Apprentice Chef Case

Apprentice Chef, Inc. is an innovative company with a unique spin on cooking at home. Developed for the busy professional that has little to no skills in the kitchen, they offer a wide selection of daily-prepared gourmet meals delivered directly to your door. Each meal set takes at most 30 minutes to finish cooking at home and also comes with Apprentice Chef's award-winning disposable cookware (i.e. pots, pans, baking trays, and utensils), allowing for fast and easy cleanup. Ordering meals is very easy given their user-friendly online platform and mobile app.

Case Challenge Part I:

  • After three years serving customers across the San Francisco Bay Area, the executives at Apprentice Chef have decided to take on an analytics project to better understand how much revenue to expect from each customer within their first year of using their services. Thus, they have hired you on a full-time contract to analyze their data, develop your top insights, and build a machine learning model to predict revenue over the first year of each customer’s life cycle. They have explained to you that for this project, they are not interested in a time series analysis and instead would like to “keep things simple” by providing you with a dataset of aggregated customer information.

Case Challenge Part II:

  • In an effort to diversify their revenue stream, Apprentice Chef, Inc. has launched Halfway There, a cross-selling promotion where subscribers receive a half bottle of wine from a local California vineyard every Wednesday (halfway through the work week). The executives at Apprentice Chef also believe this endeavor will create a competitive advantage based on its unique product offering of hard to find local wines from smaller vineyards. Halfway There has been exclusively offered to all of the customers in the dataset you received, and the executives would like to promote this service to a wider audience. They have tasked you with analyzing their data, developing your top insights, and building a machine learning model to predict which customers will subscribe to this service.

Data Preparation:

In order to appropriately prepare the data for this analysis, the data science team at Apprentice Chef has queried, sampled, and verified a dataset of approximately 2,000 customers. Each customer met at least one of the following criteria:

  • at least one purchase per month for a total of 11 of their first 12 months
  • at least one purchase per quarter and at least 15 purchases throughout their first year

The data science team assures you that their dataset engineering techniques are statistically sound and represent the true picture of Apprentice Chef’s customers. Additionally, in your domain knowledge gathering meetings with stakeholders for this project, you have attained the following information about the company.