Retail: customer satisfaction linked to product quality
Challenge
A leading multinational retailer needed to understand, in quantitative terms, the relationship between how its customers perceive product quality and the real impact on revenue. Customer opinions are now generated across dozens of channels at once (product reviews, social media, marketplaces, surveys and support tickets) and in volumes that no manual review can cover. Without a system to consolidate and analyse all of it, signs of dissatisfaction are picked up late, once they have already affected brand reputation or customer retention, and product and customer service teams end up reacting to problems instead of getting ahead of them.
Solution
We developed an applied AI solution (natural language processing and predictive analytics) that analyses more than a million customer reviews, linking customer perception and mentions of product quality to business results.
QALEON has built a customer satisfaction platform based on sentiment analysis with NLP techniques, which processes product reviews and mentions across multiple channels on a continuous basis, automatically classifying the tone, the intent and the reason behind every comment. That information feeds a risk matrix combining volume, severity and trend by product, category or region, which prioritises the most critical cases objectively and raises alerts so that teams can act.
Results achieved
Early visibility of pockets of dissatisfaction before they escalate
Faster response times to critical incidents
Objective prioritisation of resources towards the highest-risk products and segments
A sustained improvement in customer satisfaction (CSAT and NPS)
Greater brand loyalty and a measurable reduction in customer churn
