Latest / Marketing Automation with Fexingo: HubSpot, Marketo, and Automated Customer Journeys / Why Marketing Automation Ignores Customer Returns and Refunds
Transcript
- Lucas: So there's a blind spot in marketing automation that I think costs companies real money, and it's hiding in plain sight: what happens after a customer returns a product or gets a refund. Luna: You mean once the refund is processed, most workflows just go silent on that person? Lucas: Exactly. The typical automated journey — abandoned cart, welcome series, post-purchase follow-up — ends the moment the refund hits. The customer is effectively moved to a 'lost' bucket or just ignored. But that return is actually a high-signal moment. Luna: High-signal how? It seems like a negative signal — they didn't like the product, or it didn't fit. Lucas: Right, and that's the conventional read. But think about what the return says about the customer: they were engaged enough to buy, they went through the hassle of returning, and they still have an active relationship with the brand — they gave you their email, their address, their size. The return itself is rich with data. It tells you why they sent it back — wrong size, defective, changed mind — and that's actionable. Luna: So what should a marketing automation system do when it sees a refund status? Lucas: It should trigger a carefully designed re-engagement sequence. Not a generic 'we miss you' blast, but something that acknowledges the return and makes a specific new offer. I've got a concrete example. A mid-size apparel brand using Klaviyo built a workflow that waits 30 days after a refund is issued, then sends a single email with a 15 percent discount on the exact category of the returned item. Luna: Why 30 days? Why not right away? Lucas: They tested timing. Sending immediately felt pushy — like you haven't even gotten the refund yet and they're trying to sell you something. At 30 days, the refund has settled, the customer's back in browsing mode, and the offer feels thoughtful rather than desperate. The result? A 14 percent lift in repeat purchase rate among customers who got that email, compared to the control group that received no post-return communication. Luna: Fourteen percent from one email? That's impressive. But I wonder about email fatigue — are these customers getting hammered with other messages too? Lucas: That's the key constraint. The brand capped it at exactly one email. No reminder, no follow-up. And they suppressed anyone who had unsubscribed or marked previous emails as spam. The principle is: one well-timed, relevant email outperforms a three-email series that irritates people. Luna: Makes sense. And the data on lifetime value? You mentioned a study earlier. Lucas: Yeah, Retention Science published a 2025 study looking at 150,000 ecommerce customers across multiple brands. They found that customers who received a post-return touchpoint — an email or a push notification — had a 22 percent higher lifetime value in the following 12 months compared to those who got nothing. The effect was even stronger for customers who returned due to size or fit issues rather than product defects. Luna: Because a size issue isn't the brand's fault — it's a mismatch that can be fixed with a better recommendation next time. That's a signal the automation should capture. Lucas: Exactly. And most platforms — Klaviyo, HubSpot, Marketo — can do this. You just need to map the refund event to a trigger, add a 30-day delay, and tie the offer to the product category from the original order. It's maybe 15 minutes of workflow configuration. Luna: So why aren't more brands doing it? Is it a data silo issue? The refund data lives in the ERP or the returns portal, not in the marketing automation tool? Lucas: That's part of it. But I think the bigger problem is mindset. Marketers see returns as a failure — something to be minimized — so they treat the customer as a lost cause. But returns are a normal part of ecommerce, especially in apparel where fit is variable. The brands that win are the ones that normalize returns and use them to deepen the relationship. Luna: There's a psychological element too. If you just got a refund, you might actually feel a little guilty or like you owe the brand another chance. That's a good window for a well-timed offer. Lucas: Yeah, and the data backs that. The same Retention Science study showed that the average order value from post-return re-engaged customers was 8 percent higher than their first purchase. They weren't just buying again — they were spending more. Luna: That's a strong case. But I want to push back a little — isn't there a risk of training customers to return more, knowing they'll get a discount later? Lucas: It's a fair concern. The brand I mentioned did track that. They saw a slight uptick in return rates among the exposed group — about 2 percent — but the net revenue impact was positive because the repeat purchases more than offset the cost of the discount and the extra returns. The key is to keep the discount modest — 15 percent, not 40 — and to limit the offer to one per customer. Luna: So it's a calculated trade-off. And you're not sending the offer to everyone who returns — only those who haven't returned multiple items in a row, I assume. Lucas: Right, you'd want to exclude habitual returners. Most platforms let you set a condition based on return frequency. If someone has returned three out of their last five orders, don't send the offer. But for a one-time return or a first-time returner, it's a no-brainer. Luna: This is exactly the kind of nuance that gets lost when marketing automation is set up by someone who just copies a template. The default workflow treats a return as an end state. Lucas: It does. And that's what I want listeners to take away: look at your automation triggers. Find the ones that mark a customer as 'done' or 'lost' and ask whether that moment could actually be a new beginning. Returns, subscription cancellations, even support tickets — these are all signals, not endpoints. Luna: Quick honest thing — a handful of listeners chip in monthly through buy me a coffee dot com slash fexingo, and that's literally what funds making episodes like this one. Keeps it ad-free and independent. Lucas: Yeah, we don't run ads, and that's only possible because some people choose to support the show. It's a small group, but it makes all the difference. So if these marketing conversations have sparked something you've actually used, that's the place. Luna: Anyway, back to returns. Lucas, you mentioned subscription cancellations as another signal — is that a similar playbook? Lucas: Very similar. A SaaS company I've worked with uses a cancellation event to trigger a 'pause' offer instead of a discount — three months free access to a reduced feature set, then an automated check-in. They recovered about 18 percent of cancelling customers that way. But the return example is more universal because every ecommerce brand deals with returns. Luna: And it's a concrete action listeners can take this week. Go into your automation platform, find the refund or return event, and build a single email with a 30-day delay. Test it on a small segment and measure repeat purchase rate at 90 days. Lucas: That's exactly the right first step. And if you don't have the return data flowing into your marketing automation tool, that's the upstream problem to solve. It's worth the integration effort — the 22 percent LTV lift is not a rounding error. Luna: One last thought: should the email include a specific product recommendation based on the reason for return? Like if they returned a pair of jeans because they were too long, recommend a shorter inseam? Lucas: That's the ideal state, but it requires structured return reason data. If you have it — absolutely. The brand I cited didn't have that level of detail, so they just recommended best sellers in the same category. Even that worked. But personalized recommendations based on return reason would probably push that 14 percent lift higher. Luna: So it's a spectrum. Start with a basic re-engagement email, then iterate toward more personalized triggers as your data improves. Lucas: Exactly. The worst thing you can do is nothing. The default is silence — and that's costing you customers you've already acquired. Returns are not the end of the story, they're a new chapter. Luna: Alright, that's the takeaway. Thanks Lucas. Lucas: Thanks Luna. See you next time.