Sector
Telecommunications
Natural-language decision support and forecasting in telecoms
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The challenge
Executives and department heads depended on data analysts for any ad-hoc analysis or churn risk report, and decisions slowed down waiting for them.
Our approach and integration
We built a natural-language-to-SQL layer on top of the Snowflake warehouse. Machine-learning churn models plug into that layer, so a manager can ask, in their own language, "what do this month's high-churn-risk customers have in common?" and get charts and prose back immediately.
Results and impact
Subscribers at risk of churning are identified 35% earlier.
Retention rose by a net 18%.
Ad-hoc report requests to the data team fell by 60%.
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