Why Inflated Lead Scores are Tanking Your Marketing ROI

October 8, 2019 | Kirsten Lyons


The growth of marketing technology has given marketers access to more prospect data than ever before. Because marketers can know so much more about their prospects, efficiently prioritizing the best leads requires a system to make sense of all of that information.

Marketers use lead scoring models to sort prospect data. Lead scoring is a methodology for ranking leads in order to determine their sales readiness and likelihood to convert. Marketers often assign lead scores to prospects based on their engagement with content and campaigns, and demographic fit.

So what could be the issue with a perfectly logical system that has allowed marketers to automate and quantify their efforts? 

Unfortunately, the kind of information marketers collect for lead evaluation limits the quality of the information they can get out of their lead scoring models. 

Because marketers use prospect behavior data and basic demographic information assign lead scores, the scores are only useful if the assumptions marketers make about what those behaviors mean are correct. 

What is an inflated lead score?

Dramatic shifts in B2B buying behavior have rendered traditional lead scoring less effective. While in the past prospects followed a more linear journey—from identifying a problem, to searching for solutions, to selecting a vendor—today’s buyers follow a less direct path.

Prospects now spend the majority of their buying process doing independent research and consuming content to evaluate solutions. If your lead scoring model assigns value to prospects engaging with content, you’ll see scores increase as prospects conduct research, without getting any real information on how that content is impacting their decision-making process. 

This causes marketers to assign inflated lead scores to prospects who are engaging with content, but not showing any real signs of buying intent. 

The cost of inflated lead scores

When marketers qualify leads using inflated lead scores, both marketing and sales suffer. 

The ultimate goal of a lead scoring model is to improve the efficiency with which marketing can identify the leads most likely to convert to customers. 

But when prospects rack up lead scores that don’t reflect actual buying intent, sales is left chasing leads unlikely to become customers.

Inflated lead scores are problematic for both marketing and sales because they don’t reveal the why behind buyers’ actions.

In the example to the right, a high lead score based on activity can’t tell marketers and salespeople if George’s problems can be solved by their solution, whether Jane opted out because she purchased another tool, or if Carol is actually engaged in a buying process. 

Spending time chasing prospects who aren’t the right fit creates obvious issues for sales around their revenue goals. this dynamic also creates problems for marketers, who are increasingly held to sales-focused goals, rather than specific MQL targets. 

When marketers prioritize leads based on inflated lead scores, they pave the way for low MQL conversion rates that reduce marketing ROI. 

Score leads on buying intent

While inflated lead scores create significant challenges for both marketing and sales, marketers can rethink their approach to assigning value to leads to improve outcomes. 

Lead scoring isn’t the problem, the score’s correlation with actual buying intent is. Instead of relying on leading indicators like engagement, marketers can create lead scoring models that reliably predict buying behavior by using different criteria to assign value.

Instead of evaluating leads based on prospect activity, marketers can opt to score leads based on qualitative information that more effectively predicts sales readiness and fit. 

Marketers can use existing conversion opportunities that impact lead scores to get better information. For example, instead of increasing a prospect’s lead score simply because they registered for your webinar, ask for qualifying information in your registration form to uncover buying intent.

This information can help sales have better conversations that move prospects through the buying process more efficiently, improving marketing ROI and ultimately increasing revenue.

Learn how to ask qualifying questions that uncover buying intent here

Conclusion

When marketers use proxy measures for buying intent like engagement, lead scoring models become ineffective. Lead scores become inflated thanks to how prospects consume content within a buying process, and sales is left working leads unlikely to convert. However, when marketers can rethink how they score leads to prioritize actual sales-readiness, both marketing and sales can be more effective.

Learn more about how to deliver leads that convert in our ebook here


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