How to Prove ROI on High Stakes Decisions with Tipalti's CMO Rob Israch | SaaStr Software Community

Welcome to Sasterscale 2021. We are so thrilled to have Rob Israk, CMO and GM of Europe from Topalti with us this morning or afternoon, wherever you are, tuning in from. Rob's tuning in and coming to us live from London. So welcome, welcome. I see some more folks filtering in. We're really thrilled to have you all. We have 20 minutes with Rob. He is going to have a few minutes at the end for Q and A, so please make sure to take advantage of that by dropping your questions for Rob in the bottom section of your zoom screen. I will also send you some reminders about that, but let us know if you do have any issues. We're really thrilled to have Rob and really excited to have the next few minutes with him. So before we fully get started, let's join Rob. Let's please join me in welcoming Rob. I'm going to let you start off, Rob, with why the data matters for the session. Can you quickly tell some results that people can expect from this presentation today?

Yeah, absolutely. So we'll go through how to make decisions, decision making criteria, when to make decisions, timing of decisions, where data fits and where it doesn't. And I've got a further slide that goes into more details on the agenda, but we'll go through that kind of series of decision making criteria that are critical at various points in your maturity as a fast growing business.

We're thrilled to learn more. So Rob, please take it away.

Great. So Sastrade likes to start with the dashboard. So this is one of many clips of dashboards that we use at Topalti to run our business. Specifically, of course, my role. I oversee marketing and I also oversee our international expansion, chiefly Europe, which is the reason I've moved out to London. And so this is one of our dashboards where we monitor new leads that come into the system and the trend lines, the performance against quota, the performance by key segments, whether it's industry sectors or by lead sources or by product lines and such like that. Of course, similar. This is only a lead dashboard. So as you all know, or at least the folks on the phone who are in the marketing function, the beginning of a journey is a lead. There's many metrics prior to the journey. We measure those as well from a brand perspective and engagement. And then there's touch points after which we call nurture leads and opportunities. And then of course, sales and bookings, the things that just really matter. And then of course, in my other capacities, we have different dashboards to measure different things. But this Gives you an example of the types of dashboard that we use at the beginning of the funnel. And yes, literally probably five times a day I refresh this dashboard and five other dashboards to run the various different parts of the business. And I'll talk a little bit today from a marketing perspective as well as a Europe GM perspective. But literally every department or business, every leader and then their VPs underneath have their own set of dashboards and those all line up across the business. We tend to be a very data oriented decision making company. And so I'll talk a little bit about how we've done that at scale to hit the levels of growth that we were added the business. So a little agenda, I gave a little preview on that already. But we'll talk a little bit of criteria that's worked at topology and worked for me personally to make strong decision making. A bit of a guide in decision making on some criteria of things to do and not to do. I'll go through a use case of global expansion. So I moved out here to London and in December 2020 with the mindset of expanding into Europe, where England or UK was our first step in that foray. And so I'll go into some of the approaches we used to make those decisions that had to be made and then we'll talk a little bit about measuring success. Then we'll have time at the end for Q and A. So please do use the Q and A button that you see on the screen in the Zoom chat to add your questions in there. And then I'll do my best to get to each one of your questions that you include there. So in terms of the philosophy of decision making that I've utilized, the company utilizes with success, first step is determine the right KPIs. You have to understand where your business is going, make sure you're aligned with the rest of the business on those key goals and understanding what are the key metrics in your department or your unit that you oversee that line up to those core goals for the business. So what are your key KPIs? What are the business's key KPIs? They should all be connected as a first step. Second step, of course is creating the measurement system. I can tell you in the marketing field, I can't tell you how many marketers unfortunately don't spend a lot of time setting up the right measurement system. It requires having a strong marketing ops function, a strong sales ops function, often a strong setup of your marketing infrastructure. And if you're in a different department, maybe Your ERP infrastructure or other types of systems and technology to make sure you are able to measure what you're supposed to measure. Sometimes you talk to people and they say, oh, we don't measure this because we don't have the systems to measure it. That's the wrong answer. Get the systems to measure it. It's not only important today, it'll be important tomorrow. Make sure you have the right data that lines up with the goals in the business and you're incentivizing and measuring people to do the right things for the business, not just a checkpoint that says, I did my job well. Let data be your guide to decision making. I generally believe that some data is always better than no data. Some data doesn't mean it's statistically significant, doesn't mean it hits a certain sample size. Get the data that's available. Ideally, quantitative is better than qualitative, but both are data. Get whatever data you can and let those guide your decision making. They're not going to tell you what decisions to make. Ultimately, we're all grownups, we're all business people. We have to make decisions with or without data. You'll make a decision slightly more accurately. The more data you have and every bit more data you have, the more accurate your decision making will be and the more confident you will be. I'll tell you that. When Chen hired me about seven years ago, one of the key lines when I was interviewing with him, he asked me, how do I know that my decision is right? And of course, that's a loaded question asking a marketer. How do I know my decision is right? And what I said is, I don't know my decision is right. I will make a plan. I will use intuition. I will use as much data as I can humanly use to make that decision. But reality is, as much as I think I make fairly good decisions and have a process and the such, I'll probably be wrong 40% of the time and my plan will be right 60% of the time or maybe even closer to 50, 50. And from there, I will measure the data. I'll look at the data, I'll see what makes sense from that perspective, and I'll do more of what makes sense and less what doesn't. And so he really liked that answer. It was one of the reasons I ended up being selected for the job. But it kind of gives you a little light into that philosophy. Some data is better than no data. Let data guide the decision making. Don't think that just because you're an Intuitive genius, that you're going to be right most of the time and then own your bias right. Know that you're not perfect even with a little data, that you still may make the wrong decision. Try and be objective, look at the further data, post execution, and modify your data based on the current state. And even if you have a bias, call yourself on it. It doesn't mean a hypothesis or bias is wrong. Just make sure you're objective about assessing that. On the other hand, data won't make the decision for you folks that feel like you measure data. And you don't make any decision until you have 100% of the data or the data statistically significant. It doesn't allow you to move fast enough in business and to make decisions at the pace required. You're a leader for a reason, and part of the reason you're a leader is that you make decisions. And so ideally you have data to make decisions. One decision is to say, no, I'm not going to make a decision here. We're going to delay the decision while we gather more. But ultimately you're going to have to make a