B2B Marketers Tap New Scoring Tools To Prioritize High-Value Leads

While B2B marketers recognize the benefits of traditional lead scoring, the reality is that many are struggling to develop scoring models that work effectively for both marketing and sales.

To address these concerns, some progressive marketers are now integrating new predictive tools into the scoring systems already in place within their marketing automation platforms to help prioritize sales efforts on prospects that are most likely to convert.

These B2B marketers find that a healthy balance between traditional and predictive lead scoring tactics may be the secret sauce to success.

For example, Concur, a travel expense management company, was able to leverage predictive capabilities with insights gained from its past traditional scoring models to formulate a hybrid-scoring model.

"We went through three overhauls of our scoring model," said Greg Forrest, Director of Marketing Operations at Concur. "We fast-tracked the crucial information through our traditional lead scoring efforts, [and] then predictive scoring helped enhance those efforts to boost accuracy,” he said. “The information we don't provide over to sales is then used to enhance marketing efforts."

As a result, Concur saw a 150% quarter over quarter increase in the number of leads moving from MQL to close. The company also ended up uncovering 5,000 previously unidentified MQLs within its database.

Marketing teams need to build a foundation before they can go forward with growing their scoring models, according to Forrest. A simple switch from traditional to predictive is tempting, but not wise. "Many marketers feel that they need to make a complete transition to predictive because expectations were not met during their initial run with traditional lead scoring.”

B2B marketers have come to rely on a steady stream of data to help identify hot leads, sources noted. However, the top two reasons marketers lack confidence in their lead scoring are incomplete or inconsistent data (59%) and insufficient insight into which attributes indicate buying behavior (44%), according to a study from Decision Tree Labs.

"One of the biggest factors is whether or not [marketing teams] have enough data, and also whether or not that data is accurate,” said Derek Slayton, VP of Marketing for Dun & Bradstreet NetProspex, in an interview with Demand Gen Report.

This lack of accurate data can lead to several false positives, resulting in scoring rules based on best guesses, according to Yaron Zakai-Or, Co-founder and CEO of SalesPredict. "Marketers can also get false positives by relying on last-touch attribution instead of considering the full buyer’s journey and all the content they interact with, both on your site and across the web, along the way," he added.

The latest predictive modeling tools are designed to leverage data to automate the process of identifying the leads with the highest propensity to buy. Predictive lead scoring solutions have upped the ante by incorporating data from external sources.

"Traditional lead scoring has gaps, and predictive [modeling] can help fill those gaps and make scores more accurate," said Nipul Chokshi, Head of Product Marketing for Lattice Engines.

Another industry expert agreed. "A good predictive solution can help take the guess work out the traditional scoring model," said David Lewis, Founder and CEO of DemandGen International. "Machine learning and algorithms help suggest the fit instead of guessing."

Lewis identified several categories of tools for B2B marketers that can result in more accurate lead scores, including:

• Pure predictive engines designed to tap external data sources for behavioral data;
• Account-based scoring solutions;
• Targeting solutions; and
• Ancillary products positioned to enhance a user's data.

While many solutions incorporate predictive analytics into their processes, sources noted that it is important not to overlook the internal data within a company's CRM and marketing automation platform.

"If there are behaviors that predictive can't incorporate, you might be missing important buying signals that identify high-quality leads," Lewis said. "If that predictive engine doesn't incorporate that MAP or CRM data, you're missing out."

Test And Adapt

Effective lead scoring also requires continuous testing and feedback.

"There are a lot of shiny toys out there, but at the end of the day you have to identify if [a specific tool] is going to help or hinder your team's ability to accurately score leads," said Damon Waldron, Director of Demand Generation for Leadspace. "It's important to test, in a measured way, how these products can help your business."

Sources noted the move from traditional to predictive should be gradual. "Hopefully, the marketing team has learned a lot from their lead scoring initiatives in the past," said Justin Gray, CEO of LeadMD. "Variable comparisons are crucial to gaining a better understanding of what a quality lead looks like. You need to capture the data that drives the reporting. Thankfully, marketing automation platforms have been automating data collection; now predictive is helping companies use all of the data they been collecting."

The ability for marketers to regularly adjust lead scoring models to the ever-changing B2B buying landscape is also crucial to lead scoring success. "Marketers need to recognize that this is a project that never ends," Lewis noted.

IT automation provider Chef continuously adapts its scoring model to improve its targeting based on feedback from its sales team.

"Marketers need to understand the customer journey and start with a proof of concept," said Mike Korch, Demand Generation Manger for Chef. "Marketers need to be selective on where they start when it comes to audience, product and market."

Sources noted that B2B organizations update their scoring models every quarter, on average. However, fine-tuning models more or less frequently can work — depending on their industry, size and number of leads being generated.

Predictive can help marketers keep their scoring methods up-to-date because it has to use the most up-to-date information to be effective, according to Vik Singh, CEO of Infer. "The idea of building different models to keep scores adaptive on-the-fly is also a great idea."

Incorporating insight from closed and lost business also helps identify key targets where sales reps can put their focus.

"It's critical to use the filtering capabilities of lead grading tools to get the whole picture," said Adam Blitzer, SVP and GM for Salesforce Pardot. "This allows marketers to assess whether a prospect will be a good fit for a company’s product."