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