LIVE from SaaStr Europa 2023: Workshop Stage - Tuesday

Ex WeWork and Matterport's founding member and advisor, Robin Daniels. Planet's CMO, Nick Neuer, call sign CMO.

And head of GTM strategy, Sarah Whipp.

And RTP Global's chief marketing Officer, Laura Brooks.

How's it going, everyone? You doing well? All right. All right. Good venue here. London. Being back in London, it's a little cold today, a little colder than expected. What do you think?

Definitely cold.

Definitely colder than expected. All right, we have quite a treat for you today. The goal of today is to share some actionable insights, tips and advice from the front lines of marketing so you can grow and scale your revenue even faster. But before we get there, I want to introduce ourselves. So I'm Robin Daniels. I spent the last 20 years of my life in California working for various tech companies. You might have heard about some of them. Salesforce, Box, where I met Nick Neuer. Then I worked at a company called LinkedIn, who you probably use every single day. I worked at WeWork. I came in during a meteoric rise and a meteoric crash, which was very painful. And then my last company was a company called Matterport, which I took public in 2021. I love marketing. I love storytelling. I love the art of actually getting customers to fall in love with what you do. And we're gonna talk a lot about that today. But that's me. Nick, what about you? Introduce yourself, please.

So, Nick Neuer, originally from California. I've been here for 11 years. So British now as well, but still kept the accent. I'm not like Madonna, so stick with the American accent. I started at Box when we were about 40 people, left when we were 3000ish, so that was a great journey there. And then for the last decade or so, I've been in fintech, with the majority of that spent at Stripe, leading marketing for the EMEA market, which we grew from a very small portion of our new business to our largest new business driver of any region. So it was a great growth story.

Can't wait to hear all about it.

Sarah.

Yeah.

Hi. So I'm Sarah Whipp. I'm originally from the UK and apparently stayed here, so not that exciting again. Career marketeer, loving in marketing. I've worked across B2B, B2C, working for cyber companies like McAfee and Symantec. I've also worked in regulated industries, running marketing for S and P Global and some of their subsidiaries, and more recently, just started as marketing employee number one, a company called Call Sign, which is in the identity space, signed to Fintechs and have been growing the marketing team there over the last few years.

Amazing.

And Laura, last but not least. So I'm Laura Brooks. I currently head up marketing at RTP Global, which is a VC firm specializing in early tech companies. So I don't only just do the marketing for the VC firm, but I also help our founders out with their marketing and PR activities. Prior to joining rtp, I was actually startup side, so I ran PR and comms at a sort of series C cybersecurity company called Tessian, which I can share some stories about today. And then prior to that, I was agency side as well. So I've done the whole spectrum around pr, comms, brand. That's my. That's my passion and what I'm really excited to talk about today.

Awesome. So we assembled this panel in particular because of the diverse backgrounds of all of us. We've come from sales, from development, from pr, and we want to give you all the different perspectives. We've also worked in companies that were just getting off the ground that nobody had ever heard of to. To companies that were already like, you know, had product, market fit and were scaling to companies that were already public. So we've gone through all the different journeys and we want to share the stories from those journeys today. And last but not least, this is also about you getting the answers that you need. If you have anything that we haven't covered during Today in these 10 tips, there's a mic. We're going to do an open Q and A at the end, and if you don't get to answer your question, you can come find us afterwards. We're all going to be around as well, but the way we've done it, based on these 10 tips that we're gonna share with you today, we basically set up in three different sections. The first section that we're gonna talk about is really about how do you set up marketing for success? What does hiring look like? What does your first 30, 60, 90 days look like? And so on. We're gonna talk a lot about that. Then we're gonna talk about go to market. Market success. What does that look like? What kind of programs do you put into market? How do you measure the success of them? And so on. And then we're gonna share with you some marketing strategies that really work that we have done before in our careers that we can say, go do these. And they might not be guaranteed to work, but they're pretty close to being guaranteed to work. All right, so let's get started. The first area we're going to cover, of course, is really about how do you set marketing up for success? Sarah, we're going to start with you. Let's talk about like team and hiring and skills. What do you do when you're thinking about getting your marketing department off the ground? What do you look for?

Okay, so it's a great question. It's the one that I think I'm really passionate about, which is the first thing you've got to do is draft pick. Number one for me is product marketing. It's making sure you can have someone in house that understands your message, the customers and the competitive set. If you can set that up, you can be set up for success. Literally. For me, anything else can be hired, but that one is the one that you need to have knowledge within the company.

Product marketing.

Okay.

Yeah.

I think the second thing on teams is making sure you're balancing that performance marketing engine with demand. Performance will work short term, but it doesn't set up for customers of tomorrow and the year after that and the year after that. So make sure that you've got some element of brand going on. And the third thing for me from a skills perspective is curiosity. We hear a lot about the marketing playbook and yes, we've all got thousands of playbooks, but guess what? The pandemic didn't help with a playbook. Lots of changes with the market isn't helping with the playbook. You need people to be curious, wanting to learn and constantly evolving within the marketing team. So that's my hiring, my team and my skills.

Amazing.

I think to add on to that particularly, I built a number of teams from the ground up. And what I've really learned is there's no blueprint that you should be following because just someone's been in product marketing for so many years or been doing demand general, whatever the case may be, they may have that curiosity or have some overlap with different skills. And so rather than going in and saying this is what the org is going to look like, I think feeling out as you build, see your stars are, see where your gaps are and go from there and be able to flex.

I have a question. Do you think it applies to B2C companies as well? What do you describe it to me? Seems very B2B.

I think that's a fair comment with it, particularly on the performance versus the brand side because that's not a challenge within the B2 cider because of course you have to have the brand side of it. But the curiosity, yes. And beginning someone to be able to get the messaging that resonates, right?

Yeah.

I would stand by those two. Two points. Personally, I don't know.

No, no, I agree.

I agree.

And what about the. What do you look for in a marketing leader in this early stage? Let's say at seed stage versus a B stage versus maybe a growth stage.

What would you, what would you look for me as well?

Go for it, everyone.

What would I look for?

Again?

I think that that ability to maintain that curiosity. You need someone to be a great leader because you're not just being a leader internally, you're also being a leader within the market. So someone is prepared to go out and be able to sell the products. And you need someone who understands commercial realities and can absolutely align the needs of the organization with the needs of the market. Having to work at Call Sign, where I was the first marketing employee coming straight from S and P, which was really established. You need to be able to stretch to scale and understand how you can position your company in the best possible way to be able to see it in the market and sell it. You've got to get your hands dirty.

Yeah.

And you need people with no ego at the beginning.

That's critical.

Well, at any point. But I think people that are hungry and trying to prove themselves as well, even if they've proven themselves before, they.

Want to come do it again.

Yeah. You've got to not be afraid to get your hands dirty. Similar to Sarah coming in. I am a sole marketer in the VC firm I'm working in at the moment and I had to go in and switch Google Analytics on and I had no idea how to do that. It's always just been set up for me. So just finding, you know, getting your hands dirty and doing that I think about early stage is super important. Like you say, no ego, be humble.

Yeah.

I mean in my career I've had the honor and privilege of hiring lots of people and I always look for three non negotiables. It's grit, passion and aptitude. Those have to be there. Because startup life is tough. You go through a lot of like ups and downs. You get two steps forward and you get punched in the face. Something falls through, something doesn't work. But really also I think you need to hire somebody as your first marketer who has some experience. They don't need to have a lot of experience, but they have to have a little bit the only. Because who are they going to learn from? Who's going to actually teach them how to do things? So if you hire somebody, I'm A huge fan of hiring people who have again, you know, just a hunger to go in and kick some ass. But at the same time, if there's nobody in the organization to teach him or guide them, they're just going to be coming to you or they're going to be flailing around and trying too many things that don't really work. I think that works well. If you have a CEO who actually knows marketing quite well, then it's okay to hire people who are just super hungry and young and you can just kind of go and teach them all the skills and so on. But if it's your first marketing high and you don't really have anybody in the company who knows marketing, it can also end in disaster. So just something to be be mindful of. Okay, let's move on. What are you doing your first 30, 60, 90 days? Nick?

Okay, this is a tough question, right? It's like chicken, what came first, chicken or egg? I think the challenge that you have coming in as a marketing leader in your first 30, 60, 90, you may have a team you're inheriting. You certainly have other execs you're inheriting who all have different perspectives on what successful in marketing looks like. You have a board who has a different perspective of what success looks like in marketing. And so I think your two.

Main.

Focus areas really have to be listening, but also educating. And it's your opportunity to say, these are the benchmarks with which we're going to measure success and these are the time frames with it. And I think it's important. Just like we're telling a story as we're building our acquisition engine, right? We're not going to speak to someone once and they're going to buy maybe if we're lucky. But that doesn't work. It takes a lot of touches. You have to be telling that story. And building that story of this has to come before this, before this. I'm not going to switch on demand gen if we don't have a CRM working yet. And so thinking through those things and making sure that they're understanding the logic because just because we understand how to do marketing, they understand marketing's percentage contribution to revenue or whatever they think it should look like. And so I think it's that education.

But how do you balance then the short term? Because there's always like, we need results quickly. That's why you're there as a CMO versus the let's go create a category. And you know it's going to take five years. How do you balance that?

Again, the listening at the beginning is important. Understanding the objectives of the company, you know, your investors have strong perspectives as well. And so I think making sure that you're weighing those which are super important, but then also making sure that you're educating them. Actually this has all these things have to happen before you're going to start seeing that, you know, great return on investment that we want to see. You know, at early days at Stripe it was spend $3 to get one in the door. Just figure out how we can build an engine. And obviously it's not like that anymore, but you have to do those things. You have to experiment and be willing to say success is not going to look the same today as it is a year from now.

Anything to add?

Yeah, I think on top of that, I'd say the -10 days are equally as important. And what I mean by that is before you start as a cmo, make sure you've done your research, that you've got that clear head time to assess the market, look outside in and write down your notes, but don't come with your notes and recommendations on day one because that's the time to listen internally and to really understand what's going on. But if you've got that time before, prep and do that with a clear head because I think those new eyes are brilliant. To be able to assess the market before you understand the internal pieces.

Sounds good.

Let's move on to the next one. Tip number three. Experiment, Experiment, experiment and scale. What works? Okay, Laura, you want to start with that one?

Yeah, I mean we, we did a lot of experimenting at Tessian. We, we tried a lot of different taglines, we tried a lot of different. Do we do with the humor approach, do we go with the same serious approach with cyber security? It is so completely crowded and noisy that trying to get your just a little bit ahead of the competition is so difficult. So we're constantly experimenting and actually what we ended up finding was having a bit of a sort of cheekier, more humorous approach to our marketing started to get us to cut through the noise a little bit from all the doom and gloom that happens in cybersecurity. But obviously we're ab testing, we're doing everything we possibly can. So experiment and scaling what works is it kind of how we got ourselves from the series B to the series C stage.

We found and I think also.

When.

You talk about experiments in marketing, a lot of times you go directly to performance marketing or demand gen type things that you can measure the digital you have to break beyond that and build a culture within your marketing organization that, whether, whether you're in PR or product marketing or marketing ops or wherever, all of your objectives of measurement goals against them. And you know, if we can't measure it, don't ship it. That's what I tell everybody on the team every time. And so if you build that culture into it, you start to learn not just about the performance marketing, but what types of content are working out in the market, what types of themes and messages are working. Yeah, and that helps, you know, what assets are we developing from a sales enablement perspective that our sales team is actually using to help move deals forward?

And I will add one thing we had in our. So we set okrs for our teams as well and we'd have our sort of three main buckets to make sure that it affected what we were looking after. But we also had an experimentation okr. So everyone in the team was measured on something that they could do where they could experiment. And at the end of their sort of quarterly review, we would look at what they did to experiments. We called it our labs section. And every quarter you'd come together and you'd have to present what you were going to experiment for the next quarter. And. And we were measured on that. And that was part of our, you know, scale of how well we were doing as a team.

I think it's a 70, 2010 rule.

Right.

70%, you know what you're doing, 20% the longer term pieces and the 10%, the moonshots.

Yeah.

I think the big thing I've learned is the really most important thing to do is have the hypothesis, as Nick is saying, but make sure you're sharing that internally because if people don't have context about what you're experimenting on, you can have wildly different expectations of results. And in terms of the measuring thing, don't assume you can measure everything. So really check that you can actually measure what the results are going to be before you start. It sounds really obvious, but. But I've been part of, I think, too many experimentation projects where people suddenly realized they couldn't measure the okr and it all went horribly wrong at that point.

Yeah, I think that's so true. One of the tips I would give is very much in line with this is so many companies, whether you're a CEO or founder or maybe ahead of marketing, they start with the channel versus starting with the story or what you want to get out of it. And so often I've come in to meet with founder and they've got great ideas, and they say we should try more on TikTok. Okay, but what. What is it you want to try or we should. We should amplify on Twitter. We haven't really tracked the code there. Okay, but what's the story? And then usually I get this, like, blank stare back at me, like, what? What do you mean, what's the story? Well, that's the problem right there. If you don't have an interesting story, it's not so much about the channel. Of course the channel matters. You gotta crack the code on that, too. But it starts with the story, the message, the positioning. How are you different than anything else that's out there in the market? If you don't have that, you're gonna come back and be like, it didn't really work. Unfortunately, a lot of it probably comes back to that. You're not differentiating the market. You're not different. You're not saying anything new. Quirky, different, funny, whatever it is. So you got to think of all the different aspects, and then you can scale what works once you've done those experiments. All right, tip number four, just to move along. Okay, I love this one. Sarah, why don't you share what you had in mind here?

Yeah.

And it sounds kind of obvious, but too often, marketing is an adjunct to the business rather than part of the target operating model. And I've always kind of thought it's nuts, because at the end of the day, marketing is the curator of the brand. And the brand is not just what we say in marketing. The brand is the experience that clients have with every single touch point, and it has to be part of an ecosystem. I know Nick's going to talk a little bit about partnerships later on, so I'm not going to steal the thunder because he's too close to me, so. But it's important about making sure the brand is the same when you're working with partners, when you're working as part of the employer brand with investors. So you have to make sure that you're absolutely. I would see marketing as the glue within an organization that brings everyone together and works together. And if marketing is an island, then you're losing money.

Beautifully said. I mean, everybody in here probably wants to create a brand that people find fall in love with.

Right.

I mean, that's kind of like the holy grail of good company building. Well, you do that exactly through every single touch point. It's not just about what you perceive in your marketing, but it's all the brand promises through Every single touch point. I think it's beautifully said. I think I wish more people understood that as well. Anything to add?

No. I was gonna say we used to have a saying at Tessie in our marketing team. It takes a village. And we used to put that across everything because anything we did in marketing went across every department. And as I said, I looked after PR and comms, but I had nothing to say if I didn't work with the data science team to produce some data insights. I had nothing to give to the news if I wasn't working with the threat intelligence team to get threats that I could take to the media. I also didn't have any spokespeople or subject matter experts if I didn't know the CISO and how to speak to him and how he could then speak to the audience for me as well. So I made it my job, and everyone in marketing did, to have weekly catch ups with these people. Get to know them, get to know what they're like outside of work as well. Just get to know them. And then suddenly your job just becomes so much easier. Similarly, customer success, without them, you don't have case studies. So just making sure that you're working together, they respect you a lot more as well as a marketing team, rather than just seeing you as a, you know, a cost spending your money, basically. But they also, you build that relationship and over time it just makes your life so much easier.

