SaaStr 2025 Partner/Workshop Stage

It's sam, Sa, Sa, Sam, it's. It, It, Sa. And analytics. And recently as we've been working with our customers, what we've seen is the era of AI marketing is here. When we work with marketing leaders, they are constantly being pressed for headcount for roi. And what we're seeing is marketing leaders wanting to scale their workforce, leveraging AI to drive outcomes. And so we're working with these organizations to focus on delivering real roi, reshaping their workforces and using agents to scale. And I'll share a bit about what we're seeing from a marketing AI agent perspective. This is great timing for us to be here at Saster. We actually just relaunched Opal, our AI platform, which is pervasive throughout our DXP last week. And from an Opal perspective, we're really helping marketers leverage AI in their existing workflows. So we've embedded AI in their workflows. We're using their own brand information context to actually deliver outcomes at scale. And it's fully connected across Optimizely1. So wherever you're working in Optimizely, whether it's that content marketing platform for ideation and work workflow, it's the cms, it's the experimentation, the same Opal AI is meeting you where you work today and we're doing this through agents. And so what we've seen as we've worked with marketers, you know, we often get asked like, why can't I just use ChatGPT for that? Really having purpose built agents to deliver on specific functions is the path to success for digital and marketing teams from an AI perspective. So I'll talk you a bit through what we've observed and how we've approached AI as we work with marketing teams. As mentioned, you know, we've built a purpose built AI platform as opposed to a generic LLM. You know, we're focused on the outcomes that we see our customers in marketing digital want to drive. And from a capabilities perspective, first and foremost, we've introduced chat, very familiar to all of us from a, you know, chatgpt or anthropic perspective. And our customers are interacting with Opal by entering prompts, uploading files, and again, it's meeting them where they live. Today we're preserving chats and threads so you can continue that conversation over time. But our secret sauce is really what we do after you enter that chat input and before your prompt actually hits the LLM. So number one, we've built a library of instructions. We're allowing our customers to actually tailor these instructions. So think about an agent that's gonna help you write a campaign brief. You could tell chatgpt, hey, write me a campaign brief for this and it will do it. But does it understand the typical format of your company and how you want to build a campaign brief? Does it understand your brand and your brand voice? So we're using instructions once you enter that prompt to actually enrich the prompt before it hits the LLM and say, hey, here's how we want this campaign brief to look. So it's not just generic. Another piece of how we approach it is tools. Tools are pre built functions and workflows that agents can kick off across Optimizely's dxp. So again, there are kind of pre created actions, whether it's creating a new campaign, adding tasks to that campaign that the agents already know how to do. So you're not having to go back and forth as much with an LLM to get that outcome. And all of this, you know, is combined with agents. So again, we've built specialized agents, we're helping our customers build agents. We have a full agent marketplace where partners are adding based on typical use cases. And these pre built agents out of the box are helping our customers accelerate specific outcomes. We have a lot of CMOs come to us and say, hey, I'm being asked by the board like how are you adopting AI? And if you try and do it too broadly, what we found is you're unsuccessful, you're dead in the water from day one. So really starting with one to two use cases, building those purpose built agents is critical. And in the coming weeks we'll actually launch workflows. So we'll allow our customers to then stitch together agents to create true end to end workflows to support that overall marketing process. So in terms of what makes Optimizely unique, and again, this is based on the learnings from our customers. Number one, we're context and brand aware. We're connected throughout the workflow and fully agentic, we have intelligent tools. Again, the pre built tools are helping us actually drive actions and outcomes via our agents. And this is all built on our domain Expertise. So with Optimizely1 and Opal, you know, you'll hear us talk a lot in the coming months about this concept of an infinite workforce. We believe that we will help marketing and digital teams scale to that infinite workforce, drive more outcomes, more content creation with the same number of people in the coming years. Now, what we're seeing in the market today, our customers are Using agents to run more impactful tests, whether that's coming up with ideas on what to test on the site, validating that test plan, understanding the results. They're scaling high quality content. When we work with customers around personalization, the biggest bottleneck is content creation. And so they're leveraging our agents to scale high quality content. And then finally they're making data actionable. So they're leveraging their insights, their analytics to actually drive next steps and outcomes. And I did want to take a couple minutes just to show you some examples of our agents in real time. So in my role as strategy and operations, I also lead our industry strategy and go to market programs. And one of our big bottlenecks has been the ability to create industry specific content. So our industry marketing or agent and hopefully this video will play one more. You guys able to get me playing? All right, so our industry. Oh, you go back to the. Can you click play on this? Well, I'll talk through it while we're hoping it'll play. But our industry marketer agent, what it does, you can see here we have a brief for a campaign for financial services and a brief for a blog post we want to create. And what industry marketer agent is going to do if we could get it to play is we'll now feed it a generic blog post. We'll say, hey, here's a generic blog post on, I think it's on personalization. Can you create the financial services version of this? And based on its instructions, which include its knowledge of Optimizely's customer case studies and financial services, it will create that blog post in seconds. So it'll take this generic blog post and set it up as an industry blog post. So my team has used this to take Optimizely's thought leadership on personalization. And we have a healthcare version, we have a financial services version. It's taken us minutes to do this experiment. Summarizer is another great use case. I'm not sure based on our last one if we'll get this to play, but we'll try. You know, this is a view of Optimizely as a B testing result page. And on our results page, you know, you can see you have some data here. But you know, if I'm a marketer, coming into this, I'm like, so what? I have to go figure out what all these stats mean. And so with our experiment Summarizer, you can actually just ask Opal and Opal will give you a well written kind of outcome overview of your test and a recommendation on what to do next. And so again we're helping to demystify some of the data and analytics which is really helping our customers. I do have a minute left, so I'm gonna maybe be able to take a question or two. But one bonus for the group here, we also did recently launch our website analyzer. So if you go to optimize ly analyze, you can put in your own URL and it'll come back with a number of ideas on how you could run tests to improve different parts of your website to drive conversion. Again, that's Optimize Lyannaly. So team, I want to first and foremost thank you all for being here. Hopefully helpful and we are more than happy to share more about how we're approaching AI, how our customers are leveraging AI and agents to drive outcomes. We are at booth 210 in the west hangar, so feel free to stop by and I will pause there if there's any questions. You want to hop on the mic. They're recording.

