Returns Agent

AI root-cause analytics to cut returns.

See how the returns agent unifies return reasons, reviews, call logs, tickets, and logistics data, enriches every record with SKU, customer, and operations metadata, and uses AI to pinpoint root causes of returns: fit, quality, packaging, delivery, and expectation gaps. The demo shows SKU-level risk scoring, differentiating between preventable and structural issues, and role-based digests that reduce return rates, protect margins, and strengthen product-market fit.

Frequently asked questions (FAQs)

What is an AI-powered returns agent and why do brands need it?

A returns agent is an AI layer that analyzes all return-related customer signals to explain why products come back, not just how often. It unifies returns, tickets, calls, reviews, and logistics data by SKU and customer segment to surface true root causes and high-risk patterns. This helps brands reduce avoidable returns, protect margins, and improve product-market fit.

How does the returns agent actually reduce return rates and refund costs?

The agent quantifies return risk by SKU, category, channel, and segment, then links it to specific issues like fit, defects, damage, or expectation gaps. It ranks opportunities by return-reduction potential and profitability impact—such as fixing size charts, packaging, product content, or supplier issues - so teams know exactly which changes will cut returns fastest.

What data sources does the returns agent connect for returns analytics?

It consolidates return and refund reason codes, support calls, tickets, emails, chat/WhatsApp logs, product reviews, social complaints, logistics tracking, warranty claims, and survey feedback. All records are cleaned, translated, and stitched into a single “return journey,” giving a complete view of why each order was returned.

How does the returns agent identify root causes and high-risk SKUs?

Using LLM-powered analysis, the agent classifies each return into granular reasons like size/fit, material quality, damage in transit, misleading photos, wrong item shipped, or setup difficulty. Combined with SKU, batch, warehouse, and carrier metadata, it flags SKUs, variants, and locations with abnormal return patterns so teams can prioritize fixes.

Can the returns agent predict which products or segments are most likely to return?

Yes. The agent scores SKUs, categories, and customer segments based on historical return behavior, sentiment shifts, rating drops, and recurring complaint themes. This allows teams to proactively adjust inventory, sizing guidance, packaging, and expectations for high-risk items before return costs spike.

How does the returns agent separate preventable returns from structural product issues?

The agent distinguishes returns caused by content, expectations, packaging, or process (preventable) from those driven by core design or material flaws (structural). Each return is tagged accordingly and rolled up by SKU and category, so teams know what can be fixed quickly via content and operations - and what requires deeper product or supplier changes.

How do product, retail, and operations teams use returns agent insights day to day?

Product teams get digests on high-return SKUs, defects, and sizing issues; retail and merchandising see expectation gaps and store-level patterns; operations see warehouse, carrier, and packaging problems. Because insights are prioritized by return reduction and profit impact, teams can align on a focused roadmap to steadily lower return rates and strengthen margins.