In an ideal world, all marketing teams would apply proven business-to-business lead-generation best practices to help sales close the highest number of the highest value deals possible.
But the real world is quite different. B2B lead generation performance is continually grouped—based on processes and results—into three categories: best-in-class, average and laggard. The results gap across this spectrum is stunning. SiriusDecisions, for example, reports best-in-class B2B lead generation organizations outperform average ones by a ratio of five to one. That is, the top group closes 14 deals per 1,000 prospects, while those using average processes close fewer than three deals per 1,000.
Given that many self-educating buyers aren't reaching out to connect with sales reps until they get 75% of the way through their buying journeys, it is imperative to identify the B2B lead generation that best connects with the best prospects early, helps optimize lead quality, and closes more deals.
Enter Marketing Analytics
The purpose of marketing analytics is straightforward: leverage data to identify and engage with those B2B prospects who are more likely to buy at higher deal sizes. In essence, we are predicting higher lead generation performance by microtargeting subsets inside the target universe. We do this by combining business acumen with proven sales-lead management processes and technology.
A note of caution is in order. Sometimes the term "marketing analytics" generates an immediate association with technology solutions. Indeed, technology (solutions like CRM, marketing automation, social media and business intelligence) has been elevated by some to the role of savior in the quest for best-in-class lead generation performance. It's important to realize that none of these solutions—alone or in concert—can deliver desired results without very human analytics expertise and the right lead management processes.
The Marketing Analytics Approach
Applying marketing analytics as a predictive microtargeting process requires the following three steps.
1. Track, aggregate and enhance data
Once prospect data has been tracked and aggregated into a central repository, records should be enhanced with three types of information. Firmographic data—static and readily available—includes information like annual revenue, employee count, growth rate, industry code and decision-maker title. Then current environmental information about the prospect company should be added; this may include the technology infrastructure or trigger events like an executive change. And finally, behavioral data is appended. This might be website downloads, response to emails or trade show attendance. Not all data may be available when a marketing analytics initiative is undertaken, but you will come to have very rich data over time.
2. Assess data and recommend a testing strategy
This is where business acumen is so important. You're reviewing metrics to find links among seemingly unrelated data sets and trending. Recognize what's compelling, define subsets, and identify the bull's-eye where the three types of data intersect. Brainstorm alternative "what if" scenarios. Finally, formulate the core assumptions to be tested.
3. Segment prospects and test assumptions
Once data points have been appended and hypotheses defined, group "like" firms into cells sharing common characteristics. These segments can then be tested with identical lead generation initiatives to identify the high-value prospects—those more likely to close at higher deal values.
When high-performing segments have been identified, fully deploy lead generation programs focusing on them. You've essentially reduced the target universe to get the same or greater return with much greater operating efficiency.
Marketing Analytics in Action
Here's how a marketing analytics program looks in action. Working with a provider of security penetration-testing solutions, we began testing variables and learned that job title was critical. The word "security" in a prospect's title delivered a high-value target. With changes driven by marketing analytics, this program converted 22% of 20,000 raw prospects into qualified leads, added $23.4 million in new business to the pipeline, and resulted in $2.2 million in closed business. And the client changed its lead scoring criteria for what a good prospect looked like.
One doesn't typically use "fun" and "analytics" in the same sentence, but engaging in "what ifs" and experimentation is challenging and rewarding. Results always drive new assumptions, and you can always make adjustments and test again.
The "science" portion of marketing analytics documents value and helps gain sales buy-in. Give reps the facts—the hard numbers—and they'll act on that opportunity. Showing them, for example, that an opportunity has a chance to close at a rate four-times higher compared to another—that just makes sense and they can buy into that.
The bottom line: Overlaying best practice marketing analytics onto B2B lead generation delivers improved operating efficiencies, greater marketing ROI and higher sales performance.
Karla Blalock (karla.blalock@pointclearcom) is COO of PointClear.
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