Retail Analytics

Exhibit 21.1   Forming outlet groups.

Continuous outlet level transaction and shopper data is invaluable for retailers and marketers. It yields aggregate metrics such as market share, sales and distribution, which are fundamental to formulating marketing strategies and sales plans. It is also a mine of disaggregate data, that can help diagnose and address business issues.

Whereas the chapter on retail tracking focussed at the aggregate level (i.e. market breakdown — chain, channel, country, region), in this chapter, we analyse disaggregate or outlet level data which tends to be more diagnostic in nature.


The term “retail analytics” is commonly in use in recent years, often in ways that tend to overlap with related areas such as “consumer analytics” and “retail tracking”. Since each of these topics is covered separately in this text, let me clarify by outlining the scope of this chapter and how it differs from the other two.

In the current context, Retail analytics is defined as the analysis of continuous outlet level transaction and shopper data to address business issues.

Retail Tracking Databases for Retail Analytics

Retail tracking, which sources data from point-of-sale (POS) scan terminals and retail audits, generate continuous transaction databases that feed into retail analytics. Whereas the retail tracking service aggregates the data to capture market size, share and distribution over time, retail analytics diagnoses the disaggregate or outlet level data.

Fundamental to retail analytics is the filtering of retail outlets to form outlet groups. For instance a retailer or a supplier might be interested in examining outlets that stocked a particular brand compared with outlets that did not stock that brand. Or outlets that offered a promotion versus outlets that did not do so.

This chapter describes a wide array of outlet group analysis addressing aspects such as brand handlers’ analysis, brand overlap, assortment, shelf space, pricing, promotion and rate of sales.

Customer Transaction Databases for Retail Analytics

Customer transaction databases (e.g. sales transaction data, consumer panel data, loyalty panel data) also feed into retail analytics. These databases have the customer dimension in addition to outlet dimension, thus adding an additional layer of diagnostic capabilities. This data can be used to compute chain or outlet penetration, spend per customer, chain loyalty, cannibalization among outlets and so on, using similar concepts, tools and techniques that are applicable for the analysis of consumer panels.

The analysis outlined in this chapter include customer profile, loyalty and propensity, assortment analysis, overlap, outlet group, outlet repertoire, gain–loss, trial and repeat visit, penetration and repeat rate, and sales forecasting. A case example pertaining to the opening of a new petrol station illustrates a number of these analyses.


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What they SHOULD TEACH at Business Schools

What they SHOULD TEACH at Business Schools

Marketing has changed. More so in practical terms, and marketing education is lagging.

The fundamental change lies in the application of analytics and research. Every aspect of the marketing mix can be sensed, tracked and measured.

That does not mean that marketers need to become expert statisticians. We don't need to learn to develop marketing mix models or create perceptual maps. But we should be able to understand and interpret them.

MarketingMind helps. But the real challenge lies in developing expertise in the interpretation and the application of market intelligence.

The Destiny market simulator was developed in response to this challenge. Traversing business years within days, it imparts a concentrated dose of analytics-based strategic marketing experiences.

Dare to Play

Dare to Play

Like fighter pilots, marketers too can be trained with combat simulators that authentically reflect market realities.

But be careful. There are plenty of toys that masquerade as simulators.

Destiny is unique. It is an authentic FMCG (CPG) market simulator that accurately imitates the way consumers shop, and replicates the reports and information that marketers use at leading consumer marketing firms.

While in a classroom setting you are pitted against others, as an independent learner, you get to play against the computer. Either way you learn to implement effective marketing strategies, develop an understanding of what drives store choice and brand choice, and become proficient in the use of market knowledge and financial data for day-to-day business decisions.