A New Approach: Targeting Audiences With Behaviors, Emotions, and Moments

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The BEM approach uses behavioral customer data to define a brand’s target audience more precisely than with demographic data alone. Source: Google Images

With the increased use of digital marketing, using only demographic data to target audiences may not be the most productive and accurate way for a brand to get its message in front of the right potential customer. In today’s digital world, companies have access to customer data that goes well beyond the rigid parameters of demographic data. The BEM model uses behaviors, emotions, and moments to target audiences more effectively while allowing new audiences to grow, according to a recent Advertising Age article.

The use of demographic data in targeting online audiences limits customer profiles to the existing rigid demographic groups (such as age range, income range, gender, level of education, etc.) and produces narrow targets. A brand’s most productive audience might not fit into the narrow demographic profiles, creating “valuable wastage” by missing these important potential customers, according to the Advertising Age article. The article refers to an analysis of a well-established company in which 35% of sales came from customers other than those in its 18-34 target age range, and 50% came from those outside of its more precise 18-24 target age range. This shows the amount of “valuable wastage” created by using only demographic data to target online audiences. In this case, an unsuccessful marketing campaign could have misled the company to reevaluate the effectiveness of its marketing campaign, when the marketing message was simply communicated to the wrong audience.

The BEM approach mentioned in the article uses behaviors, emotions, and moments to target audiences in a more precise and sophisticated manner. The “behaviors” data shows consumers who have demonstrated interest in a certain product/service area. This can include the consumer seeking the product/service, or simply mentioning it online. “Emotions” data is based on whether a consumer has expressed emotions that would lead a certain product/service to be especially relevant to them now. Lastly, “moments” data is based on certain scenarios or situations that make a product/service more relevant than ever to a consumer. With this more detailed and individualized data, companies are able to better tailor messages to more targeted audiences. Programmatic media buying also improves the usefulness of the BEM model, as marketing messages can be automatically placed in front of the right customer when triggered by the data.

One example the article provides of a company that has benefited from using the BEM approach is Air Asia. In addition to demographic data, the airline uses its CRM data such as customers’ travel frequency and promotion of the brand on social media (behaviors) and programmatic buying to define audiences and deliver custom messaging to them. In doing so, the company saw 58x better return on ad spend.

Overall, while the use of demographic data should not be abandoned, it’s combination with data such as that in the BEM approach produces much more precise and valuable target audiences. As the article states, adding BEM data to the targeting process “[turns] data from filter to active facilitator”, and the difference is very meaningful to a marketer.

From a marketing management perspective, here are some questions to consider:

  • Research another example of a company that uses behavioral data such as in the BEM approach. How has the company benefited from this, if at all?
  • Provide an example of each data-type: behavior, emotion, and moment.
  • How does the BEM approach help marketers reduce “valuable wastage”?