Using Predictive Analytics to Manage Risk and Capture Opportunity Bootcamp

Welcome to the Revenue Room, presented by H2K Labs. Here's your host, Heather Holst-Knudsen. well, welcome everybody to the Revenue Room Bootcamp, this is an event that is hosted by H2K Labs and, my pal over here, Chad Rose from Inside Out. What we try to do is provide a combination of data and business insights to help you drive data fueled revenue growth. So today, we'll kick off and go through, a few areas. One is unpacking the hype.

Every, everyone's talking about predictive analytics, but you know, there's a lot that goes into it. What's realistic, what's not the opportunities, both in term of identifying risk and also revenue opportunity, the process that you need to go by and also landmines to avoid. So I'm Heather Holtz Knudsen. I'm the CEO of H2K Labs. I am a veteran in the B2B media and events and digital information space. Chad is the CEO of InsideOut. And Chad, want to give a real quick background?

Yeah, thank you, Heather. So my background is primarily in data engineering and analytics. So I've been doing that since for my entire career and helping companies specifically within the middle market and enterprise space to, make the most out of data, get the most out of the data. Great. And what we do at H2K labs is we help in three ways. We help through consulting across data, revenue and product strategy. We also help with planning and execution through workshops and capability audits.

And we have two platforms, one of which we've partnered with chat on called insightify, which is a modern data management and predictive analytics platform with very industry specific capabilities for media events, digital information and another solution called channel metrics, which helps on the customer delivery side. So to get the most out of the boot camp, if you feel up to it, put your camera on. If you have questions please use the comment section and we'll stop.

It, this is meant to be a discussion forum and it says polling. We don't have polling for this one but after the boot camp, you're also going to get some great information, including a playbook, the deck, and a link to the recording, which you can share with your peers. So the focus is really on data. If you'll revenue and profitability when we talk about data, it really is on the revenue side. There are many people out there already handling audience.

We connect with audience when we talk about things, but we're really about the commercial side of the equation and our content while free. It is highly specialized and there's a lot of depth to it. So we hope that you do walk away. Thank you. Either finding out how to figure something you didn't know, or you didn't know something and now you do. And then after the bootcamp, as you a series of content items that can help you.

And also we're offering office hours an hour to dive deeper into some of the things we talked about to help you as you build out your strategy. So let's talk about the hype. So what are predictive analytics? It's the way you're taking your data past and current and blending that together with new insights, utilizing structured and unstructured formats and applying statistical and machine learning to predict future outcomes. It's not just to predict, it's actually to activate.

Like, how do we change the course of something if it's Looking not so great or how do we activate and leverage an opportunity if it has if that has come to surface and predictive analytics our means it's a way to get to a better business outcome, a leaner, more proactive company, better and greater transparency and accountability into processes and outcomes and deeper insights into unmet customer needs to gain market share. But Chad, did I miss anything on this slide? Do you think?

No, I think you got it. I think on the hype side, it's just one thing to add there. Oftentimes folks think that they can apply predictive analytics across the entire organization in every domain, every department. As you'll see, as we go through this, there are some precursors, there's some requirements, so to speak, that make it more feasible in some areas than others.

And so You know, to your point here, Heather, it's really there are means that it's not the end all, be all at for every part of the company and every person in the company agree 100%. And the other point I like to make is because this is a real. Real big issue, especially for like CEOs and CFOs is, we're investing in data, but We don't see it.

What's the ROI you're investing in your business and data is the enabler of those investments so you need to look at it across things like Building data driven decision making across the organization How do I make my team? And every function that requires this able to use data to improve how they perform and how the business performs. How am I using data to grow revenue?

I always like to say there's not a CRO, CFO, or CEO I've ever spoken to that says, we're really satisfied with the revenue right now and we don't need to do anything. No it's an investment in figuring out growth, innovative growth, and it's a way to gain operational efficiency in smart ways. Like where can we look at areas? to recalibrate resources so they're applied to the most profitable high growth areas versus the not so high growth areas.

And frankly, from, an investment standpoint or you want to be acquired, it's a way to increase your multiple your valuation, your acquisition attractiveness and the speed. to which this, an acquisition takes place. And it is definitely an improved board and investor. It improves the way you communicate with your board and investors. I was speaking to another CRO in B2B media. And one of the big things they said is, they're owned by private equity is their investors are asking for data.

