📍 welcome to the Revenue Room, presented by H2K Labs. Here's your host, Heather Holst-Knudsen.
Welcome to the Revenue Room, our first episode. I'm here today with Chad Rose, the CEO of Treehouse Technology Group and InsightOut, and we're gonna talk about all things data and revenue. But first, a little bit about H2K Labs. H2K Labs orchestrates profitable, scalable, and efficient revenue growth through the smart activation of data.
We do this through strategy, execution, and a purpose-built data platform. We specialize in business environments characterized by complex data ecosystems, including media events, business information, and two-sided marketplaces. Our services and technology solutions are architected to help you expand your focus from business intelligence to real-time decisions using predictive analytics and models to ignite profitable revenue growth, embed data driven decisions in every part of the organization, gain deeper customer understanding and generate new revenue streams through data monetization. But to provide our customers with the best in class data management and analytics platform. We partner with Treehouse Technology Group, who are the developers of InsightOut, which we actually call Insightify.
We're white labeling InsightOut and naming it Insightify so that we can deliver better value to you so that we can offer things like specific customized dashboards that allow you to do cross portfolio and channel rollups, predictive and prescriptive pacing and forecasting in events and other things that are unique to the industry we serve.
So with that, let me introduce Chad. Chad, you wanna tell everyone a little bit about yourself and Treehouse Technology Group?
Absolutely. Thanks Heather and happy to be here. Thanks for inviting me. So my background prior to Treehouse is really in the data analytics and engineering space. I started out, after school as a data engineer working in the financial services industry and at Standard Poors worked my way up to, eventually kind of overseeing and managing about a third of their global data and doing things, within that space from everything from data integration and connecting systems to building predictive models and distributing that data out to our customers. And doing so learned quite a bit about what makes a successful analytics project work and in how to really properly handle data for the, use of in more intelligent decision making. So our customers, there were the, leading financial services firms within the country and the world, and we obviously had to provide extremely robust, accurate realtime data for their decision making purposes. So we were serving, very impactful data to these customers and that was where I learned a lot of my lessons and approaches to managing and delivering quality data to, end users. From there, we three founders started Treehouse Technology Group, myself, Phil West and Darton Rose.
We started that in about 2014. And really did so to take some of those skills that we all had from the enterprise space and bring it down to the middle market. So we saw that there was a demand in the middle market for better data, better analytics, better capabilities. A lot of you had a lot of the big players as always making heavy use of predictive analytics and really advanced usage of data and the middle market didn't really have the tool set or the internal capabilities to, leverage their data or to really use it as a means to drive growth. So within Treehouse, we served that middle market for a number of years, we had clients, from the lower middle market all the way up to Fortune 500, the likes of Dick's Sporting Goods, Teleflex Bain Capital a number of customers on the service side of our business.
And about four years ago or so, we started to do more work in the private equity space. We came in and helped our private equity clients evaluate the companies they were looking to invest in from a data perspective tell them how, sophisticated or unsophisticated they might be, especially if their platforms or their technology was data focused.
And in doing so alongside the work we did with those middle market companies, we just identified that there was a real gap within the business intelligence landscape and tool set that the middle market did not have and that was really a tool or a set of capabilities that were more business user focused something a little less technical and something that could deliver enterprise level analytics, at the cost that a middle market company could afford. So a lot of those companies in that space that our customers today are great companies they're, they're growing quickly they're often owned by private equity, but they don't have internal data scientists at the level of Google or Amazon. They don't have the budget to, to go out and hire a team of engineers to build out the types of technology that you need to leverage data. We developed InsightOut on our own, to meet that demand and then since then we've been deploying it within our customer base and really helping them scale up their expertise in that space and delivering the, again, that level of enterprise level analytics for a very reduced cost and timeline.
That's really excellent and there's a lot to your story and the evolution of InsightOut that really resonates with what, we're seeing in the industries we serve with media and events, business information.
So with that, you've actually been on a lot of our calls with media and event organizers as we're trying to bring to market Insightify and you've learned a lot about their business models. And I remember talking to you a little bit about it and you were like, wow, this is just very different than some of the customers that we're used to working with ie a one-sided business model, traditional SaaS.
So, one is I'd love your perspective on what you are learning about the complexity of the business models in media and events and business information. But two, there are similarities like that we've uncovered, including the private equity angle or the, being very acquisitive, with mergers and acquisitions and all of the data complexity that results from that.
