New surveys show that marketers find only limited value in traditional lead scoring.  By replacing cumbersome rules-of-thumb with powerful predictive analytics, this is about to change.

It seems to make perfect sense.  You keep giving leads points for every action that they take, and when they reach a threshold score—voila! They are sales ready.  However, as many marketers realized, this simplistic model hardly provided a robust qualification for leads’ tendency to buy.  This has two main reasons: first, the methodology seems not robust enough, and second, these programs are hard to implement.

Lead scoring weighs two major factors in order to determine a lead’s score:

• Demographic: Scoring on dimensions such as job title, industry, revenue and number of employees.
• Behavioral: Engagement with content such as click on emails, eBook downloads, whitepaper downloads and website visits.  All of these should suggest interest.

Lead nurturing is used to engage leads with a series of touches and engagements aiming to educate them about the product and service, as well as keep brand awareness high.  There is also another role for lead nurturing—to keep increasing leads’ behavioral score by eliciting them to download more and more content.

However, according to this way of thinking, eBook lovers are the perfect prospects, moving up the ladder quickly, while busy executives, who may not be avid consumers of content could be overlooked. In reality, this should have been the other way around.

Scoring also gave marketers adverse incentive.  The main aim was to create crowd-pleasing content to attract wide audience and push leads’ score higher.  The challenge here is that while this more general content was successful in generating clicks and downloads, it did not necessarily teach people more about the product and service, and therefore have pushed scores up artificially.

However, the biggest challenge is that lead scoring did not yield the results that justify the effort in setting it up.  According to a research published in David Raab’s blog, lead scoring and nurturing are among the least effective marketing tactics and are clearly among the hardest to execute.

Source: David Raab

The difficulty of setting up lead nurturing and scoring as compared to the average returns is hurting Marketing Automation. Shockingly, the same study shows that 82% of companies that adopted marketing automation are making limited use of it, or are not using it at all.

In another post, David Raab explores the share of marketers that are using the full features of marketing automation.  Raab compared results from four different reports and normalized the results, so that the best answer equals to 100.

According to his account, email is the most commonly used feature in marketing automation.  Lead nurturing is used a lot less, while scoring is trailing way at the bottom. This study shows that in its current state, marketers use marketing automation platforms mostly as a fancy email marketing software.

Source: David Raab

Why is that? There are two main reasons why so many of the lead nurturing and scoring programs fail:

• Not enough data: The demographic data in most companies’ CRM typically does not include a lot more than contact information, job title and company.  These hardly make for robust profiling.
• Not enough value:  Lead scoring and nurturing use a complex set of “rules of thumb” that are based on “common sense” rather than statistical validation.  These rules are hard to set up and maintain, specifically if they don’t drive superior results.

How does predictive lead scoring and nurturing work?

Predictive lead scoring and nurturing prioritizes leads and suggests content that pushes them down the funnel by using statistical probability rather than rules of thumb.  It uses learning algorithms to identify the leads with the highest probability to buy—as well as which solution they are most likely to need.  In addition, it segments the leads to buyer personas and suggests the type of content that is most likely to resonate with them.

Predictive lead scoring and nurturing resolves the challenges of traditional lead scoring and nurturing by both expanding the data and improving results.

Data

Powerful databases and online data mining are the basis of predictive lead scoring and nurturing.  CRM and behavioral data is augmented with thousands of additional data points, allowing scoring and nurturing to be based on a robust set of data.  Data sources include:

• Social networks activity
• Business contact databases
• Mining companies’ websites
• Technologies and SaaS products used
• Government databases
• Financial reports
• Investors
• News and press releases
• Job boards
• Marketing activity such as PPC and retargeting
• Sales and marketing tactics such as whitepapers, demos

And much more…

Improving results

This is where the “predictive” part comes in.  Unlike traditional lead nurturing and scoring that uses rules of thumb and best practices to score leads, predictive nurturing and scoring uses learning algorithms that constantly evolve to measure which leads are most likely to buy at any time.

The secret sauce is to continuously analyze the attributes of the highest value customers and find prospects like them in the lead database.  The similarity between your highest value customers and the lead in addition to the level of engagement is the actual score.

Predictive nurturing can leverage data in order to identify needs, and find the solution with the best fit and the content that is most likely to be engaging.  Unlike metrics such as CTR or form fill rates, predictive lead nurturing focuses on revenue.  Therefore, even if content gets high engagement but fails to engage the high value leads (such as interesting content with low relevancy) it will not be considered a success.

In conclusion, traditional lead scoring was based on rules of thumb, was hard to set up and most importantly, did not deliver the value that it promised.  Predictive lead scoring, on the other hand, is based on robust data from the web and statistical validation.  Predictive scoring will give marketers the power to prioritize a prospect who likes your product, from a prospect that simply likes your content.