Retention & Growth marketers are often interested in taking action on a segmented base of users. Classic segmentation methods include
- Lifecycle based segments: new, active, lapsed etc.
- Behavioral segments based on user behavior on the website/app
- Demographic: Age, gender, location, household income, education based
- Traffic source based
- First purchase product/category etc.
Given all these ways of segmenting, how should any marketer approach segmented marketing, for the purpose of improving their core retention metrics? Some of the metrics CRM or growth marketers might be interested in improving through a segmented marketing strategy may be around activation rates, repeat purchase rates, or churn/retention rates.
Here are 2 steps for how you can use segmented marketing to drive higher response rate on any metric, say, repeat purchase rate:
- Use criteria that lead to a big spread in response rates in the steady state: Researchers on segmentation, have pointed out the need for segments to be identifiable, substantial, accessible, stable, differentiable and actionable. In the digital world, the tests on identifiable, accessible, and actionable are often easy to meet with segments. However, what really separates useful segments from the rest is the differentiability of the segment especially in response rates, their stability (i.e. whether the same criteria continue to map to differentiated responses over time), and whether the segments are substantial in size. In other words, you need to identify large segments of users whose response rates are substantially (3-10X) different from the average
- Test what happens to the steady state when you introduce a message or an offer: Once you have identified your segments in a steady state, you want to be able to test how you could increase the response rates further by introducing new variables like product features, offers, or content.
We will focus this first post in a 2-part series on the 1st of these challenges:large segments of users whose response rates are substantially (3-10X) different from the average. While this sounds simple enough, in practice, quickly figuring out the criteria that lead to a big spread in response rates could take some work. For example, in a typical setting, your gender based response rates might not look very dissimilar for repeat purchase: Let’s say your average repeat rate is 50%, and that men have a repeat purchase rate of 45% and women 55%. Now, there is clearly a difference in repeat purchase rates, but not enough for you to build compelling campaigns around it. At the other end of the spectrum, you might be able to identify outlier users, whose response rates are very high – but these outlier users may not meet either the stability test (e.g. will they continue buying the same way), or may not be a substantial enough segment.
This is where predictive segmentation & machine learning have a role to play. Machine learning can quickly figure out variables that are important, and combine them to come up with models that give you a 3-10X separation on large buckets of users. At Blueshift, we make this easy by making predictive segmentation scores available “on tap” for you to use.
For instance, in this graph, you can see how multiple variables have been combined to generate a repeat purchase score that gives you a nice spread in predicted response rates. In this example, users in the top 10 percentile of scores have a 90% repeat rate, whereas users in the bottom 10 percentile of scores have a repeat purchase rate of only 9%.
Now that you have identified a way to find segments of users who have a much higher response rates than the average in the steady state, how do you test the actions that will help you improve these metrics? Our second post in this series will look at that.
