Episode: 143 |
Jonathan Stern:
Predictive Analytics:


Jonathan Stern

Predictive Analytics

Show Notes

Our guest today is Jonathan Stern, a former partner at Bain and the CEO and Founder of SnapStrat.

SnapStrat builds tools for its clients to bring prescriptive analytics and machine learning to the tasks of regularly recurring strategic decisions.

In this episode, he shares a case example of SnapStrat’s work with a top beauty products retailer, helping them use analytics to determine which free samples to give out.

To learn more about Jonathan, visit his firm’s website, SnapStrat.

One weekly email with bonus materials and summaries of each new episode:

Will Bachman: Hello Jonathan, welcome to the show.
Jonathan Stern: Thank you very much.
Will Bachman: So Jonathan, after 25 years as a consultant, you have now a software startup. Tell me about the switch, and about what you’re doing.
Jonathan Stern: Well, as I went through my consulting career, I realized that increasingly the 27 tab sell model and PowerPoint deck we were handing off, and maybe it had become a Tableau front end, it wasn’t sustainable for our clients over the long term as they tried to take the strategy recommendations and execute them. And the reason that had changed was because this pace of business change had increased so dramatically, so that the half life of that spreadsheet, or that PowerPoint deck was getting shorter and shorter. And I recognized that there was a opportunity to fulfill that gap and bring data science, and machine learning, scenario modeling and so forth, to the category of recurring strategic decisions that actually link a strategy with execution.
Jonathan Stern: For example, a location strategy might define a set of countries you’re going to expand to. The value of that strategy is realized on a quarter by quarter basis, when you decide which locations you’re gonna open next and how much you’re gonna invest in them and so forth. It’s that second class of decision. Not the initial strategy, but the set of decisions that link the strategy to actually getting the value from that strategy that is where SnapStrat is focused.
Will Bachman: So, give me some examples of those.
Jonathan Stern: Yeah, there’s two … So, two primary use cases that we’re focused on, though it’s applicable across a broad set. One is marketing allocation, and when you think and look at the products in the market that help CMOs allocate marketing spend, they tend to be focused way down towards the customer. Which channel should I put this campaign on? Should I put it on … How much should I put on radio versus the internet, versus social or what have you. That’s a valuable product, but the strategy for a marketer is thinking through which product should I allocate my marketing spend to, or which customer segments, or which geographies. We think about that problem at that level, the strategic level.
Jonathan Stern: Another example is something that all organizations do, which is strategic planning. In the case of strategic planning, you’re trying to allocate a scarce set of resources, capital, and expense dollars, to a set of discretionary products. They might be IT projects, they might be other kinds of projects. That process is highly strategic, and most of the tools that handle program management are focused not on making the decision of how you allocate those scarce resources, but in fact kind of tracking those resources after they’ve been allocated. So we focus on the front end of those kinds of allocation and optimization decisions.
Jonathan Stern: So those are a couple of examples we’re focused on, and there’s a wealth more in different functions and industries.
Will Bachman: Great. Let’s take that marketing allocation example. Maybe you could walk me through sanitized case example or a more sort of generalized case example of that. So I’m a chief marketing officer, let’s say, and we’re discussing … Give me an example of what would be an appropriate industry, and I’d really love to understand in depth kind of how you would work with a person. Where is your data coming from? Are you scraping the universe? And how would you go about supporting someone in this scalable way on allocating across products, or channels, or customer segments?
Jonathan Stern: Yeah, let me [inaudible 00:03:48]. Let’s explore a little bit our launch customer, which is a leading, or the leading beauty retailer. And the allocation is about interesting there, because instead of allocating dollars to customers, their big lever in marketing is actually giving away product samples that they get from their brands. And so, they give away hundreds of millions of these samples, and they do it through a series of different kinds of marketing programs. They do it through a points redemption program, they do it through coupon codes, they do it as a birthday gift, all kinds of ways you can get samples from them. And they do understand a bit about the effectiveness of what happens when they give a way a sample, but they actually weren’t doing anything with that data.
