The Evolution of Forecast Management - with Guy Rubin, Founder and CEO ebsta

**Ray Reich** (0:00)
Hello, I'm Ray Reich, founder and CEO of RevOpSquared, and your host of the Metrics That Measure Up podcast. We talked to a wide variety of B2B, SaaS and Cloud thought leaders, executives, investors, and people just like you to discuss the metrics and benchmarks they use to make metrics informed decisions. Now on to today's show. Welcome to today's episode of the Metrics It Measure Up podcast. Today, we are joined by Guy Rubin, the founder and CEO of APSTA. We're going to be covering three main topics with Guy today. First, the historic forecast management process and challenges that we face. Number two, modern forecast management, what has changed and what is possible. And third, blending forecast management process discipline in automation to achieve increased forecast accuracy. Guy, please take a moment to give a brief overview of your journey to becoming a guest on the Metrics That Measure Up podcast.

**Guy Rubin** (1:13)
Well, Ray, first of all, thank you very much for having me. I'm a long time listener to your podcast and really, really pleased to be here today. So I suppose a little bit about my background. We started Epster in 2012, and we really set out to address a very simple challenge that everybody has with their CRM, which was around automatically logging all of their activity in the system of record itself. Very quickly, it became apparent that a lot of the activity that we were capturing from things like your call records, your email traffic, and your calendar events, was happening to people that weren't registered as contacts inside Salesforce. We then extended the functionality to capture contact records, and we built an engine to expand the logging, not just of activity, but also contacts into CRM. Then that product went viral. I think we had over 50,000 companies in the Salesforce ecosystem kick off free trials of that product, and use EBSDA to log their activities, create contacts, and keep them up to date. Then very quickly, it became apparent that the contacts that had the most activity were leading to the most revenue.
That really led to a breakthrough for us, where we decided to try and codify what a relationship is. We ended up scoring relationships for our customers, and what we call relationship intelligence. Once we could see the momentum and engagement scores trend up when deals were closing, we then extended that functionality to deliver more accurate forecasts and then ultimately pipeline insights and benchmarking on live pipeline against previously closed one and lost deals.

**Ray Reich** (2:53)
Guy, very interesting that the start of your journey to forecast accuracy as a solution provider was the same challenge that still exists for far too many companies today, and that is, is the right data in the CRM and what's the quality of that data? And we actually did some benchmarking on this topic, and we found that the third largest challenge for companies regarding their forecast is still the quality of the data in their CRM, the opportunity information, et cetera. So I guess here's my question. Why here 11 years later, is data quoting the CRM still one of the biggest challenges to accurate forecasting?

**Guy Rubin** (3:34)
Yeah, it certainly is.
I think the problem is that everyone recognizes they've got a problem with the quality of the data in the CRM. The problem is that fixing that problem is usually a 7 out of 10 issue, and everyone's dealing with 11 out of 10s. So no one ever really gets around to fixing the problem. But we're living in 2023 now, and if you're still manually logging activity or relying on reps to create contacts and keep them up to date, well, then the data is never going to be consistent enough. So whether you're using Epster or any other platform to do it, it's not an expensive task. It doesn't take very long to set up and you really should have an engine looking after the data in CRM for you rather than relying on the very expensive sales reps to do that work for you.

**Ray Reich** (4:18)
So let's double-click on that little bit, Guy, because I know that with our listening audience, people are still going to say, well, one of my biggest challenges is the data in CRM is just not in-depth enough, it's not timely enough, or it's not accurate. So can you give two or three ideas of how using automation, we can eliminate the sales reps not putting the data in, but it's still there? How is that done, Guy?

**Guy Rubin** (4:41)
Yeah. I mean, it's not rocket science. Every company already has the data, it's just in the wrong place. So every human your team have ever engaged with sits within your mail server or on your calendars. So we built an engine that's now a decade old and has gone through lots of iterations and evolutions where you can connect to mail server in about 20 minutes to our platform, and we can go back one, two, or three years, and build a profile for every contact you've ever engaged with every company you've ever spoken to. Then behind those contacts, you've got an audit trail of all of the activities that have gone back and forth. Then in addition to that, our relationship intelligence piece will tell you who in the business holds the relationship the day they last engaged and whether the relationship is trending up or trending down because we're giving every relationship a score over time and it's always out of 100 So it makes it super easy to know who you've been engaging with and how strong that relationship is. Now, where that becomes really powerful is if you've recently acquired two or three other companies, you can just connect up all the disparate mail servers very quickly and find all the disparate relationships and highlight who within the organization you've just acquired, holds the relationship with the prospect you're interested in targeting. So that's really how we solve the data quality issue. While it's not the most sexy of topics, it gets you on a journey towards predictable revenue growth. Because once you've got consistent maintained up-to-date data, you can start relying on it. A good example I would give would be LinkedIn.