Yeah, there are quite a few similarities, but it's just within media and events, it feels like there are managing them all at once. Right? Managing all these different scenarios at once. You have as it relates to a recurring revenue model, much like the SaaS business, the software as a service business, you have an element of that in some cases where you have that recurring revenue stream you wanna keep an eye on it, you wanna keep, you wanna have your head around the metrics that pertain to that side of the business, churn and renewals and AR and those metrics are in and of themselves oftentimes very difficult to report on and to get a handle of.
And then you have these events which act almost like their own independent little companies, right? Where they have this sales cycle that is dependent on when the event is happening. And again, it acts almost like an individual fiscal year that you're managing. That's, another type of business, that we would serve.
And then the mergers and acquisitions, obviously that's pretty common within our customer base, outside of media events, and it's common there as well. But I would just say these again, these businesses seem to manage so much in terms of complexity within the data, in terms of complexity within the business, trying to meet the targets that they're trying to achieve, and they're very different business units that act differently underneath that make it, in my view, a little bit harder to get a full picture on the entire business and just to get a ongoing pulse, through, an analytics implementation.
Oh, absolutely and I actually that's, I love what you said. It's it's like a mini it's like a mini business within the business with its own p and l and its own unique fiscal requirements that Yeah, absolutely. And I, we didn't even touch on the fact that with the data that's being generated and both an event and a media business, you've got the two independent customer bases the data that's being generated is very much outside the CRM in many cases because of customer behaviors or all the people that are connecting with an event or, a media customer. But yeah, the data it's huge and there's a lot of value in the data that they're sitting on. But you're right, I think it's been very overwhelming in terms of figuring out where to start, right?
We can get a little bit of it on the audience side, how are we gonna actually activate it on the revenue side? Which actually leads me to my next question. And that would be very interesting to hear your response to this, both in terms of your customers like that are using InsightOut, right? On the Treehouse technology side and what you're hearing from the customers that we are seeking to serve, which is, the data issues.
What do you see in terms of glaring issues across your customers in a more general look at it versus what you're seeing with the media and event companies that we're talking to, and I believe there's some shared glaring issues like, doesn't matter what kind of company you are, you're seeing it across the board and then there are some that may be specific to our industries.
Yeah, absolutely. So I think generally, the type of issues that are not specific to media events are more around the data cleanliness and the data management practices within the organizations, or lack thereof.
Based on what I've seen so far, a lot of organizations are trying to get a better handle on, how to input data and get the team, the sales team to input data consistently within the crm. That's not uncommon, especially in the middle market, for organizations to have for free for all within the crm.
I think that is a very common pattern that we see one, One that comes with that is that data quality and cleanliness issue that prevents a lot of people from thinking they can start on an analytics initiative or thinking they can start thinking that they can get anything out of the data.
They often view it as a linear process where you have to get the data in shape and that only then would we be able to start reporting on it, or only then would we be able to start getting value out of it. And that often isn't the case you can often do the two in parallel and they actually reinforce each other if done properly. Within the median events space, it's the revenue recognition tends to be something that would be a challenge within the single source of truth. So you have these Products that you're selling and you have to deliver on what has been sold in order to recognize that revenue.
So having a handle on what has actually been delivered against what's been sold. So booked, not yet delivered, those types of scenarios within any business, but particular media events seems to be a pretty big challenge. You have to somehow reconcile what's being done on the operational side against what's been done on the sales side and the financial side.
That definitely seems like a big challenge within that space and not one that hasn't been overcome before I think it's just a matter of, again, having the right view on how are we gonna manage the data, the source systems, and how are we gonna connect them? How are we gonna establish a common data model where we can reconcile all this information together.
And then the recurring revenue business model, again, that's a pretty challenging one, especially if you don't have good systems to get a handle on. So I do view that as something that within the business can be a challenge to bring together. Again not impossible by any means, but definitely within this space it seems like a another unique challenge.
Yeah many of these media and events companies, they're doing a lot, at least some are real ahead of it in terms of data monetization. Others are starting to touch on it, but this recurring revenue model is where that's going because they want people to subscribe to a data product that they're getting over and over again.
So now you're gonna add into the equation the revenue recognition challenges across all of their revenue streams. The, lead generation CPM based advertising, you've got event revenue and now you're gonna have the recurring revenue. So imagine trying to get a revenue waterfall forecast with all of these revenue streams.