decision. And data very rarely will tell you exactly what decision to make. It'll simply guide and direct the decisions to get them more accurate over time. Certainly past results don't predict future success. So it's a tricky balance on this one. You do want to get data, and most data is past results. Sometimes you actually have to take a stab in the dark at something, invest in something. Let's just say SAS street, for example. We're right here. But if you were to invest in a vehicle, sometimes you can look at as much data in terms of who the attendees are and what they look like and their makeup and such, but you have to invest upfront and then you find you get the results and then you measure it. And there you have to be objective and say, did it work or did it not, based on the KPIs we set up. So you do have to do some things, execute first to get data. You also sometimes need to look at the past data and use that to guide your decision making going forward. On the other hand, your business will change, trends change. There's strategic decision making and factors that are mixed in the decision. There's conflicting data, and so it won't predict the future. It can guide you to make better decisions. But sometimes as your business changes, you have to try new things. Maybe things that you tried in the past that didn't work, but the business has now changed. You have a new product line or you have a better fit in a certain segment. And now it's worth trying new things because your business has changed. So don't let data be a jail cell for you. It should guide you, but it also shouldn't hold back your growth. Ultimately, we all need to grow our companies. That means we have to take chances and do new things and try new things. Of course, include your team in decision making, get their ideas. Oftentimes their ideas will be better than yours. I can tell you I'm very far away from the ground at this point and so I need inputs from folks on my team, ideas, data, insights to guide those decisions. But at the end of the day, you're the leader, you have to make decisions and you can't use collaboration and teamwork as an excuse to not make a decision. To be accountable and finally understand decision making cycles. So decisions do change over time. A decision that's made today on a certain criteria based on certain data may not be the same thing in the future. With different situations, different criteria. Certainly evaluating, let's say again, a live event would have been different in pre Covid world versus in Covid world. That's an example of situation changing beneath you and the decision making criteria then therefore changing as well. A few do's and don'ts, I think I've actually hit on most of the items on the left hand side. Ultimately you own the decision with data without make it bring others of course to help guide the decision. But it's your decision at the end of the day. Try and be as objective as humanly possible and use data to guide your decision. Check your biases. Sometimes it's healthy to be devil's advocate. Or when you're asking peers or asking your teammates for feedback, purposely include people who can be devil's advocate to test acid test things. And certainly if you're not certain about a decision, that's the perfect time to bring in devil's advocate opinions into decision. You constantly have to revisit decisions to ensure they're valid. Just because a decision was made at a point in time, the world changes, the business changes, the business has different goals over time and you have to constantly realign and certainly testing alignment with business strategy. So for example, let's say marketing runs a program and the program actually shows it generated leads, a generator, ops, the cost prop is good, maybe the op to sell conversion rate and the ROI is actually satisfactory. When you dig into even those deals or those ops, perhaps they're let's say $5,000 deals that have been sourced or $5,000 ops and maybe there's a different data set out there, a different analysis out there that says CAC looks at the cost of customer acquisition, says the cost of customer acquisition is strongest in these segments. They have to be 50 employees and above to hit a certain ROI threshold, for example. And you look back at that test that looked ROI positive from that one measurement system. But now it conflicts with this because when you actually look into those data points, most of Those companies were 30 employees or the such. You should make sure that your decisions aren't just isolated in your department and based on the data you have, but also what other factors are happening to the business? What is the target market? Where are we going from a caf perspective or strategic direction? Is that aligned? Are you doing things in a silo? So make sure that generally Your strategy or KPIs and your measurement system is aligned and you're at least looking at it. From balanced scorecard approach I'm a big advocate of a balanced scorecard philosophy. I don't often think that one data point leads you to the right direction, but it's usually a balance of a few key metrics on the other side. Again, we've talked about only trusting your instinct and intuition. Make sure you test your theories you do use data wherever you can and be objective about it. Definitely don't be afraid to try new things. You often have to try new things to get data to inform future decision making. And that's a trap you see set a lot, particularly by people not in marketing or the go to market function. Frankly, don't ignore new information. The world changes, your point of view changes, the business strategy changes, the competitive landscape changes, the economy changes, situations like Covid change. And so you have to be factoring in these new inputs constantly. And don't assume just because you've seen this from previous jobs, previous experience and such. That's exactly the same. Different businesses at different points in time, they're maturity of different target markets, different ICPs, different strategic outcomes, different goals. And while that is data and it should be considered, you should also consider that it might not be appropriate for the current condition. So this is an example that we thought would be useful. Certainly part of my new role was to help basically initiate our international growth, which is a big opportunity in Fort's palty. The product we sell is a payables automation platform. It's particularly robust when it comes to global expansion, whether it's a multi subsidiary or a global supplier base. And so from that perspective we started Looking at global expansion and the question always was when. We always knew that this was actually a pretty big opportunity. One, our product, our product differentiation, our product strategy was always robust and differentiated from a global perspective. We're certainly the strongest from a global payables perspective in the world. And we always knew that. The other data point that supported that is without trying, literally the go to market engine of this company has been focused on us because it's a huge market and a huge opportunity for the business. But without trying, we literally got 20% of our customers not from outside of the U.S. from that perspective, you look at the data and literally the demand from the world is telling you we should be in that market whether we want to or not. We're trying to focus on us and we're getting customers from all around the world. And so that was a compelling data point that led us to say, you know what? Yes, going global is a key long term growth opportunity for the business. Yes, it does create a bit of a distraction from us because it's hard enough to focus and execute effectively in the US alone. And creating a new international unit is complex and can create some distractions and complexity for the business. We have to find the right time to turn that button on. When you have a data point as compelling as that, where it's 20% of your clients without trying, what's going to happen when you try? That's a great place to start. Another data point we had again is you can cut within that of where the customers are coming in, where the opportunities are coming in, where the leads are coming in from geographically. You can go down the funnel and say which markets within that 20% should be the most effective most easily. Just turn the go to market engine on. You do things right from our product and compliance and back office and you should be set up for success.

