How would you describe your store’s ideal customer? Would you be able to map out where they came from, how they ended up on your site, and what products they first purchased?
Finding answers for these sorts of questions can help your team understand which new customers are the best to acquire, but surfacing the answers to those questions can be hard.
The number of data points that a marketing team can bring to bear continues to expand, and it takes time to know which variables to focus on. It’s easy to get bogged down in this process and let valuable customer information go to waste.
To help jump-start your explorations, I’ve listed the following “essential seven” variables that we’ve found are most predictive of a customer’s lifetime value, regardless of industry or vertical. (More free resources on segmentation strategies are available on my employer’s site.)
Try segmenting on these seven key variables in different combinations to figure out which are most meaningful for your business. Then you can use that information (for example, your best customers live in rural areas and come from paid search) to find more customers like your best ones.
The Seven Essential Segmentation Variables
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.
Happy segmenting!
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