Take a Listen to “Moneyball for Marketing”
April 1, 2015
Jim and Glenn talked about all things predictive – including marketing use cases for predictive intelligence, real world success stories, and four B2B barriers to predictive analytics adoption that are rapidly disappearing. Check out this excerpt from their lively discussion…
Glenn: Tell us an example of what companies are doing in the real world and how they’re taking advantage of predictive analytics.
Jim: Well, there’s an easy example I can speak to. I was the first customer of this company Infer when I worked at Box. When I was running sales at Box, I had the luxurious challenge of dealing with very large lead flows. We had a freemium aspect to the business. We also had a very vibrant free trial aspect to allowing people to get into our service pretty quickly. So, very large flows and leads, we’re talking tens of thousands and I could never afford to apply a lead qualifier to plow through all of those leads systematically over time. I needed a way to find the proverbial needle in a haystack and I started working with Infer.
We were able to build a predictive model together and use that to score leads coming in. And immediately we more than tripled our conversion rate because before we just had reps going through long lead lists and trying to pick out the ones that had a company domain or maybe we could tell had an activity in the account. Infer automated all of that and just automatically shot leads into our qualifiers’ hands without them having to do any research on the front end. We immediately tripled the revenue that we were getting from these particular sources of leads – freemium and web trial abandoners.
Listen to the full podcast here, and hear Jim’s responses to questions such as:
- How would you define predictive analytics as it relates to the marketer?
- How is it that Infer helps find the right kind of customers for any company to focus on?
- How does it work when a lead comes in as it relates to connecting the marketing activity to sales?
- How far can predictive go in helping me as a marketer understand the likely results I might get from a campaign?
- Is it also pulling in behavioral data, along with firmographic and demographic?
- What’s prevented the adoption of predictive?
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