Beverage SalesKEVIN BERNHARDT

TROY BUCKNER

BRIAN GALVIN

IntroductionPurpose: Predict alcoholic beverage sales by customer.

Several key variables:◦ 4 brands

◦ Brian's BoonDoggle Tequila◦ Hooch McCoy's Very Fine Shine ◦ Bernhardt's Fire Lovin Bourbon◦ They Call Me T Buck

◦ 2011-2013◦ Customers (Bars, Restaurants, Liquor stores)◦ 5 wealthiest Florida zip codes

◦ http://www.esri.com/library/articles/wealthiest-zip-codes-2013.pdf

K Nearest Neighbor

K=2 Optimal K

Efforts to improve results◦ Score categorical data (normalize)

◦ Gather more data

◦ Increase training set size

Neural Network

Had the least ability to predict

Best result had an average error .97◦ That was 60% of the average order

◦ 2 layers and 10 nodes each was best model

Strangely produced lift chart similar to MLR

The alcohol must have burnt up the neurons

Multiple Linear Regression- Used best subset feature to improve accuracy

- r-squared value of .15

- Report Channel variable was the largest determinate of outcome with coefficient of 3.18

- Low p-values for bottles per case and zip code mean that they were fairly accurate predictors, but not overly influential in predicting the outcome

Results

Red: Neural Network

Black: Multiple Linear Regression

Blue: k-Nearest Neighbor

Statistical tool Total sum of squared errors RMS Error Average Error

k-Nearest Neighbor 68651.90 7.98 0.34

Multiple Linear Regression 79113.94 7.49 -0.13

Neural Net 81334.64 7.60 0.98

ConclusionMLR provided best predictive model.