Case Study: Schuh achieves rapid, actionable VoC insights and reduced customer friction with SentiSum

How award-winning retailer, Schuh, uses AI analytics to make sense of large volumes of VoC data in seconds

Schuh, an award‑winning retailer known for prioritizing customer experience, was drowning in voice‑of‑customer data — more than 720,000 reviews plus high volumes of monthly support queries — and needed a way to analyze all feedback without biased sampling, long delays, or excessive manual effort. They engaged SentiSum’s AI analytics/customer insight solution to tackle timely analysis and prioritization of customer issues across channels.

SentiSum’s machine‑learning insights engine automated the analysis of Schuh’s conversations and reviews, turning large volumes of VoC into granular, objective insights the team can trust. As a result, Schuh can quickly "take the temperature" of hundreds of thousands of customers, surface previously unseen issues, quantify how many customers are affected, prioritize fixes, speed decision‑making for web optimisation, save analyst time, and reduce customer friction — improvements SentiSum has supported since 2019.

Schuh

Sean Mckee

Director of eCommerce and customer experience