Mosaic Data Science has a long history of using the latest and greatest in AI and ML to help customers determine how to drive impactful outcomes based on their data and processes.
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.
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.
Gameplay data are a trove of information about how athletes are acting and reacting in real situations, and there are real benefits to be gained by mining this information at every level, from the athlete to the entire team. In the modern age, the team that can measure and understand itself through its own data will have the competitive edge.
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.
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.
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.
Mosaic writes an in-depth view on the synergies between design thinking & data science. We also examine how these two disciplines can be used together.
Unsupervised learning approaches are very powerful when applied to customer segmentation.