Talking Predictive Marketing with Advanced Analytics Expert Cary Correia

May 19, 2016

MRP paired up with TEMO Marketing to host a virtual roundtable during which attendees could discuss and ask questions about predictive marketing with Cary Correia, MRP’s SVP, Advanced Analytics.

In a pre-event survey, attendees said that they, for the most part, were not already using predictive analytics to hone their marketing efforts – or if they were, they were using a very basic form of it. Some of the key challenges the attendees have with their marketing include understanding the vendor landscape, leveraging the data they do collect, and navigating a lack of software integration. All respondents agreed that predictive marketing could greatly improve their efforts.

Cary opened the discussion by outlining the key concepts of the predictive model:

  • There needs to be sales and marketing alignment
  • You need to figure out which of your target accounts are active
  • Use analytics to score your target accounts based on how active and how likely to buy those accounts are
  • Create a sort of “teepee” model in which you put high-scoring accounts at the top of the priority pyramid and go downward based on lower activity and likelihood to buy

Here are some other key takeaways from the roundtable.

On Lead Scoring and Likelihood to Buy

According to Cary, lead scoring should start simple. “When you first get started, you’re going to do a logistic regression,” he says. “It’s the easiest form of predictive model: if you win, you give that account a 1. If you lose, give it a 0. Then you can go back and create a data structure with all the things you need. Once you understand your account base, you can understand how much potential each prospect has based on its probability of being like an existing customer. I.E.: a high-ranking prospect will have its score closer to 1 than to 0.”

More Complex Scoring and Likelihood to Buy

Cary then got into the more complex aspects of lead scoring. “Now, you can start collecting other kinds of data and incorporating that into your model. For example: if a large company is opening a new data center and putting 10,000 people into it, that’s a sign of big opportunity. That’s a data bite that you can take and put into your model. “

He discussed other important data bites – likelihood to grow, likelihood to contract, likelihood to upgrade their IT – that can tell you an account’s likelihood to buy, that they’re gearing up for a big spend. “There’s a lot of opportunity in timing, too,” he said, “in predicting when companies are going to hit the thresholds that will encourage them to grow. Then there’s intent to buy: you take a weblog of a customer’s web activity, what they’re researching, and use that information to actually trigger when a customer is going from learning mode to buying mode.”

Finally, there’s the “strike zone model,” which Cary said “speaks to your customers’ ability to pay your price. You don’t want people who can’t afford you, or customers that are too big for your company’s scale. You want the ones right in the middle.”

Ultimately, “You’re using a combination of factoid data and these models to tell you which customers are most probable to win. Then you put it through your map of the universe and you score them. The ones that you’re going to target are the ones that look like your existing customer base. It’s very statistical – very much based on the information you have at hand.”

Finding Talent

Not every company has their very own Cary Correia, and lots of the roundtable attendees expressed this as their biggest weakness: learning how to leverage their data. It’s a time-consuming task, which is why a lot of people outsource it – but because of the ROI associated with predictive, it’s worth finding a solution, and there’s one for companies of every size.

Later, when an attendee said that her company does their predictive work in-house, but struggles to find the talent or bandwidth to actually get something out of it, Cary suggested: “go through a vendor or third-party to find someone who can help you align your data. It takes a lot of time to fine-tune your system, but you can easily go out and find someone to do it for you. Companies like MRP sell tools like Prelytix that are easy to use and do the legwork for you, so it’s really manageable.

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