Don’t Kiss Lapsed Customers Goodbye—Win Them Back

Hey retailers, let’s talk about a difficult subject: lapsed customers. Why? Because you might be losing out on significant future business, just because you’re giving up on certain customers way too early.

Generally, customers are considered “lapsed” once 12 months has passed since their last purchase. The customer record is flagged in a Y/N fashion, and just like that, the relationship is over.

Marketing promotions, targeted advertising, contact strategy drop dramatically or stop completely. With its silence, the retailer sends a clear message: You don’t shop, so I don’t care about you anymore.

But is this really the best practice for retailers to follow? There are still many open questions to consider.

First, a customer’s frequency is not constant and does not necessarily comply with any calendar, fiscal or otherwise. In addition, a host of other factors may be in play, including unaided brand awareness, word-of-mouth, social exposure, targeted emails, and more. And in a world of digital, we keep forgetting that store signs are still a communication channel, even when people are out-and-about less frequently.

Second, customers may shop a brand at specific intervals (holidays or clearances, for example), at specific times (soon after they get paid), or in anticipation of special events (weddings, graduations, holidays). The frequency needed to replenish certain items also varies widely. Think about how often you need to buy new clothes for church or new laptops for school and work. All this to say that a customer’s activity is more complex and unstructured than a fiscal calendar.

Third, the sudden and dramatic shift of marketing dollars away from lapsed customers almost creates a self-fulfilling prophecy. The longer a customer does not shop, the less exposure to the brand they receive. This inevitably leads to lower awareness (aided and unaided), which increases the probability that the customer will continue not to shop.

Retailer Relationship Status: It’s Complicated

What is the alternative? Perhaps the whole premise of lapsed customers needs to change. Instead of a binary Y/N status for active/lapsed customers, a more appropriate approach would be a probabilistic model. This allows retailers to gauge the probability of a customer to shop during a certain time frame and adjust its contact strategy accordingly.

Customers would be assigned a probability score based on their actions—and inactions. This score is meant to measure the probability that a customer will shop during a certain time frame. This time frame will vary from industry to industry and company to company. For example, Starbucks may measure the probability of a customer to shop in a matter of days. Grocery stores would do the same. Home goods, electronics, and furniture retailers may measure the likelihood of a customer to shop in years, instead of days or weeks.

In some cases, the probability that a customer will shop next may be tied to a specific event. Fashion forward brands may assign these probabilities based on the next fashion launch. In fact, the machine-learning (ML) model that will calculate these probabilities would likely incorporate details regarding which fashion collection the customer’s last purchase came from.

The Value of Courting Lapsed but Engaged Customers

With a model like that in place, what are the repercussions to marketing, analytics, and most importantly, budgeting and contact strategy?

First, contact strategy will focus on probability. Budget will be allocated mostly as before with prospects, active customers, customers with high future LTV and new customers absorbing the majority of the budget. Now, though, new target audiences are created through a custom segmentation strategy. This could include off shopping pattern customers, customers with increased probability of NOT shopping in the next X period of time, etc.

For DTC brands still using RFM (recency, frequency, and monetary value) segmentation, there is another call to action to build more advanced segmentation strategies.

For a lot of lapsed customers in the database, that probability will be zero or near zero, which is expected. However, unlike the previous Y/N flag, that probability will not remain fixed until such time as the CONSUMER initiates by making a transaction. The brand needs to make the first move. It should be obvious that a customer who has not shopped in 12 months but continues to open emails and interact with a brand’s social platforms is, in some shape or form, still engaged with the brand.

How does this impact contact strategy? Engaged and non-shopping customers should be identified and separated out with marketing dollars allocated to them based on probability to convert. There is a near unanimous acceptance that some new customers deserve more marketing investment than others (high spending initial transaction vs low, for example). The same applies to this unique opportunity of potential reactivated customers.

Lastly, what about customers with zero or near-zero probability scores for extended periods of time? Based on an analytically backed definition of “extended”—again based on industry, sector and company—it would be better to treat some of these consumers as high potential prospects than lapsed customers. In fact, based on common budget allocations, they would probably get more marketing investment as prospects than lapsed customers! And because of their previous experience with the brand, their probability of converting will be higher.

For more on how retailers can personalize experiences and maximize customer lifetime value, check out Treasure Data’s retail story.