Lapsed vs. Defecting Customers: How to Tell Them Apart

Lapsed customers are past buyers who have gone longer than expected without another purchase. Defecting is a customer group in a loyalty grid, with rules that depend on the store's settings. Neither label proves that someone chose a competitor, so check their purchase history before deciding how to respond.

What makes a customer lapsed?

A customer becomes lapsed when their gap between purchases exceeds the buying cycle you expect for that purchase. The useful question is whether they are overdue, rather than whether an arbitrary amount of time has passed.

A coffee buyer may need a refill while a grinder buyer still has everything they need. Applying the same inactivity cutoff to both can turn a satisfied owner into an unnecessary recovery target.

Start by looking at when buyers of similar products usually return. Separate seasonal purchases, gifts, and durable goods from products people regularly use up. Write down the purchase history and inactivity rule your team will use, then review whether it identifies plausible repeat purchases.

Customers can go quiet because their needs changed, their budget tightened, or the previous order disappointed them. Ask about the reason before assuming that a discount will solve it. For example, a delayed delivery calls for a service response before another promotion.

Check online and store purchase records before calling someone lapsed; they may have changed where they shop.

Purchase inactivity also differs from email inactivity. Someone can ignore your newsletter while continuing to place orders, or click messages without buying again. Use purchases to define the audience and engagement to help choose the next conversation.

What does Defecting mean in a loyalty grid?

Defecting is a rule-based group name, rather than a diagnosis of why a customer stopped buying. Its meaning depends on the recency and order-count settings in the grid you use.

In By the Numbers, the RFM loyalty matrix uses last purchase and lifetime orders, with thresholds the merchant controls. Check those settings before treating Defecting, At risk, or Dormant as an audience for a campaign.

These descriptions explain the groups' broad roles. Their names and cutoffs can change with the store's settings, so they are not universal definitions of lapse.

A Defecting buyer might still be within the normal cycle for the product they bought. Conversely, a frequent buyer may be overdue for replenishment before reaching an older recency group. Compare the label with the purchase, rather than assuming the label settles the decision.

For the wider framework, see RFM analysis for Shopify. Once the audience is clear, the At-risk campaign walkthrough covers the next campaign step.

Separate missing second orders from quiet repeat buyers

Prior order history gives inactivity a different meaning. A customer who tried the store once has a different relationship from someone who established a buying habit.

The median was 91.8% for buyers with one prior order, across 1,280 stores. For buyers with four or more prior orders, it was 50.9%, across 1,108 stores.

This comparison does not measure why buyers stopped, how they would respond to an offer, or which message works best. It also does not give you an inactivity cutoff for your own products. Use it as a reason to inspect purchase history, then test the action in your store.

Choose the next action from the purchase history

Choose a message that addresses the missing purchase or the problem behind it. Start with relevant help before deciding whether an incentive earns its cost.

For a first buyer, check what they bought and whether it worked for them. A grinder customer might need setup advice or a compatible accessory, while a coffee buyer might need a refill reminder. A relevant recommendation gives each person a reason to return without assuming either has left the brand.

For an established buyer, look for a break in their usual pattern. Check unresolved support issues, unavailable favorites, and changes in what they need. A personal question about the last order may be more useful than a generic message announcing new products.

For a dormant buyer, confirm that the product and relationship still make sense. An old gift purchase may not justify repeated reminders. Keep outreach limited, and stop marketing to people who opt out.

An existing loyalty benefit can provide a reason to return when it matches what the customer values. Reviews can help answer product doubts, but neither rewards nor reviews repair an unresolved service problem. If you test a discount, account for its cost before calling the resulting order a success.

Choose a channel the customer has agreed to receive. Avoid piling email, texts, and ads onto the same person without a clear purpose. Remove buyers from recovery messages when they purchase, and return them to relevant customer communication.

For message order and sequence planning, use the segmented customer recovery guide. Keep this audience decision separate from building the full campaign.

Check whether buyers return profitably

Judge the campaign by purchases and the money left after the offer and outreach costs. Opens and clicks show attention, but they do not establish that a buyer has returned.

Compare groups over the same observation period and keep first buyers separate from established buyers. If the audience allows, randomly reserve a comparable group that receives no recovery message. That comparison helps distinguish orders influenced by outreach from purchases that might have happened anyway.

After buyers return, watch whether they buy again and whether their earlier service issue stays resolved. Use those results to adjust the audience, message, and offer. A useful lapse definition helps your team choose a better next action, rather than labeling more customers as lost.

How we measured these benchmarks

These benchmarks use anonymized, aggregated data from Shopify stores in the By the Numbers network. No individual store is identifiable from any figure. Stores that use an analytics app are not a random sample of all Shopify stores, so read the figures as a reference point, not as an average for the whole market.

Keep reading

If you want the full picture on RFM and customer loyalty, start with how to run RFM on Shopify.

Related reading: VIP customers on Shopify: Set your rules, Shopify Email Marketing: Segmentation Tips and Shopify Loyalty Programs That Work: Get Repeat Customers.

By the Numbers builds this into the dashboard. See the RFM matrix.