And one of the things I see also to add to that, and I agree entirely, and I think you and I were on a call with somebody recently where the data cleanliness and quality and the management practices, and even just something as simple as CRM compliance are blockers for them in their mind in terms of being able to get value out of data.
But I also call it product globalization, right? So sell there's a lot of product customization within many of these organizations, especially if they have different like industry sectors that they're serving, manufacturing and retail. They could be doing food and beverage or life sciences that the way that their products are structured in terms of even just like ad unit delivery or content structure is all different. So you're talking about product data normalization on top of it because of that operational angle in terms of what's been delivered, what's not been delivered performance.
Yeah, absolutely. And I think on top of that, a lot of them are managing these independent brands, these different brands and those.
I think within this space you have probably more systems than most in terms of where data might be stored. So there seems to be quite a few, potential data sets and those exist across a variety of systems. I don't think it's necessarily when you're developing a single source for truth, necessarily critical to start with, capturing all of them.
But I think that within this space is pretty unique in that there's a lot of untapped potential within all those different data sets that, if you are able to establish it could be quite beneficial.
Yeah. One of the ones that you and I discussed with that is the whole idea of churn risk, understanding churn risk across customers as well as a contract level, you have a customer level and a contract level based on the program performance. And right now because of the point you brought up, there's data sitting in all different areas of the business that impact revenue outside the crm connecting the product performance data with CRM data, for example to really help modify and get to predictive accurate forecasting and identify the risk before it happens is a great North Star. But there's a lot of work that needs to happen prior to getting there.
Yep. Absolutely.
So with that there's a lot of things I've been reading lately about simplifying the, what's considered the complex data stack, right?
It's all over LinkedIn, all of the charts about the whole data stack ecosystem. Can you explain what that means and do you see this trend taking place? And. Part of why I'm seeing this complex data stack story materialize is reminiscent of software in the old days, which is you bought your traditional software license but it didn't do everything so you had to start plugging in as things started becoming more available or you wanted additional functionalities, you had to buy new applications that then required integration, training, new licenses. And it seems like we're that at that point right now with the data stack, am I right with that comparison?
Yeah, I think over the, especially over the past, like few decades the landscape has changed so dramatically. You have both changes in the tool sets that are available to organizations to make use of their data as well as changes to the way these organizations are operating from a system perspective.
Where it's gotten more complex is you can have hundreds of tools that a single organization is using for different needs and that's great for them oftentimes because these applications are very specialized for a single job and, you certainly would have a different marketing system, CR, sales system, finance system, but you might also have, different systems managing your websites or different systems managing your customer engagement, customer success. And so there are just more and more of those that each company is signing up for, and you using and leveraging, but from a simplification of the tools that help make use of that data, you've, again, going back in time you started out with massive technologies that, and systems, mostly on-prem back in the day that were designed to help enterprises with very large data sets, right?
So they were very technical, very technically oriented. They're very difficult to implement, though very powerful. So what that meant was you had to have a team of engineers, again, do the implementation and do the configuration of those tools and today there are many more that are self-service and much easier to manage and use and should be used in a lot of scenarios within organizations that help, help plug into your source systems with the click of a button, right?
A lot of these, a lot of these source systems and applications that companies are using have APIs or ways to automatically integrate, and some folks don't actually end up using those or leveraging that, but it is available and so you can, you have a variety of tools that are available to integrate those systems now very quickly and you have a variety of tools that will help, visualize or manage and clean the data. And again, going back in time, they're all often very big complex implementations of those systems today. They're, AI enabled or becoming more and more AI enabled and they are much faster to get to value, get to, to get to a an actual successful implementation.
And you also have, elements that are even, newer in terms of things like reverse ETL or what we call write backs on our side so this is something that enables you to push updates from maybe your single source of truth, from your reporting system back into your source systems, back into your crm.
And so if you've aggregated all the data, you got it all together and you've, highlighted some churn risks within the dataset, you can push that data back into the CRM so the sales team knows immediately. And these are things that until very recently weren't as Possible, or, you wouldn't be able to do it within a, without having a big engineering team behind it.
And I think as the systems have become open with their APIs, as they, as the technology has become, more AI enabled the result is that these organizations can achieve Better results much faster, far cheaper. And they can do so with the modern stack that exists today versus, trying to implement the tools that were designed in prior ecosystems or prior technology stacks.
