The client was struggling to gain a granular understanding of their customers’ wants and needs to increase conversion rates and sales. The client wanted to monetize the existing in-house data, but thought it was unstructured and incomplete.
We used machine learning modeling to segment customers in clusters, based on the purchasing/usage patterns, demographics, and psychographics. We linked the result with customer satisfaction scores to micro-segment the customer pool.
We improved the client’s NPS score by 3 points and boosted sales, through the delivered cluster analysis. Data-driven segmentation also enriched the client’s classical market research.
1. Cluster analysis putting the customer into 7 clear segments with specific characteristics
2. A predictive model to segment new customers based on the available clusters
3. Compelling visual representation of segments’ satisfaction metrics
The client was struggling to gain a granular understanding of their customers’ wants and needs to increase conversion rates and sales. The client wanted to monetize the existing in-house data, but thought it was unstructured and incomplete.
We used machine learning modeling to segment customers in clusters, based on the purchasing/usage patterns, demographics, and psychographics. We linked the result with customer satisfaction scores to micro-segment the customer pool.
We improved client’s NPS score by 3 points and boosted sales, through the delivered cluster analysis. Data-driven segmentation also enriched the client’s classical market research.
1. Cluster analysis putting customer into 7 clear segments with specific characteristics
2. Predictive model to segment new customers based on the available clusters
3. Compelling visual representation of segments’ satisfaction metrics
