How to Use Combined Customer Traits in Data Driven Marketing
The heavy duty power of data analysis in data driven marketing really comes into play when you start looking at multiple traits at once. This is known as multivariate analysis.
How to find useful groupings of customers in data driven marketing
Looking across multiple customer traits at once is not easy. For one thing, it gets complicated quickly. And the number of customers that share several traits in common gets small quickly.
You may have a lot of customers in their 20s, a lot who have kids, a lot who are married, and a lot with incomes between $40–50K. But if you search your database for customers who have all of these traits, you’ll be shocked at how few you find.
This is a universal problem in dealing with customer data, or almost any data for that matter. When you focus on grouping customers together based on the values of particular variables, you end up with a huge number of very small groups.
In marketing, you want to identify groups, or segments, of customers with an eye toward their common needs and preferences. Dividing your customers into groups in this way is known as segmentation. Because your segments are focused on customer needs, they don’t necessarily need to be completely uniform. The customers in a segment don’t need to be cookie cutter copies of each other.
Because customer segments are the result of some pretty advanced analytics, it often isn’t clear how the segments are defined. It may in fact be a rather complicated process to decide which segment a customer belongs in. Leave this to your technical folks.
Concentrate instead on what the segments actually look like. In other words, focus on describing these customer groups. What do they have in common and how do the groups differ from one another?
One customer segment that’s common to almost all companies is the high-affinity customer. These are customers who are very loyal to your brand. This high-affinity segment is identified through analysis of past purchase data. But this segment is generally far from uniform with respect to age, lifestage, and other demographic data. The high-affinity audience for children’s toys includes both parents and grandparents, for example.
How to make predictions in data driven marketing
Ultimately you want to know who is likely to respond to a given marketing campaign. Many statistical techniques can help you with this goal. Again, these techniques require some advance knowledge of data analysis, which should be left to your geek. But a couple of things are worth noting.
A statistically derived prediction is known as a predictive model. In database marketing, such models are generally used to predict responses to a campaign and are therefore called response models. To develop such a model, you need to have response data from previous campaigns.
It is frequently not obvious why or how the model is making its prediction. This mysteriousness is typical of predictive models.
At some point in your life you have probably received a letter from your credit card company telling you that your interest rate has gone up or you need to start paying an annual fee. These letters can be annoying.
It’s that sentence that says, “This action may be due to one of the following” It then goes on to list a bunch of things like late payments or high balances, many or all of which don’t apply to you.
What’s going on here is that the credit card company is required to tell you not only that they are taking “adverse action,” but why. The problem is that the real reason they are taking adverse action is due to a statistical model, such as a credit score. And it isn’t easy to sort out exactly why such a model’s score went up or down.
You can certainly understand which variables the model is using. You can usually understand which ones are most important. But once everything gets thrown together, it’s best to just let the model tell you what it thinks.