Building a High-Impact EdTech Venture from Alabama: QuantHub CEO Josh Jones (Part 5)

Sramana Mitra: How did you engineer your go-to-market organization to sell large deals to enterprises?

Josh Jones: We sell all the way down to a company hiring a single person. In fact, in our base level offering, you can get free tests. You start paying when you need larger volume and a single sign-on. A lot of our customers will do custom tests that are built to their systems or their specs.

If they provide their own case studies, we can provide a candidate a spreadsheet with a business challenge. For example, one of our tests is the airline pricing model. Let’s say you’re pricing seats for an airline. You download this data set. We’re going to take you through a journey. You’re going to do the modeling and upload your results. It’s going to be graded and then provided to your recruiter. A firm might want to choose their own model based on the internal nature of their own company. A lot of that complexity will add some pricing to it.

From a go-to-market standpoint, at the smaller end, the companies looking for single or low number of hires basically find our website on their own. We have a sales team that goes after the larger corporations.

Sramana Mitra: And the larger corporation deals are in what range? Is it like 100K plus deals? Four million dollars ARR kind of deals? What is the nature of the business?

Josh Jones: It depends on the size of the corporation and their volume. Some of the larger corporations have eight data scientists and four of them don’t know the other four exist. They’re just so spread out all over the world. So you very rarely have this really nice, neat, global data science recruiting process. It’s usually like supply chain has a data science team and their data science team reaches out to us.

So as a result, the deal size can range anywhere from call it $5,000 to $10,000 a year to hundreds of thousands of dollars a year.

Sramana Mitra: So it sounds like because the data science teams are so distributed, your buyers are distributed. Even within the same company, same corporate entity, you have multiple buyers.

Josh Jones: That’s right.

Sramana Mitra: Interesting.

Josh Jones: I’ve had corporate customers where we had multiple customers in the same company who didn’t know each other existed. We don’t encourage that, obviously. It’s just sort of the nature of the beast. We’re doing business with this group in this company, and one day there’s a deal from another group, maybe in a different state, different division, but it’s the same stock symbol. That’s happened numerous times. It’s nice because we can say, we already have an MSA in place. We’re already in your procurement system.

Sramana Mitra: Yes. So I would say the big advantage is that you do need to get qualified as a buyer for large enterprises; that’s often a pretty lengthy process often. Once you are a registered buyer within the system of a large corporation, then you can sell to multiple groups.

But what does a sales engine look like? I’m asking you all these nitty gritty questions more from, how to scale an organization perspective. You land a deal somewhere in a large enterprise. Does your sales team go and look for other opportunities within the same company? Is there a process of doing that? How do you do that?

Josh Jones: Yeah, so let me think about where the best way to take this would be.

Sramana Mitra: I imagine that all the people with a reasonable title who would be a data science recruiter would be on LinkedIn. So one way to do it is do a company level search for all those kinds of titles.

Josh Jones: Actually, I need to sort of fill you in on the rest of the business because assessments is the first two years of our journey. Fast forward five years, there are now multiple companies that also offer assessments in the marketplace. So it’s a much more competitive landscape, which is the case in any business.

I will just make an observation about when we were first to market. Being first to market isn’t always the best thing because you spend all this money and time educating your customers. Then by the time you’ve got them educated, competitors pop up and you may or may not have that first mover advantage. So I think first mover advantage is in many cases a myth.

Sramana Mitra: Microsoft has built an enormous company never being the first mover.

Josh Jones: Well, many, many companies, I would say, for that matter.

Most entrepreneurs have a sort of litmus test for ideas – is anyone doing it? If so, then I’ll move on to some other idea because I only want to do something that no one else has done. I don’t know if that’s necessarily the best route.

For us, it was more of a total addressable market question. In the sense that the challenge in the assessment space when you’re dealing with data scientists is that data scientists by nature are very educated, even if they’ve educated themselves. Very often, they feel they are capable of writing their own tests. They don’t need a third-party platform. It was sort of the thing that you discover after you start getting into the sales process about why this isn’t always going to be an open and shut case.

I’ve believed for the last 15 or 20 years now that technology growth and capabilities are on an exponential curve. If you look at Moore’s law, the number of transistors is going to double every two years on a microchip. We’ve seen that continue for over 50 years now. The amount of data being created is increasing exponentially. The cost of processing power is dropping exponentially at the same time. So, the processing power per dollar is improving at the same rate.

What that means is, our ability to do things like generative AI is opening up in ways that it never has before. As we move along that exponential curve, the rise over run increases. And so the pace of what we call skill inflation is changing faster than ever before.

What we realized was more important than the hiring problem is the fact that everyone needs to be data fluent, or AI fluent or AI literate. What that means is really understanding the art of the possible. How are data and AI changing the world around me? How can I be a part of that?

Where that came in tactically in both StrategyWise and early days at QuantHub is that for most organizations that try data science projects, I think 85% of those projects fail. Look at the reasons why they fail – Gartner and Kaggle have done some studies. If you look at the top reasons, usually eight out of ten are people problems. It is the lack of the right culture or data quality.

Well, data quality doesn’t sound like a people problem, but it is when someone in the call center doesn’t see the value of that data and they press 9999 in a record as opposed to the actual phone number or social security number. They think, “Well, I don’t need that screen. I’m just going on to the next thing.” A year later, the data science team comes back to try and build some predictive model; and all of a sudden, the models don’t seem right. What’s going on here? There’s bad data quality.

Or you deploy a product in the field, and we’ve seen this numerous times, where the product got killed on the factory floor or on the front line because those people were afraid it was going to take their jobs away and they would do everything they can to subtly sabotage that effort. Not everyone has bad intentions, but that sort of thing just happened.

What we realized is, for organizations to deploy data science and AI effectively, they need to train everyone in the enterprise to be data fluent. So at QuantHub, we had built an in-depth skills ontology and taxonomy for the purposes of testing, but we realized we were uniquely suited to actually build an educational platform that would be highly efficient in delivering only the learning technology and only the learning activities that an individual would need based on both the skills required for the job, as well as their personal diagnostic profile of what they know and what they don’t.

That was when we made a big pivot into the education side.

Sramana Mitra: Very interesting. So who buys that? Is that a corporate product?

Josh Jones: So we have a number of corporate customers. We’ve definitely seen success there. We are very active in K-12 and now higher education. In our K-12 product, we saw the data skills required to be successful in any job were the same. Our customers like Nissan or Blue Cross and Blue Shield or Southern Company were coming to us and saying, “We want everyone to have these ten skills.”

If you look at those skills- how to collect data, how to do a study, how to build charts and graphs, we’re supposed to be teaching many of them in ninth, tenth, and eleventh grade. They’re your STEM courses, your ninth and tenth grade science classes, or your algebra classes. A handful of states, Alabama included, have an actual data science class that can be taken as a fourth year math credit in place of calculus or something like that.

But that’s the more advanced area. Everyone needs to have these core data skills – data wrangling, data analysis, hypothesis creation. We’ve mapped our platform so the K-12 teachers can actually see our data skills, how they map to learning standards that they’re already teaching to.

We have over 3,000 teachers now using the platform to teach data in high schools. And then students, when they complete their learning journey, can get digital credentials and certificates. We’ve launched an internship program with our corporate customers where we can put the highest scoring students, preferably in underserved and rural areas. We can map them to corporate partners to do summer internships.

This segment is part 5 in the series : Building a High-Impact EdTech Venture from Alabama: QuantHub CEO Josh Jones
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