Sunday, May 1, 2016

What Makes a Prospect Attractive for Account-Based Marketing

The basic premise of account-based marketing (ABM) is that a company should focus its marketing and sales efforts on prospects that have a strong likelihood of becoming good customers. So it shouldn't be surprising that account selection is widely regarded as the most critical step in building a successful ABM program.

Most ABM practitioners choose their target accounts by identifying businesses that "look like" their best existing customers. This technique - known as 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 optimal results. Therefore, it's important for marketers to understand the underlying factors that make companies good targets for ABM.

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 terms 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 or 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? Has the prospect bought from your company in the past?

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 to support your marketing efforts.

Earlier, I noted that look-alike modeling is an effective way for most companies to select target accounts for their ABM program in most circumstances. However, in some cases, choosing target accounts based solely on look-alike modeling won't produce optimal results. In those circumstances, you'll need to step back and use the factors described in this post. In a future post, I'll describe some of the circumstances that require more than pure look-alike modeling.

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