This case study examines how to incorporate machine learning and natural language processing techniques into a craft brewery’s research and marketing strategy.  The number of craft breweries has grown exponentially and there is a strong need to find and leverage a competitive advantage to be successful in the craft beer market.  With machine learning and natural language processing, craft brewing data relationships that otherwise would be difficult to find become apparent and actionable for the brewery.        

Data from Lion’s Tail Brewery in Neenah, Wisconsin was used to create machine learning models that built word clouds, predicted consumer beer ratings, and clustered beers by common characteristics.  Word clouds allow text data to be analyzed, helping craft brewers and consumers better understand the different beers from their respective perspectives.  With an average rating calculator and clustering model, the brewery can optimize recipes to create novel beers that craft beer consumers will truly enjoy.  These machine learning models allow for excellent understanding of the data because they are mathematically constructed from the craft brewery’s own data 

The results of this study confirm that machine learning is scalable from large breweries with many resources down to smaller craft breweries that can gain valuable business insight and use their own data to make sound business decisions.  Data Science techniques like machine learning can be complicated but are becoming more user-friendly and easier to understand, create, and deploy in smaller businesses.