All right, let's move on. Laura, why don't you keep going? Yes, we're in the golden age of storytelling. What does that mean?

Yeah, well, I'm a big advocate for storytelling in B2B and I could go on for hours, but I'm appreciate. I've probably got about two minutes, so I'm going to. You can talk to me afterwards if you want to, but I mean, historically, storytelling has always been sort of the B2C domain. But I'm so excited to see that more B2B brands are actually taking on storytelling now. And the reason why is because it humanizes your brand, it creates that trust and also builds that emotion as well. And ultimately it works. So I don't know if you saw the story Airbnb put out. They had their best quarter because they switched from performance marketing at 20 with more focus on brand and storytelling. And the reason is even though we're in B2B, we're still selling to people. And people love stories. They remember stories more so than stats. And also you're making decisions, but decision making isn't solely based on Logic or fact. It's based on feeling and emotion. And that's what, that's what storytelling does for us. It just makes it so much more compelling and you build that emotion connection which makes you more memorable over time. And when you think about, actually the majority of the buyers you're going after aren't in the market to buy your product right now. So you need to think about how am I going to build this long term compelling connection with this audience so that later down the line when they do, are ready to buy your product. Your top of mind, your top of mind mind for their consideration. And storytelling can really help you, help you do that. And I've got so many examples. Again, I don't think I have time to go into them, but it's always finding, like, what's that human interest angle? Because I will tell you, no one cares about your product. They really don't. They care about how they're going to make their lives better. So if you're a ciso, you want to stop data breaches. I don't care how I'm going to do it. I just want to make sure that I get home at the end of the day and have not had a data breach happen in my organization. Tell that story. Don't tell them how many you know how your product works. They just want to have that feeling. So that's what we did at Testeon quite a lot. And there's so many other examples that are in the B2B world that you can Google and have a look at. But Robin, I know you're a huge storytelling fan.

I talk about this incessantly. I feel like on my LinkedIn profile, I feel like we're in the golden age of storytelling. I think you've said it really well. I would just add to me, I usually try to challenge my team and say, okay, well, how do we know that we're telling a compelling story? A good marker for you is, does it make you feel something? Does it make you feel a little uncomfortable? Do you go, should we do this? Can we do this? Can we say this? That's usually a good sign that you're onto something that maybe nobody has dared do before. Because the downside of being in this golden age of storytelling is there's so much noise out there. So to break through, you can't just be incrementally different. Incrementally better, slightly better, slightly different. You have to really think about the angle. You have to be funny, quirky, different, surprising, whatever it is. But you have to Elicit an emotion. And a good test for you is when somebody comes and presents an idea to you, your team, if it makes you feel a little bit uncomfortable, probably something that you're probably a sign that you're onto the right track with storytelling.

And there's one other thing I will add about storytelling as well. I know it can seem quite, quite fluffy and I've had that feedback from executives in organizations as well. But even though it does brand building, which seems very abstract and fluffy sometimes, it does genuinely impact lead generation as well. I worked on a campaign, huge success from a PR and social media standpoint. But I spoke to the client afterwards and he said that campaign was the biggest lead generator for the company for the 12 months that followed. And hearing anecdotes like that, it really helps support your storytelling in the organization. But like I said, I could go on for hours.

So for sake of time, for sake of time, we'll keep going. Okay, now let's get into like one of the questions. And again, all of these are topics are based on the questions that we get often. Metrics is a question that we get all the time. Nick, do you want to start off with this?

Yep.

So I think as a marketing leader, it's. You have the opportunity to define and decide the metrics that matter. Obviously you have to be in agreement with, you know, your head of sales with other folks across the business. But I think and they should probably be changing year on year as your marketing team evolves, but making sure that you have those clearly aligned metrics and clearly aligned with your stakeholders. But I also believe it's really important as a marketer. You know, we know if sales hits target, you know, we don't necessarily know each person that hits quota, but we know if sales hits their target. And I think marketing has to be that open about their success, their failures, what's working, what's not. Because it builds that trust in the business and it also maybe flags up someone. If you see that one area or one product maybe isn't working in your go to market strategy, maybe someone from product is going to volunteer or someone from sales who sells that product or post sales or pre sales and come in and join and help you build those things up. So I think that transparency is key with your metrics and gives you a lot more trust. I think, you know, vulnerability as a leader, as a person is a great trait and I think showing that as well as a marketing leader and a leader of your org, I think is something that only can benefit you.

Totally agree Sarah, Totally agree.

I mean, transparency by design in terms of all metrics. I think there's an important thing to discuss, though, particularly with marketeers. Too many marketeers think that the metrics are a reflection of them rather than a reflection of the program or the activity. And you've got to decouple that when you're assessing the team because it to share all the metrics and say, look, this didn't work. This was an abject failure. It's important because you have to learn from it. It doesn't mean the people behind it were failures. And a lot of marketeers get a little bit hung up in closing that. So I try and decouple up with the team so that they feel confident that they can fail fast and then learn and iterate and succeed brilliantly. So that would be my addition.

Makes sense.

Laura.

I think one to sort of add on to what these guys have said is you can also get a bit bogged down in metrics. We used to measure everything, like, everything. And that level of granularity every quarter kind of made us lose sight of the bigger picture as well. So, for example, my ultimate goal was to raise brand awareness among a security audience. When I was at Tessian. Yes, all our, you know, we're getting more press coverage, we're getting more LinkedIn followers, for example. But ultimately, has that helped increase our brand awareness? So sometimes you do have to take a step back and just see that actually measuring that level of granularity is okay for the individuals who need to pivot and work out what they're doing. But ultimately, ultimately, what is that overarching goal? You need to measure and then report that to the execs, because that's all they care about, really. They don't care about how many tier one pieces of coverage you've got.

Really.

It's the impact that matters.

Yeah.

Because I thought it had gone away, but I've had a few conversations in the last couple of weeks and it's not do not measure marketing on MQLs.

Yeah.

Just dies, Right. I thought it had gone away. Apparently it's coming back.

Well, it's a good indicator for you and marking, but you don't need to share with like, it's a good indicator whether our programs are working, but we don't need everybody else in the org to really. And at some point it doesn't matter as much anyway. It's kind of like how many views you got on your latest video you put on YouTube. It's like it's a weenie Metric, what impact is it actually affecting?

It doesn't matter.

That's right. That's right.

I had a CEO saying, I want to get $10 leads. I'm like, oh, my God, you can have zero dollar leads. If you just get a free database, it won't convert.

All right, let's talk about influencers. Another question that we get a lot. Does it work? Should we use them? Who do we use? Where do we find them? So on. Okay, Laura, do you want to.

Sarah, do you want to start?

Okay, I'll go first. So influencers are incredibly important, but finding the right influencer is even more important. My current role, we had to get credibility with really, really large banks really, really quickly, and they will trust the people they trust more than, of course, they'll trust a company they've never heard of. So we had to get those endorsements working, and that was hard work to be able to get them on stage talking about topics that matter for them and allowing us to then put our point of view across. I'm using this as an example because people obviously will. Word of mouth matters, and that's really what influences are in terms of the B2B world. In a B2C world, absolutely important. But they've got to be credible, they've got to be authentic, or they've got to be Ryan Reynolds.

With influences. Like you say, everything's on word of mouth completely. And as I said, I keep coming back to tessiem, but we cybersecurity and CISO audience position particular are very skeptical about marketing, and they are very, very much a closed community, and it's all based on recommendations from their peers. So, yeah, working with influencers is a huge way to sort of use their voices to get your word out, and you can use external influencers. So looking at sort of who's out there and who they listen to in their community, for example. So one thing we did actually was do our own podcast. And I know a lot of podcasts are thrown around, but it's something that really worked for us because we could work with social engineers or we could work with other CISOs that were in the community. Completely not sales pitch at all. We didn't talk about product, we didn't talk about Tessie, and we just talked about issues in the. In the security community, and it worked really well for us. But one thing I will add about influences is you have influences in your organization already. You do not need to go and find them. You have your customers, they are your brand biggest. They're using your Product, get them to be your influencers and your employees. They are your brand advocates. Like, use their reach and they will be able to share your. Share your. Your company on your behalf.

It's a great point. In my last company, we were selling to residential and commercial real estate. So we went and found an influencer on TikTok and we hired her to, like, do a video about matterports, the company I was at. And it was like a massive success in terms of the reach. We paid her, not very much. I was a little shocked about the results. We got over a million views on her video and we got more leads than ever. So in those two metrics, it was actually pretty successful. But the leads were not very good. It didn't really converge into anything. So this is also where, coming back to the earlier point about experimentation, it was a great experiment. We learned a lot from it, and in some ways it was successful, but it was other ways. It was like it didn't really actually have any material impact on the business itself. So do you call that success? More awareness?

Sure.

But any impact in the revenue? Not really. So, again, got to move fast. Okay, let's move on. Leveraging your execs. This is an important one. You and I, we used to work with some great execs in our time. Why don't you kick us off, Nick? What does that mean?

Yeah, I think getting. Getting your sales leader on your team is the most important thing you can do realistically. You know, I fortunately have worked with a lot of amazing sales leaders at Stripe with a sales leader who was Salesforce's international CMO before. So big champion for marketing, which is awesome. But having those people on your side telling your story and coming down on folks who don't touch their leads in the right amount of time, or whatever the case may be, aren't tracking things and helping use their voices rather than just marketing, or rather than your individual marketers having to sort of risk deterioration of their relationship with the sales REPs or the SDRs or whoever they're working with every day because they're doing the nagging. Find their managers, find their bosses to help you achieve your shared goals.

You can reach any Laura, anything.

Yeah, executives in my world are huge because they tell your story on your behalf, like Nick was saying. And if I go back to my point around storytelling, again, you don't need big creative agencies to help you tell your story. You have your founding story, and your executives are the ones that are going to tell that story the best because they've lived it so you leverage them to tell the founding story and you've already got storytelling off the ground. And you also probably have your ICP in your organization as well. So we had the ciso or you might have a CFO or a CPO leverage them. They're. They're talking directly to your. To your company. So make sure that they're telling their story directly to your. To your customer. And also leveraging your execs for crisis comms. Invaluable. Like you need a CEO that's going to stand up there and be able to weather the storm.

It's almost like you have a PR background.

It's almost like, yes, I've done a.

Few crises, but you need to make sure that all your executives are on the same page and you are the captain steering the ship as part of marketing and comms.

Love it.

Okay.

And then the partnerships.

Yes.

So we have exacts, but I actually think your most valuable partners in the business are SDRs. As a marketing. I started as an SCR, so I may be biased.

How many calls a day did you.

Quite a few. This is what. Yeah, it was box. It was all inbound in the early days, but it's 90 days as an STR. Super invaluable. You understand, you know, the questions people are asking, the pain points that they have, but that knowledge is invaluable. And so if you're not having that close partnership directly with your SDRs, I think you're missing a huge trick there. So I think that's one thing about internal partnerships and one that's critical. Obviously other departments, super valuable, but that's one I think that can get overlooked potentially because they're straight out of uni and, you know, you sort of are maybe looking for someone more senior. I think the second thing about partnerships, which is a totally different type of partnership and especially the environment that we're in right now, I think it would be really silly to go say I need 8 headcount to build a performance marketing team.

Right now.

You probably need someone looking after performance marketing. But in reality, if you have a really great agency, make sure your agency has clear KPIs. That gets overlooked far too often. But if you have an agency that's running these things for you, and, you know, maybe they're running your paid media campaigns on, you know, whatever platforms for you, but they're also doing that and they have experts in there doing that for 10 other businesses. And so they're not just learning as if you have one paid media person internally only running for your team rather they're learning from all the other businesses and clients that they're working with and sharing those learnings back with you. And so I think where you can scale and find expertise through agency partners, I think that's a critical way to scale your marketing team without having to ask for additional headcount. And you also scale your knowledge by getting that from their other clients.

Love it. That's a great tip. Okay, just for sake of time we're going to move on to strategies that work super well for me. Let's start off with you Laura.

We'll do it quick.

Okay.

Strategies get to know your Just know your customers completely and like I say, you've got one in your organization most likely get to know them, get to know what they like, dislike, what they read, what sources they trust. Just know your customers so so well so that you can sort of reverse engineer that in your marketing. And then my other point would just be like balancing short term tactics with long term brand building. I know it can be a really long process lack of brand building but it's so worth it in the long run to make sure that those future sales come in because people like I said, the vast majority of your buyers aren't ready to buy right now. So focus on what you can build long term as well as those short term tactics. That's what I would say.

Love it.

Sarah, why don't you take us next?

For me it's no feeling do which is I've used it for events, I've used it for meetings, I've used it for programs. What do you want people to know as a result of the activity? How do you want them to feel and what do you think want them to do? If you ask that for your team before they start everything, it just changes the mindset and keeps focused on the prize. Whether you're going into an internal meeting, talking to a partner or selling to a customer. No feel do love it.

Nick.

I think there can often be a lot of great ideas coming from all parts of your marketing team, all parts of the business and ideas are great. Feedback, contribution, collaboration is great but being unfocused can really hurt your go to market success. And so I always structure my year planning it around three themes that we're going to be doing. We live in Europe, we don't really do Q3. Let's be real so you know, kick one off Q1, Q2, Q4, one theme for each and they should run for for at least 3, 4/4. But rallying your team rallying the company, rallying your thought leadership, pieces of content around it, getting everyone behind this theme and what it means and who it's for and who we're speaking to and the value that it's bringing to our audiences gets that focus. And then when you have those little ideas that come in of the oh, can we do this to this, to this too? If it doesn't fit there, you already have that momentum of these themes as a company wide objective. And I think that really helps keep your focus and get everyone behind that this is the way that we're all moving forward, not just marketing right now.

I love it. I'll add a bonus tip for myself. So I call it the relaunch, relaunch, relaunch playbook. I used it at many of the companies I've worked for, but basically nobody remembers what you say the first time or the second time, maybe not even the third time. So you have to relaunch things over and over and over again until it sticks in people's mine. So many companies that I've seen throughout time, they get an article in TechCrunch or they get an article in Recode or Wall Street Journal and think that's it, we've made it. Everybody knows about us now, but it's just not true. So in 2009, I was working at Salesforce. We launched a product called Chatter at Dreamforce in front of 50,000 people. But the product wasn't ready yet. It wasn't going to be ready for another six months or so. So we went on this relaunch cadence that every six weeks we're going to have something new in the markets. Talk about regarding this product, Chatter, new customer stories, how many people signed up for the beta, new integrations, new features. Every six weeks we had something new to anchor the news around. And it meant that when we launched the product in the summer of 2010, it was the most successful product that we had ever launched at that point in time in terms of adoption and reach and in terms of the momentum that we got as a company. And we continued that momentum for about 18 months or so, culminating at a Super with a Super bowl ad, with a Black eyed peas in 2011, which was a little crazy. Not that everybody company can do that, but the point stands around relaunching things over and over and over again till six. It's hard work, it can exhaust the team. But you also want to make sure that your customers, your prospects, the press, anybody you care about gets so excited by what you're Doing and you got to find different angles every time you're relaunching this. But at the end of maybe say a one year, year or 18 month period of time, they should know who you are, what you do and what impact you're going to, going to have for them. So that's my bonus tip. Okay, we've got basically five minutes to go. We ran a little bit over time and I wanted, we wanted to leave some time for Q and A. So if any of you have some questions, either you can raise your hand or you can come up and do the mic, but you can also. I can just repeat the question. So go ahead if you can. If I can hear you. Yes, you, yes.

