Cumul.io Revenue & Funding (2024)

[00:00] Yeah. So my name is Karel. I'm founder and CEO at cumul. Io. And for the last fifteen years, I've been living and breathing business intelligence. I started my career mainly for larger corporates, crunching numbers for years, analyzing data for years up until the moment that I realized that I wanted to bring the power of data driven decision making to all, not just to business analysts, but really to everybody. And moreover, I wanted to bring those insights [00:30] to the places where we're making the decisions, so inside the applications where we're making these decisions. And so around seven years ago, I created a company called cumul.io. We're a building block for any web platform that wants to offer analytics and dashboards to their end customers so you can help them make better data driven decisions. Excited to be here to talk about some lessons learned along the way, but also to share some of the research we've [00:59] done with the company. So let's see. Before I start, one thing that I do want to get out of the way is when we're talking about customer analytics experience and or client facing analytics, I'm talking about you as SaaS companies offering the data to your end users, so that they can make better decisions. It's not about analyzing, your customers'behavior. No. It's about giving the insights your customers deserve. So in this talk, I'll talk about, why [01:30] you should care as a SaaS founders, what, does it mean, great customer analytics experience, And for the ones looking to step up their game in client facing analytics, I've got you covered with what are the five levels of great customer analytics experience. So let's first start with the why. Why should you even care? Why should you be interested? Well, allow me to start with the research done by McKinsey six years ago. And they looked at a [01:59] vast amount of companies from small to medium to large companies, and they actually compared how companies were making decisions. And they compared data driven companies to normal companies. And they actually figured out that no matter the size, data driven companies were 23 times more likely to attract new customers, six times more likely to retain these customers, and 19 times more likely to be profitable. Given that you're in SaaS, I gather you already use data to make [02:30] decisions on on sales, on marketing, maybe on personnel planning, but I can ask you this. Are you already giving that information to your customers? Are you already helping them, to make these better decisions? Because like you, they also look at to to some assurance over the outcome of decisions that they're making. Like you, they're looking for these decisions that have the biggest positive impact for their company. And with you as a provider, they look to you [02:59] for assistance because you're the domain expert. And and if you can help them, not only are you helping them to win, but you create a competitive edge for yourself. And so what are some reasons that you should be looking into client facing analytics? I have three examples here. One is on reach, one is on retention, and the other one is on cost savings. First one, it's a customer of ours, Greenlee. It's carbon foot con accounting. And [03:24] by adding customer facing analytics, they were able to attract a vast amount of new customers. Why? Well, a pretty, but also an accurate picture paints way more than a couple of words or a dumb Excel file. And so by visually showing what they were doing with their platform on landing pages, but also interactive interactive, they were able to prove the value of what they were doing. Second one is on retention, and it's actually a very interesting [03:51] case. They started to implement client facing analytics to prove the ROI of their platform to their customers. So it's a video first customer engagement platform. And what they did is, they compared the data when people were just starting to use their platform with the data after they were using their platform for a couple of weeks, and then they came with clear ROI. And so nobody will leave your platform if you prove that it makes sense using [04:18] the platform. And then lastly, on cost savings, that's a client of ours in PropTech, they drastically reduced the cost and the time to support our customers. Why? Well, before using customer facing analytics, they had customer success and engineering teams creating reports for their customers, specific reports to help them optimize business operations building operations. Sorry. And and by adding a way for their customers to slice and dice and to interact with the data, they actually gave the [04:53] possibility for their customers to immediately react and optimize their building operations. And so if you weren't convinced already before joining the talk, I hope with these three reasons, can show you that it you shouldn't disregard, client facing analytics. It can bring you great value. But knowing why you should do it doesn't mean that we know how to do it well. And so let's talk about, what is good customer analytics experience and maybe more importantly, what it's [05:25] not, how it can help you drive sticky behavior to your product, and then [05:33] and and then what does it really make great customer analytics experience. So let's start with what it's not. Although that there are great examples out there, on average, most SaaS companies fail in doing client facing analytics well. And why is that? Well, we to to look at that, we actually did large scale research specifically focused on SaaS companies. So what we did is we looked at 250 of the top performing g two top performing companies on [06:03] g two on the main categories, and we looked at how their end users perceived the analytical analytical feature set. And we actually found out that 89% of all these companies dealt with negative reviews. And