Jonathan Stern: And so, what our objective was in working with them, was to create a tool that would allow them to strategically allocate their product samples to the most appropriate program, so that they would have the highest impact, both on their customers buying more of their product, but also to the brand that’s supplying a sample, increasing the ROI to the brands of the samples that they’re giving out. And so we brought in all the result data, at a SKU level, of all the samples they’d given out. We brought in information about their programs, their different products, we allowed them to tag products so that we could correlate the type of products with the impact of the sample distribution. And basically, we built them at a brand/category level, an optimizer that would give them an optimal allocation of these product samples based on their strategic goals in a specific marketing program, whether they care more about the return back to themselves, or the return back to the supplying brand, and they could … You think about strategic optimization, you can always think in terms of setting sliders with different weights, and running scenarios to see the impact of how different emphasis on your different strategic criteria will impact the outcome.
Jonathan Stern: And that’s what we’ve done for them, and generated 10 to 15% lift in terms of the return on those samples they give out, which is a very significant amount of improvement on a very high number of samples that they give out. So that’s a special case example around where we’ve done implementation.
Jonathan Stern: More broadly on marketing allocation, where we’re focused on is a kind of a product geography level. So, thinking through taking the data and understanding the differences by the product domain … We’re doing this for a telco. By a product domain, or how a product acts in a geography, what the different competitive nature of that geography product segment is, and then being able to allocate down to the channel level based on that. So we take, the data is actually some of the typical performance data that you would get from media mix modeling, or marketing mix modeling tools, that kind of gives you the impact of marketing spend that could range from anything from the conversions and sales from a coupon code, or an 800 number, to the Nielsen ratings and so forth. So pulling in all of that performance data, and then correlating it back, but at the product or geography level as opposed to trying to do it only at the channel level.
Will Bachman: Yeah. Let me go back to the beauty retailer. So, what your service is … Is it something that now you’ve set something up for them, some software tool, where your client is now kind of running this on an ongoing, kind of continual basis? So you don’t have to have boots on the ground?
Jonathan Stern: Exactly. In fact, we’re a staffed software company, and what’s interesting about the technology that underlies this, is it’s … Our CTO is a guy who comes from the ETL software world, if you’re familiar with that.
Will Bachman: I’m not. What’s that stand for?
Jonathan Stern: It’s basically about data transformations. So it’s transforming and loading data, and so what we’re … So we have an engine that basically ingests data that the customer has, so we don’t ask the customer to conform to our data model. We work on top of their data, and then everything is configurable to a specific customer. And so, we actually have a single code base that serves any customer, and what that allows us to do is move very, very quickly, and the product to customer is a SAS tool. What’s really, really important is we’ve found around any kind of transformational software is that you can’t just provide a better answer, you actually have to provide the workflow that [inaudible 00:09:02] people’s lives easier. So in the beauty retailer example, they have a SAS tool that they have 30 users on every day, that are at one end of the process figuring out what the optimal allocation is, and the other end of the process POs are getting spit out, because that’s actually where you create the value is writing the PO for the sample, not kind of upstream where you’ve just made the decision. You have to make sure it flows all the way through.
Jonathan Stern: So we think of our business and our product as a combination of a business analytic decision tool, but also … and scenario modeling, but also the workflow and the downstream connectivity to make sure that those decisions are turned into actions.
Will Bachman: Okay. Cool. So, what’s the process for a client of setting up your tool? Sounds a little bit complex. You have to kind of go and find all the data and figure out how to port it in there, and then figure out how to make some sense of it, and probably need some processes of who’s looking at it and making decisions, so do you have consultants that go in and help them with that? Or how do they … how do you do that?
Jonathan Stern: Well, our … and we’re a young company, so we kind of do what it takes. What I would say is we have business knowledge, and we start with a design thinking workshop, in which we take all the stakeholders and bring them together to understand sort of the ideal process, both in terms of workflow and pain points, but also in terms of the decision. So what are the criteria, and then as we think about the decision criteria, there’s two important concepts in these decision. One’s the criteria you’re using to make it, so what’s the outcomes I’m trying to generate, and the second one is the constraints, because for example, in the beauty retailer example, if I gave all of my sample quantity to a single brand out of their 300 brands, that wouldn’t be a feasible solution. That brand couldn’t produce that many samples, and it would … The other brands would [inaudible 00:11:11] and so forth.
Jonathan Stern: And so there’s a set of constraints that you have to layer on top of a decision, to make sure that that outcome is actually executable. And that’s actually generated fairly quickly, and what we can do is go in, have the discussion around that topic, and then actually before we go and design anything, just understand the data and understand the value in that data. One of the … I think the core ethos of consulting that is often missing in the enterprise software world is … There’s two things I would say. One is a focus on value, and the second is a determination that you’re gonna do whatever it takes to make that customer’s problem go away, no matter what that means. And we kind of hold true to both of those in that sort of consulting ethos at SnapStrat.
