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How do we define a “best customer” and use this profile to ensure we are targeting appropriately?
Thanks to data science, defining a best customer is easier today than ever before and can be accomplished at the individual consumer level. Gone are the days of simply using segments or broad-based audiences. Pretend you are a luxury retail brand, for example. How are you going to define who your best customers are, and how are you going to target your media to reach them appropriately? It is too simple to proclaim, “I want to reach high-net-worth consumers with an affinity for luxury goods.” And yet, this is exactly most marketers’ approach: Audiences are purchased on platforms that are broad-based segments. Instead, data should be used to understand at the individual consumer level whether or not a prospect is a good fit for a given brand.
With this in mind, CEOs should be asking their marketing teams how they build their target audiences. If the answer is anything but “offline using customer data and relevant third-party data,” you have a problem.
Building on the previous attribution question, marketing teams must deeply define who their “best customers” are before they can measure if they are winning more of them.
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Do we spend more money to acquire our “best prospects”?
Not all prospects are created equal. If you’re the aforementioned luxury brand, for example, why would you spend the same amount of money to acquire a customer who will purchase a single belt as you would to acquire a consumer who will spend thousands of dollars on a new wardrobe over the course of five visits? Using data, you can mathematically predict which customers are belt-buyers and which ones are closet-fillers.
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