During my summer internship at Anheuser-Busch, I worked with the Category Management and Solutions department to develop a predictive model for SKU unit movement. I utilized machine learning techniques to process 100+ variables including total facings, price, capacity, as well as demographics variables per store ZIP code.

1. A n h e u s e r - B u s c h S o l u t i o n s
G u i l h e r m e S o a r e s
Shelfy: Predictive Model

2. Guilherme Soares
2
2018 Solutions Intern
University:
New York University – Tandon School of Engineering
Rollins College (2014 – 2018)
Concentration: Computer Science in Engineering
Hometown: Rio de Janeiro
Interesting Fact: Instagram food blog account: @DelightSniffers
Managers: Marko Trninic & Arjun Yadav
Experience
Acquired at
Anheuser-Busch:
• Qualitative market research: in-field experience
• Quantitative market research: data analytics
• Understanding of Category Management and shelf-space
assortment
• SQL: research and pull data from databases
• Python: merging data frames, plot visualization
• Machine Learning: supervised learning, unsupervised learning
• Clustering, regression, classification

3. 1 Situation
Agenda
Answer4
Complication2
Question3
Results5
Appendix (Q&A)6

4. Current assortment planning
needs improvement
Retailer SKU Purchasing
Behavior:
• Subjective
• Based on past sales
• Based on how fast each units turn
• Choice Paradox
4

5. Unoptimized shelves do not maximize
potential sales
Unsorted shelves
• Can lead to loss of sales through out-of-stock
scenarios
• Can prevent consumers from finding the
brand they want to purchase
• Don’t optimize sales due to limited turn
5

6. 2 Complication
Agenda
Situation1
Answer4
Question3
Results5
Appendix (Q&A)6

7. Retailers are being “sold”
assortment models that favor
competition’s products
Competition has been selling a
predictive model approach to use
historical sales and trends to “predict”
what unit sales will be and allocating
shelf space to SKUs based on that
modeling.
7
How they
brand it...

8. But their models are far
from perfect…
Linear models can only show:
• Absolute growth
• Absolute decline
Does not account for:
• Seasonality
• Velocity
Result:
• Over-sell their growing brands
• Trap our brands in under-selling
cycle
8
What it
actually is.

9. Retailers expect us to lead
technology improvements
• Relationship with buyers suffered
• Made them question us
thoroughly
• AB is not living up to the
expectation of being the lead in
predictive analytics and
technology
9

10. 3 Question
Agenda
Situation1
Complication2
Answer4
Results5
Appendix (Q&A)6

11. Question
How do we enable our category teams to influence space
decisions and grow our shelf-space?
11

12. Agenda
Situation1
Complication2
Question3
Answer4
Results5
Appendix (Q&A)6

13. Three steps we must take:
• Develop a business model for category teams to predict the best
scenario for each store in terms of assortment
• Reestablish our credibility as a leading technological and predictive
analytics company
• Regain the trust of our buyers by offering a solution that benefits the
category as a whole instead of something that benefits AB alone
13

14. We developed a model that
speaks to retailer’s needs
Can learn from multiple variables:
14
• Price
• Facings
• Capacity
• Previous Unit
movement
• Consumer average
expenditure
• Pop. age, race, ethnicity
• Income distribution
• Business estimates
• Media consumption
VS. Total Volume
Utilizes one variable:
Controllable Uncontrollable

15. We developed a model that
benefits the whole category
15
VS.
Accuracy = 0.08

16. One model per segment with high degree of accuracy
Premium
Accuracy: 0.69
Mean Error: 0.93
16
Store Name IRI Sub Segment Name
Actual unit
movement
Predicted unit
movement
Speedway Hess
0002845
Premium Plus Michelob Ultra 6/12B 0.74 0.75
Speedway #2851 Premium Light Bud Light 12/12B 3.42 3.95
Speedway Hess
0002845
Premium Light Bud Light 6/12B 0.54 0.69
Speedway #2851 Premium Regular Budweiser 12/12C 3.69 3.42
Speedway Hess
0002845
Premium Regular Budweiser 6/12B 0.57 0.86
Sampleoutput

17. 1 Situation
Agenda
Results5
Complication2
Question3
Answer4
Appendix (Q&A)6

18. Two important retailers are already
considering to adopt the model
18
Before:
• 50 stores utilizing Miller Coors linear
model in South Carolina
After:
• 15 stores utilizing Miller Coors linear
model
• 25 stores utilizing Anheuser-Busch
SGB model in South Carolina
• 10 control stores
Opportunity:
• Walmart account contacted us
• Asked to run the model for 336
stores in Florida