Hey there.

Thanks for joining today. My question was actually about optimizely specifically given it sounds like you own more.

Of like the whole go to market operations.

Curious what the optimizely tech stack looks.

Like when it comes to not just.

Marketing but like sales and customer success.

And how you guys are using AI across the whole go to market function.

Yeah, that's a great question. So I do own revenue operations as well. And for those who couldn't hear, the question was what does the optimizely tech stack look like less from a digital perspective because obviously we're using optimizely and more from like a core rev ops perspective. So we are a salesforce shop. So salesforce is the foundation of our our tech stack. From a marketing ops perspective, we're leveraging marketo for marketing automation. Again we use optimizely CMP to create content and then we can push that to marketo on the customer success side. Gain site. And to your question on AI, we're now starting to look at like specific productivity tools for AI similar to what I described. For marketers we're trying to pick one to two use cases for each role for AES, for SDRs, for CSMs and go drive an outcome there. So for AES it's pre call research and what we've found is they either spend a couple of hours on it before a call going and reading the 10k the news or they just don't do it. And so that's something we want to simplify For SDRs, it's around personalizing email outreach. For CSMs, it's actually around just getting a snapshot of where a customer's at. So in gainsight, they have functionality that will summarize all recent activities alongside the health score, alongside adoption, and just give you a clear point of view on where you're at. Great question. All right, they're playing me out. I want to thank you all for being here. Again. We're at booth 210. Please feel free to stop by and have a great Saster.

Thanks.

Team. It.

Firebolt IO hey, Good morning, Saster. How's everyone doing? All right, that wasn't a big good morning. All right, how about we try it again? Good morning, Saster. How's everyone doing this morning? A lot better. All right, I think we still need some coffee. Some sugar maybe. Well, welcome. I'm here to talk about a few different things, but first is how do you leverage AI to cut customer acquisition costs? How do you reduce churn, and how do you boost ARR? These are three very, very important topics. We're at Saster. We're all trying to grow our businesses. We're all trying to boost ar, we're all trying to reduce customer acquisition costs. So how do you do it? And, you know, before we get into it, let's talk about what's going on in the SaaS world. Is SaaS growth scaling booming, or is SaaS growth crumbling? How many of you here think SaaS is booming? Just a couple of hands. That's it. So I was here, this is my second sester, probably asked the same question pre2021, and everybody was like, Yay. So 2021 20. No, 2020, 2021. SaaS was booming. Today, SaaS growth is difficult. Think about it. Just so you know, the median growth has gone down from 36% in 2021 to 17% or sub 17%. So SaaS growth today is about 16.5%. That's a big difference from the great growth that we saw in 2020, 2019, 2021. So the last five years, customer acquisition costs have gone up 55%. So if your marketeers here, thinking about customer acquisition costs or your salespeople, you're driving customer acquisition. You're focused on new logos. That's a big difference. 55% growth in customer acquisition costs, not a small number. So how do you really drive growth in areas where the customer acquisition costs are going up significantly? Is this inflation? Is this. It's got nothing to do with inflation. Customer churn is the next problem. So when you think of acquiring new logos is already so difficult on top of it now you've got to keep those customers happy. Customer churn today is $136 billion cost to the SaaS industry. That's about 7 or 8 points for the entire SaaS market. Huge, huge problem. So how many of you have a customer churn problem here? Most people don't like to admit it, but everybody does, right? You know, unless you're, you're retaining 100% of your customers, which very, very few companies do. If you're retaining 95% plus, that's a great outcome. Gone are the days when, you know, people were retaining 95 or 97, 98% of customers. It's a lot easier for customers to churn today because they have options and it's easier to move from one platform to another platform. So how do you manage these situations where you have a new logo problem, you have a churn problem, and on top of that you have a growth problem. So combined, this is a massive impact to valuations of companies. So we're all here at Saster because we want to see our valuations grow, we want to see our company valuation grow. But valuations for SaaS companies have gone down almost in half. So if you think of median SaaS valuation, SaaS was, give or take, you know, 20 plus x multiple, 27, 28 in the glorious days of, you know, 2020, 2021. We're talking really good valuations today. The valuation is 6x7x, right? Think about it for a second. The valuations have gone down significantly. If you think of just public company valuations, public company SaaS valuations were almost 17x. Today it's 6.7x. So you have a churn problem, you have a customer acquisition problem and you have a growth problem. Now let's talk about the most important needles that's moving things. But before we do that, how are companies going to market today? So if you think of historically in the last 20, 25 years, the market, the go to market's predominantly being dominated by sales led growth and product led growth and some combination of the two. Right? Companies choose product led growth, some companies choose sales led growth. Most companies have a combination of the two. The challenge with sales led growth is an expensive model. You've got.