So I have a question about the.

Experiment and then scale situation.

Yep.

So what are the students in my team when you start participating?

Right now, five people.

If you experiment and something goes well.

We start building on that and then three months later we still.

Yes, yes, yes.

Makes sense.

So the question is, so if you have a cadence of experimentation, which is great and people are like in that cadence, once you've found something that actually works and you start scaling that, how do you move back into experimenting with new things? You just don't keep doing more and more of the same all the time. Any thoughts on this?

So I think you need to be thinking about that there isn't just one thing you should be experimenting on. So like you might be thinking about experimenting based on the channel or the company size or the. But, but are you thinking about the customer profile that you're going after? Are you thinking about the message that you're putting forward? Are you thinking about the customer journey that you're sending them through? And so there are all these different levers that you can play with and start adding new things. Things and starting building lookalike audiences and start trying lots of different things and different offers.

Right.

A free trial, sign up, talk to somebody.

Right.

There's so many experiments you can try. So even if you have nailed one, just keep adding in different layers, which is going to expand your breadth.

Okay, any other questions? There's a mic up here. We're happy to take some more questions. Yes, go ahead, please. Oh yeah, so the live stream here, I also got my personal photographer here. He's actually the CEO of a company I'm joining now, which is very exciting. I haven't announced it yet, but you know, it's some news that's coming out very soon. So thank you for being here. Please go ahead.

Is this on?

I can hear You.

Okay, great.

You had mentioned Airbnb had their best.

Quarter yet with storytelling.

And most companies turn to performance marketing instead of storytelling. Agree with you 100% on storytelling, but I imagine most of us in this.

Room don't have the marketing budget that Airbnb has. Sure.

And they probably did a lot of performance marketing leading up to their storytelling.

So for companies like ours, where we're working on our, our organic growth strategy and bringing in performance marketing to try.

And help that brand awareness, because storytelling just isn't there yet, what do you.

Recommend if not performance market?

I have a thought for you. There's a myth out there in the world that Tesla doesn't do any marketing. Who's seen that Tesla doesn't spend any money on marketing? They don't do any marketing. It's actually just not true. They just don't do marketing where they spend it on performance marketing. They just do great storytelling. When they did the drone video flying through the, the gigafactory, that's storytelling. That's marketing right there. So I think that the challenge I would put back is find interesting new ways. There's so many ways to tell interesting stories to, to raise your organic inbound. You know, people coming into your company could be videos, could be audio, it could be great storytelling on LinkedIn through your executive team could be so many different things. But I don't think you have to spend a lot on storytelling. I mean, even maybe zero. But you just got to find interesting ways. And then maybe over time, you know, if you have a strong enough point of view around things like we talked about earlier, then that can convert into enough interest in people coming to you.

But like I said before, it doesn't have to cost a lot. You already have a lot of these resources in your organization already. Like, you have data from the platform that you're. If you're a tech company, you have data on the platforms that you're working on. Get to know your data science team. They can find trends that you can then spin into stories that you can tell, which then gets you. Gets that ball rolling. And then eventually to that experimentation point that becomes business as usual. That's what we started doing. You also have your customers, they can tell your stories on your behalf as well. And you can do that through video and so on your employee stories. You know, that's your employer brand. You don't have to spend a lot to get stories in your organization. They already exist. You don't have to go and employ an expensive creative agency to do that. And Then once the ball's rolling, you've started to build that muscle, it starts, it starts to become business as usual. And you find that maybe every six months you put out your quarterly, sorry, your half year report with the data that you've extracted from your own platform. And then you measure that and then you just keep making that better and better over time. And you can look at like the data breach report that Verizon puts out every year. I think there's even date of breach report port day when it comes out and everyone gets so excited. That's organic. It's come from their, from their own data. So there's little things you can do like that when you don't have those big budgets.

And I think one last thing, if you did film before I did tech, but if you think about a movie, you know, you have your lead character oftentimes in your lead plot line, but there's also subplots and supporting actors and supporting characters and lots of things go. And I think sometimes we forget and we're only going with the lead character and the lead narrative at all times. And so how are we weaving in those different things that help build to that story and help add to that story you're telling rather than just focusing on your one main theme or product.

All right, Laura, Sarah, Nick, thank you so much for joining us. Thank all of you for the attention and the questions. We'll be around if you have any more questions. You can also connect with us on LinkedIn. Thank you again. See you around.

All right, good.

Good. It. Ram. Sa.

Sa.

It.

It.

Sa. Sa.

Race.

Please welcome Daniel Bailey, VP of EMEA Amplitude.

Esther Kim Trampenau, Growth Marketing, Amplitude.

Hello, how are we doing?

Thank you.

Don't know about you, Esther. It's not often I get a round of applause, so I will take that.

I'm super scared, so bear with me, everybody. Super nervous. Hi, everybody. Really excited to have you here that you can join us on your lunch break as well. Daniel and I are going to talk about how to merge the beautiful worlds of product led growth and sales led growth Together. We're going to do a bunch of sharing on what we've tried at Amplitude, some stuff that didn't work. And I think hopefully you'll take some of the learnings and not make our mistakes and then be able to take some of these tactics back to your own organizations. But first, before we get started, who here works in product? Who here works in marketing? Not a lot of marketers and sales. All right, does somebody work not in Marketing product.

Who's an operator here or a founder CEO Case we've got.

Here in the back. Found a corner in the back. Cool.

Yeah. Well, why don't we. Just so you know who we are. If you click to the next slide, that's us. You've hopefully figured that out already. Maybe to kick off Daniel Bailey again, I am the leader of our sales business, broader go to market business here in Amir at Amplitude and I've been in this wonderful profession for many a year previously with Salesforce and Zendesk, probably the first version of PLG based growth. And so I've had the benefit and the fortune of working through those businesses and very much into Amplitude where this is front and center to our strategy. Ernesta, and before I actually hand you over to Esther for a quick introduction, please join Esther, give her a warm welcome because for a bit of a story, I was actually meant to be joined by a different colleague today.

Taller.

Taller, no. But unfortunately her flight was cancelled. She was coming over from New York. So we called Esther late yesterday and said, Esther, we've got this great opportunity for you. And if I could have sort of captured establishment Esther's face at that point, I would have shared that now. But the look of horror set in.

It was more like I time blocked my day. What's going to happen to all of the things I was supposed to do?

So Esther, and what do you do at Amplitude?

So I am not a speaker for hire. I actually work at Amplitude. I'm on the growth marketing team and I head up community. And I think what would be kind of cool is for you guys to know how growth marketing is actually set up at Amplify because I think we do it in a slightly different way at growth marketing. Amplitude, we have an acquisition pod, which is why I'm there. So acquisition is all about acquiring new users. We own community, we own website paid channels and organic channels. We also have a retention pod. They're super focused on new user activation and then also engagement and then we have a monetization pod and they're really focused on cross sell and upsell opportunities.

Very good. Should we kick off?

Yeah, let's kick it off. So product led growth. I think this is one of the things that tends to have a lot of confusion around what exactly it is. We know what sales led growth looks like. But product led growth in particular, I've heard people say is it free plan? Is it a new acquisition tactic? Like do I have to change my pricing page? People really focus in on that part of product Growth more than anything. VCs and even enterprise execs, I've heard them say we should do product led growth, whatever that means. And then so there's this question of is it a business strategy type of model, Is it a go to market strategy? So one of the things that we want to do is just kind of align on what product led growth actually means. So we all know we're talking about the same thing. All right, so product led growth is emotion across acquisition, retention and monetization. And if we were to think about pure product led growth, like if you just had product and like no humans involved to help move things through, once someone got into the product, it would look like on the acquisition side you had a product that was able to not only attract and acquire new users, but also able to get them through the signup process. On the retention side, your product has the ability to help people through activation, which is nuts to me sometimes when I think about B2B and then also keep users continuously engaged without any help or any intervention from a human. And then thirdly, on the monetization side, product led growth purely means that someone can go into your site, the product itself is able to be purchased through a self serve checkout process and nobody actually wants to talk to a human. I think this is a really rare case. Like most of us probably don't have products that's able to serve this at every single level like that. So what we usually see in terms of marketing and if you take amplitude for example, we don't have a product where I don't know if anyone knows what amplitude is, but we're a product analytics platform. We don't have the type of platform where users will come in and there's some sort of viral acquisition. Oh my God, here are my charts, here are my dashboards. Oh, I'm going to like it on LinkedIn and then someone else is going to check it out. This doesn't really happen. Nobody actually likes to share their data. One, they don't like want to give away their secret sauce and two, like people are usually embarrassed about their conversion rates and so they don't want to share it publicly. So like it's true, right? Like I'm looking for someone who's willing to share their data. So if you are like, please tell me and like come to our community. But like that's one thing that's problematic on the Amazon perspective is we need sales and marketing to help. On the acquisition side. We don't really have a viral acquisition loop on the Retention side, we also have, in addition to sales and marketing, we have professional services. They're working super hard on product adoption and this becomes more and more important when you're looking at things at an enterprise level and scaling adoption across a really large organization. And then finally on the monetization side, we have sales that's working very hard on cross sell and upsell opportunities. Today we're going to talk about product led growth with the monetization lens.

So with that in mind, the death of the salesperson, right, plg. Well, thankfully, and I think we've seen through multiple examples of some of the most disruptive, fastest growing companies, Dropbox, Atlassian, Slack, Miro, many of which amplitude customers as a site plug, that it isn't the end of the sales profession. Thankfully for me and the other eight people I think I saw in the room and the reality of these companies here, actually what you saw is they started with a PLG motion and they married that with a sales motion separately. And you typically see that for established categories that are quite crowded in nature or where you're trying to address a smaller business. S and P as a segment lowered me mid market as a segment, you want to create that ease of connection to product use and move from that motion. Whereas for companies like Amplitude, that actually are category creation. So we're building a category that doesn't yet exist and we're doing that typically focused on the enterprise space. Actually the sales led motion in that scenario is typically how you build and lead a company from the outset. But the nirvana either way is when those two worlds meet in the middle, which we call product led sales. And really that's monetization focused really on driving the revenue from your user base through product insights. And when you achieve that nirvana, you marry up the best of both worlds where you drive efficiency and speed and great interaction with product and sales team and ultimately revenue growth to boot. So that's great. But how do we do it?

How do we do it?

So one of the key ingredients to product led growth is product usage data, right? Like no secret there. And I think salespeople using data isn't something super new like in fact we have so much of an amplitude, like please do not sell any more of it to us. And like sophisticated companies are using a ton of product insights to help like sales teams and customer success teams, like identify like risk for churn. Right, like and try to mitigate that. They're also using it for cross sell and upsell opportunities. I think what we're talking about here is Actual behavioral data in the product part of it is like, yeah, this isn't a new thing, but one of the things that we want to marry this with in this moment in time is most if not all customers want to try things before they buy. Like here is the opportunity. So taking this moment like understanding like what are the right accounts at the right time, it's pretty important. For one, if you do have a free plan, you want to find the opportunities where you can start making money off of them and turning them into paying customers. And then secondly, you want your sales team who's working super hard on like building a pipeline, especially moving up market in enterprise, focusing on the right accounts at the right time. So for us, empowering like the sales team with this type of product usage data was a huge game changer at Amplitude. We're for sure like one of the companies that Daniel was talking about. Where we started product led and then we moved to sales led. And it's like your classic evolution of wanting to move up market but also have a bottoms up play. But we're also finding that sales 60% of our new paying customers started in the free plan. That's including enterprise. Right. Like, I think this is scary for a lot of salespeople because they think if a salesperson gets in there before they start trying the product there is going to like this. There's going to be this really elongated sales cycle, which may be fine, but you're more likely to close those people anyways. So that was one thing that we saw was like we really needed to jump on this opportunity to get product usage data in front of our sales team. So we did a couple things. One, we made it really easy to surface these insights to our sales teams. We focused on one, a couple like key critical product insights where we would identify like what we would call monetization readiness. These just look like somebody clicking into a paid feature, somebody visiting in product pricing page. We looked at depth of use like is this company or is this person using the same basic charts? Are they just doing event segmentation cohorts and funnels, which no shame there because that's usually where I do most of my charts. Or are they using like really advanced analytics? And we're looking at like some additional feature adoptions and opportunities to sell. We also want to look at who are the most active users and really be able to identify them. And we surfaced all of these things in three different ways for sale. One, we have like this account overview dashboard in Salesforce. Right. Like having salespeople use A different platform is really painful. I think having anybody use a different platform is really painful. So we have this account dashboard in Salesforce and they can start looking at like places where they see opportunity. The second thing is we surface some of these product insights in the account object in Salesforce itself. And then thirdly, we have this amplitude dashboard similar to this one where you can look at specific accounts and look at exactly what's happening at an.

Org level.

I think most of you here, if you're doing analytics and if you're tracking, you probably have all of the events instrumented that you need. There are probably like a few more things that you need to instrument to make sure that like the account object is always pulling through and the user level object is always pulling through, through the entire behavior. So like one little anecdote here is if you give a salesperson, I think this is true. If you give a salesperson like a trending account and you don't tell them who they're like, I don't know who, like, I can't do anything with this information. I'm gonna go back on LinkedIn and try to find this person. Like, no. So they'll move on. So that's one thing that we learned is you have to always look at it at both an account level and then also a lead level.

Okay, I'll take the clicker. I mean control. So product LED selling requires a different approach though, and that starts with a different sales motion at its heart. I'd sort of, I. My first sales career was through SAP, very traditional sort of enterprise company at the time. I see they're here and they've, they've evolved over time, of course, but at that time you were, when you met with customers, you met with prospects, you were qualified, their requirements, what were they looking to accomplish, what were their needs? And you go and build customized demonstration that goes with that. Well, of course with product LED selling, invariably they're already deeply engaged in the product. And so the conversation you're having are about, I've seen you use feature A, have you thought about using feature B? It's conversations that are, I see that you're using this capability in our product. What are the output that that's helping you to drive overall as a business? Really getting ingrained in how your product is ultimately driving revenue outcomes. And what that really means is that there are new skills required for the seller, for the sales professional. As you're interacting with customers, you need to be much more proficient in the product that you're selling. A Product expert, if you will. And it's not only sufficient to really know the product inside out, but also the trigger points for monetization. So you can understand, okay, what are the capabilities that we need to be encouraging or pointing customers towards? Both based on the outcomes they're looking to realize, but equally how we get them to pay plan more quickly. And that really means that you have to reimagine the overall enablement program as a whole for sellers. All those core skills that are dear to my heart still remain key, but you have to to supplement that with that greater product insight. And so here at Amplitude, we actually have everyone in the company, every seller in particular, go through what we call bronze certification, which is really the base level certification for any solution consultant. So anyone in the company, me included, can go and have a conversation that is much more into the detail of our product and how that product can help our customer base. But that's not enough because invariably there are going to be friction points in a business, especially with sales professionals from time to time. I don't know if you would agree, Esther. I know we're aligned, we're very much aligned. But of course I'm sure that you've seen those friction points either in current role in company or previously. And that really starts in terms of addressing that by having a clear sort of value staircase, as we'd refer to it, where you marrying up the product experience that you want to drive with the pricing and packaging that marries up or aligns with that. So on the far left, the pure play PLG motion is really about trying to drive self serve. This motion works really well where you have fairly simple use cases for customers. They might be a smaller business in nature, but not always, but simple use cases nonetheless where they don't really want to speak to a sales professional, heaven forbid, Esther, but you know, in that motion you have to have simplicity and pricing. There needs to be flexibility that sits around that. Typically, customers are expecting month to month payment options and a whole host of other things. As an aside, if I move you over to the far right, which is sales led growth in this instance or in this scenario, customers absolutely want to speak to a sales professional. Typically because they've got more complex comprehensive requirements. They might have particular concerns about data privacy or legal matters. They want to talk about deployment, they want to talk about what other customers may or may not be doing with your capability, with your product. And so in this motion, absolutely it's imperative that the sales professional is helping to lead the discussion. And invariably that tends to Align with the enterprise segments as a whole and the sales cycles or engagements tend to be over several months. Whereas in of course self serve, it can be a handful of days, a handful of hours. And when those two worlds meet, sales assisted, this is where there's a real balance to sort of drive an efficient, low touch engagement focused on getting customers to product paid products as quickly as possible. And as I'll explore on the next slide, really changing the paradigm where you're looking at long term customer value and growth as the main point of emphasis. And with that in mind, the evolution of sales compensation is really important to consider and factor in here. And it really starts with neutralizing the impact certainly initially and potentially long term. Really what I mean by that is regardless of how engaged or not the sales professional is, it's recognizing them in terms of compensation for the accounts and customers in their territory. And why is that important? Well, actually what you're optimizing for here is ensuring the sales professional helps to get customers to a product experience as soon as possible. That might start with a starter or free plan. Indeed. Actually here at Amplitude, any customer comes into the product experience is our highest conversion driver really for qualified pipeline downstream. So we want to optimize that from the get go. And we also want to make sure we're optimizing for the sales assist. So getting customers from a free plan to a pay plan as soon as possible and making that a low touch very efficient sales motion and sales cycle. And that really means that you have to rethink through your monetization, just more broadly optimizing for lifetime value and growth with customers, which enables you to do larger deals in the long run. But it ultimately may mean that you're starting at a lower entry point to your product. So how do we bring it all together? Esther?