So it seems like we're almost time to go to questions. Looks like we have just maybe two minutes to address a few of the questions that are coming in.

So. Okay, thanks Taylor. I'll jump right to that, then I'll wrap this page up. So a few things you look into really quickly. Of course, you look at the total addressable market from our perspective, that's mid market. So we size the mid market across all the different geographies and the size to make sure that the opportunity was large enough. And then we started looking at competitive sets. We started looking at product market fit. Where do the values and the differentiations that Palti brings to the table match with the product needs in these Markets. Are some markets not willing to pay enough? Are some markets not international enough to value? Some of our differentiators? Are some markets not? We tend to be adopted by tech companies and high growth companies. Are some markets better than others? From a VC funding entrepreneurship perspective, those are all factors you look at along with needs in the market. Of course you have to set up a great culture in the company, get the DNA right. It's something Topology has done great in the us. We wanted to make sure we replicate that as well. And finally complexity and things like compliance, which are we have to worry about because we're fintech. So having said that, I'm going to stop sharing and go to Q and A. So few questions on the Q and A. What is the best ways to successfully integrate new teams with existing ones? A great question actually. So thank you from anonymous attendee. It's a really good question. I think the first piece is know your values and your employee value proposition up front. And so when you're hiring your first international people, you have to be really, really sensitive to that. Really listen to do they fit in to the values. At Topology, one of our values is gsd. It stands for get shit done. And so do these people possess that? Another factor we have to consider is can they scale with us? We're growing at about 100% per year. Can these folks not only do the job today, but scale with us in the future? And that means they have to probably get their hands pretty dirty and get into grunt work at the same time. They have to think strategically and adapt really fast because we're moving fast and changing quickly. So I think the hiring up front is really, really critical. Before we started, of course, we have Covid going on as well. So before we started we had to do a series of. We did a series of dinners together and various gatherings so that we could all meet each other and build relationships and trust. Prior to kicking off onboarding, then we spent a lot of time making sure the onboarding experience was really great and then just ongoing ways to create collaboration and trust. Let teammates make decisions and not everything has to go to the top to be approved. So those were a few of those aspects. Next question, what do you do when the data goes against the anecdotes you hear from your team in the front lines? It's a really great question. I think there'll be different people that have different answers for this. But personally, as long as the data is accurate and you're measuring the right thing, data trumps opinions. If you consistently listen to the front lines and opinions without data, it will lead you astray eventually. And so I think you do have to strike a balance. It doesn't mean you don't listen to opinions and you don't question the data and make sure it's measuring the right things and understand why it's saying what it's saying. But ultimately I believe and this has treated me well throughout the course of my career and the companies I've worked for well in terms of growth that data trumps opinion. So I think that's, that's the questions we have for today. Do I, do we have seeing if we have any others here?

I think we're all set. Rob, we really appreciate the time and yes, everyone please get in touch with Rob if you have more questions after. But thanks so much. And everyone please make sure to join some of the other sessions today at Scale.

Thank you. Great talking.

Sam.