Holiday periods are the busiest in retail. But behind the high revenue figures sits a group of customers who can be hard to get a proper hold of afterwards.
29. juni 2026
It is a pattern we have followed closely in our work with loyalty programmes in retail. For businesses like bookshops and toy shops it is well known: at Christmas, at confirmations and at birthdays the traffic rises. The tills hum. And the customer club grows with new members who sign up to collect points on a gift purchase.
Weeks later, many of them have gone quiet. Because they have already done what they came to do.
There is a fundamental difference between a customer shopping for themselves and one shopping for someone else. Both can make a good purchase, but only one of them has a personal relationship with what they are buying.
We see many retail businesses treating these two customer types identically in the communication that follows. The data structure is not built to tell them apart. So you know a purchase was made. You just do not know who for.
The good news is that the data patterns often tell us more than we use them for. It might be:
A delivery address that differs from the billing address, one of the most direct signals of a gift purchase.
A basket with gift wrap.
A gift card.
A purchase in a category the customer has never visited before.
A single purchase on a date falling close to a holiday – and no activity afterwards.
Patterns like these do not necessarily require advanced models to identify. They require you to ask the basic question: is this person shopping for themselves or for someone else?
For the self-buyer the logic is familiar and already widespread practice: we show them more of what they are already interested in. We build on what they knew when they arrived, and give them reasons to come back within the category that brought them in.
For the gift buyer the options are different – and in our experience often underused.
A parent buying at Bog&Idé or Legekæden in the run-up to Christmas is probably a parent who also buys for birthdays, for confirmations and for the year's other occasions. The purchases are seasonal – and follow a logic defined by the child's age and the family's plans rather than by their own wishes.
The communication that lands well here is not about the product they have already bought. It takes the gift buyer's calendar as its starting point and is about who they are. But that insight does not come from looking at behavioural data alone. It comes from dialogue – from asking the customer directly who they are shopping for, and when. As a genuine expression of interest.
Behaviour can give us a clue. Dialogue gives us an answer.
This is where we find many automated flows have something to gain. The generic "We miss you" email does not reach the gift buyer the way it is intended to, because it communicates from an assumption that the person has an unmet need for a product. But the gift buyer does not. Not in the way the self-buyer does. The gift buyer has a calendar full of occasions they play a part in.
That shift in perspective – from "when will you buy again?" to "who do you typically shop for, and when does that happen?" – can change the entire follow-up logic. But the shift assumes you have asked. That the communication is built on an answer rather than a guess. You have to know someone before you can acknowledge them.
Dialogue is, in our view, the precondition for communicating something of real value – and therefore for showing the understanding that actually creates attachment.
We return to this distinction between perspectives consistently when we work on customer club setups in retail. Relevance is goal number one. Volume comes second.
Loyalty among holiday customers starts with understanding who stood at the till that day – and what brought them there. Data can hint at that understanding. But it really emerges when we ask. For a good many of them the answer is a child, a mother, a friend. And that insight is worth more in the long run than the number of opened emails.