So this bit is actually really important, the sales compensation, because a lot of sales teams are working a lot of things right. Like you go to Saster, you're going to get a bunch of SDRs probably reaching out to you. So you're on their lead list. There are lots of high value targets that are also on lead list. There are existing open opportunities. One of the things that we learned at Amplitude is in order for us to all get on the same table, like we needed to do something really drastic. Part of it was compensation, but we needed all to like have this win, win, win situation. Otherwise the leads that we get from product qualified accounts weren't going to get the right experience, if any attention at all from a really overwhelmed sdr. Ae. So one of the things that we did build, which is super interesting because we thought like oh, if we just generate hand raisers in the product, whether they visit the pricing page or they request a demo from sales, like this would be enough to like surface leads to sales and those would be super effective. What we actually found is we needed to proactively surface accounts that were showing intent to buy. And what we did was we built a really simple model where we took close one accounts, looked at their product behavior, compared that against free plan users and then we kind of stack ranked them to create our own product qualified account scoring. And then from there we were able to surface those accounts to SDRs and then two AES. We started this in a small pilot and what we learned was these product qualified leads closed from lead to pipeline conversion 3x higher than any inbound lead or even a hand raiser. This was super exciting but it wasn't enough plans hyper to change. And then another thing that we had to learn was if we didn't all share a metric, and that's not just sales and not just marketing, but if we didn't share a metric with product then we wouldn't actually get anywhere. And that's something that we had to learn fairly recently is we all had to share the same pipeline target. And I think this is the first time in amplitude that the product team has ever had a revenue target. And I think it was a little bit scary for a lot of products because they're generally far away from like GTM motions like revenue generation and pipeline and opportunity creation. And then I think that was like one of the key moments is knowing like who we needed to work with within the company, build a small group and then be able to have a shared target across like sales, marketing and product. So I'll give you a little example of what went wrong. Maybe you guys can learn from that. So the growth marketing team and the product team were surface seeing a bunch of product qualified leads. We were really exciting measuring success on the number of product qualified leads that we generated. And we were throwing them over to sales and we were really drinking our own Kool aid. We're like why wouldn't you go after them? They're doing so many things in their product. They're checking the pricing page, they're clicking on Govern, which is an add on feature. Why wouldn't you actually go after them? But you're talking to a group of people who are not compensated in the right way, not sharing a pipeline target and also like inundated with all of the other marketing and sales motions we have at amplitude. And so we were throwing all these leads over to sales and we didn't realize they weren't getting the kind of meaningful interaction that we needed. And that's really what shifted and what created the change to knowing we needed to have a shared pipeline target. I think from there there was quite a bit of enablement that needed to happen both on the sales side, not just the bronze certification, but really kind of structuring teams and understanding accounts on what to prioritize. And you need to have a few wins first for people to get really bought in and then even on the product team, getting them properly enabled, getting them really comfortable and understanding what it was like to now actually hit a GTM strategy and have a revenue goal required a bunch of enablement as well. So that's one thing that we learned as well is it doesn't stop at PQLs at product qualified leads. The second thing is a lot of times your product qualified lead, especially in a product like Amplitude, your power user is likely your admin, not necessarily your economic buyer. So we would throw these leads over to sales and throw these accounts over to sales and sales would have to do a lot of work to find who was the actual decision maker, who was like the real buying committee that they needed to engage with. And that gap was really important for us to be able to fill. And right now growth marketing fills this gap. Like we do a lot of the scoring, we surface the product insights, we're looking at the model to see if it's really accurate. We're working on a lot of the retention pod, is working on a lot of the enablement and the communication to be able to build and surface these insights and work with them with sales. Same thing with the monetization pod. It works really well at amplitude to have a growth marketing function sit between product and sales to be able to be the middleman. I don't know if any every company can do that, but it's a core function that needs to exist because product and sales usually don't work together. Not in a bad way. It's just not how they're structured.

I agree.

Yeah. So key takeaways. And we have tons of time for Q and A after this is one product led sales doesn't just require product usage data. We hoped that it would because how easy would that be? But it does require like understanding and identifying who the actual buying committee is. And it also means evolving your sales approach. The second thing is exactly what Daniel Covered, which is we have to eliminate the friction when it comes to sharing the playground. I don't know how long did it take for us to have a shared pipeline metric? Like 10 years?

I wouldn't say quite 10 years, but certainly it did feel like a long time. It took a while. Right. I want to say probably a year. In getting to that point, it was.

A lot of like internal buy in and like changing people's minds. Like there's a lot of fear around missing targets in quarter. There's still a lot of fear around that. So like it's making a bet. And then the last thing, I think the biggest, most important thing is like shared metrics equal shared success. If you don't have all three functions bought in with the same revenue targets, then you're going to lose. Likely you're going to see really low conversion rates and most likely you're going to have sales go after the wrong person. Right account, wrong person. They're going to give up and not want to continue forward.

Yeah. And I think I'd probably add a bonus fourth which is just then the, the Learn Evolve motion. I mean it's stating the obvious, but it's the Learn Evolve and we're still on our journey, I would say right now, early journey, you know, and there's an irony within that because actually we sell a product that enables this for our customer base. But when we're sort of retrofitting that onto what was a traditional sales led motion, there are some things that we have to unpick. And I think back to the things that you've spoken about, the shared metrics. We have universal buy in across our organization, the product leaders, the sales leaders, the marketing leaders. And we acknowledge that we're not going to be perfect from the outset. So it's learning, it's evolving and it's doing that as one team. Yep.

And it didn't start that way. A lot of minds had to change. So if you're facing an uphill battle, like if we can do it, then you can do it. Like we're your classic B2B. We want to eat all of the Enterprise World SaaS company.

So there we go. Didn't Esther do brilliantly, by the way? They're going to invite you back.

Join my community.

We do have some time for Q and A. If we can be helpful in any way, either in this audience or just.

Need to go up to the mic.

There's a intimidating microphone there.

Hi, G, from Captain Data.

I have a question.

It's a simple one.

In a way, do you think PLS.

Is compatible with consultative selling?

Because it's more on the enterprise level at some point. But have you ever seen this?

Wait, sorry, what was it?

So I think the, the question just to play it back to you is so if you move away from the enterprise, like do you think PLS is compatible with consultative setting?

Like no, you need to talk with people to understand the need and so on.

So yeah, I really don't know. Yeah. And look, you know, here is. So if I kind of answer your question, maybe through the lens of what we're doing at Amplify. So in its very nature, for those not familiar with what we do, so products analytics platform at its core, but we're really digital analytics. And so if you're a startup business and you're building a digital product, you're going to buy Amplitude or maybe a competitor of ours, heaven forbid. And you want to base that product, that product roadmap, the things that you build based on the insights you're getting back from your customers. That's pretty logical, right? But the further you go up to more traditional businesses that have a lot of legacy behind them that are making that evolution to digital products, the more evangelical we have to be. Honestly, you have a problem that we can solve and we think we have a better way of solving it, but they typically have solutions systems. What I'm really trying to describe is that is more consultative in nature for us, but what we're finding is this really interesting paradigm where actually and a lot of Esther's work is trying to create this community of more engaged users. And what we're finding is actually more and more, even those enterprise customers are getting product experience with amplitude in some capacity. They might have come from another business that was smaller, that used amplitude, or they might have had familiarity in a different way. And so what we're finding is you can marry those two worlds together where it is more consultative and actually you still do want to drive that product experience. So one of the motions that we have in that sales led endeavor is actually a POC motion. So we get customers hands on with products, which is really because we know hands on with product equals higher conversion. So that's a long way of saying yes. Just from our perspective and certainly in the nature of what we do, we think it has its place even with a consultative based sales approach. What do you think, Esther?

I have maybe an unpopular opinion, but it depends on where the consultation needs to happen. And my guess is your product needs to work harder. Right? Like if the product is letting people down in key like onboarding and activation moments, then got to work the product a lot harder and product has to lean in those moments. If you're talking about like these really advanced use cases, super complex, they want an integration for everything. You don't know why. Yeah, that makes a lot of sense. But if you're looking at your journey along the way and things are falling apart very early on, then that's just throwing more humans at the problem. When you really need to just push the product and understand where do you need to surface more paid feature insights, different things in the product to help on the onboarding. But I think, honestly, I think we let the product get away with like not doing enough.

Does that answer or answer your question or is that helpful? Happy to discuss after as well. Yeah.

Sorry, y' all product people. I know it's hard.

Yeah, I think we've lost about a third of the audience there, Esther.

But I think a lot of people.

Can easily conceptualize pure play product LED growth and easily conceptualize sales LED growth, but it's very hard to understand what it looks like. Together we're trying to run our own experiments right now.

I would love to know what does.

Your funnel look like and right from what role do different people play? Is it sales that's driving top of funnel or is it marketing? Yeah, once the lead comes in, who's doing what?

Are you trying to drive?

You know, you said you had a freemium model there. Are you trying to get results for them and minimize time to value, or do you just want to have a play around and someone comes in and upsells? So what does that end to end funnel look like? And what do all the different teams play in getting to a sale?

So first free plan is legit and amplitude. It's a really good, legitimate free plan. You get a lot. Like the stuff you don't get is when you have weird taxonomy things because you have a lot of people in the organization, one thing five different ways, like that's a problem. Or if you want to run like a lot of experimentation and move really quickly. Like we have different add ons for that and there are like things with audiences. But the free plan we feel really strongly about this is like free and really high value for a reason. Because we want some teams to be able to use that forever and always get value out of it. So that is, that is definitely, definitely one of the acquisition plays for product LED growth. I think when you're talking about funnel filling the funnel, there's It's a really good question. So there's like your classic marketing and sales motion to feed the direct sales funnel looks like events like this, classic marketing types of campaigns where we talk about things like MQLs and then we have like the growth marketing team is really focused on the acquisition side, not so much the MQL to direct sales number, although that is a goal that we have and we hit it every time. But we're also really focused on qualified business orgs that we're driving through different channels and we have experimental budget and teams available that we're running a ton of experiments to understand where can we drive higher qualified traffic to open business. You know, free plans that are coming from somebody who's not like a hobby project but has a legitimate company. So the growth team is actually really focused on both things which is driving top of the funnel leads to direct sales and then also driving users into the free plan. From there we hand it off to the retention pod and they're working really, really closely with our growth product team to understand like how can we improve activation. Right now I don't know if anybody here uses amplitude, but it's super hard to get started. So time to value is something that we're extremely focused on and improving like time to value for a free plan user and then finally improving like that free to paid conversion because that's a critical thing. And then community led growth which is new but amazing. So you should definitely join the community as well.

Maybe just to compliment that. So I mean it sort of comes back. We look, we drive top of the funnel pipeline and all the ways that you might imagine the traditional event based marketing, we look at our ad tech spend but fundamentally we orientate back to trying to create a community that are engaged with our product. So if I look at how that breaks down the composition of our segments, typically if you're a smaller business, you're starting with a product based experience and we optimize for that because actually outbounding that or going and trying to attend events to go and get an average lead value or opportunity size that's much lower, it's very expensive motion right to build a business around. Whereas obviously the enterprise end of the segment, that's a different proposition because we optimize to getting them to a product based experience. But we also acknowledge we have to meet them where they're at and often that is finding whether it's events like this or otherwise to go drive that funnel and frankly just get into more active dialogues with customers so we can Educate them about what we do and how we can help. But the common denominator, if you look at our close one opportunities, is they've had absolutely some form of product based experience. So even with that traditional outbound motion, I'd like to come and talk to you about amplitude and the problems that can help you solve. Even in that sales motion. We're invariably introducing a product experience pretty early. That might be a poc. It might be. Actually you're not quite ready to sort of look at this holistically across the company. So why don't we go and pinpoint a particular brand or subsidiary and start small, but start with the product and we'll build success from there. So it absolutely has that path, but it's also acknowledging meeting customers, where they're at and you know, almost acknowledging where you are as a business and the segments that you're focused on as well. Does that help?

I mean, I think we must have run 100 tests on our pricing page alone. Right? Like that pricing and packaging moment is really, really important. Figuring out what to offer in the free plan where it's super valuable but not too much is really important. So like growth through the pricing and packaging and that pricing page, like I wouldn't, I don't. I think a lot of conversion rate optimization tests are pretty lame. This one is super, super important.

And happy to try and get most of the way through the hundred after.

Yeah.

It's a challenge to get here.

So when you have a world where.

Product led sales meets sales assisted and.

Ultimately you have a sales team and.

A product team working together, how do you, and especially within amplitude, I guess.

Is the best question.

How do you then structure commissions and bonuses and ote? Because ultimately who did the sale?

Right.

It goes back to the whole attribution. Is it first click, last click, even click across?

Well, I think and to sort of highlight a couple of the things that showed up in the presentation, maybe to share a bit more context behind them. I think it starts the shared metrics. What does good look like? Where are we trying to drive the bus to what does good look like? And then ultimately does it matter? And that's where I talk about. I'm probably not clearly enough about removing the friction certainly with the sales led, the sales professional. Because actually really what you're optimizing for is getting a customer into a product experience, optimizing for that. And what we found in our journey thus far is that actually the more barriers you have of okay, well that started as a product experience. You're not going to get full compensation or we're going to remove the sales salesperson because we want it to be completely self serving instances. The more you build friction, the more the experience for a customer becomes suboptimal and the more you have this siloed motion. Product sales, marketing. So shared metric, frankly, remove the friction through removing the barriers of things like compensation and making that one combined objective where everyone is paid against that objective accordingly. Does that make sense? Esther, anything to add there?

Yeah, I'm like, I'm glad we got there.

Yeah. Does that help? Yeah.

