We’ve just wrapped up the Mechanics of Predicting Customer Churn series—if you haven’t checked that out, I’d highly suggest you read through it. But even after we create models to predict customer churn and coordinate campaigns to prevent as much of it as we can, there are still going to be customers who cease to buy our product or service. It’s a natural step in the business cycle. Hopefully, they’ll eventually become reactivated customers—once that happens, it’s our job as marketers to make sure they stay happily consuming our product for as long as possible.
Reactivated customers present unique marketing challenges, and require campaigns distinct from ones targeting those who are already buying, or haven’t yet bought a product. Many companies ignore this group of customers, missing out on a potentially huge source of revenue; but using the right data to power the right campaign can ensure a reactivated customer stays loyal for as long as possible.
Data! Data! Data!
You can’t be expected to make bricks without clay. To target any group of customers, you need to take more than your best guess at what will drive them to buy—this is especially true with customers who have previously left you. Not all previous customers will leave for the same reason, but you can look at past purchasing behavior to develop micro-segments, grouping reactivated customers by a best-fit explanation for why they left. How much did they spend when they were customers? How did they reactivate—was it seasonal, a price discount, or some other factor? These and many others can be used to group customers back together. Be sure not to ignore demographic information, the amount of time that has lapsed since they last bought from you, and any other information you might have on a group of customers.
Okay, I’ve got data. Bombs away, right?
Not quite. Recall that these individuals had previously left your business—maybe for a competitor, maybe because they felt they no longer needed your product. But to get them back to buying steadily from your company, you need to personalize as much as you can. You can’t expect loyalty from a customer with a minimal effort spent into grouping and customizing messages for each prospective buyer.
For example, take a customer that reactivated once your company did a sale on a product they used to buy frequently. This information, combined with past purchasing behavior, might lead you to conclude that they’ll never buy a product at full price—so why waste your money and their time sending them promotions for full-priced goods? Instead, allocate your marketing dollars intelligently, by sending them promotions, complementary offers (“buy this jacket at 40% off and get a pair of jeans half off!”) or other ways to draw them in.
So what’s the bottom line here?
Keeping reactivated customers loyal isn’t easy. It’s a unique challenge for each customer, and you have to responsively look at why they left, and proactively mitigate future factors that might cause them to leave again. Having fresh data, robust insights, and a holistic understanding of each customer is the only way to gain this level of rich understanding. For most businesses, this is too much information for any data science or marketing team to look over manually. That’s where automated systems, AI, and other cutting-edge marketing technologies come in.
Josh Fayer manages Marketing Communications at Zylotech, where he brings an extensive background in public relations and computer science. Josh Loves music! He sings with an a cappella group, Orange Appeal, (whose recent album Unpeeled he shamelessly plugs at work), and at home he loves to mess around with a variety of instruments. Josh enjoys spending quality time with his adorable dog, and exploring around his new home near the South Shore of Boston.
If you liked this post, check out our other blog post on hyper-personalization in marketing.