Jonathan Stern: So we’ll work with a customer to figure out the right way to do it. What I’d say is there’s often a lot of bigger issues around process change, and insuring, and even maybe figuring out what those right criteria are. That’s not our business and that’s where we want to work with consultants, is to make sure that in fact, this customer’s set up for success around this decision and they’re gonna make the right process changes, that they have the right strategy in place and so forth, and so there’s a certain set of services provide not only just once, but on an ongoing basis as part of our SAS agreement, which has been things like retuning the model and making sure that the data flows in consistently, and that we can reflect changes in their business in the tool. But the broader set of change requirements for the organization itself is not something that would fit what we do, but rather partner with others to do.
Will Bachman: That’s fascinating, so one kind of use case would be around figuring how to allocate a certain amount of resources, or allocate some kind of activity. What are some other types of things that this sort of decision tool can be used for? I think you mention-
Jonathan Stern: Yeah, let … I can go through a few examples where we’ve had discussions on. One interesting one is if you think about a physical network of retail stores, and which stores I should close and which stores I should remodel. The value in thinking through that in a systematic way, and it’s typically there’s a budget for that, and it’s in the class of what we call recurring strategic decisions. There’s a strategy behind it, it’s multi-criteria, and we love multi-criteria decisions, particularly ones with both qualitative and quantitative inputs, and there’s a lot of value at stake in making that decision. So that’s an interesting one.
Jonathan Stern: We’ve looked at things like labor rate optimization, where there’s a whole set of trade offs between service levels to customers, retention, seniority of employee and so forth. We’ve looked at … Similarly, we’ve looked at the staffing levels, because staffing levels have … Again, it’s a set of trade offs. It’s a cost trade off versus a service level, and there’s things like a customer … How much do I care about customer retention or basket size versus the cost of operations, and it’s different depending on the nature of the store. There’s certain categories they want a minimum number of people in, so looked at that.
Jonathan Stern: We’re having a-
Will Bachman: How would that labor rate optimization work?
Jonathan Stern: So, labor rate optimization, what you need to be able to do is look historically at the … It really becomes … It’s really an HR question, right? So you’re looking historically at swings in retention, at swings in the … It could be, depending on the nature of that employee, if they’re customer facing, it could be an end, a store level NPS. You could look at their kind of … the employee record, in terms of their … how well they’ve done in their job. You can look at number of applicants, and the quality of applicants you’re getting into the funnel. It’s one of these things that you have a lot of different, kind of diverse criteria, and it’s again something we’re built around, that can all impact that decision. And understanding those correlations to get to a sweet spot, and it might be different by stores. There may be certain stores where you have a certain demographic that has a different need, puts a different weighting on service level, and another one puts a different weighting on price, and you want to be able to refine that decision at a level where you can be strategic at a store level. It would hold with other kinds of activities like customer service, as well.
Will Bachman: So this would be then, you would set up this, in the first case, so the beauty retailer, you’d set up this tool as a SAS tool, and they would … client would continue to use it over time to continue as market conditions change, they would keep referring to the tool to update their sample mix, or in this case every quarter, or whatever periodicity, as they are updating their wage that they’re gonna offer, they would refer to the tool. It’s like an ongoing piece of their infrastructure, as opposed to a one time consulting project.
Jonathan Stern: Yeah. Exactly. And so that’s what I mean about sustainability, right? Because inevitably, these decisions are being made more and more often, because the business context is changing faster and faster, and it’s core to what we’re trying to do that we not only give them that. If in fact, we just gave them a one time solution, we’re effectively technology-enabled consultants, but we’re actually providing technology solutions that sustain and can transform an organization to be better at this category of decision, not just once, but over time.
Will Bachman: And it seems like it’s these recurring strategic decisions that have multiple criteria. This is fascinating. Tell me a few other of these kind of use cases. We talked about labor rates, talked about allocating marketing budget, what are some other places where these recurring strategic decisions happen?
