A customer who stops coming in isn't necessarily lost. They may simply have changed their habits, started buying less often, found another option or had no reason to come back lately. The challenge is to tell a normal absence from a real change in behavior, then choose the right follow-up. An inactive (or lapsed) customer is one whose absence has become unusually long compared with their expected purchase cycle. And that timeframe isn't the same for every business or every customer.
When does a customer become inactive?
There's no universal threshold. In a business where customers buy every week, 30 days without a purchase can already be a warning sign. In a store where purchases are naturally several months apart, the same gap would mean nothing. A fixed threshold such as:
“Inactive customer = no purchase in 90 days”
can work as a simple rule, but it has to match your business's real purchase cycle. A more precise approach is to ask: has this customer been away much longer than usual? Take a customer who used to buy about twice a month. If they haven't been back in three months, their behavior has clearly changed. On the other hand, three months without a purchase can be perfectly normal for a customer who usually buys twice a year.
Which signals show that a customer is drifting away?
The date of the last purchase matters, but it isn't always enough on its own. You can combine several data points.
Recency
How long ago was the last purchase? This is the first indicator to check for possible inactivity.
Historical frequency
How often did the customer buy before? This puts their current absence in the context of their usual behavior.
Regularity
Two customers who made twelve purchases during the year can behave very differently. The first might buy about once a month. The second might have made all twelve purchases within three months and never come back. The annual frequency is the same, but the inactivity signal is not.
Amount spent
The revenue a customer has generated shows how much they've contributed to sales over a given period. It shouldn't be confused with their overall “value” or profitability, though.
Products or categories purchased
Purchase history helps you understand the customer's profile and, where it makes sense, write a more relevant message.
Not all inactive customers need the same follow-up
Sending the same message to every customer who hasn't bought anything in six months is easy. It isn't necessarily the right move. Consider three profiles:
Customer A
A single purchase eight months ago.
Customer B
One purchase a month for two years, then nothing for three months.
Customer C
Several large purchases last year, but a naturally irregular buying pattern.
By some thresholds, all three are “inactive.” Yet their situations aren't comparable. Customer B shows a real change in behavior. Customer A may never have been a loyal customer. For customer C, you need more context before concluding they're drifting away. So a good follow-up starts with a good segment, built on the same criteria that customer segmentation uses to tell apart profiles that are still active.
Which inactive customers should you contact first?
It depends on your goal. For example, you might target:
- former regulars who no longer come in;
- customers who generated significant revenue and have stopped buying;
- regular buyers of a specific category;
- customers whose absence is clearly longer than their usual buying cycle.
It's often more effective to work on a well-defined segment than to contact every inactive customer. Customer segmentation lets you build these groups from measurable criteria.
A 5-step win-back method
- IdentifySpot customers whose absence is becoming significant.
- SegmentGroup profiles by past behavior: frequency, recency, purchases or amount spent.
- Choose a reason to come backBefore writing the message, decide what the business really has to offer: something new, relevant information, a perk, a promotional offer or a simple reminder.
- Reach outTailor the message and channel to the chosen segment.
- MeasureAfter the campaign, check how many customers actually came back and under what conditions.
This last step is essential for improving future campaigns.
Do you have to offer a discount to win customers back?
No. A discount can work in some cases, but it shouldn't be your default reflex. It cuts into your margin and can train some customers to wait for a promotion before coming back. There are other approaches:
Showcase something new
A new product, a new collection or a new service can give you a natural reason to get back in touch.
Highlight a relevant category
If a customer regularly bought a particular category, news about that category can be more relevant than a general promotion.
Offer a loyalty perk
A perk can acknowledge your past relationship without having to be an immediate discount.
Send a simple reminder
Sometimes a low-key message is enough to put your store back on the customer's radar.
The right choice depends on the segment, the business and the likely reason the customer dropped off.
Personalize without reading too much into the data
Purchase history lets you tailor a follow-up, but don't read too much into it. A customer who bought from a product category three times doesn't necessarily want offers only about that category. The data shows what happened, not what the customer will definitely want tomorrow. Good personalization uses history as a signal, not as the absolute truth about a customer's preferences.