19. The next step:
optimization to
maximize sales
• Forecast multiple scenarios
• Manipulate price, facings and capacity
• Offer retailers optimal recommendations based on
our statistical model
19

20. 1 Situation
Agenda
Appendix (Q&A)6
Complication2
Question3
Answer4
Results5

21. One model per segment with high degree of accuracy
Value
Score (r^2): 0.57
Mean Error: 1.15
21
Wholesaler Customer Party
ID
IRI Sub Segment Name Actual unit movement Predicted unit movement
82469644 Value Light Natural Light 18/12C 0.31 0.38
232599100 Value Regular Bud Ice 18/12C 0.72 1.14
232599100 Value Regular Bud Ice 3/25C 3.73 4.63
232599100 Value Regular Busch 12/12C 0.41 1.78
232599100 Value Regular Busch 18/12C 0.38 1.30

22. One model per segment with high degree of accuracy
Craft
Score (r^2): 0.58
Mean Error: 0.67
22
Wholesaler Customer Party
ID
IRI Sub Segment Name Actual unit movement Predicted unit movement
232599100 Craft
Angry Orchard Crisp Apple
6/12B
0.73 1.11
232599100 Craft
Blue Moon Belgian White Ale
6/12B
0.88 1.04
232599100 Craft
New Belgium Fat Tire Amber Ale
6/12B
1.39 1.24
232599100 Craft Sweetwater 420 Pale 6/12B 0.79 1.12
232599100 Craft
Angry Orchard Crisp Apple
6/12B
0.92 1.01

23. One model per segment with high degree of accuracy
Import
Score (r^2): 0.59
Mean Error: 1.12
23
Wholesaler Customer Party
ID
IRI Sub Segment Name Actual unit movement Predicted unit movement
232599100 Import Corona Extra 12/12B 1.09 1.89
232599100 Import Corona Extra 6/12B 0.58 0.45
232599100 Import Coronita Extra 24/7B 0.54 0.22
232599100 Import Heineken 6/12B 0.33 0.41
232599100 Import Modelo 12/12C 0.84 1.02

24. One model per segment with high degree of accuracy
FMB
Score (r^2): 0.35
Mean Error: 0.59
24
Wholesaler Customer Party
ID
IRI Sub Segment Name Actual unit movement Predicted unit movement
232599100 FMB Redd's Apple Ale 6/12B 0.64 1.21
232599230 FMB Redd's Apple Ale 6/12B 1.28 1.59
232599230 FMB Redd's Blueberry Ale 6/12B 0.50 1.14
232599349 FMB Redd's Apple Ale 6/12B 1.10 1.20
232599349 FMB Redd's Blueberry Ale 6/12B 0.44 0.89

25. 25
Train and Fit
Train:
• Feed the model 2016 and 2017 data such as
average price, volume, total facings, etc.
Fit:
• Utilize 2018 data to see how accurately the
model predicted SKU unit movement

26. 26
Stochastic Gradient Boosting Regressor (SGB)
Decision-Tree Based Model
1. Constructing a decision tree 2. The wisdom of a random and diverse
crowd

27. 27
Stochastic Gradient Boosting Regressor (SGB)
Decision-Tree Based Model
3. Utilize the error of the previous
prediction
4. Each tree is trained on a random subset of
rows and columns of the training data

28. 28
• Chain stores
• Off-premise
• Walmart and Speedway
• Small and large format
• Looking 2 years back
• SKU information must be available for previous 2 years
Current Scope:

29. Using previous sales data to illustrate model accuracy.
A linear prediction would have Speedway under forecasted by $6.7MM on the
busiest week of the year…
Linear Regression RF Regression
Prediction Accuracy .08 .84
Actual Sales Week 131 $ 19,400,473.00 $ 19,400,473.00
Predicted Sales Week 131 $ 12,633,402.92 $ 19,243,603.40
Prediction on Week 131
$ 6.7MM

30. Model Type: Single Variable Linear Regression
Under spaced by
up to
100% for 13
weeks
Over space spaced
by up to
50% for 13 weeks
Linear Prediction
Speedway would
be allocating space
on
- Predicts the absolute mean
of a data set
- Assortment
recommendations would be
over/under spaced all but 2
weeks of the year
- Does not factor in
seasonality, or any outside
variables