Now that you have your data in order, you’ll want to start parceling out relevant customer segments.
The goal is to identify segments that are practical and helps you make better marketing decisions. You’ll want to strike a balance between segments granular enough to execute targeting strategies, but broad enough to scale your targeted marketing efficiently.
There’s no shortage of advanced statistical models to determine customer segments, but in this post we’ll provide a few straightforward, practical recommendations.
Start segmenting based on customer value.
The 80/20 rule, which states that 80% of your profitability comes from 20% of your customers, is a good place to start your segmentation effort. You might actually find your customer profitability distribution is even more extreme with over 100% of your profits coming from your top customers, and your worst customers actually taking away from your bottom line. However, at the most basic level, the distribution might look like the above graph.
In this case, we’ve identified high CLV, medium CLV and low CLV segments. However, these segments are still too broad for much use. From here, you’ll need to rely on your data to help unearth demographic or behavioral commonalities within each segment.
Ideally, you’ll want to try to identify segments with commonalities that you can act upon and measure changes in performance.
For example, you might find that:
- Customers of a particular demographic or geography might have different CLV characteristics
- Certain acquisition channels may be more effective at driving higher CLV customers
- A customer segment may show high loyalty, but lower CLV, suggesting opportunities to drive increased business
Once you’ve identified different customer segments, you can identify opportunities to optimize for your higher CLV segments. We’ll explore some ideas to optimize for lifetime value in our next post.