Let’s say that you were to sit down to create a sketch of the customer you’d most like to acquire tomorrow. Would you have a clear idea of where they came from, how they reached your site, and what they started out buying?
Unfortunately, data overload can derail even the most dedicated efforts to profile high-value customers. With good reason: it can be difficult to know what segmentation dimensions to focus on when you have dozens of fields on each and every customer and transaction. But it’s essential to understand who your most valuable shoppers are so that you can find more like them.
So think of the following seven items as a “targeting wish-list”: essential segmentation variables that almost always tend to be predictive of a customer’s long-term value, regardless of your industry or vertical. Using some or all of them can help you hone in on the customers who are most likely to help you grow your business.
Acquisition Path: How a customer ended up converting says a good deal about how he or she is likely to behave over time. While there do tend to be some across-the-board channel trends (more on this in our Acquisition report), it is often retailer-specific and depends on the interplay between your brand and your presence across these touchpoints.
Use case: A retailer cut spending on affiliates after discovering that subscribers acquired through that channel converted to paying customers at a much lower rate than those acquired elsewhere.
First Purchase: A shopper’s first purchase interaction with your site contains loads of clues on what type of customer he or she is likely to develop into. Brand (for multi-brand retailers), category, and sub-category can proxy a variety of information like price sensitivity, utilitarian vs. hedonic shopping attitude, and level of attachment to your store.
Use case: A fashion retailer changed its retention marketing program after learning that customers whose first purchase was high heels were more than three times more likely to repeat in their first 90 days than customers who started by buying other categories.
Device Type: For many retailers, shoppers who come in on certain device types – for instance, mobile or tablet devices – tend to differ systematically from those who arrive through the more conventional desktop route. Mobile usage, for example, might proxy affluence, age group, or seriousness of purchase intent. (You can read our Mobile report for more on the behavior of mobile shoppers.)
Use case: A daily deal site discovered that its iPad customers are worth twice as much as desktop customers – and changed the way it targeted and communicated with these customers accordingly.
Geography: Geography embeds a surprising amount of valuable data on your customers. Beyond the obvious conclusions (e.g., cities like New York and San Francisco tend to be both wealthier and more fashion-forward – a big win for fashion retailers), geography can also capture information on the density of brick-and-mortar shopping options as well as regional shopping preferences. For example, an electronics retailer may find that customers from Wyoming or Nebraska are among their most valuable – because there are fewer local retail options to lure them away. (See our E-commerce report for more on this phenomenon.)
Use case: An apparel retailer discovered that their Midwestern customers are worth far more than average because they tend to buy pricey knits and outerwear during the cold winter months – so their acquisition marketing team began targeting new customers from the region.
Income: Product preferences and even repeat rate tend to vary widely with disposable income. Where can you find info on your customers’ wallets so you can talk to them as relevantly as possible? Many data providers will provide you with their best guess on an individual’s income or assets: median income or median home price in a particular ZIP+4.
Use case: A retailer uses a customer’s predicted income level to determine the most relevant inventory to show him or her in emails.
Gender: Gender often encodes valuable information about a shopper’s predicted spend. A fashion retailer, for example, may find that female customers are more valuable than male shoppers because of larger orders and higher repeat rate. For retailers in other sectors, the split may vary based on who the primary end user is and who is making the purchase decision.
Use case: A lifestyle retailer was surprised to discover that although its target demographic is male, its female customers are actually more valuable. Their marketing used this insight to transform the messaging and creative of their display advertisements.
Age: The relationship between age and lifetime value tends to vary from retailer to retailer – but is almost always a source of meaningful insights 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.
Use case: A fashion retailer started targeting an older demographic after discovering that their older customers tend to be more affluent, more brand-loyal, and less prone to comparison shopping.
If you’d like to get started with customer segmentation in your email marketing program, but are unsure how to start, we created a handy cheatsheet full of segmentation ideas to get you started.
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