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.
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.
Algorithms can be trained to mimic human behavior, but what happens when the human developing the algorithm inadvertently allows bias into the training process?
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.
Working in conjunction with subject matter experts, data scientists can swiftly apply statistical tools and uncover emerging trends. This is extremely valuable for companies trying to operate in a disruption. Not only will executives have an accurate representation of their present situation, but new products & services can be devised from these insights.
Weather has a high impact on operations in many industries, and therefore is of great value to integrate into strategic decision making. Mosaic has roots in aviation research & development, giving us deep expertise in combining weather data streams with planning applications to facilitate efficient resource allocation.
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.