In this episode of the Full Throttle Leaders podcast, host Michael Falato interviews Bill Kantor, CEO and founder of Funnelcast. Kantor explains how Funnelcast uses advanced mathematical and statistical techniques to analyze CRM data (primarily from Salesforce and HubSpot, with CSV fallback) and generate time‑based win‑rate models that improve sales forecasting and optimize selling effort. The platform claims a typical 60% productivity boost for users. Kantor describes the product’s pricing ($15,000‑$25,000+ per year, company‑wide, not per seat) and shares a case study where analyzing market‑segment productivity revealed a 30× difference, leading the client to reallocate resources and achieve 30‑40% annual growth. He addresses the common “garbage‑in, garbage‑out” CRM hygiene problem by employing separate models for existing pipeline and prospective deals, allowing the system to work even with back‑dated or incomplete data. The ideal customer profile is defined as companies closing at least 25 deals per quarter and having around 10 quota‑carrying sellers. Listeners are directed to funnelcast.com and provided with a contact email for Bill Kantor. The conversation blends technical explanation with sales‑focused storytelling, highlighting Funnelcast’s disruptive approach to revenue operations.
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All right, everybody, another Full Throttle Leaders podcast today. I'm your host, Michael Falato. As you know, I am the CEO and founder of Full Throttle Falato Leads, and one of my new ventures in life is to do a podcast to talk about go-to-market strategies and funnel and just how do you run a business? How do you succeed? What's your path that got you there? Today, I have Bill Kanter, who is, I think, the CEO and founder of Funnelcast. Is that right?
That's right. Yes. Yeah.
So tell me a little bit about your company and what you do and why you're even on the roundtable today.
Yeah. So, well, we're doing something very disruptive.
I'm sorry, not the roundtable, but the podcast. I'm losing my mind.
So we're doing something very disruptive to help sales leaders make more money. and, you know, basically, uh, what we do is we help them, uh, forecast better, and more important than forecasting, optimize their selling so they can make more money. And we do that using, uh, some advanced math techniques and statistical analyses of their, their prior sales history to make specific recommendations about where they should be focusing their efforts. And if you follow our Our advice, we can demonstrate typically about a 60% productivity boost. So it's pretty substantial, but it's disruptive because people don't really know how important focus is. We show them that, and when they get that under their belt and see how powerful it is, they can be much more productive, make more money.
So they're based— you're basically hooking into their CRM?
Yeah, we look at— we analyze— we take in all the CRM data that, that's available for the basic fields that are, um, you know, made available, uh, or basically tracked for every deal and analyze that and then come up with recommendations based on that. Yeah.
Is there particular CRMs that you work better with than others?
Well, we have API connections to Salesforce and to HubSpot. And so really pretty much all of our customers are there. We have ways to do it manually through CSV files to other CRM, but it's really, uh, the business is mostly driven off of Salesforce and HubSpot customers.
And you've been doing this for about 9 years, right?
A little bit less than that. We started out as a technology-enabled service, uh, where we were doing this in spreadsheets and, uh, kind of custom crafting things that would take us a few weeks to build a model and, uh, come up with some recommendations. We can do that now in about 5 minutes and, and do it much more robustly, much more precisely. So the application has really enabled our productivity if we were doing it. So anybody who wants to do this as a service, talk to us. We'll be happy to enable your service as a consultancy to use our product to do it for your customers.
And what's the average revenue per client, or what's the average cost to a client to work with you guys?
Well, you can get started pretty inexpensively. For our core product, it's only $15,000 per year, and the enterprise product starts at $25,000 a year and goes up. And that's, that's for the whole company. We don't charge per seat. We charge based on the, the size of the company and the use.
So tell me a story, Bill. Like, you went into a company, you took in their HubSpot, and lay out a path of what you did that blew their mind away?
Well, so one of the most important things we do is we build a time-based model of the sales process. And so, you know, here's just one example. Is the first thing we do is we look at win rate curves as a function of time. You know, when you say you win, your win rate is 20%, it really makes no sense to say that if you don't also say how long it takes to get to 20%, right?
Yeah.
So, so we measure that and we measure that by stage. And so I had a CRO that was forecasting all of his last stage deals to close in the quarter. And I showed him that in fact, they only closed about 50% of those deals. Within 90 days. So he was really over-forecasting and getting in trouble with the board because, uh, being, you know, optimistic about, uh, how long it would take to close the deals that were in his last stage before close. Um, so that's, that's just one example of the, the analytics that, that you can use. But more important is, um, you know, if you— we had, we've had a customer, for example, where we said, look, if you look at your demand generation your, your, your market segment, you have multiple market segments and you're putting resources kind of evenly across all of these different market segments. What if I could, you know, analyze them and we see which ones are more productive in terms of the time to close a deal, the amount of the deal, and so forth. All that falls out immediately out of the model and you can see in a few seconds that market segment 1 is, in the case that I'm thinking of, was actually 30 times more productive than market segment 2. And so a 30x productivity difference, they were allocating resources evenly across all of the market segments. There were 8 in that case. But from the highest to the lowest, that was about a 30x. And they kind of knew something was up, but they didn't know it was 30x. And so the analysis gave them the the power to make decisions about resource allocation in different markets that they actually did implement and they grew about 30, 40% over the next 2 years annually.
So what do you say to people that are putting, not updating their CRM or garbage in, garbage out type scenario? How do you fix that approach? Because there's people that are not good stewards of their CRM.
Yeah.
And then if you don't have the good CRM data, then how do you help them?
Yeah. So, well, they eventually get updated in the CRM. They show up, you know, as sometimes people have a practice of not recording deals until they're essentially closed.
Yeah. And so, unless they backdate it and then they're, then it's a little more obvious.
Those backdating things cause difficulties. We know how to get around that and understand that. But, Um, the answer is it's, it's pretty robust model for, uh, that kind of behavior because we have two separate models, one for existing pipeline. So if you're recording deals as you should be, as they move through the sales cycle, you'll have more fidelity on those deals, um, because you'll be able to predict, you know, later stage deals are higher probability than earlier stage deals. And, uh, then a separate model for what we call prospective or new pipeline. These are deals that are not yet created that haven't been added to the CRM yet. And by modeling those two separately, people who are not recording deals until they actually are essentially done, we still capture that behavior and we model that separately so you can still optimize in those markets or across all of those.
So your last question here, or last two questions, Who is your ideal customer profile? Obviously it's Salesforce or HubSpot integrations or CRM users, but what stage?
So we need, we need someone who's doing sufficient volume in transactions to train the model and to evaluate the model. So what does that mean? It's, it's a little bit of a rubber band because it depends upon the uniformity of the deal sizes. But as a rule of thumb, if you're closing 25 deals a quarter of new and expansion opportunities, you usually have enough transactional history to train the model and to score it so that you can use it for sales forecasting and optimization. Most of our customers are doing more than that. I have a few that are doing less and they're still— the model produces results. It's just a higher variance output. If you're under that threshold, you have to take a look at the uniformity of the deal sizes and it's a more complicated issue. That's one metric. Another metric is if you've got about 10 quota-carrying sellers, that's typically enough.
Volume, you start to see how do people get a hold of you, like, what's your information so that people that are watching this podcast can reach out to you?
Funnelcast.com. You put— it's sales funnel and forecast, put them together, funnelcast.com. And you can contact us there, sign up for a demo if you want, or even get a free trial started on your data in just a few seconds.
And what's your email?
Bill— or excuse me, B Cantor, K-A-N-T-O-R, at funnelcast.com. Awesome.
Thanks for having— thanks for coming on my show today. It was good seeing you again.
