The terms aggregate and disaggregate data provide for a broad categorization of the nature of data. The data that Tesco was accustomed to seeing before the launch of Clubcard was aggregate in nature. They knew how much of each item they sold each week or each day. This is the data that is captured every time a bar code is scanned at the checkout; what it does not reveal is who bought the item.
Disaggregate consumer data on the other hand is consumer or household level data. It is captured for instance when the consumer’s loyalty card (e.g. Clubcard) is also scanned at the checkout. The data is continuous in nature — we have data for the same households/individuals continuously over time.
The term consumer analytics has been defined in a number of different ways. I would like to use it specifically in the context of consumer level disaggregate data. Consumer panels, loyalty panels, consumer/customer transaction, e-commerce sites, social networking sites, search engines, websites in general — all these sources yield disaggregate, continuous data on the behaviour of individuals, customers or households. It is the analysis of this type of consumer level disaggregate data that I am classifying as consumer analytics.
In the past, the data pertained mainly to consumers’ buying and consumption habits, and their tastes and preferences. Now it increasingly also includes their browsing or interaction behaviour on the net.
Interactions include the clicks, navigation paths and browsing activities on websites. The field of web analytics, or the analysis of behaviour of web users, falls largely within the scope of consumer analytics. In addition to refining the elements of the marketing mix, the focus of web analytics lies in improving the effectiveness of the website, in terms of conversion rates and other performance parameters. This subject is covered in brief in Chapter Digital Marketing.
While behaviour is the key characteristic of consumer analytics data, it often is enriched by demographic, geographic, psychographic and socialgraphic information.
The methods and techniques covered in Chapter Consumer Panels, fall within the field of consumer analytics. The focus in this chapter lies mainly on data management tools and technologies, machine learning techniques, data mining, crowd sourcing and co-creation, optimization techniques and visualization techniques. Big data and cognitive systems are also covered, and so too some of the application areas.
Consumer analytics is not as recent a phenomenon as it is popularly thought to be. Some companies at the forefront of consumer analytics were founded in the 1980s and 1990s. The biggest change over the years is not the science, but rather the technology, and the advent of big data.
Back in the 1990s, to run a resource intense consumer panel analysis over about 35,000 homes in urban India, I would leave one of my PCs on overnight, and it may still be running the next morning. Today a similar analysis would take a second or two to run on my laptop.
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Marketing simulators impart much needed combat experiences, equipping practitioners with the skills to succeed in the consumer market battleground. They combine theory with practice, linking the classroom with the consumer marketplace.