What message should you send to an inactive customer?
Above all, the message should give the customer a clear reason to come back. A simple structure can do the job:
- remind them of their relationship with your business;
- explain why you're reaching out;
- suggest a simple action.
For example:
“We haven't seen you in a while! Our new [category] collection just arrived. Stop by the store and take a look.”
Or, when an offer is really warranted:
“You used to buy [category] regularly, so we have a special perk for you on this selection until [date].”
The message should stay consistent with what you actually know about the customer.
Choose the right timing for your win-back message
Reaching out too early can be pointless. Reaching out too late lowers your chances of reviving a habit that stopped long ago. So the right moment depends on the purchase cycle. For a customer who normally comes in every two weeks, waiting six months probably makes no sense. For a purchase made once or twice a year, following up after a few weeks would be premature. Purchase history lets you compare the time since the last purchase with the customer's usual pace.
Don't overwhelm customers with messages
Reactivating a customer doesn't mean contacting them over and over. Someone who doesn't respond to several campaigns shouldn't necessarily get more and more messages. Keep your sending frequency in check and respect the preferences and consent rules that apply to each channel. One relevant follow-up usually beats a string of generic messages.
How do you measure a win-back campaign?
Open and click rates tell you something about the message. But the end goal is usually getting the customer to come back. So, depending on the data available, track:
- the number of customers contacted;
- the number of customers who came back;
- how long it took them to buy again;
- the revenue generated after the follow-up;
- the basket size of returning customers;
- the margin generated, if available.
You can then calculate a reactivation rate:
Reactivation rate = customers who came back ÷ customers contacted × 100
Decide in advance how long the window is for a customer to count as having “come back.” Without this observation window, the result is hard to interpret.
Test instead of assuming
Two messages can seem equally relevant and still produce different results. If you have enough customers, it's worth comparing different approaches:
- with or without a perk;
- new arrivals vs. a simple reminder;
- different send times;
- different segments.
Over time, you'll learn which follow-ups really work for your business. The goal isn't to find a universal formula, but to refine your strategy based on actual results.
Automate detection without blindly automating your messages
Looking for customers who are starting to drift away is repetitive work, so it can be automated (see automating your store's tasks for details on how routines work). For example, a system can regularly check which customers pass an inactivity threshold or fall behind their usual frequency. But automatically detecting an inactive customer doesn't mean a message has to go out automatically. It may be better to keep an approval step before certain messages, especially when the segment or offer needs checking. That way, you automate the monitoring without losing control of your marketing.
How can AI help win back inactive customers?
With access to the right data, AI makes it easier to find and analyze the customers in question. For example:
“Which customers who used to buy at least once a month haven't bought anything in three months?”
Then:
“Of those, which ones regularly bought category X?”
And finally:
“Draft a win-back message for this segment.”
The benefit is that you move step by step from detection to targeting to preparing the action, without lumping all inactive customers into a single group.
How Gillia helps you win back inactive customers
Gillia, an AI agent for independent businesses, lets you query your customer data to spot customers who are dropping off. For example, you can ask:
“Which regular customers haven't bought anything in 90 days?”
Then narrow it down:
“Keep only those who made at least six purchases in the previous twelve months.”
Once you've checked the segment, Gillia can help you prepare the matching message from its customer loyalty capability. When you need to run a search regularly, it can also become a routine that surfaces new customers who match the criteria. Detection is automated, while you stay in control of the list, the message and the action before anything is sent. That's the principle of a true AI agent for retail.
Win customers back, don't just follow up
A good reactivation strategy isn't about sending more messages. It's about identifying:
- who has really changed their behavior;
- when that change becomes significant;
- what relevant reason you can give them to come back;
- and whether the follow-up actually brings them back.
That's what turns a list of inactive customers into a real loyalty strategy. You'll find more loyalty examples in our catalog of Gillia use cases: identify and follow up with inactive customers or get an alert when a regular customer drifts away.