Exploring the Iowa Liquor Sales data set.
Exploratory data analysis
Taking a closer look at the data
Who drinks the most?
Who are the top 10 vendors in Iowa?
|ID||Sales (Millions of $)||Vendor|
|370||103.29||Pernod Ricard USA/Austin Nichols|
|65||100.22||Jim Beam Brands|
|115||85.89||“Constellation Wine Company, Inc.”|
|421||79.48||“Sazerac Co., Inc.”|
|35||67.17||“Bacardi U.S.A., Inc.”|
|55||46.03||Sazerac North America|
What are Jim Beam’s top selling liquors?
|Item ID||Sales (Millions of $)||Vendor ID||Item||Volume (ml)|
|15248||3.97||65||Windsor Canadian Pet||1750|
|24458||3.26||65||Kessler Blend Whiskey||1750|
|10628||2.63||65||Canadian Club Whisky||1750|
What is the price-response for Jim Beam 1750ml?
Taking a closer look at demand (our response)
How has demand for Jim Beam 1750ml varied over time?
How has change in demand varied over time?
Comparing demand, % change, and log diff distributions.
How has demand varied month-to-month?
How has change in demand varied month-to-month?
Taking a closer look at price (our predictor)
How has price for Jim Beam 1750ml varied over time?
How has change in price varied over time?
Comparing price, % change, and log diff distributions.
How has price varied month-to-month?
How has change in price varied month-to-month?
Developing a demand model
We take month (one hot encoded) and log price as our features or predictors. We take log demand as our response. Then we fit a regression model using linear least squares with L2 regularization (i.e. ridge regression).
RMSE Mean = 779 Bottles
Inferring seasonality and price elasticity
Price elasticity coefficient
Price Elasticity of Demand (PED) = -0.02
PED = % Change in Quantity Demanded / % Change in Price
% Change in Quantity Demanded = PED x % Change in Price
So a +1% change in price will only result in a (-0.02) x 1% = -0.02% change in demand (very inelastic).
Therefore, it’s no wonder Jim Beam has continued to raise prices and stopped offering promotions all together in 2016.
Optimizing promotions to maximize profit