Data Analysis: Product Sell-Through & Speed of Sales

Our product (alcohol-free beer!) is currently available in a number of select Whole Foods & regional supermarket locations in the Eastern United States. We see increased sales & great customer adoption when we show up for in-person samplings and sales events. In order to identify opportunities for greater market saturation, faster sell-through potential, and more in-person events, this project will analyze data sets and create models to show our business advisors the results of our sales efforts. Historical store data, including location, initial wholesale purchasing, dates of in-person sampling, and speed of sell-through to the customer, will be provided for the analysis and modeling. Historical buyer and buying data should help us identify spaces for growth, areas of declining sales and stagnation in our own business to inform future business development. This information is fundamental to the allocation of our resources and the efforts to choose to pursue. From this analysis, we hope to not only improve our processes and understanding but also gain insights that will enhance our strategy. These insights will be directly related to business outcomes. Using historical buyer data, help us evaluate our current audience. Using the curation of this data, we would like a group of students to assist in a report. About Us Atmosphere Brewery is a quirky and chic upmarket N/A beer brand with a German made super-premium alcohol-free beer that aims to inspire joy, celebration, good deeds and healthy behavior through engaging social content and IRL experiences. We’re looking to make a big impression locally, nationally and eventually internationally. The team is enthusiastic about the emerging non-alcoholic adult beverage category without being discriminatory to alcohol or other legal substances.  We have a great tasting 0.0 ABV Pilsner from a 400-year-old German brewing partner which is getting great reviews and is sold in key US markets with international interest. We’d like students to help analyze our datasets to draw helpful insights. Students will connect directly with an internal specialist (COO) for mentorship and supervision throughout the project.