It’s your job to know your customers, what makes them buy and how to find more like them. The hard part of that job has always been separating the wheat from the chaff.
If your marketing budget is being tracked to sales and ROI more than ever, you just can’t afford for half of it to be wasted. Even in media that aren’t constrained by budget (like email) there’s a price to be paid for wasting marketing touches (like being labeled a spammer).
It’s more important than ever to be able to target your marketing efforts not just to people who might be interested, but to the people who are most likely to be interested.
The best way to do that is to use past performance to predict future behavior, and data modeling with predictive analytics is the key to that. Data modeling takes into account the interaction of data elements that, in combination, allow you to identify the people on a list who are most likely to take the desired action.
So stop targeting the names you happen to get, and use these techniques to target people who’ll be your model customers.
Data Modeling Increases Response Rates
Data models can be separated into two main types and strategies: Customer Models analyze the behavior of people who have already done business with you, and Acquisition Models help you identify prospects most likely to respond to your offers.
In each case, the idea is to sort the list by significant variables so you can contact a smaller subset of it to get more response. In the Lift Curve table below, the “Random” line represents your typical campaign, where each 10% of the contacts made bring in roughly 10% of the total response—so to get 80% of the responses, you need to mail 80% of the list. The “Wizard” line is an ideal world where you could get 100% of the response by mailing just 10% of the list (essentially mailing only the people you know will convert). In the real world, no model is going to let you get that level of response, but it helps to illustrate the idea.
The “Validation” and “Estimating” lines represent what data modeling lets you do. These are the results of modeling in the real world from cases we did for our clients. The “Estimation” line shows the results the data model predicted, the “Validation” line shows how that model would have performed based on those individuals’ reactions to previous campaigns.
In short, the model shows how you can get 80% of the response with only 55% of the names, and the data validation shows that those gains were real and repeatable for the clients.
While those are simple concepts, they can be applied in many ways to ensure successful campaigns. For example, customer models can be very valuable when used to identify current customers who will respond best to campaigns built to optimize:
• Lifetime Value
Used properly, acquisition models can increase response rates in your efforts to generate new customers. This means your prospecting can be conducted more profitably, and your prospecting budget will yield more new customers.
Two Types of Data Models
There are generally two types of data models you can use to identify better customers. You can look at how prospects have responded to other marketing campaigns, or you can look at your existing “good” customers and use them to build a model that will help identify more customers like them.
In each case, you’re creating the model to find characteristics responders have in common that you can use to refine prospect lists and target only the segments that share those characteristics.
1. Response Model
Data set: Analyze a sample of solicitations and responses from prior campaigns.
Action: Identify variables that differ between those who took the action (response) vs. those who did not.
2. Good Customer Match Model
Data set: Analyze a sample of your best customers.
Action: Compare that to a sample of your list that have not yet taken the buying action to identify variables that set them apart.
Marketing Example of Data Modeling
Going back to the case examined in the Lift Curve chart above, here are the 15 variables the model identified as key contributors. The variables include some demographics and some buying behavior indicators. In this model, AGE has the highest contribution and Year of Vehicle has the lowest contribution.
When scoring a data set, each individual will be assigned a point-value for each one of these variables to create a total score. The total scores can then be ranked from high propensity to respond to low propensity, and can be broken out into segments, such as 10% increments (“deciles”) based on score.
Here’s what the scorecard looks like for the data used in the Lift Curve chart above.
The top four deciles individually produce index levels greater than 100%. In this case, we would recommend the client select records in the top one or two deciles in an initial test. We would also recommend a random sample among all deciles, just to validate results in a live solicitation.
If the test verifies the model is working, this list can be solicited at a much higher rate of return than unmodeled data, because you can sort it to zero-in on just those customers who are more likely to respond—either because their response patterns dictate higher response, or because they already fit the model of your best customers.