Any other questions?

I think just at the back.

Oh, cool.

I'm interested in driving usage in premium enterprise accounts. So some of what we do is kind of pre sales cs, we're kind.

Of building micro communities and we're kind.

Of getting advocates and doing training, all.

That kind of thing. It's difficult to know where that sits. If it's customer success, if it's SDRs.

If it's sales, it's product to some degree.

I don't know if you come across that at all.

But they're existing customers, they're trying to expand.

No, sorry. So freemium accounts identified like an enterprise.

Account with a bunch of premium users that were then trying to.

Trying to turn into an enterprise.

This is like the unicorn PLG example.

So we have like a B to C kind of B2C is all product led and then B2B is where sales step in and there's kind of a. Yeah.

Pulling all those B2C users together to.

Create a B2B as such.

I mean, I think this is what we're talking about, right? Like being able to identify and know like these monetization readiness moments. So like you're really engaged enterprise person in the free plan. Like what are they using it for? Are you able to identify like how actively engaged in the product they are and then like starting to surface that with like the sales team and helping them like this is what the growth marketing team does all the time, is helping one keeping that person engaged and continually adopting their product. But the sales team, I don't know if they pulled in like a solutions consultant as well, is like, is also honing in on that and that like account as well to try to expand it and land it.

Yeah. And just sort of playing back the question. So it's in that enterprise sort of motion. It's who does what essentially or where does the ownership sit primarily. Yeah, Yeah, yeah. And I think just to sort of add to what Esther shared in that situation, I mean it's the. It's understanding the different sort of trigger points sort of clearly like not not every starter plan is sort of equal in nature. And we look at, you know, one of the North Star single North Star metric is actually something called a weekly learning user, which is really the ability for tracking or understanding which users are going and getting insights on a weekly basis and sharing those insights with other people around the company. The reason I reference that is that that's a good indicator, right. Of people that we can go speak with that are clearly champions of the solution, amplitude solution, then also understanding the use case that they're deploying or supporting with amplitude the trigger points that may take them from a starter plan, free plan to pay plan. But the motion is very much trying to get them to that pay plan as efficiently and quickly as possible. And I think the mistake certainly we maybe sales professionals make is that gotta land big, right? So taking time to go maybe do a larger deal as opposed to optimizing for getting a customer to pay plan and building that motion thereafter. Hopefully that helps.

Yeah, I mean that's a good point. Like don't let them be alone in the product, right? Like Dropbox does this super well. Every great enterprise product led growth SaaS Co. Does as well as they make. They make it like a social event, a shareable event. We try to do that with Amplitude by like many ways to share insights through dashboards, through charts, through notebooks too. But that's another thing is like don't leave them alone, help them find a friend.

It's a bit of a slalom to get to the front.

Cool. Topline team. You guys have the best memes, best copy. Do you write them?

No, it's my team back home. Just quick question. You mentioned yourself that it's often the last thing sales people want is another tool to sit out of. And just identifying trigger points aside, there's a ton of valuable information in Amplitude, like signals that users fire within the product that can be used to personalize sales pitches.

Yeah, right.

So how do you solve. I'm curious how Amplitude contextualizes product data for your sales team to use in these conversations and how have you seen data being best used by salespeople?

Yeah, well, it helps that we're. The product we're selling is a database platform. Right. So if our sales team are not embracing that, then there's probably a broader question of whether we have the right sales professional. But, but, but actually sort of comes back like at its core our salespeople are fully enabled on very proficient in the product that we, we have, but we make this the basis of how we run and operate our business. So as a sales leader, excuse me, as a sales leader, you know, I'm running the business based on those insights alongside all of the other levers that you might think about the traditional forecasting motion. And as an aside, we look at amplitude and understand where we are, where our sales team are engaged and we, we quiz, challenge coach them through that lens as well. So it really is in the day to day fabric of how we run our business. I think like anything like you inspect what you expect but you know, it's sort of absorbing that top down, bottoms up, so you become data led in nature because whether it's an amplitude sort of set of insights or platform or any other tooling, I think there's a lot of tooling out there for sales professionals. And so it's being very intentional of what you're trying to drive them towards, what good looks like, and then having a cadence that continually looks at that. So you drive the outcomes that you care about the most. Would you agree, Esther?

Yeah. I mean the model works too, right. Like we like there's getting enablement super critical, like what we Learned with our PQLs, just generating them isn't enough. But the model that we evolved and evolved and looking at what were the patterns on Closed one Ops and how does that translate into free plan users and building cohorts and understanding behavior? We have just a few key monetization readiness indicators that we always surface with the team. They're used to these now and they believe in them because they see them closing from lead to pipeline conversion and 3x higher than anything else.

That's a good, it's a good point which is that the, the kind of quick wins, the early wins. I mean, yeah, I can speak as a sales professional, but we're easily distracted and often like you're not seeing that immediate return. So it's kind of trying to get to the quick wins and then building that into the muscle of how you operate. Does that help?

Thank you.

Yeah, thank you. We have time for one last question. A fun one. Anyone? Fun question. What's your favorite analytics platform? Doesn't have to be amplitude. It's okay if it's GA4, no worries.

I think we think that might be it. Unless there's another question. Yep. We're happy to come on to any questions you have after.

Yep.

And also tell you a bit more about amplitude if you'd like to listen. Okay. But thank you for your, for Your time for listening in.

Thanks, everyone.

Thank you. Esther.

Is that my water?

It.

D.

Sa. It's.

Sa.

No, we did not.

Okay, so here.

Okay.

Yes.

Please welcome mailchimp's head of data platform products, John Humphrey.

Good afternoon. Thank you. I'm John Humphrey and I'm here to talk to you today about a data scientist guide to product development, how data informs product roadmaps. First off, a little bit about me. Over the years, I've led data orgs a number of places. I was chief data officer at Mailchimp. I was the first data hire and employee number 30 at Goodreads coming in before the acquisition by Amazon, I've led data teams at intuit on the QuickBooks business. I've even worked as an ads data scientist at Meta a couple of times. And I'm here to tell you it does not matter where you go, data are always going to be messy. I've also had the privilege of being able to make this clicker work. There we go. I've had the privilege of helping take a company public. And in 2021 I joined LegalZoom as their head of data and helped us get through the IPO process later that year. And nowadays I'm a product manager at mailchimp where I help ship data platform products. And so what I hope to do for you today is share a little bit of this kind of combined perspective of being a data leader and a product leader and help kind of mesh this together as to how best to use data in order to drive your products and businesses forward. So, enough about me, let's talk about what we're doing to do together. And what we're going to do today is this is going to be the day that we break through some of the data fomo, because I know it's really easy to see some of the stuff in the press and hear things on the news and kind of wonder to yourself, like, what am I doing wrong? Why can't I do some of the amazing things that some of these rather large, well funded companies can do? And I'm here to tell you that in reality, in most cases, you do not have big data. And you know what? That's perfectly okay. So cut yourself a little bit of slack. But by the same token, cutting yourself some slack doesn't mean do nothing. Instead, today we're going to talk about three things. First, we're going to talk about how you measure performance, and then we're going to talk about how you use that measurement of performance in order to sort of assess your business and drive decisions. And then along the way, I'll sprinkle in some ways that you can accelerate progress. So let's dive in and talk a little bit about how we measure performance. And I get to start the conversation with some good news, namely that a lot of this is already done for you.

You.

What I mean by that is the moment you picked an industry, a lot of your metrics have been sort of selected for you. Investors and customers already have a way of thinking about your business the moment you pick that history, and so you inherit a lot of that. And we're going to talk about how to use that to your advantage. First off are business outcomes and business outcomes. The way I like to think about that is the value that your business is creating for investors. This tends to be incredibly similar by industry. So, for example, if you're in A, like B2B SaaS sort of subscription business, I can tell you that there's certain metrics everyone's absolutely going to expect you to look at. And so take advantage of this prior art. And so one of the things I will do throughout our conversation today is I will use sort of an example company and imagine that this company basically is in the business of, you know, doing payments and invoicing. And, you know, for example, in the case of this particular hypothetical business, something that they might talk about as a business outcome is retention rate. In addition to thinking about the business outcomes, customer benefit is another thing that you will care about. And here too, this actually tends to be pretty similar for all the entrants within an industry. And this measure of value for customer ends up being the same. And it intuitively has to be, because when a set of customers come to a particular industry looking for a solution, they're looking for the same thing. And so then naturally you're going to largely measure that benefit in the same way. And again, and to sort of build on our example here again, this sort of invoicing and payments company, something as simple as invoices sent, would be an example of a customer benefit that they might measure. The things we've talked about thus far tend to be fairly homogenous across players within an industry. There's one area where that's generally not true, and that's when you start getting into some of your experience metrics. And the way that I would think about these things is this is how do you generate value for your customers? It's getting into the specifics of how your product works. And if this isn't unique to your business, this might be A sign that you're not necessarily as differentiated as you should be. One other comment I would also make here is where the first two sets of metrics we talked about largely are pretty durable over time, experience metrics often change to the extent that you might pivot your business, to the extent that you might change how you go about establishing product market fit, naturally your experience will change and how you measure it will change. And so again, to sort of build on this hypothetical example, one of the things that let's say this business focuses on using AI to sort of generate invoices in some capacity, they might want to look at the percentage of invoices that are AI generated as a measure of how that experience is coming along. I want to stop here and give a really big sort of pro tip as to how to think about metric establishment. Many of you are trying to take your company public at some point or have some sort of big exit. And as you go through that process, for example, companies filing to go public in the US have to put together an S1 doc. This is very much an artifact that tells the story to investors in the street of how your company is doing. Look at companies already in your industry or companies that are disruptive in the way in which you hope to be disruptive, and go go back and read the docs that they've already published as they went public. In many cases, this gives you the blueprint. This blueprint is useful for two reasons. One, it tells you literally what metrics people are going to expect to see. But two, it also gives you some ideas to how to start thinking about your business and evaluating it now, knowing that that moment is coming. And as I think about my experience leading data at LegalZoom, this is one where now that I know what the end game looks like, if I were to ever do something like that again in the future, I would definitely start at the end and work backwards. And so that would be my advice to you is how to get started here. So of course I sort of. We were saying a moment ago that we have some good news here and that a lot of this work is already done for us. The bad news is there's parts of this where we have to do our own homework, and specifically that comes down to where we set our own goals. And the way that I think about what a goal is, is a goal is a way of contextualizing a metric. So, for example, let's say Your revenue was 5 million last year.

Is that good?