More frequently, more granular and more like on things that literally it's it takes them weeks to pull it together. By taking this approach, it can greatly enhance that communication, which had on this area. I know you deal a lot with private equity, for example. Are there other areas that you think that impact the business when you invest in data? I think you've covered some of the key ones here. Heather double down on the impact it has on the team.

And so as you as if you do this correctly and you're able to embed predictive analytics into the business the team will largely or oftentimes kind of take take that on. And what I mean by that is they'll be more interested in pursuing. Other areas of their day to day where they might see a value in adding additional predictive analytics. So they might even, research how to do it themselves or create models themselves.

So as soon as you start to embed this within the business, the team members do take a big interest and take ownership of it. And that's in the best case scenario. And so I think that's a really important point. Small wins spark the appetite, right? Another area that we see when working with compliance is that, looking, taking a huge bite or trying to eat the whole cake. It's really start small, savor and really learn what the ingredients are before you move on to the next one.

But small wins will not only enable a much faster, better path to R Y. It actually will prove the value and which will enable it to scale. And there is, there's definitely we always see low hanging fruit that you can go after first, whether it's in a very specific division like the finance department or it could be sales. But again, small, quick wins are the way to go. And then it's a journey, right? So you have to start somewhere.

And I guess the question that I would ask everyone is, Is what I'm doing today. Okay. Can I continue to make my business grow the way I needed to grow just doing what we're doing? Or do I have to take this on and I need to run my business better. I have to become data driven. And so where do I start? And then finally, it takes a village. It's, and I'm going to actually, Chad, because you deal with this so much is there so much, there's a process which we'll dive into later into this discussion.

Bye. Bye. Bye. And all of these elements are absolutely critical. What can you talk, tell, talk to us about what you see when you look at the, how important this process is. Yeah. I mean, in terms of, this message here, it really starts at the executive level, and having sponsorship there and taking on the challenge of really becoming more data focused and oriented. And then, you have.

Impacts on the organization in the source systems, like your CRM, where you're going to, if you have predictive models that are built on top of the data that you're collecting, there's a dependency on the people who are putting the data in there. And so in the way that the systems are configured, and so there are elements across the business that start to impact the outcomes of the analytics. And so it does hit a lot of people in a lot of different areas as you start to really operationalize it.

Absolutely. Okay. Does anyone want to, talk about what your, what are your main business objectives when you think about predictive analytics? Maybe post it in the comments or, and this will help us make sure that we're talking about things that and hitting squarely on some of your top priorities. Feel free to throw that in here and Chad will be watching as I go through so let's talk about capabilities.

I took this course and I recommend anyone who's interested in data monetization or how to improve your business and predictive analytics falls under data monetization. It's by MIT Sizer. It's fabulous. It is it really helps with, organizing the thought process and how you approach it. But there are two really big outcomes that I took from this. And that is you need to have data capabilities that map to this to where you want to be in terms of a data maturity model and how you're activating that.

And there are guiding principles that, that underscore. So in terms of predictive analytics, these are the five areas. If you're pursuing data monetization, As it relates to predictive analytics, which falls under the internal, right? You're using data to add cash to the bottom line by improving what you're doing internally. You need to have very sophist on your data assets, single source of truth.

It needs to be able to be blended and you need to apply data science and the machine learning side. You need to be able to provide access to that data to your team members, because it needs to be transparent. So you need a platform. I mentioned the machine learning. And that's not just about one of the big myths about predictive analytics is like, Oh, I put a predictive analytics platform in and like, let me push a button.

Predictive analytics, actually, the data needs to mature and learn for a period of time. So having this capability is important. Thank you. You don't need to be super advanced on the acceptable data you side, but you do need to have internal oversight and permissioning who sees what, et cetera. And on customer understanding again, not you don't need to be out in what I call the outward commercialization area. But you do need to understand what the data is telling you about. your customers.

And then the guiding principles about that underscore this is that, data monetization is the direct or indirect conversion of data into financial games data liquidity. And this is the area where we specialize in our businesses that have very high data liquidity. But this is the ease of which your data can be monetized.

And in the area of events, media, business information, marketing services you're dealing with huge amounts of data that changes every single day, every minute, actually every action on your website is a new data event.

