Account-based marketing (ABM) is rapidly becoming the preferred approach to marketing for many B2B companies. The defining characteristic of ABM is that it focuses marketing efforts on a specified group of target accounts. Therefore, choosing which accounts to target is an essential step in implementing ABM, and most ABM thought leaders and practitioners agree that account selection is the most critical component of any ABM program. Choosing the right target accounts isn’t the only requirement for success, but it will be impossible to build a successful account-based marketing program if you target the wrong accounts.
ABM can be used for acquiring new customers and for marketing to existing customers. In this article, I’m focusing on account selection for new customer acquisition.
Most companies choose target accounts based on how closely those organizations resemble their existing customers. This approach is known as look-alike modeling, and the process is fairly straight forward. Companies identify the attributes and behaviors that their best existing customers have in common. These attributes might include company size, industry vertical, number of employees, and location. Then, they use these shared attributes to create a profile of their “ideal customer.” Lastly, they choose target accounts based on how closely each organization matches the ideal customer profile.
Most companies select target accounts manually, but a growing number of companies are using predictive analytics to support the account selection process. Virtually all predictive analytics solutions use a sophisticated version of look-alike modeling to identify target accounts. They extract data regarding existing customers from your CRM and marketing automation solutions and combine that information with external data about those customers to construct a customer data model. The solution provider then runs your customer data model against its database of businesses and/or applies the model to prospects already in your marketing database to identify the accounts that resemble your existing customers.
The advantage of predictive analytics is that it can incorporate and process far more data points than humans can realistically use. Therefore, predictive analytics solutions enable companies to build and use more comprehensive customer data models and thus do a better job of identifying accounts that most closely resemble their existing customers.
Look-alike modeling is an effective way for most companies to select target accounts in most circumstances. However, like any business tool, look-alike modeling must be used correctly, and in some cases, choosing target accounts based solely on look-alike modeling may not produce optimum results.
For look-alike modeling to be effective, your company needs to have enough existing customers to build a customer data model that’s reliably predictive. Some experts contend that you need at least 500 “successes” to build a sound customer data model. While 500 may not be an absolute minimum, you do need a substantial number of existing customers, and there are two circumstances when this may pose a problem. First, a start-up or young company may not have acquired enough customers to create an accurate profile of its ideal customer.
A similar problem can arise when a mature business wants to use ABM for a new product or service. If the new product or service appeals to a different type of customer than the company’s other products or services, a customer model based on existing customers may not be all that useful.
Choosing target accounts for an ABM program also becomes more complex if your company needs to sell to new types of customers in order to reach growth objectives.
In all of these situations, look-alike modeling isn’t likely to product optimum results. Therefore, it’s important for marketers to understand the underlying factors that make companies good targets for account-based marketing.
The following diagram depicts the factors that make a prospect organization attractive for account-based marketing. At the highest level, attractiveness is a function of value and buying potential. In this context, value simply means that a prospect has the potential to be a large and profitable customer for your company. The best measure of this factor is the estimated lifetime value that the prospect would produce for your company.
Buying potential refers to the likelihood that a prospect will purchase your company’s products or services, and as the diagram shows, buying potential is a function of two factors – fit and interest.
Fit is one of those business concepts that’s hard to define in a precise and formal way. The underlying idea is suitability, and one dimension of fit is whether your company’s products or services can effectively address a need, problem, or challenge that the prospect is likely to have. In the diagram, I call this solution fit.
The second dimension of fit is more subtle, but equally important. I call this dimension company fit, and it refers to whether your company can effectively market to, sell to, and serve a particular prospect. Company fit is often a function of geography for small and mid-size companies. For example, if your company is based in Atlanta and primarily serves customers located in the southeastern United States, you may not be able to effectively market to, sell to, or serve a prospect located on the west coast, no matter how well your products or services fit the prospect’s needs.
The second component of buying potential is interest, which refers to whether a prospect has shown an inclination to evaluate or purchase the kinds of products or services that your company offers. Interest also has two components – engagement and buying signals. Engagement refers to whether a prospect has had direct interactions with your company. Has anyone affiliated with the prospect visited your website, consumed your marketing content, or met with one of your sales reps?
The other dimension of interest is buying signals, and this refers to prospect behaviors (other than direct interactions with your company) that indicate the prospect may be interested in the kinds of products or services your company provides. Today, most accessible buying signals consist of online behaviors such as website visits and content consumption behaviors. These behaviors are represented as intent data, which is collected and sold by B2B publishers. Some providers of predictive analytics acquire access to this data and incorporate it into their PA solutions. Therefore, as a practical matter, you will only have access to this type of intent data if you are using a predictive analytics solution.
Selecting the right target accounts for your ABM program is critical to success, and look-alike modeling works well in most circumstances. But it’s also important to understand the underlying factors that determine whether an organization is a good candidate for account-based marketing.
Top image courtesy of Emilio Kuffer via Flickr CC.