Yeah, that seems to really go with your, when you introduced Treehouse. And the rationale for for being is, really serving the middle market, right? The complex data stack is just too unwieldy, inexpensive, yes. For a mid-market company. And to be frank, in events and media and business information companies that we talked to, middle market just has as much complexity with data as a large entity. It's just,
Yeah
Maybe they're not doing 300 events, but they could be doing, a hundred and you have 50 media products and 30 websites that don't talk to each other. You still have that data complexity.
That's right and that's really the couple points there so that's historically been the big difference. They are almost equally complex it's just that the large organizations can afford to invest in the business intelligence solution, whereas the smaller companies cannot. And that has definitely changed, I think, in the most recent years.
And that decision as to what tools you use is where we see a lot of companies go wrong you're obviously more likely than not to purchase or to hire the well-known name that is used by the enterprises, but without understanding what it comes with, without understanding, how much you need in support to implement those tools a lot of companies get stuck and end up with failed implementations of these very powerful tools, but they're just not the right fit. And so that, as I mentioned, there are a ton of SaaS companies out there, a ton of products, ton of options for companies to use now it's, now the problem is almost, which one do I go with? Which one's right for me in my business?
Yep. That's one of the things we're trying to solve here at H2K Labs, with white labeling Insightify. Again, having been an operator myself of a integrated media and event company And just going through evaluation and, selection of, sophisticated SaaS software, Salesforce Eloqua, Marketo, you name it what always ended up happening was it was a huge implementation. Lots of customization, months and months of trying to get what you want and hyper customizing the platform. By the time it launched you got close-ish maybe, but there was always a glaring gap of something that got missed.
And then the amount of money that was spent and the, a level of frustration when people started using it and you, you had to start all over again. Like I've been through that story a few times.
Yeah.
And from what I understand from the customers we talked to that has not changed. But let me move on to another question, which I think actually is a great follow on to this is that I took this terrific course at m i t about creating value through data monetization and one of the biggest pillars of successful data monetization, whether it's an internal data monetization strategy where you're adding cash to the bottom line. Using predictive analytics, by the way, I would consider a very important internal data monetization strategy. Or external where it's a wrap around an existing product or you're building a brand new one is you have got to make data.
You have to democratize it. It has to be operationalized. You need to make data driven decision making and skills development, a part of the daily flow of work. What is your thought on that and how did you build Insightify, to meet that, that data democratization need?
Yeah, so we started with I think it's a good point.
We started with the assumption that, folks are not data scientists, they're not technically trained in this. They're not experts in determining the right KPIs or what to do with the data as it is displayed and deliberate to them. And we also started with the assumption that today, users want to have the same experience in their business tools that they have in their personal B2C tools that are, extremely easy to use and extremely user-friendly. And so we developed, Insightify to really simplify the presentation of data to simplify the application itself to make it less like a dashboard and more like an application.
And so by giving the platform, the level of flexibility that our customers ended up needing while also keeping it very simple. We were able to strike that balance and what I mean by that is really you have to have a tool that is going to give end users the data in the way they need it, when they need it.
So many times we've seen failed implementations of, analytics solutions that have never been adopted because in the end, all they did was take the data from excel or from the source system and give a couple charts and graphs that A didn't solve the automation problem. End users still had to bring the data outta that system and compose a different cut of the data, a different view of the data in order to serve the monthly reporting or the quarterly board meeting reporting.
It didn't even accomplish the automation component of the work that it should have. And, it didn't give them the answers quite how they wanted to view it or give the data to them in a way that really they can make use of. So with the platform, we're able to, we focused on a handful, as an example.
We focus on a handful of visualizations. So we don't have, sunbursts and every other type of chart, but we have the ones that business users use and the executive use, and those are extremely customizable so that they can be sliced and dice cut and displayed in a way that fully automates the data preparation and the report preparation for our customers, but also gives it to them with all the data points they need to get to the answer they're looking for.
I would say the level of adoption is extremely high within the companies we have implemented it, where it's, one of the most common or most popular tools they're using on a day-to-day basis. They log in the beginning of the day, they log out at the end of the day. And the level of, exporting the data to then prepare in Excel is pretty low compared to what we've seen on the service side of our business.
Where you, you end up having to Work around the shortcomings of the existing tools versus, making the tool work for the customer.
That's really there's a lot more to the platform by the way, that I believe, in terms of business usage and democratizing it but that was a great explanation.
There's one part about that though that I always find interesting when we talk to customers together is that you believe that the actual data scientist role will no longer be around in, let's say 10 years. Tell us a little bit more about why you believe that to be the case?
