How Point-of-Sale Marketing Mix Impacts National Brand Purchase Shares Across Stores

Purchase shares of major national brands in consumer packaged goods industries vary substantially across stores, both between geographic markets and and across stores within markets. Furthermore, store-specific POS marketing mix factors (such as price, assortment share, feature, and display, and promotion intensity) show similar variation across stores. This suggests that POS marketing mix factors may be responsible for the observed dispersion in purchase shares. Shown below are examples from toilet paper brand: Charmin. We can clearly see that there are significant dispersion of purchase shares across stores. In addition, dispersion in purchase shares across stores are highly correlated with dispersion in assortment shares across stores.

From the large scale analyses, we find that on average, approximately 56% of variations in national brand purchase shares can be attributed to this five POS marketing mixes. Moreover, we also find that asymmetric POS marketing mix effectiveness (especially in price), which favors brands closer to their city-of-origins, is another underlying mechanism behind observed differences in purchase shares across geographic markets.

For more details, please refer to our POS paper (together with Prof. Raphael Thomadsen), which was accepted in Management Science (October, 2014). The link is shown below.

https://www.academia.edu/4509938/When_Push_Comes_to_Shelf_How_Point-of-Sale_Marketing_Mix_Impacts_National_Brand_Purchase_Shares

Estimating linear regression models with a VERY LARGE number of fixed effects in R - Part 1/2

It is very common for researchers (or data scientists) not to have an access to some the (independent) variables, which can potentially influence your dependent variable (e.g. market share, sales, choice of brand, etc). These omitted variables can potentially cause biased estimates of marketing mix effects.  

Fixed effect regressions (i.e. dummy variable regression) is widely used to control potential omitted variable biases. In a typical panel data or multi-level data settings in Marketing and Economics, you can easily end up with a VERY LARGE number of fixed effects to obtain consistent estimates. As an example, if you are trying to estimate price elasticities for each of 1,000 stores in 30 markets, which belong to the same/different retail chain, you might need to put in store-fixed effects (1,000) to control potential endogeneity. In addition, you might be worried about the potential correlations between unobserved market-level advertising and prices, thus you may need to put in market-time fixed effects. Assuming weekly data for 2 years (i.e. 104 weeks), this will result in 30 x 104 = 3,120 fixed effects. If we consider both store fixed effects and market-time fixed effects, a linear regression of unit sales of brands may need to control 4,120 fixed effects with 100,000 observations (or sometimes with millions of observations.) Most of common statistical software are not very suitable for this type of estimation. As a example, STATA can only handle up to 11,000 variables (or dummies). Even with 1000 dummies, the estimation takes really long to finish.

An extension of "within" transformation with more than one factor, which is called as "Methods of Alternating Projection" allows us to estimate linear regression models with a VERY LARGE number of dummies. lfe-package in R (written by Simon Gaure; based on alternating projection methods) is a handy tool for the estimation of linear regression models with a VERY LARGE number of dummies. The package has been tested on datasets with 15 covariates and approximately 2,300,000 and 270,000 group levels. The estimation of effects (felm) took ~ 50 minutes on a 8 CPU machine. Recovery of fixed effects (getfe) took 5 minutes.

Shown below are useful links for the lfe package (in R). I will show the test results of this package with fake data in the next posting.

1. CRAN site to download R package (lfe)

2. lfe manual (R package)

3. R documentation (felm)

4. R documentation (getfe)