so why was that? Well, the main reason, 61% of all negative reviews indicated that it was too clunky to use. They couldn't find their way in there. Second reason, it didn't present the data that they needed to make the right decisions. And [06:33] then three, they weren't able to configure or edit the reports to their likings. And when we actually started digging deeper, we saw that most SaaS companies look at client facing analytics as necessary, you have to do to check a box, a cost center, but they all fail to see the huge potential and the benefits that can bring with it. And those benefits can be immense. They can very well be the difference between losing or winning against [07:01] your competition. Think about Strava. So back in the days, there were many running and cycling apps. How many winning cycling apps do you still know today? Strava largely beat their competition through great customer analytics experience. Because if you do it right, your customers will go to your platform because in a nice looking interface, they'll have access to real time, insights that help them make better decisions. And they'll share these with their peers, with their colleagues, which [07:29] brings in a community effect. And if you allow them to dig deeper and to uncover new insights and maybe to create even their own reports, you actively empower them. And all while you're educating them in your platform where you become the trusted partner, the partner they depend on, and then you're building a strong, innovative brand. [07:51] So in a nutshell, where is great customer analytics to be found? Well, it's in the intersection of these three areas. Of course, end user business value. It has to make sense. But then you have personalization, and premium user experience. So with that, now that we know what great customer analytics experience or KAX is, let's look at how you can get there. There's actually five levels that we well, we created these five levels. It's based on research, [08:20] but also customer experiences. And you can go over them one by one, but you can definitely also combine them or skip them to get quicker to the. [08:31] Let's get with let's start with the first one, and this is really about getting the basics right. So you look at the data you already have at hand. You look at what metrics, what charts, or dashboards would matter to your end customers for them to make informed decisions. And at this stage, you only look at your entire customer base. So you don't, look at their specific context. Great example here is it's from one of our government [08:59] clients. Typically, what they do is they share on fertility rates. So it's about birth, birth's numbers. But here, they looked at how can we matter to our end users, which are the Belgian civilians. And, they created a tool which was based on popularity of baby names, and that allowed, people to parents to look at a name that would be popular enough so that everybody would like the name, but not so popular, for example, being in the [09:26] top five, that you would risk having your kid in a class where three or more would share the same name. So really bringing value for their end users. Once we have step one, let's move to step two. And then here, we're really going to customer specific. Here, we're making sure that our customers feel special. So the charts and dashboards that you'll be creating are specific to the data that that user is looking at. And so you [09:53] want to make sure that it's, secure so that the people only with the right authorizations have access to it. But how do you start with this? Well, a good way in starting in it, is you're the domain expert. So you know what the important decisions in your industries are that need to be made and also the metrics that influence that. So start with these metrics and gradually add data as you go. User testing here is crucial [10:19] because you'll want to build these dashboard templates that actually help your customers. So throw away metrics if you see that there aren't used, and make sure that you're not cluttering any dashboards. It needs to be easy to consume, easy to play with. It doesn't need all the data. Just make sure that it contains the data that actually matters, to them. So an example here, it's a broker report on a portfolio, of an investment. So weekly report [10:46] that is sent through email. Level three. Here, things get interesting in terms of customer engagements because the customer specific insights from level one from level two, you're actually now embedding in your platform. And so here, it's very important that you do it in a seamless way. So it has to fit the style, the languages, the screen modes, but also the data that is in there should be in real time so that any change in the platform [11:13] is reflected in the insights. And if you do that well, well, the, value of those customer specific insights is now directly attributed to your application and your brands. What you can do to even increase, the engagement here is every now and then send them an email with metrics that you know that matter to them because they'll likely return to your platform and uncover new metrics that help them make better decisions. Example here is a banking app [11:44] that shows spend behavior, historical spend, but also the categories where they were spending. Then level four. Now that analytics are native to your platform, you can start to actively empower your customers. And here, interactivity with, the metrics and the dashboards become important. And maybe even more important, driving the actions or making the decisions from that data. And at this stage, what you'll see is that your charts, your dashboards, your analytics become entangled with the day to [12:17] day workflows. They burst out of your typical one page analytics step. Now they start appearing in different places in your platform where you