Athleta B Corporation Case Study

As a B Corporation, Athleta has the unique challenge of identifying a competitive strategy that will guide the company to success in the highly competitive athleisure market while also remaining consistent with its mission, vision, and values and supporting its parent company, Gap Inc. This case study was developed to evaluate this challenge through internal and external analysis of the company and to encourage readers to consider Athleta’s future priorities as the company undergoes changes in strategic leadership. The case study begins with a brief overview of the competitive landscape of the Athleisure market and a review of the history of Athleta before diving into the company’s positioning in the market as a company that values sustainability and female empowerment. Athleta’s focus on inclusive sizing, sustainability, partnerships with female athletes, and customer engagement provide strong tools for the company to differentiate itself from competitors. These strengths will be critical for Athleta to utilize in the future as it seeks to become a leading Athleisure brand and a strong brand in Gap Inc.’s portfolio

Persistent Customers

The goal of this project was to produce a predictive model that scores a policyholder’s likelihood to churn within a given time window. Mutual of Omaha understood policyholder persistence through an actuarial lens which is limited to variables that the industry understands well and to probability theories and statistical techniques that have a long history. Machine learning driven model discovery, by contrast, avails itself of a wider set of predictive variables and leverages the unusual size of datasets in modern enterprises to validate the patterns it discovers

Machine Learning Improves Customer Satisfaction

Within DMSi’s customer service platform, The Wedge, customers are able to submit new ideas for features using the Idea Portal. Within the Idea Portal, there are too many ideas with limited filtering capabilities. The goal of this project was to improve customer satisfaction and DMSi’s internal teams’ pain points through data available on The Wedge and the Design Studio team’s knowledge in machine learning. The final solution resulted in a recommendation engine geared toward customer satisfaction, and two reports: the Strategic Classification Report, which identifies ideas in line with DMSi’s company-wide strategic initiatives, and the Duplicate Detection Report, which identifies similar or identical ideas that can easily be merged