Why is there so much confusion about the meaning of account based marketing (ABM)? Because it’s a brilliant idea with a lousy name.
The fact is we’ve always marketed to accounts. What distinguishes account-based marketing is the hyper-targeting of accounts that all have critical traits in common. In short, what makes ABM unique is its focus on account selection.
The problem is that there’s just too much data—too many traits—to analyze during the selection process. This often leads to the status quo: analysis paralysis and a lack of focus. Ironically, the solution to proper account selection is data. Let me explain.
Marketers need to distinguish between marketing to accounts and account-based marketing
Even in some ABM programs, the account selection process may sound familiar: “Let’s target everyone in the healthcare industry”; “Our target account is any company in the Fortune 2000”; or even “Our target account is any company that will buy from us.” This may narrow the audience but not in a meaningful or actionable way. Unfortunately, it’s also the approach used in most account selection and most B2B marketing programs. But, let’s be clear: this is really marketing to accounts.
When it comes to choosing accounts for ABM, marketers must sacrifice volume for precision.
Marketing to accounts is based on firmographic attributes, such as company size, revenue or industry, and it has become the de facto approach to account selection and prioritization. The fact is that firmographic data may be a good start, but it’s just a start. When the rubber hits the proverbial road, failure to complete the account selection process accounts for many of the failures in ABM.
It’s understandable that the Sales and Marketing teams want to target as many accounts as possible, with the belief that volume will lead to revenue. This thinking is a hangover from an era when advertisers measured success in terms of reach and impressions. This approach, though, leads to wasted resources and time, the very problems that ABM should overcome. When it comes to choosing accounts for ABM, marketers must sacrifice volume for precision.
A methodical and deliberate targeting and account selection process can uncover accounts that are showing buying signals and that will help achieve the business outcomes the company needs. And that’s where data comes in.
Data can give us insight into account behavior that can be used for targeting, segmentation and personalization
What could be more powerful than a Google search? Think of how easy it is to use, yet how insightful and powerful responses can be to even the most arcane questions. And it’s all thanks to Google’s algorithms and the vast data that it commands. Google has made buyers more informed and empowered than ever.
But that doesn’t mean marketers are defenseless. Far from it. How can marketers shift this balance of power, to regain traction? With data.
But, for all you know, you’re sitting on a treasure trove of data already.
I’m not saying that that shift will come with something as easy as a Google search. And I understand why marketers feel overwhelmed by the sheer volume of available data—demographic data, firmographic data, predictive data, intent data, sales data, experiential data, social data, survey data—the list goes on.
But, for all you know, you’re sitting on a treasure trove of data already; CRM data, billing data, event data and marketing data can be pure gold in the account selection process. Clearly, to help companies achieve their marketing goals, we’re going to have to roll up our sleeves and critically examine the various types of data and understand how these can help us perform precise account selection for an ABM campaign.
Predictive data and machine learning algorithms can help us uncover accounts in our database that are actively shopping now.
Let’s agree that the most valuable kind of data is that which reveals not who people are, but how they behave generally and who is in the market to purchase now. By selecting accounts based on their online behavior, not just firmographics, marketers are able to create stronger strategies, campaigns, content and creative assets. Certainly, these efforts will drive higher ROI than a general campaign that targets a list of all Fortune 2000 companies.
Predictive data and machine learning algorithms can help us uncover accounts in our database that are actively shopping and are truly hot prospects. Layer in intent intelligence—that is, data about an account’s behavior on third-party websites—and we can refine our list even more. Imagine being able to extract intelligence on accounts that are searching for competitive solutions. By using this insight, we could then create targeted outreach campaigns to accounts that are in market for a solution but that have not necessarily heard of your company.
All of these types of analytics are available now.
Marketers have at their disposal an enormous volume of data that can be leveraged to select accounts. Long gone are the days when the only account attributes we have are basic company information. By relying on insight gained from account behavior, we can find and target accounts that are ready to buy. A data-driven account selection process minimizes the strain on resources and budget and opens up opportunities to deliver personalized messages based on an account’s interest.