The 7 Most Meaningful Dimensions For Customer Segmentation

1. Acquisition Path: How a customer ended up on your site to make the first purchase says a good deal about how he or she is likely to shop over time. Do some digging on your own as the implications fluctuate from retailer to retailer.

Example: A retailer could cut spending on Facebook after discovering that subscribers acquired through that channel converted to paying customers at a much lower rate than those acquired elsewhere.

2. First Purchase: A shopper’s first purchase says a lot about what type of customer he or she is likely to become. Brand, category and sub-category can give you strong conclusions on things like price sensitivity, shopping persona, and level of attachment to your store.

Example: A fashion retailer could change its retention marketing program after learning that customers whose first purchase was a sweater were over three times more likely to repeat in their first 90 days than customers who started by buying in other categories.

3. Device Type: For many retailers, shoppers who come in on certain device types are inherently different from those who arrive through the more conventional desktop route.

Example: A daily deal site might discover that its iPhone customers are worth twice as much as desktop customers – and change the way it targets and communicate with these customers accordingly.

4. Geography: Geography gives a surprising amount of valuable info on your customers. Beyond the low-hanging fruit (e.g., cities like New York and San Francisco tend to be both wealthier and more fashion-forward), geography can also give insight into the density of brick-and-mortar shopping options as well as regional shopping preferences.

Example: An apparel retailer might discover that its Midwestern customers are worth far more than average because they tend to buy pricey knits and outerwear during the cold winter months – so its acquisition marketing team could begin targeting new customers from the region.

5. Income: Product preferences and even repeat rate can vary widely with disposable income. Tip on finding this info: Many data providers will provide you with their best guess on an individual’s income or assets by looking at median income or median home price in particular ZIP+4’s.

Example: A retailer could use a customer’s predicted income level to determine the right items to show him or her in emails.

6. Gender: Gender can say a lot about a shopper’s predicted spend. For example, a lifestyle retailer may be surprised to discover that although its target demographic is male, its female customers are actually more valuable.

Example: This luxury retailer’s marketing team could use this insight to transform the messaging and creative of their display advertisements.

7. Age: The relationship between age and lifetime value tends to vary from retailer to retailer, but is almost always a source of insight for lifetime value segmentation. Some retailers may find that their younger customers skew more valuable because of a greater level of comfort with e-commerce transactions. Other retailers may find that older customers tend to be more affluent, more brand-loyal, and less prone to price comparison.

Example: A fashion retailer could start targeting an older demographic after discovering that its older customers tend to be more affluent, more brand-loyal, and less prone to comparison shopping.