I think it's pretty clear, especially this year with the advances that we're seeing within ai, that these capabilities or the capabilities that exist to interpret data and to decipher what's happening within the data sets are becoming very powerful and I think that Will inevitably lead to, a situation and a vision that we hold within our company that, as a business user, you should be able to log into, to a system that has pulled in these different data sets, automatically that has interpreted and the data and is giving you the outliers or the trends or the specific data points that you should be paying attention to today or the, for this week or for this month. And I think while there will always be data scientists, I think the qualification here is there will always be data scientists at the very edge of the development of analytics where they're looking at, coming up with brand new ways of determining what's happening or, investigating brand new data sets. I think that a lot of these companies that share similar business models should and will have the predictive models, automated to a certain degree for them to leverage and, I just don't view in the, in 10 years down the road, if you're a middle market company looking to become, an enterprise level company you would go out and hire, 10 engineers to stitch together a bunch of data. That's the way it's been done in the past. I don't believe that's the way it's really going to be going forward. I really think that, the applications will become exceedingly powerful more and more powerful to help enable the business users as they intend to make use of this data.
Yeah. And actually with CHATGPT I imagine that's just going to be and adding that into the equation, which we will do another podcast on that.
Yeah. We'll actually make that even more, more profound. My last question, and I think this is a shared problem across all businesses, doesn't matter what type is that becoming data driven is. Is it is a critical imperative if you're going to succeed and thrive in the future.
However, it requires financial, capital time commitment, leadership commitment, a lot of persistence because it's a, it's, there's no end to becoming great at how you're activating your data. And it does require significant culture and organizational change. Many businesses are unable or unwilling in certain cases to justify this level of investment.
And one of the things that, we try to do is how do we help customers, one, define success, identify the business value, and ensure it's measured across the journey? What tips do you have for listeners on how to approach this big elephant in the room about how am I gonna measure the return on investment?
Yeah, absolutely. I think the short and sweet answer is to start small. So I think the problem is that These types of transformations are only going to happen and often only start from the executive level. So the CFO, CEO, that level of the executives within the business have to push this, and they're typically the ones who want to push that transformation, although they are also the ones who you know, might not know or may know the least in terms of technically what's available and what's possible for them to do within their business as it relates to data. So their view of it is, okay, I wanna become data driven. I wanna, I want to aggregate this data, or I want to get to some sort of single source of truth.
But they have a thousand different systems. They have, their team is telling them the data is a mess and not clean. And so their perception is that this is going to be a giant lift and it has to be a giant lift, but it really doesn't. And if you go into it with that mindset of we're gonna have to tackle all the different systems all at once and create this massive single source of truth as a single project, then you're most likely going to fail.
And I haven't seen that done very well. If ever so I come back to the start small concept. There are certain reports, there are certain things that need to take place within the date, within the business on a monthly, quarterly basis. If you don't have the technology in place, you're probably doing that manually today.
If you're doing that manually, there's a clear ROI to automate that. If you're not automating that, you are gonna be behind very quickly. Again, going back to the AI and automation part of the conversation, that's where we're going. So you need to get AI and In tech enabled, otherwise, you're gonna fall significantly far behind and your expenses will be just much larger than a competitor who's actually leveraging this technology.
You start very small and you automate what you can , within the existing reports that are being done in the business today. Even within those, you have to have a certain eye towards what's you know what's worth automating? In some cases it's a little bit, questionable as to whether or not you should automate a report.
We could get into those types of details, but really if you start small, then you've alleviated work from your team so they can see the value. You have a working solution that's answering some of the questions that you are otherwise getting from manual exercises, and you start to generate some buy-in.
And once you've done that, then the team can see a little bit more around what's possible and they can start, asking other questions as to what could come next. And if you take Those iterative steps one month, two month types of projects that should get a result at the end of those and you deliver those, then you build that internal support you clear the ROI threshold and you can continue to build on top of that.
The data cleanup part in the data quality part, again, I'll mention that I mentioned that in the beginning. That again oftentimes feels like a barrier to entry and really should not be viewed as that way. You don't need to take on a full data governance and data management data clean up effort prior to doing the implementation of an analytics solution.
Again, if you go back to this automation concept, the reports that you're getting today, Are being done in one way or another. There's manual cleanup, there's manual effort. You can look at those at a case by case basis and really start to deliver value by taking them one at a time. So this is the approach that we fol
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