Giving thanks for predictive B2B marketing analytics tools

by in Marketing

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Thanksgiving is an opportune time to express gratitude for the blessings that have been bestowed upon us. This Thanksgiving, the marketing professional has much to be thankful for. 2016 has been a year of enormous innovation and transformation in marketing. We’ve seen the monumental growth in predictive B2B analytics, giving rise to new tools that enable marketers to enhance lead generation and more effectively target specific customer segments. This year, for the very first time, predictive B2B marketing analytics has been added to the Gartner Digital Marketing and Advertising Hype Cycle.

Gartner explicitly credits the rise of account-based marketing (ABM) as an important contributor to the inclusion of predictive B2B marketing analytics in the 2016 Digital Marketing and Advertising Hype Cycle. ABM is an approach that involves targeting specific accounts or account segments using highly customized marketing tactics. It goes beyond the mere use of predictive lead scoring, which has been around and leveraged for several years (and was included in last year’s Digital Marketing and Advertising Hype Cycle).

The rise in ABM has caused marketing professionals to eagerly seek out more intelligent means of identifying buyers that meet their ideal customer profiles and who exhibit high buying propensities. Predictive marketing analytics tools offer great potential to advance these efforts, especially in the B2B world, which is characterized by longer sales cycles, more decision makers, and smaller lead pools as compared to the B2C ecosystem. Not surprisingly, B2B companies outspend B2C companies on marketing analytics – 45% compared to 26%.

Gartner identified ten key players in the predictive B2B marketing analytics domain:

  1. 6sense: a predictive intelligence engine that amasses buying intent interactions to determine customers’ propensities to buy.
  2. Datanyze: a sales intelligence platform that helps companies pinpoint when accounts have started or stopped using a specific technology provider.
  3. EverString: a predictive analytics platform that helps companies build statistical models of their ideal customer and identify target accounts.
  4. Infer: a predictive sales and marketing platform that analyzes internal and external buying signals to determine which accounts are most likely to convert.
  5. InsideSales.com: a predictive analytics platform that helps companies determine the optimal time to contact customers, as well as optimal pricing and discounting strategies.
  6. Lattice Engines: a predictive platform that helps companies find new contacts that match their ideal customer profile and create tiered lists of accounts.
  7. Leadspace: a predictive analytics platform that helps companies score prospects and accounts according to an ideal customer profile.
  8. Mintigo: a predictive marketing platform that helps companies identify accounts that are most likely to buy, as well as which customers are most likely to cross-sell or up-sell.
  9. Radius: a predictive marketing platform that helps companies analyze and profile existing customers to identify new prospects.
  10. SalesPredict: a cloud based sales intelligence solution that helps companies determine the accounts that are most likely to convert, as well as the associated rationale.

Although Gartner estimates 2-5 years until mainstream adoption of predictive B2B marketing analytics, many marketing professionals have already jumped on the bandwagon: 89% had predictive analytics on their 2016 roadmap. Indeed, there’s no doubt that predictive analytics tools have driven notable results for marketing professionals: 86% of marketing executives credit marketing technology and predictive analytics with generating a positive ROI. This Thanksgiving, marketing professionals ought to take a moment and give thanks for this.

While Thanksgiving is a time to give thanks, it’s also a time to reflect, reinvent, and prepare for the upcoming year. In this light, we must appreciate that B2B predictive analytics tools are limited in their ultimate potential (Gartner appropriately classifies them as in the “peak of inflated expectation”). Predictive analytics tools can be likened to a Band-Aid solution. Since they rely on historical data to generate predictions, they give rise to reactive rather than proactive results.

To truly reach their full potential, marketing professionals must be proactive. They must embrace prescriptive – rather than predictive – B2B analytics tools. In sharp contrast to predictive analytics, prescriptive analytics tools aim to anticipate future outcomes by leveraging machine learning. Whereas predictive analytics tools require many assumptions (for example, that you’ve correctly identified your ideal customer persona and that your CRM platform contains accurate information, neither of which is usually the case), prescriptive analytics tools require a minimal number of rules and underlying assumptions. Machine learning algorithms are programmed so that they learn over time and adapt according to changing conditions. Falon Fatemi, CEO of Node, explains: “Prescriptive is a dynamic, relationship-based model, not an analytics platform focused on static data points.” The potential is enormous: prescriptive analytics tools empower marketers to accurately predict how specific channels, content, and customers will respond to various marketing initiatives. Doug Henschen, vice president and principal analyst at Constellation Research explains, “Predictive analytics can provide a probable view of the future, but users don’t always know what action to take. Prescriptive analytics software takes it one step further, offering up action items and potential outcomes.”

At Node, we’re preparing for 2017 by making prescriptive analytics a reality. We’re not alone in recognizing the promise and potential of prescriptive analytics. While prescriptive analytics isn’t as mature or widely adopted as predictive analytics, Gartner estimates that the market will reach $1.1B by 2019. We’re looking forward to sitting around next year’s Thanksgiving dinner table and toasting to the rise of prescriptive B2B marketing analytics tools.

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About The Author

Rebecca Hinds
Rebecca Hinds - View more articles

Rebecca Hinds graduated from Stanford University in 2014 with a M.S. in Management Science and Engineering. In 2013, Rebecca co-founded Stratio, a semi-conductor company developing infrared sensors. The company was selected by the Kairos Society as one of the 50 most innovative student-run businesses in the world.