Designing and deploying computer vision is a powerful technology that humans can employ to improve their decision making. The only limits to these technologies lie within our ability to think of problems for them to solve.
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.
In post 2 of 4 on biometric modeling, we discuss how sporting teams and goods manufacturers can segment their consumer base using biometric insights.
This is post 1 of a 4-part series focusing how data science can incorporate biometrics for sporting good manufacturers and professional sports teams.
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.