Case Study: Bell achieves actionable customer and employee insights with Keatext

How Keatext's AI Data Analysis Tool Can Help Telecom Giant Bell Canada Improve Both Customer and Employee Satisfaction

Bell, Canada’s telecommunications giant, was facing divergent signals from customers and employees on public review sites and needed a scalable way to understand root causes. Keatext used its AI-powered text analytics platform to analyze 1,698 employee reviews (3,720 comments; 7,592 statements) from Indeed and 834 customer reviews (2,796 comments; 3,501 statements) from Consumer Affairs and the Better Business Bureau to surface themes, sentiments and suggestions.

Keatext’s analysis revealed high employee praise (over 65% of employees rated Bell 4–5 stars; 2,460 praises vs. 1,124 problems) alongside strongly negative customer feedback (over 94% 1‑star ratings on Consumer Affairs; 2,119 problems vs. 522 praises), and pinpointed actionable issues such as tech‑support failures, billing errors and training/management gaps. By delivering these targeted insights, Keatext gave Bell measurable priorities—timing peaks in negative sentiment (Jan 2014/2015), technician reliability and training improvements—that Bell can use to boost employee engagement and customer experience (research links a 5% engagement rise to ~3% revenue growth).

Bell