B2B Marketers Use Predictive Tools To Boost Account-Based Marketing Results

Published: June 16, 2015

While it is not a new strategy, account-based marketing has recently become a critical part of the marketing mix. More than 90% of marketers believe that account-based marketing is a “must-have,” according to SiriusDecisions’ 2015 State of Account-Based Marketing (ABM) Study. While just 20% of respondents are currently using an ABM approach, 60% expect to adopt a targeted account approach over the next year, according to the survey.

“Account-based marketing has been a viable and successful strategy for B2B for the past decade, but the tools that are available now make it much more attractive to implement,” said Megan Heuer, VP and Group Director at SiriusDecisions, in an interview with Demand Gen Report. “There is a greater opportunity to take advantage of ABM because today’s technology makes it much less labor intensive.”

To succeed at ABM, many savvy B2B marketers are relying on predictive analytics to focus on the accounts on their targeted list that are showing the highest propensity to buy. “The majority of ABM programs have a list of targeted accounts in the 500 to 2,000 range, so that is still a lot of activity that is hard to track manually,” said Heuer. “Predictive is the one thing that enables companies to scale their ABM efforts, something which was not possible even a few years ago.”

Demandbase, which offers an ABM platform, is using predictive tools to boost its own ABM strategy. “To accelerate our account-based marketing efforts, we required a scientific understanding of the highest value companies to target,” said Peter Isaacson, CMO of Demandbase. “By organizing our sales and marketing efforts around these high value accounts, we have better aligned our teams and significantly increased our pipeline.”

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Using predictive tools from Lattice Engines, Demandbase identified the key characteristics of its closed accounts and late-stage pipeline. Armed with that data, the sales and marketing team then developed a list of target accounts. The result was a 75% increase in close rates and 72% increase in average selling price compared to accounts identified through traditional lead scoring tactics.

Demandbase measured the success of its ABM strategy using three metrics:

  • Annual Contract Value (ACV);
  • Close rate; and
  • Funnel velocity (i.e., how quickly an account moved from MQL to close).

“Where predictive can help in ABM is looking beyond the firmographic and demographic data to really identify the buying intent data of that account,” Nipul Chokshi, Head of Product Marketing for Lattice Engines. “Predictive can zero in on intent data such as participation in forums on third-party networks or when members of a buying committee at a targeted company download syndicated white papers, attend webinars, view videos and click on ads.”

Using buyer intent data is a critical component of predicting which accounts are more likely to purchase, according to observers. “If you have an account in which for the past few weeks, multiple contacts have been researching a new phone system and downloading white papers about new phone systems, that not only tells you if they meet your buyer criteria, but it tells you they’re in the market now,” said Alison Murdock, VP of Marketing for 6sense.

CSC, a provider of IT products and services, is piloting a predictive platform in partnership with 6sense to identify new buying intent within its strategic accounts as well as new business opportunities. CSC uses models and digital buying signals to understand if a specific targeted account is showing increased interest in a particular solution area. The marketing and sales teams then work together on a coordinated approach.

“ABM isn’t a choice; it’s a necessity,” said Nick Panayi, Director of Global Brand and Digital Marketing at CSC. “We have to go to market in a focused, highly targeted way. Our products and solutions are quite sophisticated, and we support mission critical environments in large customers and governments.”

Predictive Helps Improve Messaging To Targeted Accounts

Panayi noted that the company also offers content recommendations to all website visitors based on their digital body language, but those recommendations are even more accurate for targeted accounts. “How well you can predict a buyer’s behavior is a direct function of what you know about them. When it comes to ABM accounts, we clearly know more about their content consumption habits and are able to make more accurate predictions about the content that will engage them.”

Overlaying predictive analytics with other attributes of successful accounts can help marketers further target their messaging. “This has helped many of our clients have more fact-based and relevant conversations with their targeted accounts,” said Lattice’s Chokshi.

Chokshi noted that one client, a tech consulting firm, has used predictive tools to identify target accounts that have had a recent change in IT leadership. The firm then developed specific content for those specific accounts.

In another client example, Chokshi said a storage device manufacturer determined that companies that have recently invested in content management systems are more likely to buy than companies with other characteristics. They targeted those accounts with use cases around the role of storage needs and content management.

The future of ABM will likely involve tighter integration with CRM, marketing automation and additional tools for more precise ad targeting.

“We’re very excited about additional integration with marketing automation and some of the new things we’re seeing and new things from LinkedIn to help with targeted ads,” said Jessica Cross, VP of Marketing for Fliptop. “Anything to make those connection points work more seamlessly will make it easier to succeed at account-based marketing.”

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