Too often businesses struggle to fully understand their customers and as a result their attempts at personalized messaging fall flat. Fortunately, there are solutions available to address this issue such as predictive account-based marketing.
Using advanced analytical techniques (machine learning, data modeling, etc.), predictive marketing has become a defining trend in how brands attract, convert, and retain customers. Essentially, it refers to the practice of leveraging technology to forecast future consumer needs and behaviors. This leads to improved marketing outcomes. The Predictive Intelligence Benchmark Report found the average lift in conversion rate for sessions influenced by predictive intelligence to be 22.66%. 1
These positive outcomes stem from the fact that with predictive marketing companies have a wealth of strategy-defining knowledge at their fingertips to help acquire or convert a customer. A perfect example of this is the homepage for any Amazon Prime user. Using data from previous purchases, Amazon creates personalized recommendations for possible future purchases.
Amazon uses predictive technologies to create personalized product recommendations.
Account-based marketing (ABM) is when the marketing and sales team of a business collaborate to execute a set strategy. The focus of this is on creating personalized buying experiences for an identified set of high-value accounts.
The difference between ABM and traditional marketing
For quite awhile, ABM was the main focus for marketers. According to the 2019 ABM Market Research Report, 40% of B2B marketing teams were involved in company ABM initiatives in 2019.2 However, in order to find success amidst challenges brought on by the pandemic, companies need to broaden their approach to recognize business stakeholders as people, not as “accounts.” This involves understanding each person, who they are, and their own unique journey.
While some might consider these two tactics siloed strategies, that is not necessarily the case. In fact, leveraging predictive account-based marketing can be transformative for a business. To do this, brands need to optimize ABM strategies with a predictive analytics focus.
Any marketer knows that a successful ABM program requires a deep understanding of the target’s business, each contact’s role, peers, and reporting relationships in their organization. Leveraging predictive marketing alongside this only strengthens your approach.
In order for predictive ABM to work for your organization, you’ll need to to consider implementing the following strategies:
Firmographics refers to the profiles developed on the qualities of a prospective business organization, not an individual. This can include everything from annual revenue, acquisition cost, sales cycle, geographic location, etc. Leveraging this information should be at the foundation of any ABM campaigns or tactics. It ensures that you are not only using the right approach but that you are also targeting the right business in the first place.
With predictive marketing tools, you’re able to craft more detailed profiles. AI-powered data platforms more efficiently analyze information to make sure your marketing team is targeting the organizations that are most like your ideal customer base.
Once you’ve utilized firmographics to identify an organization, you’ll want to tap into contact data for necessary insight for the buying group. In fact, Gartner finds that it isn’t uncommon for B2B solutions to involve 6 to 10 decision makers.3 Knowing more about these individuals will help your team craft a more personalized and effective omnichannel journey.
A critical analysis of quality data can provide the following:
With this information, you are able to surround the buying group with a more coordinated effort (email, direct mail, etc.)
It is impossible to capture this kind of in-depth information from a lead generation form only. Today, lead generation without third-party data does not make the cut in terms of quality and accuracy.
Consider the difference between first and third party data as you look at implementing an ABM campaign to sell more of your product, for example, an HR platform to make the hiring process more efficient. While first party data is a great starting point, there is a good chance you only have an email address or phone number for someone–maybe not even a key decision maker–in the HR department.
With a third party data partner like Data Axle, you’re able to fill the gaps to help you reach beyond that single contact to the people you need. Plus, you’ll have the information to tailor your message to them, their pain points, and industry.
Conclusion
ABM is an effective strategy for B2B marketers for a reason. However, combining this approach with predictive marketing not only allows organizations to more fully understand their customers and prospects, but it also allows them to increase customer acquisition with more personalized messaging.
1 https://brandcdn.exacttarget.com/sites/exacttarget/files/deliverables/etmc-predictiveintelligencebenchmarkreport.pdf 2 https://www.pardot.com/whitepapers/2019-abm-market-research-report/ 3 https://www.gartner.com/en/sales/insights/b2b-buying-journey
As Content Marketing Manager, Natasia is responsible for helping strategize, produce and execute Data Axle's content. With a passion for writing and an enthusiasm for data management and technology, Natasia creates content that is designed to deliver nuggets of wisdom to help brands and individuals elevate their data governance policies. A native New Yorker, when Natasia is not at work she can be found enjoying New York’s food scene, at one of NYC’s many museums, or at one of the city’s many parks with her two teacup yorkies.