The Future is Here With Predictive Account-Based Marketing
Account-based marketing (ABM) isn’t a new topic, but today it’s catching fire like never before. This explosion in popularity is due to parallel economic and technological trends that have made account-based marketing an essential strategy for B2B marketers.
The growing adoption of customer relationship management (CRM) and marketing automation technology (MAT) systems has created an unprecedented amount of data for optimizing ABM initiatives. Simultaneously, the evolution of the corporate buying team has added complexity to the B2B sales process, requiring marketers to take a more surgical approach toward identifying target accounts, key decision makers, and personalized messages and buying experiences that will resonate with prospects.
These changes in the B2B landscape are already driving more companies to adopt account-based marketing, but the methodology is likely to become even more prominent and successful as it converges with another B2B marketing trend: the rise of predictive analytics.
What’s behind the growth of Account-Based Marketing?
The emergence of the ABM tech stack
Account-based marketing is nearly impossible without CRM and MAT, and the rapid adoption of these technologies in recent years has enabled more marketers to use the methodology.
Customer Relationship Management
CRM is the foundation of ABM because it serves as the system of record for data about accounts and contacts. This information is vital for keeping track of target accounts through the buying cycle and beyond as prospects become customers.
Marketing Automation Technology
MAT is equally important because it serves as the system of engagement through which marketers can orchestrate campaigns that reach prospects at top accounts with personalized messages and appropriate cadence.
As a result of these technologies’ growth, B2B marketers are now able to execute account-based marketing more efficiently and effectively than ever before. No longer is information about prospects and customers stored in rolodexes and piles of business cards; now it’s digitized in CRM and MAT systems and available for marketers who are seeking to increase the precision and scale of their ABM efforts.
Source: Gartner, Market Share Analysis: Customer Relationship Management Software, Worldwide, 2015, Joanne M. Correia, Yanna Dharmasthira, Julian Poulter, 12 May 2016
Source: Raab Associates 2014 VEST
The Changing Landscape of B2B Buying
At the same time that technological changes have made account-based marketing more feasible, we’ve seen corresponding shifts on the buying side of the B2B equation that have made ABM more important. Notably, the concept of the buying team has evolved so that any significant B2B purchase now involves numerous stakeholders.
This transformation of the buying process has come about partly because technological advances have made software systems increasingly interconnected, meaning the B2B buying process now requires additional stakeholders to make sure that new technology purchases play nicely with existing systems.
In fact, an IDG Enterprise report found that the average number of individuals involved in major technology purchases at enterprises increased from 11.5 to 17 between 2011 and 2014.
The Rise of Predictive Analytics
These trends on both the supply and demand sides of the B2B economy are creating opportunities and challenges for ABM practitioners. Massive quantities of data in CRM, MAT, and external systems such as ad platforms represent tremendous possibilities for improving target account selection, contact coverage, and message personalization, but marketers need a way to cut through the data noise to find the signals that can improve their efforts. And marketers now need to influence more people during the buying process with consistent and relevant messaging delivered through a variety of channels.
That’s why more marketers are using predictive analytics software to enhance their account-based marketing initiatives. The promise of predictive analytics is simple: Leverage the data in your CRM and MAT, as well as a robust and dynamic external data set, to analyze patterns of attributes and signals that characterize your best customers. Once these patterns are identified, predictive analytics can locate prospects both within and beyond your CRM and MAT that are likely to convert.
Transforming the ABM Target Account List
This functionality is important for marketers because it can help with the first, critical step in the account-based marketing process: target account selection. Until now, the fragmented and incomplete nature of data in CRMs and MATs made it very difficult to select target accounts for ABM in an objective, data-driven way. Rather, Marketing and Sales would have to fall back on anecdotes and gut feelings to make their target account selections. This process was both inefficient and ineffective: Marketing and Sales would have no way to narrow down a potentially extensive list of potential targets without reviewing them all, and the anecdotal information used to support target account decisions could not always reliably predict which accounts would convert to customers in the future.
Predictive analytics solutions, however, can assess which of your potential target accounts are most likely to convert based on the data in your CRM and MAT as well as external information such as your prospects’ technology stacks, buying intent, and recent news and events. These insights enable Marketing and Sales to shrink the universe of potential target accounts into a manageable group that represents the most promising opportunities, which builds alignment between the two teams and produces a target account list that is more accurate and less likely to result in missed opportunities or wasted resources.
The Future of Predictive Account-Based Marketing
As macroeconomic and technological changes have made ABM more essential and achievable for B2B marketers, they have begun to experiment with implementing predictive analytics solutions to enhance their efforts. But target account selection is merely one use case where predictive analytics can have a major impact on account-based marketing.
Gartner recently highlighted this fact in their 2016 Hype Cycle for Customer Analytic Applications: “Predictive lead scoring was included in last year’s Hype Cycle as it was the most common use case, but predictive segmentation and demand generation have quickly gained traction, fueled by the rise of account-based marketing.”1
Specifically, B2B marketers can also use predictive analytics to identify and prioritize the right contacts within their target accounts, the right products to sell to those prospects, and the right messages that will resonate and enhance the buyer’s journey.
At Radius, for example, we’ve built a feature that automatically generates high-propensity segments of accounts with shared attributes that can inform the development of outreach programs and messaging. We’ve seen customers succeed with micro-segmenting the target account lists generated using our Radius scoring feature so that each group of similar accounts receives a tailored experience based on the signals surfaced in the Radius application.
As account-based marketers gain more experience with the methodology and predictive technologies become even more advanced, additional opportunities will emerge for B2B marketers. Predictive analytics may be able to provide insights about what channels to use for reaching specific prospects, the best time to contact those prospects, and the right offers to make to increase the odds of a conversion.
With these possibilities on the horizon, there has never been a better time to get started or double down on account-based marketing. Learn how to build the right ABM tech stack for your needs by downloading our Account-Based Marketing Technology Stack Blueprint.
1Gartner, Hype Cycle for Customer Analytic Applications,2016, Melissa Davis, Gareth Herschel, 26 July 2016
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