Is that bad? I don't know. But if Your goal was 10 million, then 5 million doesn't look so good. And conversely, if your goal was 1 million, that actually looks pretty good. And so I want to talk a little bit about how you go about setting goals, because this is harder and you kind of have to do it for yourself. And so there's a bit of a framework that I like to follow in terms of goal maturity, and we'll kind of work our way from the bottom up today. So first off, at the very bottom is this notion of a ship goal. And that's quite simply, did I get a product out on time? Again, not a very great or sophisticated goal, and ultimately not your end game, but at least starts you down the path of getting into the discipline of measuring your progress, holding yourself accountable, and ultimately then being able to drive improvement. As we move up from there, functionality becomes the next thing that you think about. And this might be things like the uptime of your application or the sort of TP 95 or 99 of how performing your app is. These are metrics often, again, that your sort of engineering partners are looking at, and they really speak to the quality of the thing that you're delivering. But again, still pretty far away from what your customer cares about. Customer experience and business outcomes. I won't dwell on that too much because those are essentially the things we just talked about a moment ago. But you're now starting to get closer and closer to those sort of top level of maturity. And that top level of maturity is customer outcomes. And this is really the thing that your customer is trying to accomplish. If you're in a B2B business, this is, you know, to what extent are they growing their revenue? Or if you're in a B2C business, this might be whatever need that they're trying to fulfill. And as you start to look at this, you probably start to see there's a tension emerging, which is, on one hand, you want to move further and further up the continuum because that gets you closer and closer to the customer benefit that you're trying to deliver. But as you do so, you get further away from your ability to influence and measure the thing that you're observing. And so then, then you have to sort of strike the right balance in the moment of those two factors. And the general rule of thumb, I would say, is for the companies of this sort of size, you really need to at least get to the business outcome stage. The reason I'm not necessarily prodding you towards customer outcomes is that while customer outcomes are a fantastic provocation and certainly something that should inspire everything you do. The reality is they're incredibly hard to observe. I can tell you even now, as you know, Mailchimp is part of Intuit, which is over $100 billion company. Even we have a hard time observing customer outcomes sometime. So certainly in early stages this is not a particularly good place to prioritize. Use it as a provocation, be inspired by it, but then sort of move on. And the big pro tip that I would offer here is this is hard. So start early, start Basic, and then sort of iterate your way into a better and better place. We've talked a little bit now about how to sort of pick metrics, how to set goals around them. Let's talk about how to actually start doing this stuff. And the good news I have for you here is that you have more data already than you probably realize. First off, there's back end data. And these are the data that often are sitting in your product many times already in a database and are essential to your product functioning properly. So for our example company, again, if you wanted to count things like how many customers are creating invoices on the platform or how many invoices were created yesterday, all of that stuff has to exist in some backend data store or else the product won't work when the customer comes back the next time. The bad news about back end data or the challenge you'll run into is that these are data stores that were purpose built in order to make your product function. What they were not purpose built to do is to provide metrics and insights. So there is some level of curation that you will have to do to these data in order to to get the answers you want, but at least you have them at all and they're a great starting place. So if back end data are the story of what happened, front end data becomes the story of how it happened. So generally speaking, you won't find in your backend data anything about what journey the customer went through in order to create that invoice. And this is really important information as you try to improve the quality of your experience. And so one of the places that you can sometimes have data are these sort of front end events. Now you may have to do some level of work to go in and actually instrument this stuff. But if you've heard of platforms like Google Analytics or Amplitude, these are all sorts of platforms that are ultimately consumers of front end data and then sort of render it in a way that's relatively easy to consume. And so, yeah, that's another place that I would be looking for information as well. We've talked a little bit about what you can learn about your product. You also probably want to learn a thing or two about the marketing of your product and that that's where ad spend data can be really interesting. And this is if you think about you're running ads on social or on search. As you spend that money with those platforms, they usually provide back some out of the box reporting that gives you some idea as to what's going on. Now the challenge you will run into here though is that marketing attribution is hard. What you want is to be able to say, okay, I spent money over here and I got conversion over here. But the reality is the world is not that linear of a place. Customers are exposed to many different sort of stimuli and purchase for many different reasons. And so there is some level of interpretation you as the consumer of the information must perform in order to really get to good decisions. The other thing I would point out here is all of these things are inherently biased and not even necessarily intentionally so. Facebook knows nothing of what you're doing on Google and so when index out they're going to assume that the conversion they saw where you spent money with them belongs to them. And so there will be duplication and things again that you have to reconcile for yourself. But at least this stuff comes out of the box and gives you a starting point. And then last but not least, there's always qualitative data. You can always run a survey, you can always talk to a customer. And so these things are great because they're very free form and they can answer all sorts of questions in so sort of fill in the cracks in between the other data sources. What's challenging about this and why you can't just rely on this is it doesn't scale particularly well. This all involves manual effort and it's just going to slow you down. So don't just default to relying on qualitative data. Instead, kind of look at it as a way to sort of fill in the gaps. Next thing I would advise you on is the time to start is now. And there's two things in particular I want to highlight here. First off, get in there and start building dashboards. And remember that function is more important than form. A Excel spreadsheet emailed manually every day with the right metrics is going to beat the most elegant tableau or looker dashboard that is just kind of there to look pretty. And part of why I encourage you to start building sooner rather than later is that Your first attempts to do this are are going to fail. And understanding why they fail is instructive. When you start trying to calculate a retention rate and you realize that there's a hole in your data, that's going to happen. The sooner you can figure out and start to recover, the better. Now once you start building, then the next thing to do is start using them. And not just in a perfunctory like, yep, this came into my inbox. I looked at it and I then never thought about it again. But really get in there and try to start making, making some decisions and ask yourself, does this make decision making easier? To the extent that you find yourself saying, yes, I'm clearly making decisions off of this, you're on the right path. To the extent that you're not, that's where I would encourage you to iterate a little bit. And the thing I would think about here is that simple metrics reviewed regularly will beat trying to create the perfect sort of panacea metric that incorporates all the insight. Just being able to look at simple things like how many customers came and used a feature day by day over time you'll start to get some intuition as to what's going on. You'll see that spike a couple of weeks ago and you'll be like, oh yeah, that's right, we did some sort of uptick in SEM spend that day. Or you might see something get better and you're like oh yeah, that's right, we fixed a site wide bug and you start to kind of connect the dots in your head. And so get in there and give yourself a chance to start learning these things about your product and your business. Because ultimately failing fast means that you're learning, learning fast. So now we've talked a lot about how to get started. I want to talk a little bit about some pitfalls that we can run into along the way as well. First thing I would talk about is being dogmatic. It's easy to get in there and start expecting everything to be perfect. Like oh, if I look at all the different parts of my experience, I can account for all of my attrition. The reality is that you just can't my much. Like with an attribution, the world is not that perfect and linear a place. And two, especially in early days, your data just aren't going to be that clean and so there's just inherently going to be gaps. I think it's good to start principled but recognize that as you start to execute that you are going to have to make Concessions. Similarly, I would also talk a little bit about being a perfectionist. And when you start going down the path of you just cold stop whenever data are wrong or missing, now you're potentially falling into this trap. And part of why I think this is a trap is that the reality was never that data were going to solve all your problems and give you a deterministic answer. It's just that it was going to reduce some of the uncertainty. And so just get in there and start working with what you have. And as long as you're working with less uncertainty or making better decisions, not even perfect decisions, you're headed in the right direction and keep going. Also along these lines, I'd say not setting goals is a thing to watch out for. And that was that one's pretty self evident. If you have metrics and you don't have goals against them, you probably need to revisit that. And my advice here would be just set any goal. Because what happens when you set a goal, even a somewhat arbitrarily selected one, is you start comparing. And when you start comparing and you see differences, the next thing you do is you start asking why. And why is an incredibly powerful question. The more you ask it, the more you try to answer it, the closer you'll get to the truth, faster and better understand your business. Corollary to this though, is not resetting goals. And you know, you've arrived in this spot when missing a goal becomes uninteresting, when the same metric is in red week in and week out, and you just kind of give it a shoulder shrug. And what I would do here is I would first I would start by reevaluating the goal. Maybe it's just not a realistic goal for where your business is at this point. So then, you know, adjust it accordingly. But then if even that leaves you in this spot where you're still indifferent to misses, then I would say maybe it's time to reevaluate the metric itself. Because again, you're in early days, you're starting to do this and you're not going to get it all right on the first iteration. And that's okay. Now let's talk a little bit more about how we start driving actual decisions off of the frameworks we've put in place here. And my big piece of advice, first off would be to gain early momentum by focusing on the right questions. And what I mean by that is first start with the big existential questions and deal with them now. Things like, should we pivot this business altogether? Go ahead and answer them for two reasons. One, it's big and it's existential. It's going to be top of mind. And let's be honest, you're not going to think about much of anything else until you get past it. But two, this is a place where data can be high leverage. Again, data are not going to lead you to a perfect deterministic answer. It's just going to reduce some of the haystack around the needle, so to speak, and help you get closer to a good answer. Once you've done that, then the advice I would give is actually go to the small tactical stuff. Rack up some nice quick wins, little things like why is the invoice API slow? This has two nice effects. One is that it starts to work very quickly because you can ask these questions and answer them and take action relatively quickly, which starts to develop muscle memory in your organizations as to how to use data to drive the business forward. But also you'd be surprised how much these like seemingly straightforward wins can really make a difference for your business. And so the sooner that you start to realize those things, the better. And then last but not least, then I would say sort of end with the questions that are kind of in the middle. And this is a sort of place where like, the effort sometimes doesn't match the outcome. And I'll give you an example. You might want to look at your marketing spend and say, like, do we have the right sort of channel mix between search and social, et cetera. And I'm not here to tell you that you shouldn't throw data at this question. You absolutely should. But I would advise against going here first because this is actually a big, hard, unwieldy question. You will spend months trying to get to the answer to this. All the while you're not necessarily getting benefit of having solved any of the things that you could have been working on in the short run. And you're not necessarily enforcing that sort of muscle memory with your teams. And so again, I'm not going to tell you that these aren't important questions, but I'm saying like, they're not the highest leverage in terms of quick wins. So don't go there first. Another thing that I would recommend is make data backed decisions a cultural norm. And there's three things that I would particularly highlight on how you do that. First off is distribute data, whether it's in an automated dashboard or an Excel spreadsheet you send out every morning, just any means necessary, get in there and start doing it. And the more Context you can provide with the data, things like defining what different metrics are, the better, because that'll make it more meaningful to the people who receive it. Which leads into the second point, which is involve everyone. Database decisioning is a team sport. And I know sometimes, especially in a tough market, when maybe your company's not doing as well as you'd like it to be, it's kind of tempting to keep data close to the vest and maybe not necessarily disseminate bad news. But the thing that you give up in the process of doing that is now there's all these people that you're missing out on who could also be asking the question why and getting closer to the truth and getting better answers to help ultimately drive the business forward. As you're going through this process of involving everyone, I would also say that it's really important to be good at capturing assumptions and capturing decisions, because ultimately, when you go to take action, having this context will be really, really useful to you. You will get in there, you'll start doing things. Some of them will work, others will not. And being able to remember what you thought and why will then sort of bring the learnings full circle for you. One thing I often like to highlight when talking about this topic is people often think about, like when they run experiments, for example, that a winner is good and a loser is bad. And I tend to think about it as, of course a winner is good, like you like it when things go well. But an experiment that's a loser is almost like the next best, because you still learned something. You just learned that what you thought was true, the opposite was probably true. It's the stuff that comes back flat, where you sort of come out the other side not knowing anything you didn't know already. And the pro tip I would offer here is that culture comes from the top of an organization and so exemplify the behavior that you seek in your teams. As I think about the companies where I have worked over the years that do this best, it starts with CEOs and leaders who are particularly intellectual, curious, and very keen consumers of data. And then naturally kind of everyone sort of follows suit. So this is where you as leaders and your teams can have the same effect on your companies. And to ask to get better answers, one of the best things you can do is ask better questions. And so I wanted to quickly run through a couple of examples of questions that I've seen as a data leader over the years that again, are completely reasonable good questions and how we can make them better and Drive towards better outcomes, outcomes for your business. For example, a question I might get is describe our best customers. And the way that I would improve this is I'd be a little bit clearer about the call to action. And let me give you an example of what I mean by that. So if you take a question like, how can we acquire more customers like our top spenders? That question is helpful in two ways. One, I know I'm clear what you mean by a best customer, it's a top spender. But the more important word here is acquire. Because let's say, for example, I'm looking at this from a product perspective and I provide you a whole bunch of data as to how our best customers use the product. Well, that's not going to be terribly helpful at acquisition phase when I know nothing about how the customer uses the product because they haven't used it yet. And so this sort of clarity around the question is really meaningful because now I know as your data partner, how to come back with an answer that you will be able to take action upon. Another sort of question that I get at from time to time is, is our retention rate low? And you know, this is one where I think focusing on the outcome and contextualizing it a bit better helps. So, for example, if I was asked instead, what must our retention rate be in order to hit our margin goal? Now I can offer you a more objective answer. When you ask me if it's low, that's subjective. I might think, yes, no, might even depend on the day. But when I say, okay, I know how much it acquire it costs me to acquire acquire a customer, I know what the lifetime value of that customer is. I now know basically what retention has to be in order for the math to work out to hit some margin goal. And now I can give you a very objective answer to your question, which features drive retention? Another question I get, and this is one, whereas your former product data partner, I would love to know the answer to this question too. But the trouble is I simply can't disentangle a product perfectly and know that this feature is driving three points of retention. This one five and one two and so on. And so then instead I focus on something knowable, such as which features seem to see the most repeat usage. It's not perfectly the thing I want to know, but it's a pretty reasonable proxy to assume that if a customer keeps coming back to the same part of the experience again and again, that it probably has something to do with why they pay me again and again and then Last but not least questions like should we fix the site wide bug? And how would I improve this question? Recognize sometimes you just the question I'd be asking is what are you waiting for? Just go fix the bug already. And so yeah, just recognize that while it's tempting to throw data everything, sometimes you just know what you need to do. And if I were to sort of summarize this into a pro tip, I think it's good to brainstorm in terms of broad questions like the ones you see on the left. But when it comes time to get to work and start answering questions, focus on something a bit more specific that will tie to an action or outcome that you can drive. So let's talk a little bit now about how we can sort of accelerate progress on this journey. And I like to call this portion an AB testing guide for companies without 2 billion users. Because it's great when you're at a scale like a Facebook and you can run a test of significance in hours, maybe days at most. But when you're doing subscription B2B SaaS early stage and you've got a customer count that's barely got a comma, that's same test is going to take you months, maybe even quarters, you don't have that kind of time. So let's talk about some ways that we can make some reasonable trade offs and accelerate progress. Three ways I would talk about how do you test faster, ensure that the desired outcome is even possible. An example of where I see this often go wrong is people are trying to move a go to market metric with non go to market levers. So for example, you want to move revenue and the thing that you're playing with is say some product experiencing like the left nav. In my experience over two plus decades of doing this, pretty much the one thing that reliably moves a go to market metric is a go to market lever. If you play with pricing you will probably move revenue. If you play with subscription terms you will probably move revenue. If you move something about the product experience, you almost certainly won't directly move revenue in most cases. And so rather than than send weeks or months down the drain running a test where you're probably going to get a flat result anyway, just probably don't even go there. The next piece of advice I'd give is make the precision right for the decision. You're probably looking at a business that's trying to grow by 20, 50, 100%, maybe more year over year. You do not care about testing at 3%, Delta at 95% confidence, you can probably look for a substantially larger delta. You can probably run at 80% confidence and be perfectly fine and still make a better decision than you would have made otherwise. And the difference between those two things can be months of testing time. And so don't overlook the fact that just kind of taking the sort of default answer that you get from your website stats calculator of how long you need to run a test is making some assumptions that might not be appropriate for your situation. And then last but not least here, you can also test faster by finding leading indicators, because sometimes a test is over before it's over. An example I would give here is let's say you're trying to move something like 90 day retention and you believe you're going to move 90 day retention by first having some interstitial that pops up and creates awareness of a feature, second gets them to sign up and start using the feature somehow. And then retention. Well, you can safely assume that if, okay, the interstitial viewing goes up, but then steps two and three don't go up, retention almost certainly is not going to go up either. Because once you've sort of broken the causal chain, the game is sort of over. And so no need to wait 90 plus days to see what happens to that experiment. You can probably assume it's a loser and move on to the next thing. That doesn't necessarily help you call winning experiments faster faster, but if you can end the losers faster, that's still progress too. But in some cases you really just can't test. And in those situations I'd call out two things. First is sometimes people will do pre post analyses or some sort of time series comparison. That's okay to do, but do it with a little bit of caution. Of course, things such as seasonality or exogenous factors like the macroeconomic conditions will always make time series comparisons hard when there's no control. But the other thing you have to watch out for is there's just some inherent level of volatility in your metrics. And especially with smaller companies that can be fairly substantial. And so if you've got a metric that like month over month tends to bounce around like 10%, know that you're never going to be able to detect a 5% delta based on something that you just changed. And so just be realistic and go in with your eyes open. And then, you know, like we talked about before, qualitative data are always a really good way as well to start to understand what's going on when you can't Run a test, talk to a customer, send a survey, just get out there and do something. And the pro tip I would offer here is know that not every change needs to be tested. Sometimes you do just have to make a decision and go the other way that you can sort of accelerate your progress is by throwing money at the problem. And I want to talk quickly about two things here that are potentially helpful. One is let's talk about your people and your first data hire. Think Swiss army knife, not specialist. I've literally been data hire number one. And why I was successful in that role was I knew a little of everything and not a lot of anything. So this is not the time to go hire your expert in generative AI. Hold on to that thought for later. But the person will come in, they will start to help you figure out where the bodies are buried, start to figure out, how do we even define revenue here? And they will probably also give you some clues as to what your second data hyper should be. Also, probably not a generative AI person just yet either. The other accelerant that we have at our disposal is platforms. And the big thing I want you to take away here is that in the year 2023, pretty much all the platform choices you have are good. So pick one and go. Sometimes people ask me, should I use redshift? Should I use snowflake, Should I use bigquery? And the answer is yes, pick one and go. That's not to say that they don't have their strengths and weaknesses, but all these platforms have gotten so relatively good in the grand scheme of, of things. The best thing you can do is just pick one and get going. And a big pro tip that I would offer up here is that data engineering can be a real force multiplier on all these fronts. Having even one, maybe two data engineers can help you get a lot more out of that first data hire because they can help curate the data at scale, especially like the back end data that are probably all over the place. And they can also help you make particularly optimal platform investments. And so this is one of the of those where it's not necessarily the first thing you think of, but getting that higher sooner than later can make a real difference. So I want to now pause and close out by putting it all together for you. Hopefully today's the day that we get to say so long to data fomo. And I want you to remember that data driven decisions are closer than you think. You already have a lot more data than you probably realize. And in many cases, things such as metrics are largely already chosen for you. So just, just take advantage of all that stuff and start going. And start with the end in mind, work backwards and iterate. Looking at artifacts such as S1s of companies that are in your space, looking at how they did it and drawing inspiration from that is completely the right thing to do here. As leaders, you can also help create a culture of data driven decisions and focus on asking good questions. And the quality of the question has more to do with the quality of answer that you get than you would realize. And also not having the resources of a massive public company doesn't have to hold you back. It just means you have to be more intentional. No running tests to 95% confidence for you. And so ultimately, I hope what you take away from all this is that if you start simple, ask questions asking why, iterate quickly and focus on outcomes, you too can bid ado to data FOMO and really start to bring your roadmaps and businesses together. Thank you. And with that I'd like to sort of offer up the opportunity to take any questions that you may have. And I believe the way it'll work is there's a mic up there and so just if you want to ask a question, just come up to the mic.

Sorry, it's been a long way from dinner. Anyways, thanks for your insights about the data and how we should, you know, define the roadmap probably. But I would assume at a company like Milchimp, you know, which is ultra huge and number one in the world, you know, in his industry, there is lots of different ideas. How do you prioritize? Is there any business case calculation before prioritizing and getting the certain ideas on top of other great.

