Latest / The Retention Marketing Podcast with Fexingo: Email, SMS, and Loyalty for Repeat Customers / Why One Brand Uses Return Data to Trigger Repeat Emails
Transcript
- Lucas: There is a premium apparel brand I have been watching for a while — they sell mostly direct to consumer, high price point, low return rate for the category, around 12 percent. But what they do with that 12 percent is what caught my attention. Luna: Okay, I am curious. Most brands treat a return as a cost center and a dead end for that customer relationship. Lucas: Exactly. This brand sees a return as a signal. When someone returns an item, they trigger an automated email sequence — but not a generic 'we got your return' message. It is a personalized email that offers alternatives. Luna: So if I return a pair of jeans because they don't fit, they send me a different size or a similar cut? Lucas: Yes, and they do it fast. The email goes out within 24 hours of the return being scanned at the warehouse. It includes the exact item returned, the reason code if the customer selected one, and then one to three product recommendations based on that data. If the reason was 'too small,' they show the same item in a larger size. If the reason was 'not my style,' they show a different color or pattern from the same collection. Luna: That is smart. The data is right there — the customer already raised their hand and said, 'I wanted to love this, but it didn't work.' Lucas: Right. And the results bear it out. The brand told me that customers who receive this return-triggered email are 22 percent more likely to make a repeat purchase within 30 days compared to customers who return something and get no email at all. Luna: Twenty-two percent is significant. What about customers who don't open the email? Do they see a lower repeat rate? Lucas: Good question. The brand tracks that too. Among customers who open the email, the repeat rate is even higher — around 38 percent lift. But even among non-openers, the repeat rate is still slightly above baseline, which suggests the mere act of acknowledging the return with a follow-up has some effect. Luna: It is like the customer feels seen. 'They know I returned something, and they still want my business.' Lucas: Exactly. That emotional component matters. But there is also a practical layer. The brand integrates real-time inventory into those emails. If the alternative size or color is out of stock, the email doesn't show it. They only recommend products that are available to purchase right then. Luna: That is crucial. Nothing kills the goodwill faster than clicking a link and seeing 'sold out.' Lucas: Absolutely. And they also give customers the option to opt out of these emails. Which, counterintuitively, makes the ones who stay more engaged. The unsubscribe rate on this particular email is under 1 percent. Luna: That is a really low opt-out rate for a triggered email that comes after a potentially negative experience. Lucas: It is. And I think it is because the email is genuinely helpful. It is not trying to sell you something random. It is saying, 'We noticed this didn't work. Here is an alternative that might.' That is a very different tone from most post-return communications. Luna: I have seen some brands send a 'we received your return' email that basically just says, 'Your refund is processing.' No attempt to re-engage at all. Lucas: Exactly. That is a missed opportunity. The customer is already on your site or has just had a touchpoint with your brand. They are thinking about you. Why not offer something relevant? Luna: One thing I wonder about — does this work for all categories? Apparel makes sense because fit and style are subjective. But what about something like electronics or home goods? Lucas: It probably works best where returns are driven by preference rather than defect. For electronics, a return might signal a broken product or incompatibility, and the alternative might be a different model or accessory. I have seen some electronics brands use return data to suggest a different version of the same product. But apparel is the most natural fit because the reasons are so varied and actionable. Luna: So how does a brand implement this? What data infrastructure do you need? Lucas: You need two things. First, a return management system that captures reason codes at a granular level. Not just 'defective' or 'changed mind,' but specific attributes like size, fit, color, material. Second, an email platform that can ingest that data in real time and trigger a campaign within a day. Luna: That is a pretty big lift for a small brand on Shopify with a basic email tool. Lucas: It is. But there are workarounds. Some third-party return platforms, like Loop or AfterShip, now offer integration with email tools like Klaviyo. You can set up a flow that triggers when a return is marked as received. The recommendations might be less sophisticated — maybe just showing the same product in a different size — but the concept still works. Luna: And what about brands that don't collect reason codes at all? Can they still do something similar? Lucas: They can still trigger an email based on the return event itself, even without a reason code. The email could say something