1 Berkeley January 2004 Teck H. Ho 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

2 Berkeley January 2004 Teck H. Ho Purchase History of Panelist

3 Berkeley January 2004 Teck H. Ho The consumer choice setting u Household i chooses a product (or stock-keeping-unit (SKU)) from a choice menu on a series of purchase incidences indexed by t u Before each purchase incidence, each SKU j is characterized by a set of marketing-mix activities: Hprice (P), display (D), and feature advertisement (AD) u The modeler also observes: H Household i’s SKU choices on purchase incidences 1, 2, …, t-1

4 Berkeley January 2004 Teck H. Ho Research question u To develop a good descriptive model of SKU choice to predict the probability of household i choosing SKU j on purchase incidence t

5 Berkeley January 2004 Teck H. Ho 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)

6 Berkeley January 2004 Teck H. Ho Complex menus & increasing number of items

7 Berkeley January 2004 Teck H. Ho Criteria of a “good” model – Estimation u Does not aggregate choice (i.e., at the SKU level) è Heterogeneity across products (biased estimates); avoid “average” variables; inventory planning u Does not throw away observations è Choice-based sampling (biased estimates) (Ben-Akiva and Lerman, 1985)

8 Berkeley January 2004 Teck H. Ho Criteria “violations” u Model specification èModel complexity HMany models have complexity increases with number of items èPlausible principles of behavior HFew attempts to incorporate findings from psychological research in consumer behavior u Model estimation èAggregate choice HViolation examples: Brand-size combination; “other” product èThrowing away observations HViolation examples: Top n SKUs; ignore SKUs that have few purchases

9 Berkeley January 2004 Teck H. Ho Notations u Household iPanelist u SKU jUPC u Purchase Occasion tJuly 17, 97 u Attribute kBrand u Attribute level lCOKE Examples For estimation, every product category is assumed to have three attributes (Brand, Size, Flavor)

10 Berkeley January 2004 Teck H. Ho Utility specification u Utility = intrinsic value + value associated with marketing-mix activities u Error structure captures serial correlations in attribute-level and product-specific utilities u Uses latent class to capture heterogeneity u No product or attribute-level specific intercept terms!

11 Berkeley January 2004 Teck H. Ho Intrinsic Value u Intrinsic value consists of both product-specific and attribute-level experiences u Example: SKU 14 = {PEPSI, 9.0, DIET}, Panelist = Grace

12 Berkeley January 2004 Teck H. Ho Marketing-mix response u Control for price, display, and feature advertisement on local newspapers u Display and feature advertisement are dummy variables u Actual price paid (incorporating coupons and etc.)

13 Berkeley January 2004 Teck H. Ho An overview of the model Utility Intrinsic Value 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 Brand Incremental Reinforcement Consumption Shopping Previous Cumulative Reinforcement Size Flavor Attribute- level Experience Product- specific Experience Incremental Reinforcement Previous Cumulative Reinforcement Consumption Shopping

14 Berkeley January 2004 Teck H. Ho Cumulative attribute-level reinforcement, A ikl (t) u Cumulative attribute-level reinforcement = Decayed previous reinforcement + immediate incremental reinforcement u Incremental reinforcement consists of consumption as well as “shopping” experience for chosen level and “shopping” experience only for unchosen levels

15 Berkeley January 2004 Teck H. Ho An overview of the model Utility Intrinsic Value Marketing-mix Response Brand Incremental Reinforcement Consumption Shopping Previous Cumulative Reinforcement Size Flavor Attribute- level Experience Product- specific Experience Incremental Reinforcement Previous Cumulative Reinforcement Consumption Shopping

16 Berkeley January 2004 Teck H. Ho Consumption (C ikl (t-1)) & shopping (S ikl (t)) experiences u Consumption & shopping experiences depend on consumer’s familiarity with the level u “Shopping” experience because people care about foregone utilities from actions/products that they could have chosen (Camerer and Ho, 1999) u C k1 < 0 captures “law of diminishing marginal utility” u S k1 > 0 captures “memory-based decision making” (Alba, Hucthinson, and Lynch, 1991)

17 Berkeley January 2004 Teck H. Ho Variety-seeking behavior u Modeled as negative reinforcement ( e.g., Lattin, 1987 ) u Under our model setup, it is driven by C k1 0 (“grass is greener” effect) ( Kahn, 1998 )

18 Berkeley January 2004 Teck H. Ho Product and attribute-level familiarities u Product and attribute-level familiarity is concave in number of times the product and attribute levels are consumed (T ikl (t) & T ij (t)) u Also tried linear and step functions u Log function fits best and is also the most appealing conceptually

19 Berkeley January 2004 Teck H. Ho Main ideas u Utility consists of intrinsic value and value associated with marketing-mix response u Intrinsic value has two components: product-specific and attribute level experiences u Incremental reinforcement has both consumption and shopping experience, which depends on product and attribute-level familiarity u Each unchosen attribute level receives a different “shopping” reinforcement u The model has parameters for a K- attribute product category èExample: K=3 (brand, size, flavor), the model has 29 parameters

20 Berkeley January 2004 Teck H. Ho Data Set u Panel-level market basket data set u 124,000 product purchases across 15 product categories (10 food + 5 non-food) u Purchases made by 513 households at 5 stores located within the same neighborhood over a 2-year period u + Data from Fader and Hardie (1996)

21 Berkeley January 2004 Teck H. Ho Data Set

22 Berkeley January 2004 Teck H. Ho Estimation u Maximize the likelihood of observing the data u The first 13 weeks of data for initialization; the next 65 weeks for calibration and the last 26 weeks for model validation u Benchmark against Fader and Hardie (1996)’s model

23 Berkeley January 2004 Teck H. Ho FH Model u Has attribute-level specific terms u Does not capture familiarity-based consumption as well as shopping experience

24 Berkeley January 2004 Teck H. Ho Key Results (Small Categories) u Number of parameters èOur model = 59 (two-segment models); FH model = èComparison was made on small product categories (less than 200 parameters) u Calibration èThe hit probability is 7% better than F&H model èBetter in every single product category u Validation èThe hit probability is 8% better than F&H model èBetter in every single product category

25 Berkeley January 2004 Teck H. Ho Key Results (Small Categories) - Calibration

26 Berkeley January 2004 Teck H. Ho Key Results (Small Categories) - Validation

27 Berkeley January 2004 Teck H. Ho Key Results (Large Categories)

28 Berkeley January 2004 Teck H. Ho Tests of Key Behavioral Premises

29 Berkeley January 2004 Teck H. Ho Conclusion u Our model èSimple but fits and predicts better è Neither aggregates choice nor discards data èShows both product and attribute-level experiences matter è Shows consumers accumulate both shopping and consumption experiences u IRI has implemented this model at a leading consumer packaged goods firm