Jonathan Stern: So there … One of the easy ways to think about is to start to go sort of at an intersection. Sometimes, a lot of them are cross industry. Some of them are industry specific, but in an industry sort of function perspective. So, one we’re … We’re just talking, this week down at one of the movie studios. We had a couple of different discussions there that were really interesting. One of them was about one of the studios’ interactive platforms that’s a subscription model, and their question is, “What’s the right strategy around offers to be able to optimize the value of the subscription platform?”
Jonathan Stern: And if you think about offer management, it’s been again, typically, we see this in kind of the vast majority of the decisions. If there’s tools, they’re way down at the D tab level. So there’ll be offer management that helps me understand for a specific, what’s the right pricing of a specific offer for … that I’m going to make to a specific customer set. They were interested in a very different question, which is, “What are the right group of offers and what should be my strategy in terms of things like discounting for new customers, and how that might hurt retention of existing customers, versus retention offers, versus increasing promotional spend,” those kinds of trade offs on a fixed platform. So, sort of the investment in various levers that will impact customer revenue on a interactive platform. So that’s one we’re looking at.
Jonathan Stern: We had another with another studio, actually had a very different conversation, which is an example of an industry specific one. There we were talking about licensing windows. So, as it turns out, if you … In the media entertainment business, if you take a film and you go through its life cycle, it will go, it will get distributed across different means of distribution, from … It might jump from theater, to online and various permutations, but also there’s a whole geographic component to that, and the way that, and timing of those distribution windows by geography is complex, and it has a lot of benefit to getting it done right. And there’s factors you wouldn’t even think of, like if you go … Depending on the demand in a specific country, if you wait too long to distribute into that country online, you risk a higher amount of piracy, but if you do it too early, you lose all the higher margin theater revenues and so on and so forth.
Jonathan Stern: And so that’s another one we were looking at that’s kind of very special purpose, so it ranges pretty broadly. There’s … You can think about supply chain, and optimizing my DC network in supply chain is another problem that’s highly strategic, and most of the tools are around sort of shelf space optimization, not really thinking through, which is now very fluid, how do I … How many DCs do I want to build? What 3PL should I use to augment that space? In what cities? How do I manage the value of having a centralized network versus having a distributed one, where I can get more same day or next day service out to my customers and so forth. You can sort of see it would … You can take any function and think about the core strategic decisions that that function makes, and apply this kind of tool, and in general you’ll find that those decisions today are being made in either spreadsheets or in meeting rooms, for the most part, or in big kind of one off efforts with a business analytic team that is not repeatable for that customer. Yet they’re having to make these decisions more and more often.
Will Bachman: Wow. Sure. I imagine you pay attention to what other folks in the industry are doing, kind of that’s similar to what you are. What do you see kind of the big consulting firms, McKinsey, Bain, BCG, so forth, doing around kind of building these kind of software tools that can kind of be the leave behind kind of that can continue to generate revenue for the firm over time? What do you see those firms doing that is somewhat analogous or competitive to your product?
Jonathan Stern: Well, it’s a huge challenge for those firms, and you know, I’ve talked to all of those firms around partnering and I’m sure we will at some point. I think the … You know, MBB, for sure, recognizes that the product has to change. I think what’s a little less clear is what their role in that, sort of the new consulting value chain is. The challenge … And so, I think about the large consulting firms, and frankly the small ones and individuals, much more as partners than competitors. They are building a lot of prototyping capabilities, and I think in some cases, McKinsey probably further ahead than others, are buying firms that particularly around that have data. And then sometimes it’ll have some kind of capabilities around software, but the challenge with software in a consulting firm is that software’s a capital intensive business, where you’re building a platform that you’re going to invest in over time. And it just has very, very different economics than a consulting firm, and very different skillset.
Jonathan Stern: And so I think what you’ll see is a lot of partnerships. You’ll see some, for the very high velocity kinds of tooling, particularly tooling that the software, the consulting case teams are gonna be able to use, you’ll see they may invest in their own proprietary software. But for the multitude of decision making, there’re increasing number of partnerships that consulting firms used to avoid. You know, in the name of objectivity, they’re realizing that they actually now need those partnerships to be able to quickly build capability in their customer. So I think they’re still … It’s still shaking out. Certainly developers in all the firms now, but they’re also being much more aggressive about finding the right partners to offer differentiated solutions to this just wide, wide diversity of challenges that their clients face in this space, that frankly there are very poor software solutions today.
