A Parsimonious Model of Stock-keeping-Unit (SKU) Choice

1. A Parsimonious Model of Stock-keeping-Unit (SKU) Choice Teck H. Ho Haas School of Business UC, Berkeley Joint work with Juin-Kuan Chong, NUS The Goal Search for best-fitting model in SKU choice Teck H. Ho

2. Purchase History of Panelist 14110874 Teck H. Ho

3. Household i chooses a product (or stock-keeping-unit (SKU)) from a choice menu on a series of purchase incidences indexed by t Before each purchase incidence, each SKU j is characterized by a set of marketing-mix activities: price (P), display (D), and feature advertisement (AD) The modeler also observes: Household i’s SKU choices on purchase incidences 1, 2, …, t-1 The consumer choice setting Teck H. Ho

4. To develop a good descriptive model of SKU choice to predict the probability of household i choosing SKU j on purchase incidence t Research question Teck H. Ho

5. Criteria of a “good” model - Specification • Simple (i.e, small number of parameters) • Model complexity does not increase with number of items/feature levels in the choice menu • Increasing number of items • Satisfies plausible principles of human behavior • Incorporate psychological findings into model • Fits and predicts choice behaviors well (e.g., Guadagni and Little, 1983; Fader and Hardie, 1996) Teck H. Ho

6. Complex menus & increasing number of items Teck H. Ho

7. Criteria of a “good” model – Estimation • Does not aggregate choice (i.e., at the SKU level) • Heterogeneity across products (biased estimates); avoid “average” variables; inventory planning • Does not throw away observations • Choice-based sampling (biased estimates) (Ben-Akiva and Lerman, 1985) Teck H. Ho

8. Criteria “violations” • Model specification • Model complexity • Many models have complexity increases with number of items • Plausible principles of behavior • Few attempts to incorporate findings from psychological research in consumer behavior • Model estimation • Aggregate choice • Violation examples: Brand-size combination; “other” product • Throwing away observations • Violation examples: Top n SKUs; ignore SKUs that have few purchases Teck H. Ho

9. For estimation, every product category is assumed to have three attributes (Brand, Size, Flavor) Notations Examples • Household i Panelist 14110874 • SKU j UPC 11200000847 • Purchase Occasion t July 17, 97 • Attribute k Brand • Attribute level l COKE Teck H. Ho

10. Utility specification • Utility = intrinsic value + value associated with marketing-mix activities • Error structure captures serial correlations in attribute-level and product-specific utilities • Uses latent class to capture heterogeneity • No product or attribute-level specific intercept terms! Teck H. Ho

11. Intrinsic Value • Intrinsic value consists of both product-specific and attribute-level experiences • Example: SKU 14 = {PEPSI, 9.0, DIET}, Panelist = Grace Teck H. Ho

12. Marketing-mix response • Control for price, display, and feature advertisement on local newspapers • Display and feature advertisement are dummy variables • Actual price paid (incorporating coupons and etc.) Teck H. Ho

13. Previous Cumulative Reinforcement Product-specific Experience Consumption Incremental Reinforcement Shopping Previous Cumulative Reinforcement Size Attribute-level Experience Consumption Brand Incremental Reinforcement Flavor Shopping An overview of the model Intrinsic Value Utility Marketing-mix Response • Intrinsic value consists of both product-specific and attribute-level experiences • Consumption and shopping experiences depend on product and attribute-level familiarity Teck H. Ho

14. Cumulative attribute-level reinforcement, Aikl(t) • Cumulative attribute-level reinforcement = Decayed previous reinforcement + immediate incremental reinforcement • Incremental reinforcement consists of consumption as well as “shopping” experience for chosen level and “shopping” experience only for unchosen levels Teck H. Ho

15. Previous Cumulative Reinforcement Product-specific Experience Consumption Incremental Reinforcement Shopping Previous Cumulative Reinforcement Size Attribute-level Experience Consumption Brand Incremental Reinforcement Flavor Shopping An overview of the model Intrinsic Value Utility Marketing-mix Response Teck H. Ho

16. Consumption (Cikl(t-1)) & shopping (Sikl(t)) experiences • Consumption & shopping experiences depend on consumer’s familiarity with the level • “Shopping” experience because people care about foregone utilities from actions/products that they could have chosen(Camerer and Ho, 1999) • Ck1 < 0 captures “law of diminishing marginal utility” • Sk1 > 0 captures “memory-based decision making”(Alba, Hucthinson, and Lynch, 1991) Teck H. Ho

17. Variety-seeking behavior • Modeled as negative reinforcement (e.g., Lattin, 1987) • Under our model setup, it is driven by Ck1 < 0 (satiation) (Erdem, 1992; McAlister, 1982) or Sk1 > 0 (“grass is greener” effect) (Kahn, 1998) Teck H. Ho

18. Product and attribute-level familiarities • Product and attribute-level familiarity is concave in number of times the product and attribute levels are consumed (Tikl(t) & Tij(t)) • Also tried linear and step functions • Log function fits best and is also the most appealing conceptually Teck H. Ho

19. Main ideas • Utility consists of intrinsic value and value associated with marketing-mix response • Intrinsic value has two components: product-specific and attribute level experiences • Incremental reinforcement has both consumption and shopping experience, which depends on product and attribute-level familiarity • Each unchosen attribute level receives a different “shopping” reinforcement • The model has parameters for a K-attribute product category • Example: K=3 (brand, size, flavor), the model has 29 parameters Teck H. Ho

20. Data Set • Panel-level market basket data set • 124,000 product purchases across 15 product categories (10 food + 5 non-food) • Purchases made by 513 households at 5 stores located within the same neighborhood over a 2-year period • + Data from Fader and Hardie (1996) Teck H. Ho

21. Data Set Teck H. Ho

22. Estimation • Maximize the likelihood of observing the data • The first 13 weeks of data for initialization; the next 65 weeks for calibration and the last 26 weeks for model validation • Benchmark against Fader and Hardie (1996)’s model Teck H. Ho

23. FH Model • Has attribute-level specific terms • Does not capture familiarity-based consumption as well as shopping experience Teck H. Ho

24. Key Results (Small Categories) • Number of parameters • Our model = 59 (two-segment models); FH model = 75-163 • Comparison was made on small product categories (less than 200 parameters) • Calibration • The hit probability is 7% better than F&H model • Better in every single product category • Validation • The hit probability is 8% better than F&H model • Better in every single product category Teck H. Ho

25. Key Results (Small Categories) - Calibration Teck H. Ho

26. Key Results (Small Categories) - Validation Teck H. Ho

27. Key Results (Large Categories) Teck H. Ho

28. Tests of Key Behavioral Premises Teck H. Ho

29. Conclusion • Our model • Simple but fits and predicts better • Neither aggregates choice nor discards data • Shows both product and attribute-level experiences matter • Shows consumers accumulate both shopping and consumption experiences • IRI has implemented this model at a leading consumer packaged goods firm Teck H. Ho