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.
Case Studies
Automated Cooking Prediction & Optimizer | Deep RL
Mosaic built an automated cooking prediction & optimizer using deep reinforcement learning to improve short term cooking operations.
Case Studies
Loyalty Program Discounting
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.
Case Studies
Wholesale Cannibalization Analysis
A leading CPG company wanted to diagnose cannibalization hypotheses using a data-analytics-driven approach.
Case Studies
Price Optimization for Major Clearance Sales
A leading clothing manufacturer distributor and retailer of clothing realized they needed to fortify their pricing decisions with machine learning insights.
Case Studies
Predictive Modeling for Clinical Trial Recruitment
For many pharmaceutical firms, trial recruitment forecasting plays a role in trial recruitment planning. However, these forecasts may be generated with relatively simplistic approaches based on only a small subset of available internal & external data.
Case Studies
Automated CPG Sales Force Route Forecasting
Data Science, specifically machine learning forecasting techniques, enables decision makers across a spectrum of industries to set more effective sales targets and plan profitable customer routes that boost top and bottom line growth.
Case Studies
Customer Segmentation for Geo-Targeted Marketing
Unsupervised learning approaches are very powerful when applied to customer segmentation.
Case Studies
Community Healthcare Utilization Risk Score
Hospitals can change the way they deliver care by understanding their communities through the application of machine learning and statistics.
Case Studies
Predicting Signal Degradation using Weather Data
Mosaic built a model that predicted signal disruption, up to 16 hours in advance, based on North American precipitation forecasts and historical signal quality measurements.