So I think the question is how do you prioritize which problems you throw data at first? And I would say both for this question of prioritization as well as just in general, don't so much think of data as a foreign object, but like any other part of your business. And so to that end, how you would prioritize data resources really shouldn't be that different than how you would prioritize resources for anything else. You know, to the extent that, you know a decision is big and existential, it's going to come first. To the extent that you've put together a case, you know, that says there's a massive opportunity here, here that's going to get resources first. To the extent, honestly something is some exec's pet project, yeah, that's going to eat first too. But in Many cases it's sometimes just don't overthink it. You don't necessarily need to calculate out to six decimal places how much benefit is associated with the decision because there's going to be false precision anyway. And so just use good judgment and go just like you would do do in any other space. And so I think in many regards data, really your default assumption should be data doesn't work that differently than any other function. And in many ways I would say if I were going to pick one function in particular, that in many ways data mirrors. Think about it the way you would think about engineering. It actually works very similarly in terms of especially now with more modern platforms and how teams work delivery works more similarly than you would think. And so whatever mental models you're using there in terms of prioritization, work planning, et cetera, use that as your default and then course correct incrementally where appropriate. But yeah, it's really actually not that different. Other questions.

Yeah, so we have a data team.

Of maybe five or six.

They concentrate on data that actually powers our products.

And we're probably quite embarrassingly not data.

Driven in terms of decision making.

So if we were to go in.

The direction you've been talking about and using data to drive decisions at a business level, would you use the same team that are delivering data for your.

Products to do the data engineering and.

Decision making analysis for business decisions?

Great question. So I think to sort of summarize the question, it's, you know, especially in earlier stages when you have data people, they are often data in terms of powering the sort of core product experience. Are those necessarily the same data people that you want as you start moving into more analytical and strategic type decision making and especially at the like, you know, five to six person org size? I think the answer truly is it depends because sometimes you have that person who is very versatile in a way and like they will not naturally sort of pivot effortlessly into dealing with different sorts of stakeholders. But what I would say, especially as the orgs start getting a little larger, what you'll find is the answer is it varies. So if you think about data as really consisting of sort of three sort of sub functions, there's data analytics, which is really how do I use data to sort of answer like big strategic questions. There's data science, which is probably more how I answer sort of the little micro questions that happen many times a second in a product such as should I approve this transaction? And then there's data engineering, which is really the flow of data Sort of to and from different parts of the product and experience. What I would say is that sort of team you're describing probably indexes a bit towards data engineering, maybe a little bit towards data science, and probably least towards the data analytics kind of stuff. And so I think what you'll find is that some of the folks will work very well in terms of that sort of curation of data and at scale, which is very much what data engineering is trying to solve. But the second that you ask that same person to stand in front of your CEO and explain what does this all mean, that's like just a very different muscle. And so I would think of it as, you know, if you have five to six people and like, let's say to use an extreme, you were to pivot all of them tomorrow to be like in service of sort of decision making, if you will, I would assume roughly like a capacity reduction of maybe like 50ish percent and terms of like, they will do some things very, very well. But then like that last mile, for example of like, then how do I sort of package it up to somebody will probably not be as natural in adjacency. One last thing I want to come back to is in sort of setting up the question, you were making the point of like, almost like kind of sheepishly like we're doing something a little bit wrong. And here's the thing, you're not. You must have a business and a viable product in order to be at the point where you have the right to have the problem of, okay, how do I make the business more optimal through the sorts of things we're talking about. And so the fact that you've arrived at this point in many ways is a good and successful thing. I think really just the way I would think about it now is, okay, great, you've made it to this point. This is a way to accelerate and go further. Not necessarily think of like, oh, we're doing this thing wrong. Because again, like, I'm kind of trying to make the point of just in general, everyone's all kind of feeling the same, sort of like FOMO or insecurity, and so know that you're not alone. Other questions?

Hey, hi everybody.

My question is a little bit complicated.

I'll try to keep it short.

We're trying to start a data initiative. We are about a 50, 60 person company. And my question is, there's kind of two strategies we could apply.

We could either.

Look at a data driven strategy where all our nerds generate a bunch of data and provide Statistical.

Control mechanisms to reduce variance, et cetera.

And then ask the business stakeholders to make decisions based on that. Or we could have it question driven, where the business stakeholders pose several questions.

That then we can try to provide answers for.

Is it which strategy would be more advantageous? Is it a combination of the two? Is it a migration from left to right? Can you speak to that? Yeah, and let me play that back and make sure I'm grokking fully. But basically what you're deliberating between is to what extent is it sort of of a push strategy versus a pull strategy, one in which I start with a question and work my way backwards, versus one in which I just sort of publish data as fast as I can? Is that fair? That is exactly what I'm talking. Okay, great. Yeah, so I'd say in terms of overarching approach, I would definitely index towards, like start at the end and work backwards. It's basically impossible to start from the bottoms up with the data and know what's going to be useful. And especially if the team is set up in such a way that they don't particularly have good sort of connections with leadership and the people making decisions, because that context is so incredibly powerful to taking a vast sea of data and getting something coherent and useful out of it. Where I would caution slightly, though, on taking sort of that first part of my answer literally is that what you can also fall into the trap of is you get a bunch of people who solve things in very bespoke ways, who think in terms of PowerPoint and Excel exclusively. And that more like engineering and technical mindset of like, how do I manipulate data at scale? Quickly becomes really, really important. Because back to a point we were talking about earlier, a bunch of time series metrics presented consistently and frequently are incredibly powerful things. And so you want a little bit of both. The way I would say it in short, is the people really setting direction. You want them starting with the problem in the context and working backwards. But you also need people who are mindful of how to do this at scale. The very best data teams, much like the best engineering teams, tend to be full stack. The unicorn hire is the person who can get in and understand the pipeline code and talk to the CEO about it. And so if that's the ideal and you can't get that in every hire, in fact, you can't get that in most hires. In many cases, you do want to get as close to close to that as you can. And you certainly want to structure teams in such a way that these Two worlds coexist as close together as possible and it's not an us versus them sort of thing, but rather a we. Other questions.

Hi, I'd like to to know your perspective on this kind of two different concepts. First one, when we have like a structured data structured and then everyone like a product team and anyone in the organization can write an SQL code and find the answers and they're free to discover. And the other one, when the data analytics teams are managing all like a dashboards, data lakes, updates and they are saying like ask the question, we'll provide you with a dashboard but we won't get you into the data.

So let me play that back and make sure I have the question. So the question is to what extent do you index towards a team that sort of abstracts away all of the complexity and just like we will tell you the answer versus having people sort of just have at the data directly. That's a great question and I think this is one where it's like know your people, know your organization. Obviously the closer people can be to being end to end in some ways the better that works because what you have in that scenario is less context is lost in translation. There is less like the person who answers the question and the person who applies the answer to the question are different and then something gets lost. So you want to minimize that, but you can't, practically speaking always drive that to zero. Sometimes you have an organization where the PMs can write SQL and that can be a magical moment. They can just answer questions as fast as they can ask them and in other cases that's just not where you are. And so just this is one where principles are good, but be pragmatic and just know the people you have and figure out how to sort of play it as it lies as opposed to trying to address things with the team you wish you had. But again, to the extent that you are making higher decisions going forward, forward, the fuller stack people can be, the better that versatility is worth it. Especially in early days. Yes, thank you, that was very insightful.

I just had a thought about information.

Versus intelligence and a data engineer can perhaps compile the data that you have in order to produce information.

What sort of skill set do you.

Need to be able to translate that information into something which then can help.

Guide a business outcome.

Got it. So the question is what sort of skill sets does one need to be able to take sort of information and then translate it into the sort of intelligence that drives decisions? I think there are many ways that one can get there. I think one of the interesting things you'll notice is if you look at like a good high performing data team, the backgrounds that they come from are all over the place. It's not just a bunch of people with data science degrees because for starters, those degree programs, most of them didn't even exist 10 years ago. And so it can be like random things like anthropology, it can be very obvious things like math or computer science, they come from everywhere. But I'd say if there's one thing that all of the people who do this, well, have in common, it's that just intense intellectual curiosity. They can't stop like asking why. In fact, one way that I've described it to teams of mine in the past is that a good analyst gets to the end, has a number, answers the question, and says, ta da, here, I'm done. The great ones get to that same point instead of being like, ta da, here, I'm done. All are constantly asking themselves, okay, how did I screw this up? Where did I make a bad assumption? Where did I make an assumption and not even realize I made an assumption? So. So again, that intense intellectual curiosity is the thing that makes you keep looking and digging and asking why and finding the truth of like, oh, wait a minute, I totally made an assumption here. What if that's not true? And that I think more than anything else is what allows people to sort of transcend and become really good at this stuff, is just insatiably curious and just constantly like doubting and asking why. You've heard me say the word why a lot today and I probably still haven't said it enough. And that would be, I think, again, how I would think about what would make somebody strong at this stuff. I think we have time for one more question if anyone would like to ask. Okay. Yes. Yes. So the question is a team of literally five people, a tsunami of data coming their way. What do you do? Start with the end and work backwards. If you stand there in awe of all this data, you will get crushed by, by it. If you stay focused on the most important questions. And what do I need to answer that question? Nothing more, nothing less. That ability to maintain focus is what will allow you to extract value. Will you miss things? Will there be opportunities squandered? Of course, but that's not the goal here. The goal here is just to do better than you would have done otherwise. And I think if you stay focused on the question as you navigate all of this, that will give you your best chance. Thank you much, very much, everybody I've really enjoyed getting to chat with you today and best of luck to you. Sa.

Wait.

Sa.

It, Sam.

Sa.

It.

Sa.

It.

Baby.

Please welcome Hyper Growth Partners co founder.

And general partner Guillaume Cabanet.

Hello. Thank you. Thank you. Hello everybody. Okay, and if I can get my slides on here would be good.

Great.

Awesome.

Cool.

So everybody calls me G, so please feel free to call me G. And I used to run growth at a couple of great companies like Segment Drift or Gorgeous. Can we have a show of hands? Who here has used one of those three products? That's like a good third of the rooms. That's pretty solid. Okay. And I've advised like more than 20, probably more than 30 companies now invest in more than 50. So like there's really strong odds that on a weekly basis you use one of the products that I've contributed to. So people often ask me, gee, like do you have like a bias when you recommend like companies like I am the bias. So think about that when you're asking me for advice on software. And these days I've actually productized advisory. So I've co founded Hyper Growth Partners. We are really amazing crew of VPs and CMOs who do advisory for equity. So really like sweat advisory. We invest our time in exchange for equity mostly around like series A to series C companies and we accelerate their path to hypergop. You can see a couple of the great logos here and a few of the amazing people on our crew here. That's just half, maybe even less than half these days. But you can see we've got people all over the board, all CMOs, marketers, growth people dedicated to making your pipeline, your demand gen scale. Work with that. The topic for today is efficient Hyper Growth because we're in 2023. So what are the strategies that work that I employ, that my crew employs in all those companies? What's the stack that you need, the tech stack that you need and what's the crew? What's the team? The org chart that looks like what do you need? So first, what is hybrid of growth? What's that Magic hockey stick? I can share a couple of numbers from my experience. So when I was at Drift, we grew the revenue from 7 million to 27 million in just one year, which is pretty darn impressive. A lot of things broke for sure, but we got there. When I was at Gorgias, we did 1.5 to 12 in two years, which is annualized 3x year over year. And more recently at ramp, we increased the revenue 10x year over year. We went from 100 million in annual transactions to 1 billion. So maybe a better title for this presentation today is Hey G, just tell me how the heck is Ramp going so fast? So let me tell you, there's three key things. The first one is low CAC growth strategies. I want to cover that. The second is clean prospect data, a complete coverage of all your prospects that goes into your growth stack. And the last one is a growth team that's working on the top of funnel and that is taking risks. We're going to talk about that. Let's cover the first one. Low CAC and scalable growth marketing strategies. So this is ramp. You can see when we joined, what's the difference? They were going fast, but not in a very repeatable way. They were landing some wins, but those wins weren't very scalable. The one key thing, the one win I implemented is outbound. Not any kind of outbound, fully automated outbound, extremely high scale. We're talking of like hundreds of thousands of contacts enrolled per month fully automatically. And people often ask me, gee, what are all the colors on the this chart here? What do they mean? Each color is a different intent signal. Okay, so when a CFO is hired, hiring signal, automated email. When somebody anonymous comes to the site, automated email. When a potential buyer goes to G.2 automated email. All of those are buying intent signals which automatically go through the entire pipeline of evaluation, fit modeling, contact inspection, sends emails without SDRs being involved. What's important here is you can see the two key stats at the bottom here. The outbound is now about one third of the total pipeline. The automated outbound and the CAC to LTV is 3x lower. It's 1/3 of paid. So it is high scale, low CAC, exactly what we want. This is one of the key competitive moats that Ramp has. So how do you do that? The first thing that I recommend always is to map your entire total addressable market. Okay, so you're going to find the list of all the companies you could sell to. Like all of them. You're going to score them, then you're going to find all of the contacts at those companies, going to build a fit model and you really going to build an understanding of your icp, your ideal customer profile. It is important that you map out all companies beforehand instead of like doing it progressively. Makes a pretty big difference. So the key difference that I see is the clean customer data. That is the difference. And more than customers, prospect data, people who you are not engaged with at this time. So if we Take for example Ramp. They sell to finance orgs, CFOs, VPs of finance in the US for companies above 10 employees. They mapped the 3 million US businesses that are a fit and then all finance people at all of those companies in a database and they're tracking all of the intent signals that match those companies. So you have to invest in that clean data. So three key steps. TAM database with company prospect data, second first party intent data. So what's happening on your site, on your app, if you have an app, and then the third party intent data, whether you get it from G2, as we said, you get it from maybe a cognizant or any other place. There's a lot of data vendors these days. You have to aggregate all of that into a central database. It's fairly technological marketing, fair enough, but that's the competitive modes. So the four pillars of a scalable low CAC outbound are like first, like getting the best contact data out there. So the coverage, it's not only how many contacts, how many contacts at all of the companies and how many companies can you get? Second is the quality. I see and I do audits all the time. I see so many companies get that wrong. They look for like, oh, we get like 90% coverage. And look at the data quality and it's all crap. The profiles are either wrong or outdated. You think of one stat. The mean tenure in marketing these days is 18 months, which means that if your data is three months old, one fifth of the prospects have moved on and changed jobs. So you're going to get a 20% bounce rate. So you can monitor by the bounce rate. But the cleanliness of the data is really what matters. The third is the depth. Can you get more than just first name and last name? Can you know about their education? Can you know about where they live, how the company is doing, what is the org chart like? And the least important is the cost, right? Because eventually you do want to get to that cactual tv. So here is Ramp's secret tool that we're unveiling for the first time and it's called waterfall. What they used to do before is to increase the coverage stack multiple data vendors. So they would go to vendor A and buy, I'd say maybe like 30% coverage for 30 cents. And then to get more coverage they go to vendor vendor B and then vendor C and vendor D. And all of that creates complexity, right? There's overlapping data. They're buying for the same contacts multiple times. They have to maintain the integrations they have to pay with sometimes annual contracts. They have engineers dedicated to that. All that is painful and problematic and they have to pay upfront. And they found this company, European company, that is called Waterfall. It's funny, interesting because it actually aggregates all the data vendors in one. One API call, one cost, it's flat. And they have one contract, one integration to maintain great high quality data and all in real time. So that is another competitive moat that reamp has. And yeah, you can pay and you go and get the best vendors. So why does this work? Why does having better data have an impact? Because everybody's saying you buy data. Buy data, buy data, sure, but why. So here's the thought experiment. I'm going to need a show of hands in a second. So who here, who has a mailbox at home? Can I have a show of hands? Who has a mailbox at home? Ok, who gets junk ads like this? Raise your hands if you have junk ads like this. Okay, put the hands down. Ok, who throws out this mail without opening it? Okay, let's say almost 2/3 of the people here. Okay, so 2/3 throw out junk mail without opening it. Makes sense. Now, let's imagine you get this. You get what is very obviously a personal mail. Like when grandma wrote your name on the envelope, licked the stamp and put the stamp on the envelope and sent it to you. Now I want to ask, who here throws out this envelope without opening it? No one. Right. You have to be a psychopath to do that.