Being a data driven organization, it's an organization that's constantly innovating and scaling using data to improve business outcomes and this data democratization, which anyone who's on this call and has spoken to me before, I'm very passionate about is, Your whole team needs to have access to data. It needs to be built for business users. You are not going to be able to acquire data skills. You have to actually build them internally. So one way to do that is democratizing the data.

So another question, if you want to throw it into the chat is areas we're going to talk about are like gaps that are stopping you and based on what we just reviewed. All right. So this is the risk and opportunity side. You have more data than you ever had before. And again, if you're in the media events or any type of business that has a two sided business model, or is dealing with multi channel marketing, you've got more data than you ever had beyond what I call a traditional business.

It's where do you start? And it's not just enough to acquire it. You have to understand it, have to be able to operationalize it, right? How am I activating behaviors internally? Based on what we're learning to impact, and I should have a fourth hour here. And that is how am I measuring it? Okay. So what Chad and I did is we broke down three ways that predictive analytics can help your business. There's one is growing sales.

Identifying expansion opportunities, personalizing campaigns to have better impact on pipeline conversion, maximizing your lifetime value, whether it's your audience or your your exhibitor or advertiser side reducing churn and optimizing pricing, you can improve or optimize performance by. Bringing the right products to market or sunsetting less profitable ones. Optimizing resource allocation based on customer spend, not all customers are alike and you need to, measure that.

How can I look through the data, what's working from a sales standpoint and scale that across all of my sales team players so that I can improve my A game and reduce my C game. And how do I optimize content and how we're investing in it and how we're producing it, audience and the conversion side to programs. And then clearly there's the risk, deal risk, customer churn risk.

And again, in the world of, that we operate in it's churn is actually a very complicated matter because there could be a lot of revenue reduction happening along the way. Well, before the churn takes place. So how can you utilize predictive analytics to identify that erosion before the actual logo goes away payment risk? It's another area attrition on actually, which is Repeat of the employee fight flight risk and then product adoption risk. So we put together some use cases.

There are about six right now that we have in here. And this first one is the customer churn risk. This is and you could slice and dice this one in many different ways, but in this use case, let's, it's a marketing services company that's serving diverse customer segments across multiple brands in different countries. And the channels include events, digital advertising and legion. You could add. More the point is that there are all these different factors that create data complexity.

And what they're trying to do is they want to identify red flag alerts in time to action and fix. Right? Because as I mentioned, the churn is a painful, slow trickle sometimes. But unfortunately, as I've experienced personally, and I have seen with customers by the time that red flag really gets raised, it's too late, right? And we're all running around with our, like chickens with their head cut off trying to improve, but the damage has been done.

So how this customer wants to identify this further. So you build a stakeholder team, you put sales, customer success, operations and finance together, and you map things that are in your business that would say this constitutes a red flag alert, right? And in which in this case, CRM, we're going to map CRM data with operational data and program. Program performance data.

We actually also want to map order management because we want to ensure, one of the biggest issues with churn risk is the the program doesn't deliver. So I need to know what was promised and what they paid for and when it was supposed to be delivered. And I also want to put in some other things like third party data.

So LinkedIn, for example and another churn risk is if someone departs The actual customer company and LinkedIn, for example, has a, an alert system that tells you if someone moved or there's a change in LinkedIn profile. So you then create insights through the data and it could be the program performance dashboard. Is there if you're doing surveys at events? the customer turnover, the third party data that I just mentioned, and also obviously the salesperson traction and pipeline traction.

And you create dashboards based on what you believe are these red flag alerts. And you also, on top of it, have an actioning process, right? What are we going to do when these alerts are surfaced. So and it's a process. Everything that impacts the sales team, the ops team, the customer success team and the finance team. Another data point you may want to put in here is if the customer typically pays on time. If payment is delayed, that could be an indicator of financial troubles.

And then you assign some ROI metrics, churn rate, net retained revenue, customer satisfaction. And these all go into the dashboard that you're looking at so that you can actually not only see what's happening and action it, but is it making an impact? Are there any questions on this use case or does anyone have an example where they're doing this right now? Okay. The next use case, and this is from an outlier, right? This is Chad has customers with the product we sell.

And this one I love because it is I actually can liken this to acquiring attendees for an event or selling audience subscriptions because there's seasonality to it. But Chad, you want to talk through this use case? Sure. Absolutely. So in this use case, it's really about trying to get ahead of Any sort of downturn or reduction in sale and future sales.