Will Bachman: What does it look like for a client to use a product like yours, in terms of what’s the engagement look like? You mentioned that you’d start with some design thinking, and some workshops, so it sounds like your tool is very much multipurpose. We talked about supply chain, and which stores should we close, and marketing spend, so really quite a multipurpose software. So what does a client have to do to customize it for their particular data needs and decision needs?
Jonathan Stern: Yeah, so we … There’s two stages to an engagement, which is one is what you talked about, sort of the problem definition. So, the problem definition is the design thinking, and making sure there’s value in the data, understanding how we’re gonna derive the correlations, and then at least for the foreseeable future, we do the lifting on configuration at our end. And so, what we do is once we understand the workflow and the screen flow, we build out the screens, which is a configuration exercise, but it’s non-trivial, and so typically the first month is the let’s make sure there’s value in the data, let’s make sure that we understand the right focus to make sure that we get that data value as quickly as possible. Typically, after the first month we’ll come back with kind of a dashboard view of the data, and then in the subsequent probably two months, we kind of build the app that we discussed.
Jonathan Stern: Depending on the scope, there may be multiple phases of that app. You know, fit for … Think about it as sort of a three month, end to end, we’ll get you to a app that delivers value around one of these decisions, and depending on sort of how comprehensive and where you want the workflow to go, you may decide that, “No, we want two or three,” what I call releases or phases of that to extend across the end to end workflow of that specific decision. And then there may be enhancing decisions, or related decisions that kind of share data that then they decide we want to take advantage of the fact that we already have our data on, let’s say campaign performance. We really, beyond just a marketing allocation tool, we’d really like to understand something more around campaign optimization or whatever that thing is.
Jonathan Stern: But we try and … We want to be really fast, so we want to go from zero to you’re getting value out of this tool in a couple of months, but also in a way that feels very, very custom to our customers, because it’s their strategy, and the … I know there’s a lot of software avoiding calling customers unique, but in the intersection of industry, strategy, decision, and workflow, no one looks like anyone else, and our goal is not to force a customer onto change their workflow and so forth for our platform, but rather the other way around. Make sure we configure our platform how they want to work to make this as easy as possible to start to get value.
Will Bachman: So, you mentioned that decisions are currently being made with Excel spreadsheets or in a conference room. What are the sorts of changes internally to organization, or roles, or processes, that clients should be thinking about if they’re gonna be adopting a tool like yours for recurring strategic decisions?
Jonathan Stern: Let me start by answering that with an anecdote from the beauty retailer example, because we were … The CMO … We were giving an update and the CMO was sort of getting exposed to the tool for the first time, and this was when we were a few months into it. And in that tool, as I mentioned, there’s a need to set up some criteria that are gonna impact sort of how samples are distributed. Is it more important to get value to our organization, more important to get value to brands, or more important … the third one might be average order volume, which matters for certain programs where you’re putting a coupon code, for example. And so, someone has to set sliders to say, “Which of these is most important?” And you can set sliders at a program level, you can set sliders at an enterprise level.
Jonathan Stern: So the CMO comes in the room and is kind of looking at this. So she asked the question to the project lead, the head of brand marketing, “So, who gets to set those, and are those the right values?” And you know, that person froze, because she hadn’t really thought about this, because suddenly you’re putting a very high powered tool in the hands of an organization that is gonna have major impact on outcomes, depending on how it’s set. And so the first answer to your question is really … An organization has to really have a good handle on these decision rights, and sort of the way the criterion, how those criteria are gonna get weighted, and making decisions on that. And if you’ve made these decisions in meeting rooms, or informally, or one person just kind of dictated it, that’s a whole new practice. And then as you go downstream, you have to build the processes so that that translates into how the people on the front lines, depending on the nature of the decision, some decisions there is no front line, but maybe store remodels doesn’t … Once you make a decision which store I’m gonna remodel, it’s kind of done.
Jonathan Stern: But for … Back to our beauty retailer, yeah, I have someone who’s gotta call the brand and ask for the samples, and if they kind of ignore all of the data science behind this and do whatever they want, I’m not gonna get the value out of that whole ecosystem. So, it’s both having the right criteria, the right decision rights model, and then the link to those decision rights and insuring that the execution follows that, that’s really important. And the tool can enable that and kind of tell you when it’s not working, but it doesn’t change organizations. It doesn’t change incentives. It doesn’t change basic workflows that may have to change in order to get this stuff adopted.
