Bigger Is Better: How to Scale Up Customer Acquisition Smarter
With these insights we can now develop a model that shows us the criteria or attributes of our customers that are most impactful. They often are clear enough and may sound like your expectations, though we do find nuances, which can improve the selection of prospects we will market to.
Our models can combine data points to infer new ways of looking at who our target customer is, like ethno-demographics, the relationship between ethnic attributes and income/age, or geo-density, the concentration of a customer in a specific location, for example.
This is surely a step up from “look alike models” (different from user selected “look alike criteria” in multivariate marketing).
Multivariate modeling can also require that we enhance customer data with many, many variables and then determine the variables that are most predictive of who our ideal prospect really is. As a result, we have a fairly reliable approach for testing our target definition and validating that we’re zeroed in on our target.
True Response Modeling: The Gold Standard
For marketers seeking to scale up and grow substantially, for example thousands or tens of thousands of net new customers, a true “Response Model” is the most sophisticated, and highest value in targeting a universe of most likely responders.
This should not be confused with any of the increasingly common “look alike” approaches, which are common to programmatic display advertising, univariate or multivariate targeting. It is also substantially more effective than the multivariate modeled approach we’ve already described above.
A true response model has a number of characteristics that can dramatically distinguish it, and improve your prospect marketing performance. Firstly, it’s a custom model, you can’t get a custom response model built by someone who knows nothing about your business — and it’s a different solution altogether than buying a target based on one or more variables or targeting criteria.