So the idea here was to build a model to predict out a couple of weeks, a couple of months in advance based on current year data and prior year data, how much. The business was going to do in net new sales and how much they were going to do in same store sales year over year, which is their primary metric of success. Right? And building a predictive model here allowed them to see out into the future very accurately within the first within the next couple of weeks.

And even further out, To say if they see a downtick in bookings or in potential sales, they were able to respond with increased promotional activity, increased marketing activity and other triaging to ensure that they actually address that shortfall ahead of time. So great example of where predictive analytics is really giving them, visibility into the future and giving them, actionable things that they need to do to address problems before they, surface.

Anyone have a question on that or how that how that could apply or translate into your business? Okay, so the next use case is content optimization and one of the things for that will do better next boot camps is we actually got this from one of the participants on the call who spoke to me about like, I'm having an issue with this. Can you. Address it.

And it's how do we and this really applies if you're also selling content as part of a subscription, or you need to produce content that appeals to an audience that you are attracting for an event is if you have content that's on your website. And you're producing that all the time and there is user engagement and you can slice and dice that. How what can we use that data for in terms of helping us plan for better engagement, better commercialization.

And better investment in this content creation. So it's a B2C media company that has multiple brands, very large. And they have a paid content subscription model. And their objective is to reduce the hours they spend each week, creating a scalable education content packages for their paid subscribers. Every week they get down and sit down at a table. They're sitting there trying to, well, I think this, well, let's do that. So it's very anecdotal.

It's got, again, having been there many times that you're wrong. So how can you actually use data to do a few things? One data to reinforce and the decisions, right? No, here's the data that's telling me that this is or us. This is what we should be doing to reduce the time that's being spent agonizing over what to create. That time could be spent creating value. And three being able to actually map this content really better use the investment better.

Okay. Towards what your audience is telling you they want today and some trends of what they may want tomorrow. It's also, by the way, underlying this there, there is actually a a new product ideation component as well. But for this particular use case, you get the stakeholder teams of marketing content, audience and data together. You're you need to map website analytics, own social analytics, your C. D. P. Data, C. M. S. Data.

And really break down into audience core cohorts to content segmentation and really identify that to demographics, location, cohorts and content type. Also, by the way, it's not You know, is it product information? Is it thought leadership? Is it, do they like short and sweet? Or are they, is this type of audience looking for more detailed, bullet point type of content? So it really helps you understand not just the topic, but the type and the delivery model.

And then the actions you can take is that not only can you should you have dashboards that are telling you that, this very high, this is a very high value audience cohort, right, that we've identified. And this particular cohort, we're seeing this trend data in terms of content types. We're actually now going to build out and be able to plan for the next three to six months. Based on this the content program. And the ROI metrics you're going to use are, the one engagement.

And it's not just engagement of they're looking at it and reading it, but are they sharing it? Are they liking it? Are they coming back to it? Are they going to another, if you have carrots, IE, like this content is supposed to lead to this, are those carrots being followed? The retention, the referral rate, if it's, if It may also be something that you're doing to do new subscriber acquisition. These are all the things you would measure in this use case.

Did this use case resonate with anybody on the call here? And we have some stuff from Kathleen. This is similar case for me too. Content development based on seasonality, type, title, author, popularity, trending topics and profitability. So Kathleen, out of curiosity, is this like what we described this use case? Are you able to deploy something like this in your business, or if not, what would need to take place?

I would say that only factor that is being incorporated currently is profitability, but really it's all of those multifaceted things that are a little bit more touchy feely, like the popularity of the seasonality.

Is it Christmastime and we're offering a content on like new year, new you, all of that is a little bit more subjective and it's hard to incorporate, but I'm sure you probably have a way to do this, but I feel like the only things that are being taken into account now is just sheer profit, which is, subtracting the marketing dollars from the sales generated from the subscription, but I don't think it's taking into account a lot of other things that should be added to that.

Analyzed in the decision making process, if that makes any sense at all. Yeah, no, absolutely. That actually is goes back to my I'm the Sierra that says the revenue we have right now is enough. Right. Exactly. Yeah. And Kathleen, just for everybody, Kathleen's with everyday health. Which is owned by Spice Ziff Davis. Yes, and it's a subscription based. Almost like, I would say they purchase courses, but it's not really a subscription. It's purchasing individual courses. Exactly.

