Return Signals Texts Your Customers Before They Call Support, and They Actually Reply

The Macro: Returns Are Eating E-Commerce Alive

Online fashion returns are a disaster for everyone involved. Customers buy three sizes and send two back. Brands eat the shipping costs and deal with inventory that comes back wrinkled, worn, or unsellable. The average return rate for online apparel sits somewhere between 20% and 30%, and it represents billions in lost revenue every year.

The existing solutions attack returns from the logistics side. Returnly, Loop, and Happy Returns make the return process smoother, which is good for customer experience but does nothing to prevent the return from happening in the first place. Better size charts help. AR try-on tools help. But the fundamental problem is that nobody talks to the customer between the moment they click “buy” and the moment they click “return.”

That window, the post-delivery period where the customer has the product in hand and is deciding whether to keep it, is the most valuable and most neglected moment in the entire e-commerce lifecycle. If you can reach a customer in that window, understand their concern, and resolve it before they initiate a return, you save the sale and the relationship.

Most SMS marketing tools in e-commerce are outbound blast platforms. Attentive, Postscript, and Emotive send promotions and cart recovery messages. They are not designed for personalized, conversational engagement with individual customers about specific orders. That is a different product entirely.

The Micro: Proactive SMS That Gets 61% Response Rates

Return Signals sends post-delivery SMS check-ins to customers. Not promotional blasts. Personalized messages about specific orders. “How does the jacket fit?” “Did the color match what you expected?” Simple, direct, conversational.

The headline metric is a 61% customer response rate. That number demands context because it is extraordinary. Typical SMS marketing gets response rates in the single digits. Attentive and Postscript might see 5% to 15% on their best campaigns. 61% is four to ten times higher than anything else on the market.

The reason the response rate is so high is that the messages are genuinely useful and clearly about a specific purchase. Customers are not being sold to. They are being asked about something they care about. When someone does respond, the AI concierge handles the conversation, with human escalation available when needed.

Beyond return prevention, the platform handles cross-selling, restock alerts, exchange management, and product feedback collection. Integrations cover Shopify, Gorgias, and over 50 other platforms.

Ilya Valmianski and Alejandro Zaniolo are the cofounders. Valmianski has a background in healthcare AI, and Zaniolo brings experience in startup growth and scaling operations. The company went through Y Combinator’s W26 batch.

The strategic insight here is that proactive engagement turns a cost center (customer support) into a revenue driver (relationship building and upselling). Instead of waiting for tickets, you are starting conversations. Instead of processing returns, you are preventing them. The economics flip.

The Verdict

Return Signals is doing something I have not seen anyone else do well: making post-purchase SMS feel like a conversation rather than a campaign. The 61% response rate is not a vanity metric. It is evidence that customers actually want to be asked about their purchases if the outreach feels personal and relevant.

At 30 days: does the return rate actually drop? High response rates are great, but the business case depends on fewer returns, more exchanges, and higher customer lifetime value. The downstream metrics need to match the engagement metrics.

At 60 days: does the AI concierge handle edge cases well? “The zipper broke” and “I changed my mind” are completely different conversations that require different resolution paths. The agent needs to navigate both without frustrating the customer.

At 90 days: can it expand beyond fashion? The post-delivery check-in model could work for electronics, home goods, beauty products, and basically anything people buy online and sometimes return. The question is whether the product generalizes or stays niche.

I think Return Signals has found a genuine wedge. The post-delivery window is underserved, the response rates prove the approach works, and the economics favor the brand. If the team can execute on the return reduction metrics, this becomes an easy sell for any direct-to-consumer brand spending money on returns processing.