Will Bachman: Take me inside the black box just a little bit in terms of data science, so I imagine it’s not that you are kind of programming a specific algorithm out of your head, but is it one of these things where the machine is learning over time? Tell me just a little bit about the algorithm, how it’s developed, and how the machine is taking the inputs and translating them into recommendations.
Jonathan Stern: So first of all, there’s … and this is again, a bit of a differentiator for us. There’s two kinds of algorithms that are kind of in the broad realm of data science that we’re leveraging. The first is predictive algorithms, which say that a [inaudible 00:32:18] criteria, let’s say lift to a … for a sample to a brand, I have to be able to predict how that sample will perform in the future, based on whatever historic data I have, and that’s a very classic data science kind of multi variant regression problem. The specific algorithm may change, so we don’t have a single black box algorithm. We’ll have a library of … Right now, we just custom build the algorithm based on the needs of the customer, it’s actually a relatively small amount of the development, and make sure that we have the right regression model in place, and that type of modeling based on the specific problem.
Jonathan Stern: And then the second piece of data science is an optimizer, because you have … Once you understand what your predictive values are and you weight those predictive values, you need to then flow that through whatever kind of optimization model you’re trying to build. Typically, it’s an allocation, but not always. And so that optimizer is more of a special purpose piece of software that takes in all of the constraints, and then produces the optimal allocation.
Jonathan Stern: What you mentioned in terms of tuning is based on … Is the kind of where technology is going on predictive models, which is to say that as I see the results from previous decisions, I tune my models to continually improve my predictive capability over time. And that happens for two reasons, and it’s important, and so that’s happening in a more and more automated way that still, you still need data scientists to tune those models, and that’s part of our subscription model is actually making sure those models get tuned. But in our world, where we’re often … If you think about our decisions that are different than say what webpage should I render to someone who’s coming on my website, that’s a very high velocity decision, because millions of people do that every day.
Jonathan Stern: Our decisions are low velocity decisions. You pointed out, often it’s every quarter, and I’m trying to take some downstream data and feed the back end decision. So that changes the nature of our science, and the second thing is a lot of our criteria are things, because they’ve been done in spreadsheets for meetings, that may have been poorly measured in the past, so outcome of discretionary projects is something that people … At first, you might have a very qualitative measurement by going to interview 20 people, and we’re fine with that. We’ll take in that data and create that data, just like we would a highly quantitative, very high end piece of data. But over time, you’re gonna be able to improve that, because you’re gonna begin to build this closed loop as you automate this decision making, you’ll understand what the criteria were, you’ll understand the outcome, and so in our world, those decisions, the sort of the curve of of improving those decisions, will be very steep for customers because their data and decision hygiene is gonna improve so much.
Jonathan Stern: Yeah, so the black box is we think about it as a white box. And it’s another … When we hear the objections of a lot of our customers to what they see in the marketplace, the issue that they face is that it’s very hard to make this [inaudible 00:35:48] decisions if you don’t understand the why. And so, we open up our models and work with … We work with some firms, we’re aligned with a professor out of MIT that we’ve been partnering with who kind of focuses on these kind of white box predictive algorithms, meaning … And what I mean by white box is that the reason we’re recommending this course of action is because these factors influenced it.
Jonathan Stern: So we know that, for example, for our beauty retailer, that the type of brand it is, if it’s a modern brand versus a classic brand, has a big influence on which programs it performs well. We want people to know that, and know why we’re recommending those things, and that tooling is now available, and so it’s … We think that’s important, in terms of the kinds of algorithms we’re gonna write, and it’s, I think, very frustrating for a lot of organizations when they don’t understand those correlations, because it doesn’t help them do things like test and learn, because you don’t really know why you’re making the decisions you’re making. So we avoid that kind of technology completely, and try and really have a very open kind of data science architecture that our customers can see inside.
Will Bachman: Got it. How does a firm like yours, how do you do the business development side of raising your visibility, getting inquiries from potential clients?
Jonathan Stern: I mean, it’s funny. It’s probably … Going from being partner at Bain, to starting a software company, it’s much easier to sell a seven figure consulting project than it is to sell even an initial assessment with a new, initial project with a software platform like ours. And so you have … It’s been exhilarating. It’s hard getting into the marketplace. Fortunately, where we’ve started over the first couple of years is with my network is in a connected in an awful lot of companies, and the alums of the various consulting firms I’ve been with have been very helpful. So, initially it was … Started doing it with networking, and now we’re at the place where we’ve kind of gotten our launch customer into production, we are … We got the platform version one built out. We’re now kind of focused, now, is the next stage of it is go to market.
