Case Studies
Customer Churn Risk Scoring using Machine Learning
Mosaic designed and deployed a custom machine learning model to help this retail energy company predict customer churn and inform a geographic growth strategy.
Using data to inform marketing, sales, and customer experience decisions is increasingly essential for business success, no matter your industry. Mosaic Data Science understands that predicting customer behavior is increasingly critical. When businesses gain a deeper understanding of their consumers’ buying cycles and habits, they can better generate strategies to influence future behavior with relevant, targeted advertisements, content, promotions, or messaging. Armed with data, your company is more likely to succeed in keeping and growing your valued customer base.
Mosaic designed and deployed a custom machine learning model to help this retail energy company predict customer churn and inform a geographic growth strategy.
Executives thought they could drive even more gasoline sales through further discounting to their members. They needed to measure if dropping the price per gallon drove more sales volume, validating their beliefs. Instead of relying on ‘tribal’ knowledge.
If a decision maker is only relying on their ‘gut’, they are not only behind the times, but at high risk of losing competitive advantage, customers and market share.
A leading CPG company wanted to diagnose cannibalization hypotheses using a data-analytics-driven approach.
A leading clothing manufacturer distributor and retailer of clothing realized they needed to fortify their pricing decisions with machine learning insights.
Retail executives need to think more like tech companies, using AI and machine learning not to just predict how to stock and staff their stores, but also to dynamically recommend products and set prices at the individual consumer level.
Meeting customer expectations is more difficult than ever, more and more of market share goes to companies who are able to perceive needs rather than react. Whether e-tailing or selling in brick-n-mortar stores, inventory planning is a promising area for predictive analytics,
We examine how professional teams can deploy predictive ticket pricing to capture increased revenue and decrease empty seats.
In post 2 of 4 on biometric modeling, we discuss how sporting teams and goods manufacturers can segment their consumer base using biometric insights.
Unsupervised learning approaches are very powerful when applied to customer segmentation.