So another use case we have is a media company with deep and expansive reach into highly coveted audience segments who wants to improve pricing. And this is a really interesting topic that I am personally very fascinated with is pricing, right? And it is in this particular use case, they want to identify ways to improve pricing power for digital advertising campaigns.

And so one of the biggest assets you have is a media or an events company or digital information is the audience and not just the number, the quantity it's the depth of which, the audience and what they're doing and the signals, the purchase signals. In this case, we put together the sales marketing, digital ad ops and audience data and finance teams. And what we want to map is advertiser ICP and buyer personas. So that's actually a new data set.

So what would have to happen is in their CRM, they would have to actually add these in there. And in this case, I would say, do a test of their top 50 advertisers and start really understanding, from, sector. Title region, who are they really wanting to look at, look look to to to target and then how are we on the audience side looking at what data we're currently collecting.

Versus data that we can accrue or gather from behavior without asking someone to fill in a form that would make that audience segment map to the advertiser side very highly attractive. So the insights you're trying to gather here's my top 50 accounts. Here's who they want to reach in this particular brand. Here's the audience we have. And of that audience here are very high value prospects, right? These are the ones that they would covet the most and who are the most engaged with us.

And then you trickle that down and then you could price accordingly. based on like dynamic pricing. So the more highly coveted, the more engaged, the more valuable, the higher the prices. And you could do this across advertising. You could do this across lead generation, but that is one way to optimize pricing based on one of your most valuable assets, which is your audience. And the ROI you would look at is revenue per audience member. As well as.

On the advertiser side is, spend time to acquire the 1st or the renewal deal. The program performance metrics, customer satisfaction is anyone here on that? And the call doing this right now? Steven hasn't. I think Steven, this might have been towards the to the earlier use case. Correct? The good morning. I texted in on the on the content monetization part, but. I mean, it, it applies to both.

I mean, we chatted about this a little bit a couple weeks ago, but the, we're not we've got partial subscription, paid subscription model, and we're migrating to more paid products. And the pricing of individual products, the pricing of subscription products, and then sort of bundles. Yeah. That that applies generally. But we don't have, we so far we did some industry sort of scanning about what people are charging for what.

Content they have, and we've done a little bit of internal work, but it was not done in sort of a dashboard form. It's done more informally than that. But my interest applies to both just from a broad standpoint. Well, actually, just in your case, another area that actually comes to mind of a use cases is the packaging of the subscription, right? Certain.

And especially if you're in multiple countries there's different ways people want to purchase content, whether, for example, in, let's say the UK, they want to buy from a company standpoint, whereas in the U S it's individual. That's my gut, right. But where's the data to support it and need that. Yeah. Because the UK has successfully forged ahead with corporate subscriptions and completely gotten nowhere with individual subscriptions, but they, that's fine.

We're using that as a model for the U. S., but the U. S. can do more like smaller groups. I mean, we're going to go to our big customers. And say, here's the 5, 000 people in your company that we have on file. Here's 3000 of them that are active users, and here's the price, so to speak, to, to have them have, the full gamut, and it's going to be a lot less expensive than doing it individually or departmentally. So we have to have that conversation, but that's 20 companies, right?

Well, we'll make money with another a hundred companies that are smaller individual groups and libraries and other types of, sort of non traditional. Subscribers. Right. And actually even taking the data analytics side to that, if literally be being able to activate your marketing based on the insights you're getting from the different types of cohorts you're going after so that, it's not so manual. That would be another application. So another use case is revenue stream identification.

And this is how do I look at what's happening within my current. asset base to find new revenue streams. And again, we'll just use the media company as a use case. One that has audience segments across multiple adjacent markets. So essentially, you would get your stakeholder team together and you're mapping data across your C. D. P. Your serum and then unstructured data sources. Both internally and externally.

Again, unstructured being, website usage, your social media, your own social media and maybe even third party. And what you're finding is, where is that intersection across perhaps these brands that we're not addressing, but that there is. There's need that we're identifying. And how do we then take that? And it could be that it's a simple as it's a new topic. So we have a, spoke to somebody who is talking to, has all different markets.

