"Our customers" is rarely one group, but most marketing treats them like one. We used behavioral data to find the real groups inside a retail customer base, and what to do differently for each.
An online retailer's customers are wildly different from each other. Some buy weekly, some bought once two years ago, some only show up for sales. But marketing usually sends everyone the same message, aimed at an average customer who doesn't really exist.
The goal of this study was to find the real groups inside the customer base, then figure out what to do differently for each one.
Same six CRISP-DM steps as every project. Here's how they went.
Keeping an existing customer is much cheaper than winning a new one, and different customers need different things to stay. A loyal regular doesn't need a discount. A customer who's gone quiet might come back for one. Sending both the same email wastes money on the first and loses the second. So the goal wasn't just "find groups." It was to find groups a marketing team can treat differently.
The data was purchase history: what people bought, how often, how recently, and how much they spent. Simple data, but it took real preparation, because a customer's behavior over time has to be summarized into one profile before any grouping makes sense.
We used clustering, which finds groups of customers who behave similarly instead of starting from assumptions about who the customers are. The groups come from the data. That tends to surface segments a demographic guess would miss, like the occasional big spender, or the loyal regular who never spends much.
The test for a segmentation is practical. Are the groups clearly different from each other? Do they stay stable over time? And does each one suggest a different action? If two groups would get the same email, they aren't really two groups.
The deliverable was a plain-language description of each group: who they are, why they buy, and what to do next for them. Keep this group happy, grow that one, win back the third. Something a marketing team can act on with the tools they already have.
This was an applied study. We're happy to discuss how a segmentation project would be scoped and what data makes it worthwhile.
There are usually distinct groups worth serving differently.