A global D2C sportswear brand selling across 100+ markets knew from external sources like Trustpilot that customer experience needed improvement — but lacked the depth of insight to know what to fix first, and the data quality to trust the numbers.
90%+
AI accuracy after just hours of training
80%+
automation of sub-categories within two months
5 min
to install via standard Zendesk integration
Data they couldn't trust.
Agents were manually tagging every closed ticket — a process that was both time-consuming and notoriously imprecise. The categorization hierarchy needed for real insight was too complex for agents to apply consistently, leading to significant differences between teams.
The result: they were burdening agents with a task that produced data they couldn't trust enough to make strategic decisions.
From simple tags to hierarchical intelligence.
The technical implementation was straightforward — Labelf's standard Zendesk integration was installed in about five minutes. After installation, a scoped training dataset was imported for the AI models, defined in close collaboration with Labelf's implementation consultant.
The company could now leave their previous flat structure behind and establish a fine-grained, hierarchical categorization for deeper insights.
- Bootstrapped from existing data The first model was trained on agents' historical tagging — then Labelf's quality control surfaced suspected errors for efficient correction.
- 90%+ accuracy in hours Just a few hours of data quality review was enough for the first model to reach production-grade accuracy.
- Confidence-based automation Sub-category models used confidence thresholds — only auto-tagging when certain, and routing uncertain tickets to agents for continuous training.
- Self-improving over time Uncertain cases became training data. Sub-category models reached 80%+ automation within two months — improving continuously.
Insight they could finally trust.
For the first time, the company had a detailed, unified, and current picture of what customers are reaching out about — and how each category impacts key KPIs.
Cross-functional reporting
Trends and insights across markets and products — enabling faster, more precise actions across the entire organization, with measurable follow-up on the actual impact.
Market comparison
With a distributed support team speaking different languages, a cross-market overview was previously impossible. They quickly identified that product returns were driving volume and lower satisfaction specifically in one market — and could compare root causes across geographies.
Reliable data for decisions
Interactive dashboards with trustworthy data — including cluster analysis of product reviews — that enabled better decisions aimed at improving customer satisfaction, response time, and handling time.
Stop burdening agents with work the AI should do.
Manual categorization was consuming agent time while producing data nobody trusted. With Labelf, the same categorization is done automatically, at higher accuracy, with a hierarchical depth that was previously impossible to maintain.
The models start working from day one and get smarter over time. Uncertain predictions become training data. Within two months, what started as a handful of manually trained sub-categories reached 80%+ automation.
From unreliable tags to trusted intelligence.
Quantified Insight. Prioritized Action.
Higher accuracy, deeper categorization, cross-market visibility, and trusted data — transforming customer service from a cost center into a strategic intelligence source.
AI accuracy in hours
Sub-category automation in 2 months
Average integration window
