Thursday, April 18, 2024

How B2B marketing can see into the future with predictive lead scoring

Many B2B marketers spend an inordinate amount of time on sales lead generation. But how strong are these leads?

Traditional CRM systems provide some metrics on the likelihood of leads converting to sales. However, these metrics are based on retrospective rules. If only marketers had some source of intelligence that utilizes forward-looking data so that they could discern the good leads from the duds.

Luckily, many technology providers have entered the B2B field and their solutions enable marketers to predict which leads have the best chance of converting into sales.

According to a January 2015 Gartner report, some of the leading solutions providers in predictive lead scoring include 6sense, Fliptop, Infer,, Lattice Engines, Mintigo and others. Because predictive leading scoring is such a new discipline, many B2B marketers may still be unclear just what is involved.

“Predictive lead scoring is the process of using machine learning statistical algorithms to understand which prospect has the highest probability of converting to a customer,” says Jessica Cross, director of marketing, Fliptop. “B2B marketers benefit from predictive lead scoring as it can filter out the good leads from the bad, prioritize sales reps’ time and allow marketers to make smarter decisions on their campaign spend.”

Ability of predictive lead scoring

Much of the predictive ability of lead scoring systems comes by way of intent data provided by prospective customers themselves. Some of this intent data is related to the digital footprints that customers leave across the Internet, including changes in job titles, employment at new companies, patent filings and other disassociated data.

“Intent data can also be found when people express their ideas and interests on social media,” says Brian Kardon, CMO, Lattice Engines. “Perhaps they contribute to a LinkedIn group that is discussing ‘enterprise security’ or they just tweeted ‘thinking about a new phone.’ Facebook, of course, lets marketers target their users based on things that they have shared.”

Wasted resources, wasted time

According to B2B research firm SiriusDecisions, 68 percent of companies use marketing automation systems to do lead scoring, yet only 40 percent of sales people get value from it. This would seem to imply that both marketers and sales people are wasting their resources and time on traditional lead scoring methodologies.

“The challenge with traditional scoring found in marketing automation is that it wasn’t built with a predictive-first approach,” says Jamie Grenney, vice president of marketing, Infer. “Point values are manually assigned, and there isn’t any emphasis on combination, concentration or recency of signals. And the point differences between activities (e.g., downloading a white paper, visiting a pricing page) are arbitrary. While it’s a starting point, it is easy to see why it is prone to false positives and why it is not trusted by reps.”

Wasting resources and time is not even the half of it. For those marketers and sales people who follow through with current leading scoring processes, they can end up prematurely pursuing buyers who have not completed their journeys in the sales funnel.

“What resources have been wasted on targeting the wrong buyers, who don’t have an immediate need to buy certain products?” asks Amanda Kahlow, CEO and founder, 6Sense. “How many deals have been lost to competitors? And most importantly—how much more money would companies make if they replaced guesswork with data-fueled predictive intelligence about their next buyers?”

She goes on to say that tapping into prospect intent activity data allows marketers to know exactly where they are in the sales funnel and allows marketers to predict when they will have a need.

Who will buy and when?

To be able to tell with some level of accuracy which B2B buyers will purchase requires more than just predictive lead scoring. What is best is a way for sales people to exert some level of influence on prospects. It might necessitate prescriptive analytics.

“While predictive analytics enables sales teams to make sense of insane amounts of data, this emerging technology, by itself, is simply not enough,” says Leo Dirr of “In addition to predicting buyer behaviors, sales teams must be able to influence buyers. This requires a new generation of sophisticated technology that can not only predict the future by analyzing the past but also prescribe actions to take to achieve success.”

According to Dirr, has more than 10 billion sales interactions in its database that provide insight as to the best method for contacting prospects—whether by phone, email or text as well as the best days and times to reach out.

Others also advocate using past behavior for ways to influence buying behavior. According to Mintigo, modeling past behaviors can predict the most effective promotion channels and contact frequency for each buyer. “Such models can consider other factors such as the cost per contact in different channels and when additional contacts will annoy customers and depress future response,” the enterprise company says in a statement. Then predictive analytics can identify the most productive use of sales or call center resources to ensure that individuals receive the most effective contact, according to Mintigo.

What do you think about predictive lead marketing? Let us know in the comment section below

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Derek Handova
Derek Handova
Derek Handova is a veteran journalist writing on various B2B vertical beats. He started out as associate editor of Micro Publishing News, a pioneer in coverage of the desktop publishing space and more recently as a freelance writer for Digital Journal, Economy Lead (finance and IR beats) and Intelligent Utility (electrical transmission and distribution beats).


  1. I understand how Fliptop might help a B2C company and the logic of analyzing social media data to identify people who would buy your product, but I still don’t get how this could work in a B2B environment. Where do you find the data that could help you tell if a company would need your product ?


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