Every return tells a story. Every complaint is product intelligence.
Retail and e-commerce companies handle massive volumes of tickets about orders, returns, sizing, delivery, and product issues — across multiple markets and languages. The insights hidden in those tickets are the fastest feedback loop your product, logistics, and marketing teams will ever get.
30%+
of e-commerce orders returned in some categories
100+
markets — each with unique languages, expectations, and logistics
3x
seasonal volume spikes during peak periods
High volume. Sharp seasons. Product feedback hiding in plain sight.
Retail support teams sit on the richest source of product and customer intelligence in the company. But manual tagging is imprecise, too flat for real insight, and varies wildly between agents and markets. The data exists — it's just not structured enough to act on.
From flat tags to hierarchical product intelligence.
Manual agent tagging is too flat, too inconsistent, and too slow. Labelf replaces it with AI-powered hierarchical categorization — product line, issue type, root cause — all in real-time, across every language and market.
Your product team sees quality issues before they hit reviews. Your logistics team sees delivery failures by carrier and region. Your CX team sees exactly what drives satisfaction up or down.
- Stop defective products in production Spot design flaws, sizing inconsistencies, and material issues from support data — while the production line is still running.
- Reduce unnecessary returns Understand why customers return — wrong expectations, poor descriptions, sizing confusion — and fix the source, not the symptom.
- Compare markets instantly Same product, different markets, different problems. See why returns spike in one country but not another — regardless of language.
- Automate categorization, free up agents Replace manual tagging with 90%+ accuracy AI models that improve themselves over time. Agents focus on customers, not admin.
Support data that flows across the company.
Product, logistics, marketing, and CX — all benefit from the same AI engine analyzing every customer interaction.
Contact Reasons
Hierarchical categorization — product, issue type, root cause — so every team gets the detail they need.
Process Improvement
Find the broken processes that generate unnecessary tickets — delivery issues, return friction, payment failures — with cost attached.
Customer Experience
Connect CSAT, Trustpilot, and ticket data to see what drives satisfaction — and what destroys it — per product and market.
Operational Efficiency
Eliminate manual tagging, reduce handling time, and scale through seasonal peaks without proportional staffing increases.
Your support team is your fastest product feedback loop.
Product teams wait weeks for survey data. NPS tells you a number, not a root cause. But your support tickets tell you exactly what's wrong, with which product, in which market — every single day. Labelf structures that into intelligence your product team can act on immediately.
A D2C brand selling across 100+ markets discovered that product returns were driving volume and lower satisfaction specifically in one market. With Labelf, they could compare root causes across geographies for the first time — and fix the right thing in the right place.
Intelligence at Retail Scale.
Language-agnostic AI that handles every market. Integrates with your existing stack in minutes. Gets smarter with every ticket.
Categorization accuracy
Languages supported
Average integration window