Jonathan Stern: We think of it as two things. The first is a couple of focus use cases, because you can’t sell sort of in the market, you can’t sell improved strategic decision making as a product. You have to sell marketing allocation, or strategic planning, or what have you as a product. So that kind of pushes us into new customer acquisition in some narrower spaces, but at the same time, I think, and more importantly in the long term, is working with consultancies and individuals, individual consultants, in bringing a new set of capabilities to their clients and kind of fitting in with their product. Sometimes that will be white label, sometimes it will be just a referral, and sometimes it’ll be a partnership or something integrated into a notional product that a consultancy have. And we’re doing that with boutique firms, and larger ones. We’re having those conversations, and I think that’s where a lot of our business development effort will go.
Jonathan Stern: We’re, as I mentioned, our customers almost by definition are going through some kind of transformation in the area they want this capability in, because we’re not the … We’re not replacing the system that does something, we’re creating a whole new capability. In almost every one of those cases, there are some kinds of consultancies involved with those types of transformations, so it’s the sort of networking, core focused products that we can go to market with in a specific use case, and then most importantly, working with channel partners, and it’s not just consultancies. Agencies are also another one we’ve had discussions with, particularly around the marketing space.
Will Bachman: So, for any listener who might be interested to … A lot of listeners of the show are running boutique firms, or independent consultants, so for any listener that says, “Wow, that’d be a great capability,” and might be able to bring your firm into an opportunity, what’s the best way for people to learn more about your firm, or to reach out to you in particular, to follow up?
Jonathan Stern: Yeah, so we love having conversations, and we’re young and hungry and eager to work with folks, so we’d love to hear from your listeners, and we’re thrilled to have exploratory conversations that just educate both of us, because I find I learn an awful lot during these, both with the consultancy and their clients. You can reach out to me directly. Jonathan@snapstrat, S-N-A-P-S-T-R-A-T .com. Our website has a pretty good set of both use cases and more detail on what it is we do, and we can … I think the best way is to find a customer and just do a small pilot to test the concept, test the value, and take it from there. We’ll be very responsive, and the advantage that we have is that we understand your business. We understand how consultancies work. We understand business value, and so there’s a lot of kind of the baseline conversation around understanding the mechanics of the business and the consultancy, that we just get off the bat, and it allows us to have a very rich conversation very quickly about, can we, because we have no desire to help one of your clients that we can’t help, and if so, how, around a specific problem that they’re facing.
Jonathan Stern: We are not a product looking to find a solution. We are actually looking to apply our technology to where there’s a felt need. So if something resonates, I go, “Wow, I could really use something like this for client X on problem Y,” that’s a great conversation. And if it’s not gonna be a fit, we’ll be the first to tell you.
Will Bachman: Yeah. Now, you’re a former Bain partner, and I think you were also at McKinsey, as well. Could you give us just a snapshot as kind of what the rest of your team looks like? Is it several management consultancy type folks, and then some programmers and data science people? Or tell us just a little bit about the team at SnapStrat.
Jonathan Stern: Yeah, it’s a very senior team, so we … Our CTO has 30 plus years experience just in data engineering, which is the core of what these applications require, and so he brings the capability to bring and build enterprise class applications, which is critically important when you’re dealing with the kind of customer set and the sensitivity of data that we’re dealing with. We have folks from chief product officers, a veteran of Dow, and Apple, and several startups, and then we’ve got development talent, and bus ops talent behind that, but it’s pretty diverse in terms of both business and technology talent. I thought … The one thing I thought it felt as I took the leap from consulting into this business was if we could get the right team, who both had the right team culture, and the right ethos, and the right dedication to just caring more about our customers, that we couldn’t lose and I’m thrilled with the team we have.
Will Bachman: That’s awesome. This has been a great conversation, Jonathan. I can imagine a lot of listeners might have opportunities where their clients have a need for something like this, so really excited to have you on the show. Thanks so much for taking time out to tell us about SnapStrat.
Jonathan Stern: Thank you so much for making the time for me, and glad to have the conversation. Look forward to hearing and talking to some of your listeners.

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