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Quantitative Marketing and Economics

Erschienen in:

29.10.2021

Prices and promotions in U.S. retail markets

verfasst von: Günter J. Hitsch, Ali Hortaçsu, Xiliang Lin

Erschienen in: Quantitative Marketing and Economics | Ausgabe 3-4/2021

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Abstract

We provide generalizable results on the price and promotion tactics employed in the U.S. retail grocery industry. First, we document a large degree of price dispersion for UPCs and brands across stores, both nationally and at the local market level. Base price differences across stores and price promotions contribute to the overall price variance, and we show how to decompose the price variance into base price and promotion components. Second, we document that a large percentage of the variation in prices and promotion tactics across stores can be explained by retail chain and especially market/chain factors, whereas market factors explain only smaller percentage of the variation. Third, we show that the chain-level price and promotions similarity can be explained by similarity in demand. In particular, a large percentage of the variance in price elasticities and promotion effects can be explained by retail chain and especially market/retail chain factors. Further, price elasticities and promotion effects across stores of the same chain are hard to distinguish from the chain-market-level mean, and cross-price elasticities are typically imprecisely estimated. These findings suggest that retail managers may plausibly consider price discrimination across stores to be infeasible.

Vorheriger Artikel Measuring the competition effects of price-matching guarantees

Nächster Artikel Counterfactual inference for consumer choice across many product categories

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UPC is the acronym for “universal product code”.

As explained in Section 3.1), we impute prices in weeks when a product did not sell using the most recent base (non-promoted) price.

In work that has a different focus than our study, Kaplan et al. (2019) analyze the extent to which product-level price dispersion is due to persistent price-level differences across stores, based on a sample of 1,000 UPCs from the Nielsen RMS data.

https://www.chicagobooth.edu/research/kilts/datasets/nielsen

See the Retail Scanner Dataset Manual provided by the Kilts Center for Marketing for the Scantrack market-level data indicating the coverage of spending for the three main retail channels.

See Appendix A.2 for details on the product size (revenue) distribution in our data.

Table 8 also provides chain-level summary statistics on the geographic coverage and the total number of stores of different retail chains.

N_jt is the number of stores in \(\mathcal {S}_{jt}\).

The median product is the median of the revenue-weighted distribution of σ_j.

\(\mathcal {S}_{jmt}\) is the set of all stores that sell product j in market m in week t.

A small number of markets (2 DMAs and 45 3-digit ZIP codes) contain only one store. We exclude these markets from the analysis, and we also exclude markets where only one store carries product j.

This is equivalent to calculating π_j based on all D_jst observations, pooled across stores and weeks.

The weights are given by total product revenue.

All results are derived in detail in Appendix D.

var(p_m) and \(\text {var}(\bar {p}_{s}|m)\) are calculated as weighted averages using the number of observations in each market and the number of observations for each store as weights (see Appendix D).

The total contribution is the sum of the last two components in Eq. 2.

The R² values from the market indicator regressions are comparable to the across-market price variance component in the variance decomposition (1). The values are not identical, because the variance decomposition in Section 6 is performed using all weeks in 2010, whereas the product-level R² values in this section are obtained by averaging over the R² values from separate regressions for each week.

The first two principal components explain 33% of the price variance for the median product. See Appendix E for detailed explanations and more empirical examples.

The color labels are not mutually exclusive across the panels. For example, red dots in two different panels represent the projected prices for stores that belong to two different retail chains.

We use DMAs instead of 3-digit ZIP codes as markets because the large number of 3-digit ZIP codes is hard to visualize.

Table 5 shows analogous patterns based on the ratio of the 95th to 5th percentile of prices and base prices.

\(|\mathcal {S}_{sm}|\) is the number of stores in \(\mathcal {S}_{sm}\).

The distributions are weighted using total product revenue weights.

All but four retail chains have stores that are in this sub-sample. Among the covered retailers, feature ads are recorded for about 20% of stores, and in these stores feature advertising is measured consistently for most products and weeks. Among the covered stores, feature advertising is measured for 99% of all non-imputed product/week observations and for almost 90% of all products.

In particular, the entry of a Walmart Supercenter leads to a 16% drop in the revenue of the nearby retailers, but to no corresponding change in the prices offered by the incumbents.

If s and \(s^{\prime }\) are two stores in the same 3-digit ZIP code, and if t and \(t^{\prime }\) are two weeks in the same year and month, then \(\tau _{j}(s,t)=\tau _{j}(s^{\prime },t^{\prime }).\)

The distribution is weighted using total brand revenue. The weights are brand, not brand/store-specific.

β_js is a vector that includes the own and cross-price elasticities, β_jks, and the promotion parameters, γ_jks.

The own-price elasticity and promotion effect estimates are from the main model specification that includes 3-digit ZIP code/month fixed effects.

For example, DemandTec, which was founded in 1999 and later acquired by IBM, offered analytic services to its retail clients using such demand models.

Brand size is measured by total brand revenue.

We could potentially have obtained more precise demand estimates using data that covered a larger number of years than the three years used in our analysis. In practice, however, demand analyses performed for manufacturers and retailers have typically been based on at most two years of data. Hence, the demand estimates that we analyze are likely to overstate, not understate the precision of the estimates available in the industry practice.

See, for example, the discussion of pricing and promotion tactics in Consumer-Centric Category Management by ACNielsen (2005).

A new UPC version is created when one or more of the “core” UPC attributes change. The core attributes include the product module (category) code, brand code, pack size (volume), and a multi-pack variable indicating the number of product units bundled together.

If we define a product as a combination of UPC and UPC version (the variable upc_ver_uc) the number is 967,863.

In Kaplan and Menzio (2015) a brand aggregate is obtained using a “set of products that share the same features and the same size, but may have different brands and different UPCs.”

See, for example, Hastie et al. (2009) for a thorough introduction to principal components analysis.

See Table 8 for summary statistics on the number of stores per retail chain at the DMA and ZIP+ 3 level.

https://cran.r-project.org/web/packages/bayesm/index.html

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Titel: Prices and promotions in U.S. retail markets
verfasst von: Günter J. Hitsch
Ali Hortaçsu
Xiliang Lin
Publikationsdatum: 29.10.2021
Verlag: Springer US
Erschienen in: Quantitative Marketing and Economics / Ausgabe 3-4/2021
Print ISSN: 1570-7156
Elektronische ISSN: 1573-711X
DOI: https://doi.org/10.1007/s11129-021-09238-x

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