like, 'We hope the item worked out. If not, here are a few similar items you might like.' It is less targeted, but the mere act of following up still has a positive effect. The brand I mentioned actually tested a version without reason codes and saw a 15 percent lift in repeat purchases, compared to 22 percent with reason codes. Luna: So even a basic version is worth doing. Fifteen percent is still meaningful. Lucas: Absolutely. And the cost of sending an email is essentially zero. The only cost is setting up the flow. That seems like a no-brainer for most brands. Luna: It also ties into something we have talked about before — the idea that every customer touchpoint is a chance to deepen the relationship. Returns are usually seen as the end of a transaction, but really, they are a midpoint. Lucas: Exactly. And for the brand we are talking about, returns are not a loss. They are a second chance. They have even started using return data to inform product development. If a particular style has a high return rate for 'too small,' they adjust the sizing in the next production run. Luna: That is closing the loop. The return data feeds back into product design. That is a whole other level. Lucas: It is. And it is something that larger brands have done for years, but smaller DTC brands are just starting to adopt. The ones that do are seeing real results. Luna: I am thinking about how this could apply to a subscription brand. If someone cancels a subscription, that is like a return of the service. Could you trigger a similar email offering a different plan or a pause instead of cancellation? Lucas: Great point. Some subscription brands already do that — they call it the 'save' flow. But the data is analogous. The customer is signaling that something didn't fit, and you have a chance to offer an alternative. The return-triggered email is basically the same concept applied to a one-time purchase. Luna: So the principle is universal: when a customer says 'no,' listen to why, and offer a different 'yes.' Lucas: Perfectly put. And the beauty is that the data is already there. You just have to use it. Luna: Yeah, it is one of those ideas that seems obvious once you hear it, but most brands are not doing it. What is the biggest barrier you see? Lucas: I think it is a combination of two things. One, returns are often handled by a different team — operations or customer service — and that team doesn't talk to marketing. Two, there is a fear that contacting a customer right after a return will annoy them. But the data from this brand and others suggests the opposite. Luna: It is about the tone and relevance. If the email feels helpful, not pushy, customers appreciate it. Lucas: Exactly. And that is what makes this a retention marketing tactic, not just a sales tactic. It is about keeping the relationship going, not just making a quick sale. Luna: I want to try this with a client. Do you have any advice on the specific timing? Should it be right when the return is scanned, or wait a few days? Lucas: The brand I mentioned sends it within 24 hours. They tested 48 hours and 72 hours, and the 24-hour window performed best. The theory is that the return is still top of mind. The customer is thinking, 'I need to find something else.' If you show up quickly with a good alternative, you capture that intent. Luna: Makes sense. What about the subject line? Do you have any examples? Lucas: They keep it simple and personal. Something like, 'We saw your return — here is an idea,' or 'That didn't work? Try this instead.' They avoid anything that sounds like a sale or a promotion. The goal is to be helpful, not to push a discount. Luna: That aligns with the tone we always talk about: treat the customer like a person, not a conversion. Lucas: Exactly. And that is why I think this episode is one of the most actionable ones we have done. It is a simple idea, backed by data, and it can be implemented by brands of almost any size. Luna: And it is a reminder that retention doesn't have to be complicated. Sometimes the best tactics are the ones that use the data you already have in a smarter way. Lucas: Right. And if these conversations have sparked something you have actually used in your own marketing, you know, a handful of listeners chip in monthly through buy me a coffee dot com slash fexingo, and that is literally what funds making this many of these episodes ad-free. So if that sounds like something you would want to be part of, it helps keep the show going. Luna: Yeah, it is a small group that makes it possible for us to keep doing deep dives like this one. And we are grateful for every single one. Lucas: Back to returns — I think the key takeaway is that returns are not a failure. They are feedback. And if you act on that feedback with a timely, relevant email, you can turn a potentially negative experience into a stronger customer relationship. Luna: And a second purchase. Which is the whole point of retention marketing. Lucas: Exactly. Next time, I want to look at how brands are using return data to personalize the shopping experience before the purchase — like recommending the right size upfront based on past returns. It is a whole other layer. Luna: I am in. That sounds like a great follow-up.