need to make decisions, multiple scenarios need to be presented. [12:32] And so an example that we have here, it's a marketing technology agency where visually, their users can segment and slice and dice, according to historical behavior, according to demographics, according to other dimensions, and the result, they can actually use to as a new target list for their campaign, which makes data driven decision making a reality. Here, two eyes the case. The the cake, what you can do is alerting. So when thresholds are met, let them know, [13:03] but also, collaboration features. They've uncovered some insights. Let them share with their colleagues and peers, and that brings in, the community effect. [13:14] Of course, the last level, that's the wall hala. That's the ultimate level. It's like, with the levels of fully autonomous driving. Level five is where you want to go. What is level five? Well, level five is about laser focused automation, and that combined with large degrees of freedom. So what you'll be doing here is you'll continuously be monitoring your end users'business context. And the result of that, you will want to proactively signal to them at [13:42] that moment in time when they need to make a decision. And by doing so, you'll create a premium user experience in which they can swiftly operate in your platform, which makes them, win a huge amount of time, but also which leads to fully data driven decision making. And the time gains that they will have, they'll likely reinvest in your platform, And that is where, the large degrees of freedom enter, because you'll have normal users that will [14:10] just say, okay. These proactive insights, they're good enough for me. But you'll always have power users that are looking to investigate all the data that you have in your platform in various ways. And there, you can give them the freedom of of free form reports. Did you know that in HubSpot, 79% of users prefer their own reports compared to the dashboard templates? And so by giving free form reports, not only are you helping them to uncover [14:38] specific problems, specific solutions to their problems, but it also leads to better adoption, more engagement. And if you're lucky, they'll actually uncover a new battlefield where you as a company can win. And moreover, often, it leads to less trains on your teams because no more customer success team anymore to generate custom reports. The engineering team just gives you the gives the end users the possibility to slice and dice the data. So you gain time that your [15:09] teams can reinvest in the core of your application. [15:13] I hope with these levels, you got a clear view on how you can improve in in customer facing analytics. I think with the technological advancement that we are today, there is no excuse anymore for you to be disregarding customer facing analytics. As SaaS companies, you actually should already be on level three or at least working towards that because your customers are expecting you to do so. And if you're not doing it, you can be very sure [15:41] your competitor is doing it, and then it becomes a difference between success and extinction. Moreover, with the economical uncertainty today, you have an obligation towards your end, customers to help them because, what better way to remove that uncertainty through data driven decision making. So I hope I gave you some extra knowledge as some ways to improve on customer facing analytics. I'll be around the whole afternoon, here, so don't hesitate, to ask me any questions. I see [16:14] we still have four minutes, for some questions. [16:29] Any questions? [16:33] All clear? Well, so you're providing adapters to customers? Or how do I get to this data? So what well, what we are providing with cumul. It's a building block, that you can add to any web platform, and it allows you to have customer facing dashboards. So, today, dashboards are considered a commodity, I would say. Look at all the booths that that they're there. Everybody has dashboards. But because it's a commodity, it doesn't make sense anymore to [17:01] for you to build them themselves. So you use a building block for winning time in that. But what is important, it's not just about painting a pretty picture. Dashboards should be business should be valuable tools in business operations. They shouldn't just be visual representations of data. They should help you make better decisions and drive, better actions. And so, it's a native component that you plug in in in your own software, like you have Stripe for payments, [17:31] Algolia for search, AltZero for authentication. Well, you have cumulIO for for dashboards. You you didn't answer my question. How do I plug in? How how do you plug in? It's a simple, web component, that you add to your platform. You it's cloud first, so, you connect your database. And then the only thing you have to do if it's a dashboard, you have cumul.io dashboards. You provide the idea of the dashboards, a key and token to to [17:57] make sure that's, that it's secure, and it, it adapts to, to your platform. It is a modern day web component, so you can listen to events when people click. You can, pull in data. You can influence how the dashboard looks. So it's really back and forth communication. [18:16] But you do have to have the data. Right? Like, you need the Well, you do have to have the data or you can link APIs to it. So we have out of the box connectors. You can connect any API. We actually have, I think, an example on what's it called again? The open bikes here in New York. It's an open API that is connected to it, and then you can see which bikes, or what spots still [18:42] have, you know, bikes. As long as it's structured and available through the web, you can connect it. [18:53] Any other questions? If there if there are, feel free to, to contact me. I'll be roaming around here, for the next couple of hours. Thanks.