In the age of analytics, this multimedia platform serves as a comprehensive guide to marketing management, covering the underlying concepts and their application. As can be seen from the snippets, the focus is not on the statistical theory, but more on the application of new analytics techniques and established research methods to enhance the marketing mix.
As advances in technology transform the very nature of marketing, there has never been greater need for marketers to learn marketing.
Essentially a practitioner’s guide to marketing management in the 21st century, the Marketing Analytics web learning platform blends the art and the science of marketing to reflect how the discipline has matured in the age of analytics.
Application oriented, it fuses marketing concepts with the analytical tools that practitioners use, to impart an understanding of how to interpret and apply research information and big data.
The focus is primarily on the practical application of well-established tools, techniques and processes, as the platform sifts through all elements of the marketing mix.
It is only apt that a book on Marketing Analytics should exemplify the use of digital technology. Unlike passive eBooks that replicate print versions in their original linear state, the online guide is a full-blown, multi-media platform that greatly enhances the reader’s experience.
As a website, it is dynamic, fluid, and connected with relevant and useful content, both within and beyond the platform. That it is continually updated and enhanced, keeps the guide evergreen, abreast of the latest developments in a the rapidly evolving fields of analytics and digital marketing. (In addition to numerous updates, over 100 new sections and four new chapter have been added, in the two years since the platform was set-up).
It is interactive with the facilities such as (shareable) notes/comments at any of the approximately 500 sections in the guide. The question papers/exercises allow subscribers to view answers and explanations. The site also supports business analytic platforms so that students can practise as they learn.
The online guide is made available on an annual subscription basis. Subscribers login with their email ID and password.
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.
Retail analytics essentially involves filtering the data to form one or more outlet groups (refer Exhibit). If a particular retail issue is to be evaluated, the groups are configured so that they provide a deeper understand of that issue. For example:
Customers visit a repertoire of outlets, and they spread their transactions across these outlets. Retailers therefore get only a proportion of their customers’ spend. Behavioural loyalty tells us what that proportion is. It is defined as the retailer’s share of category sales among its customers, and it may be measured in terms of volume or value.
Take for example the consumer panel data on the FMCG fabric wash category depicted in the above Exhibit. The total market size is $1 billion. Shoppers that shop at a particular retail chain spend a total of $300 million across all of the outlets where they shop. Of this amount they spend $120 million within the retail chain. Their behavioural loyalty to the chain is therefore 40% ( = 120/300).
Related to loyalty is the notion of buyer conversion. It is the proportion, among its customers, of category buyers who buy the category at the retailer’s outlets.
The retailer can theoretically grow category sales by improving chain loyalty. In this example, sales could increase to a maximum value of $300 million, if loyalty rose to 100%. The retailer could also grow category sales by attracting more shoppers to shop at its stores (increasing store traffic), or by increasing the amount they spend on the category.
This leads to the concept of retailer propensity which is the proportion of total category sales coming from the retail chain’s shoppers. In the earlier example, the retailer’s propensity for fabric wash is 30% ( = 300/1000).
It follows from the stated definitions that a chain’s share of trade in a category is the propensity of its shoppers to shop for that category multiplied by the behavioural loyalty of the shoppers.
Due to variations in the consumption habits of their shoppers, retailers can have relatively high propensity for some categories, and low for others. For instance, if the chain’s shopper’s demographic profile is skewed towards families with babies, then its propensity for categories like infant milk and diapers is likely to be high.
Note: Loyalty and propensity cannot be computed with data that is confined to the retailer’s own transactions, as is the case with loyalty panel data. Both these measures require an assessment of customers’ transaction across the entire market.... less
Retailers are grappling constantly with a vast number of brands and items, as also the continual requests to list an ever increasing parade of new products. Their space is finite — as new items get listed, some items on the shelf need to be de-listed.
One approach to optimizing assortment is on the basis of sales volume, sales value and profitability. This is covered in detail in the chapter, Sales and Distribution. It is important also for the retailer to examine the proportion of shoppers who buy (% buyers), and particularly those who exclusively buy listed items. A low selling item might have relatively high base of shoppers who exclusively buy it. If the item is de-listed, some of the shoppers may switch to other stores, in which case the retailer stands to lose their total spend in store.
The above Exhibit depicts an analysis of exclusive buyers and cumulative duplicate buyers of items for some category. The items are listed in order starting from the item with the biggest base of buyers. Observe, at the tail end, items r and v have a high proportion of exclusive buyers. Since these shoppers exhibit high loyalty to items r and v, the retailer should probably refrain from de-listing them; or do so only after careful consideration.... less