Some of them not even adjacent to one another, but obviously this artificial intelligence topic is of huge demand. So they're launching an artificial intelligence paid newsletter that they're going to be able to market to, 500, 000 plus database independent of the markets. So again, you would create your action plan. This one's more about testing a hypothesis first and rolling out and testing. And then with a longer term view, but again, your data can tell you that if it's organized correctly.

Chad, I've been kind of taking over here. Do you have on the use case side for new revenue stream identification? Any examples from your side of the fence? Yeah, absolutely. We worked in the past with a number on a number of these types of examples, but one in particular where a company was looking to see in the market new brands that were entering into a relatively new industry. And see which ones early on. We're showing signs of high potential growth.

So that was by looking at their social media, by looking at their sales data, website, traffic, et cetera. And then identifying those brands and those companies that were new entrance as potential acquisition targets. then acquire and roll up under their own brand.

So that was very, a very interesting kind of project there to look out at, third party data, trying to get a sense of the market, trying to get a sense of where the opportunities are early on enough that they could, acquire these organizations before they grew. Before they grew too big. Yep. All right. So just from a time check, I wanted to, we've got a few other ones, which we can go over in a, if you'd like to contact us for a one off, but there's revenue expansion.

And again, there's a gazillion different ways you can use predictive analytics to help identify risk and capture opportunity. But I want to hand this over to Chad at this point. And this is the process. The process is More on the technical data side, but it is very important because one of the things that I hear and so does Chad is they're missing a huge part of what has to happen in order for them, to arrive at predictive analytics.

It's like they want to go from being a baby to a 21 year old and skip all the years in between. I'm going to, we did, we put together this process diagram and I'll have Chad Chad, I'll be driving the slide. So you'll just, let me know when to click next, but the floor is yours. Thanks Heather. So yeah, this is a pretty detailed overview of kind of the steps you need to take.

To Heather's point, this is really speaking more to fully operationalizing analytics and predictive analytics within a business. The same process could be used if you're doing a one time exploration of data and trying to get a one time kind of output of a read on certain a certain part of the business.

So you could do, and I'll speak to it as we go through, it could be used for a one time project as well, but really we're kind of focused on how do we operationalize this in a a very efficient and robust manner within a business. So go ahead, Heather. So first one, obviously you kind of want to understand what your objectives are. It's not enough to say, Hey, we want predictive analytics. We want to be like everyone else who's super cool.

And that area you have to have some sort of objective to start. Right. And then, honestly to Heather's point earlier on in the conversation, improving sales is always an objective and it's a great place to start. So there are a number of other examples here, but you really want to have something in mind specifically, otherwise you're going to be wasting time and money. So next is kind of defining the team. So the, you need, kind of a. Ownership on the project or on the initiative.

Ideally, the executive level. So someone who's going to sponsor it and make sure that people who are on the team have time to do, to partake in the work. You have to identify, as you kind of, as we'll go through, you'll see, but we have to identify folks who are able to, who have the skills necessary to make this happen and identify where you might not have that internally. So you might need to go out and get external help. So this third one here, this is also very important early on.

So you have your objective, you're trying to, improve revenue forecasting as an example, in order to do anything here, you have to have data and you have to have enough of it. So there, in the example I provided before with regards to finding acquisition opportunities within the market. Scarcity of data was a real problem there. It was tough to say, we know exactly how much these new brands are selling, or we know exactly how much activity they have in their business.

And so a big part of the project as in that example was spending time on this step here, looking at third parties who. provided data within certain areas that we could grab and use to help fuel the model for, predicting the outcomes. And not only that, you obviously have to, on the, if you want to improve revenue forecasting or sales, you have to look at the CRM. You have to look at, if you've done acquisition across a number of different companies, are they all on the same platform?

Do they have different standards of how they're managing the data and managing a sale? And you have to start to identify where those data points reside and how much of it do we have. The amount of data, becomes pretty important depending on what you want to achieve here. But this is, that exercise of saying, okay, here are the data points that we have internally, here are the systems where they reside. And here's the kind of nomenclature and the methodology we use to collect them.

So here on the tools you, that's, also a kind of a big step here, depending on where you are in the process. So if you're starting from complete greenfield and meaning you don't have any technology in house to manage information or manage data you could be looking at a decent sized list of tools that you need to go out into the market and find and install but some of the key ones that you would absolutely need, especially if you're doing this on an ongoing basis is the data warehouse side.

