SKU performance and distribution: A large-scale analysis of the role of product characteristics with store scanner data

This study investigates the relationship between distribution and market share across various consumer packaged goods (CPG) categories and specific stock keeping units (SKUs). The study identifies product-related characteristics that result in substantive deviations above or below market shares predicted by the distribution – market share relationship. The association of product price, brand (private label [PL] v. national brand [NB]) and pack size with above (or below) expected market share for a given distribution level is analysed. Results indicate larger pack sizes, PL and medium price levels result in market share above what would be predicted by an SKU’s distribution. This presents a source for competitive advantage in markets driven by push–pull dynamics.     

Consumers' SKU choices in an online supermarket: a latent class approach

Abstract

This study examines the effects of loyalty and e-marketing mix variables on the choices of online consumers at the stock-keeping-unit (SKU) level. Using a panel dataset from an online supermarket, we estimate a discrete choice model of a frequently purchased product; generate the refined smoothing constants of the loyalty variables for brand, size, and SKU; and adopt the latent class approach to address consumer heterogeneity. The findings suggest that SKU loyalty is a better predictor of consumer choices than brand and size loyalty. Although online consumers are not sensitive to the net prices of SKU alternatives, they are attracted to price promotions. While webpage display has little effect on SKU choices, speedy delivery has a positive impact. The latent class approach significantly improves model fitness and classification accuracy. Analysing consumer choices at the SKU level can help online supermarkets with promotion planning and inventory and distribution management to improve customer satisfaction and profitability.     

Predicting Under- and Overperforming SKUs within the Distribution–Market Share Relationship

This research presents a retail analytics application which uses machine learning (ML) to identify and predict under- and overperforming consumer packaged goods (CPGs) using retail scanner data. Essential to measuring market performance at the SKU level is the relationship between distribution and market share (the velocity curve). We validate that ML can reproduce the velocity curve, and ML is further used to predict underperforming, in-line performing, and overperforming SKUs relative to the velocity curve, based on a range of variables (SKU features) at a point in time. Our ML approach can correctly predict 83% of SKUs as under-, in-line-, or overperforming based on their characteristics. The research analyzes 9,321 SKUs of 2,565 brands across seven product categories of CPGs which were sold in 8,117 stores from 49 different retail chains of five different retail channels located in the US states of California, New York, Texas, and Wisconsin. The retail stores comprise convenience stores, drug stores, food stores, liquor stores, and mass merchandise retail stores. The data is Nielsen retail store scanner data for the calendar year 2014. The relationship between distribution and market share is a market-wide proxy for the ratio of relative sales in a category to, for example, aggregate shelf space, a key retail productivity metric. We further find indications that the distribution of SKUs across different store sizes, the stores’ category specialization, the line length of the brands, the overall performance of the parent brand, and sales consistency are the most important characteristics for the prediction of market share performance beyond the velocity curve. The methods and results presented will help CPG marketers (suppliers and retailers) understand which SKUs are under-, in-line-, or overperforming and the potential factors contributing to that performance. Optimizing assortments and portfolios is essential to decrease failure rates of individual SKUs. ML approaches can evolve to complementary support tools for such management problems.