Okay?

You have to be a complete psychopath to throw out this envelope without opening it. Why is that? Okay, well, the first thing here is the junk mail, as we said, never opened, always trashed. Almost always trashed. There's one third who likes seeing ads here. Okay? It's not personal. So the first key thing, it is not personal. And you know that. You know it's an ad. You know that nobody cares about sending you this envelope, this ad, this one here. Curiosity and reciprocity. Those are the two key points. You're curious. The old grandma sent you an envelope, sent you something. You don't know who she is. You just see your name. You're curious, why has somebody written to. To me? Reciprocity. Somebody went through the effort of handwriting, takes time and effort. I'm not sure I could do it today, right? Of writing an envelope and saying it to me. Okay, you feel for that person. You feel for that effort. You feel bad inside of you. If you throw out this mail without reading it, you feel ownership, you feel responsible. Okay? It is personal. That's the difference. We can use this trick in our marketing communications. And this is why we need good data and great copywriting. Okay, so here's a summary of what this thought experiment. If you're truly convinced it comes from a fellow human and there's effort, and you're convinced there's effort, you will feel bad about ignoring them. And this is why you've seen in Martech all of the people with like, you know, the, the automated videos where there was like an SDR on AE that had your name on an. On something like that. That's why all of those new mediums are meant to create that new. That same feeling. Okay, what's interesting, two key things are interesting. One is this just shows that even in B2B, people buy from people. Okay? We don't buy from a business, we buy from people. And here's the second thing. All of those new hacks, they work for a while. Eventually the conversion rate goes down. And so if you look here, like, everybody is like, at the merging of both curves in the middle here, okay? And so every time there's this new technology, there's like a new idea, new hacks, like, it works for the people on the rising edge of the curve, and then everybody gets too much of that and you get desensitized. Conversion rate falls, start over again. The best people, the best marketers, the best growth teams are already on the rising curve of the next one. That's where you want to be. That's where the conversion rate is good. That's where you have a competitive moat. Okay? And you always need to be on that leading edge before it's too late. Okay? So you need clean customer and prospect data. You need to be on the lookout for the new marketing platform solutions all the time. All the time. Okay? And the last thing on that section I want to leave you with, which is with all of what's going on in large language models, AI. Right. And I'm convinced it's soon going to be indistinguish, indistinguishable from human writing. How is that curve going to flatten? And how is the envelope thought experiment going to work out if you can't tell apart the grandma from the AI model? Are we going to be desensitized to everyone? I don't know, but it's something to be on the lookout for for number two, modern growth stacks. So we talked of great customer and prospect data. How do you make it happen? Well, you got to do two key things. You centralize the data upstream. So you collect all that data and then you unbundle the tooling downstream. Let me show you graphically what it means. So upstream you're going to have all of that data collection, whether it's like the amplitude or the, the segment and, and the clip, all that, you first party, second party, third party data. All of that is upstream. Okay. You orchestrate all of that through a cdp, whether that's a, a census, again, a segment, and you pass it to the AI model. Okay. And then you're going to have the downstream effect to all of the marketing touch points. Whether that's an email campaign, a chat, something with a mutiny. All of that actually needs to be centralized and then the output pulls down from that. So that, that is what it should look like. Interestingly, I am convinced that we're going to move from having fairly large marketing teams with specialists to smaller teams with generalists because the model will actually take care of the technical details in each channel. Speaking of which, growth teams, what do they look like? Now that's one thing that I have high conviction on, huge conviction. It's the parallel effect of growth teams. Or better put, like how what I do as an angel investor is very similar to what I do as a growth marketer. There are a few wins in a sea of failures and those wins pay back all of the experiments. Okay. So you got to diversify. Let me show you a bit more. So first you have the typical marketing team, what I call the core team. The core team is focused on the top of funnel. They do like brand and dimension and content and it's more like one off campaigns. Very classical. Okay. The VC equivalent of that is the growth stage. Why is that? They know a lot about the prospect, they have a lot of data, a lot of insights and they de risk. Okay. Another way to put it, if a marketing leader fails three or four times in a row, he or she is fired. It's a bad marketing leader. On the other side, the growth marketing team is focused on building evergreen demand gen. They're doing outbound and SEO unpaid. They're building competitive moats. They are the VC equivalent of early stage. They fail a lot. They're going to do a lot of small MVPs to find things that, that work. Okay. Very creative, very data driven. They have very little insight. They don't know if it's going to work or if it's not going to work. They try if it does, super high scale. Okay. So very different thought process. The failure rate on the growth team has to be around 70%. Otherwise they're not taking enough risks. They are not early stage investors. So you can really see the parallel here on the outcome, which is like high yield, the risk to reward, which is high, the limited available data, the high speed of velocity, it's very, very similar. So when you hire a growth marketer you got to be looking for those traits. Are they risk averse, are they risk prone? And so you might ask, hey, gee, I thought this was a talk about growth after the series B. Like why are we taking all those parallels from Angel? Well that's right. And here's one key learning ramp's key success was adopting early stage growth approach at a late stage company. They take risks like a seed or series A. Their growth team takes risks like a seed or series A company. They are late stage company. I'm amazed at how often I see series B, series C or later stage companies that say, oh, we don't do those growth tactics anymore. Like we do real marketing. And then they're surprised that the CAC12TV is bad. Of course they have no competitive moats. They do marketing like everyone else in the markets. They use the same tools with the same data. They're not going to win. So this is what the growth team looks like at most of the companies I'm involved in. I help them set up the growth team. You have this VP growth head off if you want. Interestingly you have I just put out two channels here, paid and outbound. But there's obviously more. What I want to point out is that there is this PM function in the middle. The PM function runs the growth framework, the high velocity framework, the hypothesis based approach. Is this likely to work? Yes and no. How can we descope MVP this? Do we need engineers? Yes and no. How do we analyze that? And then you have the specialists in paid or in outbound. Okay. If you zoom in into what we're going to call like a outbound pod here for a specific project. So you have this outbound framework and you have like an idea. What if we collected this intent data, say hiring signal and automated emails. That's going to require engineers. Good news, you have access to growth engineering as a team. Even though they don't report directly to the head of growth, they are dedicated. You have growth engineers, you've got the PM function and you have the special marketing specialists. You have that organization. You're capable of building unique experiments and have unique wins. So here's really, I'd say the key thing about growth, you need to have KPIs like a sales team, which means like revenue pipeline. If you can't have revenue, okay, you need to be organized like a product team. Engineers PM's ship in sprints, maybe two week sprints.

Okay.

You really work like a part team. You're going to ship products, quasi products, but your scope, your surface area is a marketing one. Okay. So if you have those three things, the center nexus, that is a top of funnel growth team. So to wrap up, okay, so high scale locac is enabled by clean data, prospect data. Map out your entire market. Just go ahead and do it. You need to understand the coverage and how much of the market you touch on the monthly basis. You have to be on the bleeding edge of Martech. If you're using something like a Pardot or a Marketo and you're coming to me and say, hey gee, like how do we win? Like the first thing I'm going to say is like change your martech, okay? Because otherwise you can have the same emails and the same strategy as everyone else using Pardot and Marketo. And the true growth mindset comes from having this like early stage risk approach. Take risks like you are not going to survive the next day on a regular basis. Go for those like early stage risky investments and it will pay out. If you have enough experiments and you ship 5, 10 every two weeks, some of those will have huge impact. And with that, thank you very much. And we have time for questions. There's a mic just here, so I was told to tell you all that you want to come here, otherwise we won't be able to hear. And you can just go here and line up behind the next person and go ahead.

Thanks.

Great presentation.

It's interesting because on one hand you kind of have the early stage kind of approach, right?

This iterative small experience, fail often, fail fast. On the other hand you're kind of.

Saying, hey, you need to buy all of the data, have all of the.

Marketing tools and really have kind of.

A sophisticated big investment on that side.

How do you reconcile the two?

Yeah, so like there's a high budget too, like. Yeah, so exactly. So that's why a few of those companies you look at segment drift, gorgeous or even ramp, interestingly are highly capitalized. And the CEO was convinced about all of this and committed a lot of dollars to building this team headcount wise and Marta W. Wise. That's why there's a competitive moat. It's not a prerequisite, but it does help. Absolutely. I'd say the cost of mapping your entire market Is maybe not as high you think it's like. But it is going to be like a couple hundred thousand dollars. Absolutely. It's not going to be millions. But there is an investment. Absolutely. Does that answer your question?

That's great.

Thank you.

Thank you.

Quick, not that tall.

Go ahead.

Quick question.

Yes.

You put up the slide with the.

Core marketing team and then the growth marketing team.

Yes.

And agree on most of it. But my one question for you is around. Yes, that one.

So there you go.

This one. So my question for you is around channels with content and social. Specifically, if we're working on top of funnel and bottom of funnel demand gen.

Demand creation campaigns and you're looking at.

Evergreen campaigns and outbound, why forego content in social?

Even podcasts.

Right. Something to get people educational interested in that.

So maybe something I was not clear on. Sorry if I wasn't. I'm not saying we should not have a core team. Actually I think a core team is not necessary. Everywhere I've been at I've had a core team. I'm just saying this is a different job and I split. There's a line in the sand and you can wobble that line however you want. But I think that's actually, that's important. Like brand is important. Social content is important. Very important. It's not something you can experiment on. You're not, you can't really like, you can't half ass brand and you can't like, like the risks of doing something bad on social are high. So you can't say, hey, let's just say crazy stuff on social, on content, let's see what happens. That has the downside effects are high. Which is different than having a small paid campaign or outbound campaign to a couple hundred. No, to a couple thousands of people and see how that lands. It's very different. So you've got to assess where can I take risks, where can I move fast on things? Where you can't move fast. That I think is core. On things that are hard to measure. Social content, hard to measure the revenue impact that also is core. All the rest, which is like revenue focused, demand gen, high expectation. You can move that to growth and have a high velocity framework there. Thank you very much. On one of your slides you showed how you are bundling data sources together.

First party and third party.

Yes. And then you were feeding it into AI and specifically the ChatGPT logo. So I want to understand what role is AI playing there once you've bundled your data. Yeah. I don't know why I Forget anyways, I think it's here too many bullet points. Bulletin fever. There we go. Yes. So great question. So how do we use AI with that? So right now the use case for AI is mostly copywriting. Okay. And so in a lot of cases we use AI to let's say make sense of all of the data we have that we collect on personal information, business information, business trends, hiring trends. And we basically have like a complex prompt for GPT that says like hey, I'm a salesperson at the at ramp. As a salesperson with this knowledge, what would I say to convince this person to talk to us? And here's examples of past emails that have worked well. Okay, so you have like a limited training sets. That's how we use it. How we will use it in the future to anticipate that question is instead of having let's say AI within set campaigns say hey, like if somebody's hired, write an email. If the company comes to our website but doesn't convert, add them to a some retargeting ad campaign. It is better to have a model that is based on, based on the company fit, company size, forecasted revenue. What should be the next best action? Should we send ads? Should we send an email? Should we place a phone call with an sdr? What is the best investment to move the needle? We're not there yet and as far as I know, no one on the market is doing that yet. But I do forecast this to happen in the next couple quarters fairly soon.

My question was actually about the great.

Outreach intents, but you just presented them.

Over here in your answer. So just on top of my head, a quick one. I see a lot of movement right now. A lot of people are saying that automated outreach is kind of dying down and people go in personal one on one trying to do that. Just kind of curious on your thoughts about that. With the help of AI, does AI help us become more human in an automated scaled outreach?

Yeah. Thank you. So outbound is not dead like at all like bad bad. Outbound is absolutely. I think I showed the stats somewhere. But some of the companies I work with, the response rates including ramp is upwards of 8%. Okay. Like if you remember the, the costs are like very low. So like this is very, very efficient at scale. Of course what's interesting is actually the difference between human LED outbound and automated outbound. And what we found that once we normalize for every other factor which is like SDR picking the, picking the audience, the everything but the copywriting, the lift on a human versus the current AI models that we are capable of using is about 30%, which is significant. It's not zero. The cost decrease is actually 8x. So it is 8x cheaper to run it automatically versus only 30% lift. So you can see where the world is moving. Right? Those emails are getting, getting extremely similar to a human. I'll give you one example actually, to get you excited, something that's being published already on LinkedIn. Reamp is a fintech company that does credit cards and financial services. Okay. Software. It's very hard to engage with a cfo. They don't care about a new credit card. So what the Ramp team did is do an email which has in the. As the copywriting. It's a sports betting offer. Most CFOs in the US went to a college. Okay. They went to college and they care about a college sports team. Very important in the U.S. and so what we do is we scrape LinkedIn. We find where, which college they went to. We have a list of all the upcoming college games. Sports games. Put twos and twos together and we write out and say, hey, Alex, I saw that your, your college sports team is going to have a tournament against that other college sports team. I'm gonna bet you 50 bucks that they're gonna lose. Overcall with me. Okay. And the back end, we ran all of the betting algorithm simplified to see, like which team were likely to, you know, which case were likely to win or lose. Okay. The response rate on that is higher than 10%. Why is that? 1. People love sporting betting on sports. People care about their college sports team. It feels very personal. Okay. No one automates that. Who the heck automates that? Especially a fintech company. So nobody's gonna think, hey, this is fully automated. Okay. And in our case, we win or we win. Either we get a free call or we pay 50 bucks for a call with a CFO. In both cases, really good CAC. So that's an example. Does it scale to tens of thousands a week? No, it doesn't. There's not enough games for that. Is it evergreen?

Yes.

Is it repeatable? Yes. Is it low cost? Absolutely. You stack ideas like that, 10, 20, 30, you have a really strong outbound program. More importantly, can you distinguish that from a human sdr? Absolutely not. There's no way. Yes. How would you adjust those tactics to.

Audiences that are mainly offline, like manufacturers? Slow moving?

Yep, that's a good question. The first thing is you got to check whether their audience is large enough.

Okay.

I recently had HP Industrial the industrial printers reach out to me for help over outbound and asked, how big is your tam? How many companies can you sell to like maximum worldwide? They said 2000.

Great.

Thank you. One recommendation, like just hire salespeople, have them like have dinner with all the clients and call them. That's it. Goodbye. There's nothing to automate. You're not going to invest, invest in like hundreds of thousands of dollars and have engineers when you could just solve the problem by having the humans do it? It's just cheaper to have the humans do it. So the first thing is to check like is, is there value in automating, building a product that automates that, replacing humans or not? Okay. And the second then is treat it as the data collection, the automation. Very similar. The touch points could be different. They might be more in person. I've got one company that I help spot AI. They compete with Verkada. They do security cameras. Number one audience is car wash in the us. The other one is industrials. You know what works? Phone calls. Phone calls and snail mail and in person events. But it doesn't matter. We know everything about them already. Like scrape Google Maps, find out all the locations, the problems they have, the feedback of the location. People are complaining about security over there. Like all that's done automatically. So maybe fewer opportunities, but they definitely exist.

Cool.

I think that's it. Thank you very much. It.

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