You need some someplace to store the single source of truth to bring the data in from a data integration standpoint. So what do we mean is just, a method to automatically extract your source data from your CRM and other systems and push it into a centralized warehouse. That way you can have it in a more flexible. environment that you can model the data. You can, run certain algorithms on it. And you can, clean the data as well. The data visualization, algorithm development.

I'll get into that a little more detail, but those are also going to be, pretty important depending on the approach you're taking. So data preparation And if we think about one, steps one through five here, a lot of this so far would be kind of the precursor that Heather was describing.

You kind of, you have to get to a point where you have your data collected and cleaned up in order to do much of anything, even if you're not trying to do predictive analytics, this is something that you would do just to get General business intelligence on the organization, right?

So it's a step that's well worth it to go through or these steps are really well worth it Even if without the predictive analytics part as the goal and the data preparation side is really coming up with a structure that unifies the data from the different systems So if you have customers in the customer success System that have different names than the customers in the sales system and the crm You have to have some way of preparing that and blending those datasets together.

So you can see from a term perspective, potentially, what's their customer, what's the customer support activity and success activity matched against the sales and the pipeline that we have in the CRM. And actually, Chad, I want to bring one thing up here. Just again, knowing the types of businesses that we work with.

There are so many tech tools and platforms that are being used to support events and media where this data is sitting for the same customer understanding how to create that taxonomy, that single source of truth. While still moving your business forward with all of those platforms, it is a real big factor in this data prep part. And there are definitely ways to do it without interrupting the business flow or the source data.

But that is something that I see stopping a lot of people because of the enormity of it all, but there are definitely are solutions. So step seven, six and seven here. This is probably where you end up without internal expertise. You might stall out a little bit. So this is going back to the first couple of steps. What are our objectives? What data do we have and how do we map that to our objectives?

Now you have to figure out, like, how do we go about actually creating the algorithm or creating the process, right? Without having a lot of expertise or experience in this, in doing this from scratch, you're going to, again, you might need to go out externally to get expertise to set it up as a one time process that you can then continue to run over time.

But I would also say that, depending on the nature of the objective There are tools in the market that can do this and have determined the right model, determine the right features or have a mechanism to determine the features automatically and by feature. We mean thinking again about the easy one that everyone knows on the sales side. I go back to a story from our own business way back when we were starting.

We had a little smaller set of data, obviously, as we were just But we decided to do a kind of an experiment and review of our CRM and the deals that we had closed, the deals that we had lost. And in doing it just as a one time evaluation, we were able to identify three key attributes on our sales. So on the opportunities data points, we collected that led to a greater than 90 percent close rate. So those three attributes, then we're pretty much our sales method and process going forward.

And we use those to close, business at a higher rate. And continue to kind of keep an eye on those attributes. And that's kind of what you mean by the feature selection. It's like what data points are really impactful. Give it to that drive the outcomes statistically. So again, there are tools that if you're looking for certain outcomes or certain predictive models, there are tools that are built in the market that will have some of this all ready to find.

If you have something that's very unique and very niche in your space, or you have data that's very complex then you might need to go and build more of it from scratch. And that's where you have to get into more of the technical expertise. So here on the next step, after you've kind of defined your model, you're really going to train it and kind of validate the results, right?

You might not have picked the right approach, might not have picked the right data sets, the date, the attributes, this step. Is a means to kind of figure that out and to over time determine what is the most optimal model and most optimal data set to use to generate the outcomes. And so you know, just simply, you kind of, you can think of it where you're, you're splitting your data sets.

into some that are fed through the model and you validate against the model and then some you are fed and you train the model on and then you ideally want to see the model work well with new data that it hasn't seen before. And that's the process that you're overseeing here. So that's, it's going, going to an example that most people probably know with, which chap with chap, she, Bt, they trained their models internally. You had a ton of people doing it.

Obviously, and then they released it in the wild, and, they're getting feedback based on its performance that incorporates itself back in to the model to get better over time. And that's kind of touched on here as well. So the evaluation, you obviously, you want to see it work well against the training data, but also in the new data as it comes in. So there are methods for determining, how accurate is this or how reliable is this given the different nuances we see in the data.

The step 10 iterative improvement again, you oftentimes won't get it completely right on the first time or in the first try. And so you kind of, you need to take this as a, known factor going into the project, into the initiative, you're going to have to, adjust over time continue to look at the performance and see if it's. Continue to perform well over time and make changes if needed. So on step 11, educate and communicate really important here.

