Thought Leaders in Artificial Intelligence: Gabe Larsen, VP of InsideSales Labs (Part 3)

Gabe Larsen: One of the things that we built into our dataset is this cross-company concept. Let’s say somebody places a phone call on our system to an individual in the East Coast, and that phone call reaches a disconnected phone number. Two days later when another rep goes to call that same phone number, the rep will be alerted that that phone number doesn’t work. I’m giving you a very tactical example.

Let’s go with Amazon. They’re doing millions and millions in transactions. In the B2B enterprise space, we’re not closing millions and millions of deals every year. I wish we were. You don’t run a Big Data play on 10 deals a quarter. That’s ridiculous. We pool the enterprise together and we start to say, “Let’s share this.” The power of cross-company becomes the only way that AI will end up working in the enterprise B2B space.

Sramana Mitra: Aside from the example that you cited, can you share an example of more modeling. Supposing I’m getting success in one account, how do you parametrize that success and look at the rest of the prospects to see which ones are like that account? Is that functionality available and how do you do that?

Gabe Larsen: It’s a little bit of what I said. Certainly, the past can help predict the future. If I can find that you’re working very well with the northeast technology companies with employee size of 50 to 500 and revenue of $50 million to $100 million and if I study these past transactions, what the dataset is telling me is there is an optimal. I could sell my product to everybody in the continental United States.

I’m bringing in some of the economic factors in the northeast at the moment. There’s this sweet spot going on. I’ve figured out that it is ideal. The question then is how do I apply that to the future. From a very simple standpoint, you say, “If this is the ideal customer profile, are there other companies in the same industry that I can work with?” If I can start to pick out these different characteristics from my past and apply it to the future, that’s definitely one of the considerations.

Then adding that secret sauce of company data, “What if I could see what other companies like me are finding success with? What if I threw that into the pot as well?” This is all a game of numbers. I’m not saying that AI could ever display to me exactly the right thing that is guaranteed to move forward, but it’s a game of percentages. If I can just increase my conversation rate by 2% to 5% or increase my close rate by a couple of percent, we’re talking of millions of dollars there.

Sramana Mitra: Let’s switch to the forecasting part of the equation.

Gabe Larsen: There are two things that we do here. One is a little bit newer, but I love it. This dips down into prospecting. When you look at a sales opportunity, there are two things interacting. There’s the individual and then there’s the entity. The two combined makes the sales process. Originally we were just looking at the opportunity, but some of the fund things we’ve been doing lately is looking at the seller.

As we look at the opportunity, think of it like a lead or an account in the prospecting notion. As a sales rep, I often have a difficult time figuring out which opportunities are best for me to spend my time on. What is the optimal next step? Because of that, I struggle from a forecasting standpoint as to which deals are going to close.

A lot of data out there says that the industry forecasting capability is at about 50/50 – 50% that I’m going to close and 50% that I’m going to close a deal that I didn’t have in my forecast. Those are going to be the three big problems. Which opportunities do I spend most of my time on? What is the next best step should I be thinking about? Which one should I actually go to and put a stake on my career?

This segment is part 3 in the series : Thought Leaders in Artificial Intelligence: Gabe Larsen, VP of InsideSales Labs
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