A regional manager from a retail chain in Malaysia oversaw dozens of stores spread across the country. He could see that performance was uneven — some stores were thriving while others were struggling — but without hard data, it was difficult to know which locations needed help most and what exactly was going wrong. He brought in Pau Analytics to get clear answers.
Across 43 stores in 7 regions, the average customer experience score sat at just 72.9%. At the worst-performing locations, scores dropped as low as 32% — nearly 38 points below the regional benchmark. Four stores had fallen so far behind that they were flagged as critical cases. Left unaddressed, these gaps would continue to quietly erode customer satisfaction, repeat visits, and long-term retention.
What the Data Showed
Pau Analytics reviewed mystery shopping assessment data covering the full customer journey across all 43 stores. Every touchpoint — from how staff greeted customers to how they explained promotions and handled bookings — was measured and compared against regional benchmarks. This allowed Pau Analytics to separate genuinely underperforming stores from those with normal variation, and to identify whether problems were isolated or widespread.
Pau Analytics advised the regional manager to act on two fronts simultaneously. First, treat the four critical stores as urgent priorities and deploy targeted coaching without delay. Second, launch a company-wide programme to fix greeting and engagement behaviour — because this weakness was found across 34.9% of stores in 15 locations, making it a network-wide issue, not a branch-level one.
What Changed
The regional manager now had a clear action plan instead of guesswork. The analysis showed that 75% of underperformance in critical stores was behavioural — meaning it could be addressed through structured coaching within 6 to 8 weeks. Process-related issues, which made up the remaining gap, could typically be resolved in 1 to 2 weeks. With this roadmap in place, Pau Analytics projected the network could lift its average score from 72.9% to above 80% within months, drawing on best practices from the top-performing regions, George Town and Melaka, which both scored 88.7%.
The Result
The regional manager now monitors store performance through a live dashboard that tracks scores against regional benchmarks in real time. When a store starts to slip, it is visible immediately — allowing the team to act early rather than wait for problems to compound. An AI assistant supports the team in reviewing performance trends and deciding where to focus next.