Impact of Sales Promotions on When, What, and How Much to Buy Sunil Gupta, JMR, 1988 Presenter: Gabe Gonzales MKTG 555

Research Question When products are promoted, what explains the sales ‘bump’? When to buy: Interpurchase time, the time between purchases of items in a category What to buy: Brand-size switching (i.e., Guadagni and Little, 1983) How Much to buy: Purchase quantity, here described as ‘stockpiling’ These three elements had not been observed/modeled together Papers modeling promotions’ effect on 2 of 3: Brand choice & Interpurchase time together (e.g., Little and Guadagni, 1986) Interpurchase time & Quantity together (e.g., Neslin, Henderson, and Quelch, 1985) Brand choice & Quantity together (e.g., Krishnamurthi and Raj, 1988)

Modeling Approach Brand Choice and Purchase Quantity are conditional on purchase decision So time is modeled separately from the other two Brand Choice and Quantity are modeled separately Brand Choice probability is a function of each week’s market conditions Weak association between brand choice and purchase quantity ( U = .053) despite inherent connection (brand-size  volume) Built 3 models

Brand Choice Model Multinomial Logit (e.g., Guadagni and Little, 1983) Modeled brand-sizes of coffee, like G & L (1983)

Interpurchase Time Model Modal interpurchase time > 0; Poisson distribution is inappropriate People are not most likely to purchase again immediately as would be suggested by an exponential distribution Other papers used Erlang-2 distribution as its mode is > 0 Use of this distribution was empirically supported by the scanner dataset

Purchase Quantity Model Linear Regression of exogenous variables on Quantity? Quantity is not continuous—so this is inappropriate Instead, modeled underlying latent variable V for the regression [ V = B’Z + ε ] Q is discrete purchase quantity—equations 7 & 8 are a cumulative logit model

Scanner Panel Data Similar to Guadagni and Little (1983), used coffee purchases from one market (Pittsfield, MA) to test their models Used random sample of 100 households Dataset included weeks 30-137 of panel Weeks 30-59: Precalibration (for brand loyalty, inventory, etc) Weeks 60-100: Calibration period Weeks 101-125: Validation period 1526 purchases 4211 opportunities

Variables Brand-specific constant (0 or 1) Regular brand price (¢/oz)—inferred from shelf price & promotions Promotional price cut (¢/oz) Feature-or-display (weighted average between 0 and 1) Feature-and-display (weighted average between 0 and 1) Brand loyalty (Exponentially weighted average of past purchases, 0-1) Size loyalty (Exponentially weighted average of past purchases, 0-1) Weekly inventory (oz) estimated: Inventory = Inventory (w – 2) – Estimated weekly consumption + quantity purchased (w – 1)

Results (Brand Choice) Hypothesized Signs: Brand Loyalty: + Size Loyalty: + Feature-and-Display: + Feature-or-Display: + Price Cut: + Regular Price: - Large effects of brand switching!

Results (Interpurchase Time) Hypothesized Signs: Average Purch time: + Feature-and-Display: - Feature-or-Display: - Inventory: + Price Cut: - Regular Price: + Interpurchase time is relatively stable

Results (Purchase Quantity) Hypothesized Signs: Average Purch Quantity: + Feature-and-Display: + Price Cut: + Regular Price: - Family Size: + Inventory: - Feature-or-Display: + Interpurchase time: + Significant, but relatively small, stockpiling effects.

Model Validation Compared proposed models to 3 naïve models: Interpurchase time, Quantity, and Brand Choice = averages in calibration period Same as Naïve model 1, but brand shares updated after each purchase simulation Same as Naïve Models 1 & 2, but using the logit model of brand choice Demonstrates additional value of time & quantity models Proposed model has lowest inequality coefficient U, meaning it produces the best predictions

Elasticity Analysis A primary goal of the paper was to disentangle and compare the relative effect of promotions on What, When, and How Much consumers purchased. Calculated elasticities for Feature-and-Display, Feature-or-Display, and Promotional Price Cut Total Elasticity: Almost all of promotional sales increase from price cuts (98%) is from brand switching (price cut has no effect on purchase time) What When How Much

Conclusions/Implications Promotion primarily affects brand switching Effects weaker for purchase acceleration/stockpiling, which are not true incremental sales Modeling approach can be used to compare effectiveness of different promotions Determine which promotions add the most new customers vs. ‘accelerated’ loyal customers who would buy eventually