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Predictive marketing tools trump old-fashioned gut feel

As customer data volumes grow, more marketers rely on predictive analytics software to zero in on their highest value customers, and it's working -- in spades.

Digital technologies may have forever changed customer behavior and challenged the concept of the sales funnel, but what affects marketing organizations even more today is the end result of digital technologies: namely, the volumes of data generated by online behavior.

While it was several years ago that customers began researching purchases on the Web, only recently have marketers been able to glean insight from the ever-growing -- and increasingly rich -- customer data volumes, Kerry Cunningham, research director at the global B2B research and advisory firm SiriusDecisions Inc., said. The big change, he said, is "what we can now do with data that allows us to get better at understanding where the buyers are."

This is why marketers increasingly turn to predictive marketing tools and techniques, according to Cunningham and Liam O'Connor, a principal at marketing and sales strategy consultancy Lenati. In fact, marketing analytics spending is expected to increase 66% in three years, from 6.7% of the marketing budget today, to 11.1% by 2019, according to the 2016 CMO Survey, with a particular focus on customer acquisition (43.6% of respondents) and segmentation (29.2%).

Predictive analytics vs. traditional lead scoring

Predictive analytics goes well beyond traditional approaches to lead scoring, Cunningham said, which prioritizes prospective customers by awarding points for attributes such as industry type, title and whether the prospect has downloaded a white paper.

With predictive analytics, marketers use data science-based techniques to segment databases, determine which behaviors and attributes of a company or individual are statistically predictive of desired outcomes, and then identify a pool of prospective customers that match those characteristics. "You're leaving the realm of making assumptions on what's important and letting data tell you what matters," he said.

That is the evolution experienced by Demandbase Inc., a marketing software vendor that turned to a predictive marketing system from Lattice Engines to support and expand its own account-based marketing program.

"We wanted to understand which companies had a high propensity to value our core proposition and were, therefore, more likely to purchase our solution," Peter Isaacson, chief marketing officer (CMO) at the San Francisco-based company, said.

We found out that predictive selection was better for close rates than actual gut instinct.
Peter IsaacsonCMO, Demandbase Inc.

Demandbase partnered with Lattice to identify the common attributes among its highest-value customers and late-stage pipeline prospects, and then applied that list to 40,000 companies in multiple industries, which resulted in 1,500 target prospects. Data was drawn from internal Demandbase systems, as well as Lattice's data cloud, which pulls from third-party vendors and independent websites.

The sales results were so positive that Demandbase doubled its list of target customers to 3,000 prospects. Over one year's time, close rates improved 150%, and the annual contract value among the targeted accounts increased 30%.

The experience also proved the value of a data-based approach to marketing and sales, Isaacson said. When marketing first provided sales with the target list of customers, it also allowed sales people to add in accounts of their own, based on their own experience.

"I wanted to find out how our Lattice-chosen accounts performed versus the ones sales chose," Isaacson said. As it turns out, close rates were much higher for the Lattice-chosen companies.

"It was fascinating, because sales was adamant that while predictive analytics is hot, they also had accounts they knew were great," he said. "We found out that predictive selection was better for close rates than actual gut instinct."

Demandbase also discovered industries that it would not have otherwise emphasized for sales, including manufacturing and healthcare. Little wonder, then, that more marketing decisions will be made using marketing analytics, according to the CMO Survey, increasing from 31% in 2015 to 35.3% this year.

An essential component of predictive analytics systems is their use of data gathered from outside the company's four walls.

"Marketers will start with traditional segmentation, based on industry, company size and what they spend in a given category, and then layer on their digital footprint -- what they do on the company's website, social media, other websites, communities, influential blogs and peer reviews," O'Connor said. "They'll be able to aggregate all that information to understand who's in the market to buy and how that compares to people who've bought solutions from you previously."

A next step, O'Connor said, is to use those insights to predict which content would be most relevant for a prospective buyer, or the best actions to convert a prospect to a customer. These could be webinars, demos, promotions or discounts, depending on where the prospect is in the journey.

"You're using analytics-based insights to compare the behaviors of people who previously purchased from you, to know which content types and offers are the most valuable to get the highest percentage change in converted customers," he said.

Predictive marketing tools improve lead scoring precision

This is starting to happen at Neustar Inc., with the help of a predictive marketing system from Mintigo. Neustar, a provider of cloud-based information and analysis, began using the system to improve its predictive lead scoring results. Its previous approach to lead scoring resulted in such a high volume of potential leads that sales struggled with prioritization.

"They didn't know where to start," Nida Chughtai, marketing operations manager at Mintigo, based in McLean, Va., said. Another problem was that leads were either highly engaged but not the right fit, or were a great fit but not ready to talk about Neustar's offerings. That meant sales had to spend a lot of time on customer education. "They ended up making judgment calls [on which leads to pursue] themselves, which was not the optimal solution," Chughtai said.

Using Mintigo, Neustar improved the quality of its customer database, as it was populated with much richer and more accurate data. "One of the strongest aspects of using Mintigo was the data we got," Chughtai said. "It decreased the volume of leads and increased their quality."

Neustar also improved its lead scoring model, separating "fit" and "engagement" into two different streams. Leads are now given separate scores for their likelihood to buy and their level of engagement with Neustar.

"Depending on where they fall on that grid, that's how we now determine if they're marketing-qualified," Chughtai said. As a result, the number of leads passed to sales has decreased by 25%, the sales acceptance rate has improved by 14% and sales qualification is up by 55%, as higher-quality leads resulted in more opportunities created. Further, marketing can now better analyze their marketing programs to determine whether particular investments are worthwhile and will reach the right buyers.

The Mintigo system also enables Neustar to measure prospective customers' intent, based on words or phrases the company has searched recently. For example, "we can see if someone at the company has searched on DDOS," she said, which is relevant because Neustar offers a system that protects against distributed denial of service, or DDOS, attacks. Not only will this information be incorporated into lead scoring, but Neustar will also use it -- along with the ranking and attributes information -- to personalize the customer experience.

"If someone comes to the website from a company we know is not highly engaged, we can provide more interactive content to reel them in," Chughtai said. Or by knowing a prospect's intent, Neustar can quickly send an email based on the search term or deliver that customized information if the prospect visits the website.

No matter how they use predictive marketing analytics or which system they choose, marketers seem convinced that it defines the future of marketing.

"Predictive analytics is really promising because we can start taking the digital body language and specific behaviors -- either at an account or individual level -- and use that to guide our marketing investment," Isaacson said. 

Next Steps

Marketers, get the memo on the sales funnel

Turning a chaotic sales funnel to your advantage

Using predictive analytics in the sales funnel

This was last published in April 2016

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