July 25, 2016
There are a number of predictive analytics firms dedicated to helping B2B revenue leaders examine their own successes and losses to inform everything from account selection to next-step action analysis and recommendation.
Last year, Laura Ramos introduced them to us in her report, New Technologies Emerge To Help Unearth Buyer Insight From Mountains Of B2B Data. Laura concluded this report with a recommendation to prepare to take the predictive analytics plunge.
Well, many of you have "taken the plunge," or are about to. Nearly two thirds of marketing decision makers plan to implement or upgrade predictive analytics solutions during the next 12 months. Since I joined Forrester a few months ago, I've spoken to many of you that wonder what lessons early adopters have learned and how to consider predictive marketing analytics in the context of your specific go-to-market strategies and organizational goals.
In my first Forrester report, What’s Really Possible With Predictive Marketing Right Now, Laura and I collaborated to look more closely at the trends driving predictive marketing and the common attributes among early successes.
What we found is that three categories of use cases dominate the current landscape, not only laying the foundation for more complex use of predictive marketing analytics, but also supporting the full scope of the customer lifecycle, from net-new prospect identification to account expansion:
- Prioritizing known prospects, leads, and accounts based on their likelihood to take action. Predictive scoring adds a scientific, mathematical dimension to conventional prioritization methods that rely on experimentation and iteration. This use case help sales and marketers identify productive accounts faster, spend less time on accounts less likely to convert, and initiate targeted cross-sell or upsell messaging to accounts that are likely to be responsive.
- Identifying and acquiring prospects with attributes similar to existing customers. In this use case, accounts that exhibit desired behavior (make a purchase, renew a contract, or purchase additional products and services) serve as the basis of an identification model. This approach helps sales and marketers find valuable prospects earlier in the sales cycle, uncover new markets, prioritize existing accounts for expansion, and power account-based marketing (ABM) initiatives by bringing to the surface accounts that resemble existing clients.
- Using uncovered attributes to personalize messaging. Traditionally, B2B marketers have been limited to personalizing by generic attributes and did so with such manual effort that it applied only to the most highly prioritized campaigns. Now, attributes used to feed predictive algorithms appended to account records support more intricate and automated segmentation. This use case help sales and marketers drive outbound communications with relevant messages, enable substantial conversations between sales and prospects, and inform content strategy more intelligently.
All indicators show that predictive capabilities are certain to be increasingly important to B2B marketing and sales leaders, and I’ll be looking much more closely at the predictive marketing space going forward.