If you want actual adoption within the business, a good example from our experiences, in some cases, if you define the model and define the outputs and they're very obscure or very difficult to comprehend. In a dashboard or in a report or anything like that and you try to distribute that to people They would just they just won't use it. They won't believe it. They won't understand it and You'll have a bunch of wasted effort.

So you really have to make sure that the outputs are something that are easily digestible just in the visual and report presentation form, but you also need to educate them on how is it, how was it constructed? What factors do we use to determine the future sales or the churn risk? As soon as they understand that, then they can have more faith in the model and have more faith in the outcomes in the outputs, and they'll be able to adopt it.

But if you don't do that it's rare that folks will really trust it from the outset. And I'll just pipe in here. What I've seen is if that's not done well, then all of a sudden, well, that's that's data is wrong, so it just gives someone if it doesn't say what they like. So this part is, I think, super critical and then after this, you get to go on and do more. So if you pick something narrow and very, contained, very Again, the idea is that you can scale this to other areas.

And we've seen that within our customers time and time again, we set up something for them. They love the output and then they might start to do things on their own, or at the very least suggest other ideas. And areas within the business that they can apply these techniques to. And so it's, from start small and then iteratively kind of scale up and have the team within the business, take on some of that some of that exploration and model development. Any questions about the process? All right.

Again, if you want to talk to us offline, we're here. So my favorite part is always the landmines. We, the landmines are and landmines happen before, during and after. So they're really important to understand. But Chad, you want to kick this off? Sure. Just a couple of that. I'll call out. So not enough data. Thank you.

If you have and, ambitious or not, maybe not even ambitious, objective you need to make sure you have enough data to actually make it statistically possible to produce predictive outputs. We, there It's a little bit of a chicken and egg, but. You do want to make sure you have sufficient historical clean data, in your single source of truth that you can run the models through. And that's oftentimes for smaller organizations. That's where that's really where they aren't able to leverage.

Predictive analytics as much as because there's just not enough data to make use or define patterns. But, that's one I would say discarding models too early as well. You might see outputs early on that don't make any sense. It doesn't mean that everything is wrong. It just means that you might have it off. You might have it configured slightly off. You might have, a couple inputs that are not clean, more couple inputs and weights within the model that aren't working. And so it's.

It's don't try to don't quit too early on. When you're starting the process. Yeah. And I think the other one is and we touched on all of these, but I think that the big one is the activation plan and process to activate and how you're going to track and measure. it's very important to show your board, your CFO the value that's being added to the business for these investments. But there's a lot of culture change and process change that's required.

So thinking that through ahead of time is critical and not doing it is definitely a way to have that landmine. Go off. I also think that not proceeding until the data is perfect. No data is perfect. And especially in the businesses we're talking about it is there's so much data. But you have to start somewhere. So figure out where that is, and you'll perfect as you go. Capabilities are evolutionary.

And I would say the the assuming everyone is on board, or I like to call the saboteur in the room. You've got to identify the person or people who are... are not willing or really trying to stop progress from happening in your business through the use of data. I personally see it as the emperor's clothes. They don't want to, they don't want to expose what's really happening. But really finding that out and understanding why any resistance and why, democratized.

data environment and everyone is better at their jobs when they have data at the ready. Those are just a few of the landmines. I don't know if any of you are encountering any that you'd be willing to share, but we have a couple left but that's our presentation. Any landmines that anyone wants to dive into we've had a few people have to leave, but there aren't any questions. I will share these last two slides. There's our, the chat on my email are there to take advantage of the office hours.

You can email us. I'm happy to sit down and talk about where you are in your journey. And again, these are the ways that we can help at H2K. And then we have another boot camp coming up on October the 19th, and we have Denise Medved, the Chief Commercial Officer. She's new in the role at Informer Markets, and Matt York, who's the CRO of Foundry, and it will be actually a really interesting discussion because Informa is very heavy on the event side. Foundry is 80 percent digital lead gen media.

And they're both doing some really exciting things in terms of a single source of truth and pursuing revenue excellence through data. So with that, I would say if there's no more comments or questions, you'll get four minutes back to your day and we'll send everyone the recording. Great. Thank you so much, everyone. Thanks, Heather.