Given the importance of data in today's marketing landscape, predictive analytics is becoming a powerful tactic for marketers. Predictive analytics—software that enables users to make predictions based off data—can help marketers make the most of the wealth of data available to them. 

For lead generation alone, predictive analytics can make a difference: Radius’ 2016 B2B Demand Generation Benchmark Study survey uncovered that B2B marketers that applied predictive analytics to demand generation met their objectives 55% of the time, compared to 30% for those not using predictive analytics.

"The marketing role is, now, more than ever, that of a revenue driver," Katie Gregorio, senior director product marketing at Radius, said. "Marketers are increasingly tasked with helping sales improve close rates."

Software analytics company New Relic knows the value of predictive analytics. The company teamed up with Infer's predictive sales and marketing platform to achieve impressive results. Using Infer's applications, New Relic was able to prioritize and forecast all of its sales and marketing efforts, ultimately seeing an almost 10x conversion for top leads.

“As a result of implementing predictive analytics into our sales and marketing stack, the marketing team now has insight into, and can more effectively target, the most valuable personas and segments for greater impact," Baxter Denney, VP of Growth Marketing at New Relic, told Marketing Dive.

Predictive analytics as a marketing tool

New Relic is active across the marketing board with services including paid search, banner ads, retargeting campaigns, and social and physical community events and conferences. Denney that predictive analytics helped the company identify potential prospects for its online trials and drive other types of engagement. 

“Our sales team needed helping keeping pace with a rapidly expanding sales pipeline," Denney said. "Using Infer’s predictive scoring, we were able to immediately identify and convert great leads that had been buried in our nurture database.”

New Relic used Infer to create fit and behavior scores based on certain conversion types among its customer base and how those types relate to the likelihood of conversion. The entire process included a joint effort between the marketing and sales teams, indicating the value of organizational alignment when putting data to use.

“Fit and behavior scores help us identify which prospects are a good fit for New Relic software analytics, as well as uncover which are currently exhibiting strong buying behaviors,” Denney explained, “So, all demand is scored using our predictive analytics solution, but since we operate an online trial model, those leads are particularly useful to apply predictive analytics to.”

Denney felt that creating the scoring model couldn’t typically be done sufficiently in-house, which is why New Relic outsourced and tapped Infer’s expertise. Once the score models were in place and operational, Denney said they were used to identify who was most likely to convert into an active trial lead, and then from that group who was most likely to convert into a qualified sales opportunity.

New Relic’s two-dimensional approach is to "score leads, help the team find what would otherwise be hidden segments of leads, interpret buying behavior and prioritize daily sales engagement,” he said.

The group used the letters A-E to represent high-to-low behavior groups of prospects, and the numbers 1-5 to represent high-to-low fit.

 "We immediately route any incoming free-trial leads that fall into the upper-left of the Infer grid directly to sales reps, which supplements the flow of leads from high-value forms or ‘contact me’ requests,” he said.

The value of predictive analytics: New Relic's experience

New Relic's data handling and manipulation have had a direct impact on improving lead generation and prioritization by helping the sales team focus its time appropriately.

“We use the lead score as a filter on sourcing outbound leads," Denney said. "We improve the efficiency of spend by only accepting leads that meet a certain scoring threshold.”​

Even with the improvements, effective predictive analytics is very much a journey and not a destination, particularly with sales enablement.

“I have found the key is to run the score without changing sales behavior for the length of an average sales cycle, then use the learnings to decide how best to incorporate the score into the workflow for reps," Denney said. "You really need to focus on front-line managers, as they're the ones directing the day-to-day activities of sales reps.”

As with any marketing activity that requires more than a set-it-and-forget-it approach, the proof is in the results. To put numbers behind those insights, New Relic has used predictive analytics to realize:

• Increased conversion performance by 9.6x for top leads
• Identified A-Leads that made up 51% of won business
• Achieved 30% higher deal sizes by doubling down on high-value prospects
• Surfaced deals that close faster and more often than New Relic’s average

“I believe in a very collaborative approach with Sales. We find the score is very useful as a focal point for conversations around lead quality and prioritization, in addition to using predictive scoring to increase raw marketing efficiency,” said Denney.