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Learn more: www.demandgen.com | main: 925.678.2500 | sales: 925.678.2511 | info@demandgen.com © 2016 DemandGen International, Inc. All rights reserved. DemandGen and the DemandGen logo are registered trademarks of DemandGen International. All other product and company names may be trademarks of their respective owners. 2 Predictive marketing is gaining momentum among marketing organizations seeking better ways of scoring leads. It goes hand in hand with the rise of Account Based Marketing (ABM), which, at its heart, is about focusing marketing efforts on a highly targeted prospect with a tailored, personalized message. B2B marketers can augment their ABM strategy with predictive marketing, which provides non-intuitive insights about customers and prospects, enabling more precise personalization of both the message and the means of outbound communications. It's true that ABM yields the highest ROI of any marketing approach. In fact, according to ITSMA, 80 percent of marketers measuring ROI say ABM outperforms other marketing investments. Altera Group found 84 percent of marketers said ABM had significant benefits to retaining and expanding existing client relationships. But ABM is only as successful if you are able to target the accounts most likely to convert with a message that speaks directly to their pain points. There are many frameworks being developed around executing ABM strategies today. This paper examines four specific ways of applying predictive technologies to refine and inform your ABM strategy with predictive data. You'll read examples of predictive technologies in action and come away with a better understanding about how to integrate predictive methods into your marketing outreach. HOW PREDICTIVE MODELING WORKS Although predictive modeling is backed by complex algorithms and machine learning technologies, the premise is rather simple. It begins with two sets of data: 1) a "positives" list, which consists of a company's existing, ideal customers, and 2) a "negatives" list of prospects. The predictive model takes these two data sets and compares them to find data points in the "positives" that are unique. Those are key characteristics of companies who are likely to buy your products or services. Most predictive technology providers offer solutions that crawl the web to collect unstructured data about companies to add to the model. This provides additional data points and characteristics to help you understand your ideal customers, and becomes part of your customer "DNA." Once you have your customer DNA profile defined, the predictive model ranks or predictively scores any company or lead against the profile. If a company shares many characteristics with the ideal profile, it is assigned a higher predictive score.

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