The Indian consumer market has two features that make it attractive and at the same time challenging for marketeers—it is large and it is complex. Expenditure patterns are dictated by income, education, occupation, family size, family type, and regional and community influences.
Even if we look at just the urban market, it includes more than 300 million people in more than 70 million households. So how many consumer segments would be needed to capture the multiple identities that Indians live with so comfortably? There is of course no definite answer, but on one point all agree—the finer the cuts, the greater the understanding of what will sell.
The SEC (A to E) are five segments that marketeers are familiar with, but there is a very high degree of variation within groups in terms of income and expenditure; these segments are homogenous only up to a certain point.
The Indicus Indian Consumer Spectrum goes beyond these five by creating a finer segmentation of 33 groups, as we have detailed in this series. We started the series by profiling the bottom of the pyramid, segment G1, and moved across the spectrum finishing with the most affluent segment, A4. Going past the broad generalizations helps to understand the complexity in the Indian urban consumer landscape.
How were these groups created? Contrary to popular perception, the Indicus Indian Consumer Spectrum is not a product of pure market research. It is of course based on household surveys, but the connect with market research ends there. To begin with, a large database was put together from various credible primary sources, with detailed insight into household characteristics, earnings and occupations, family structure, and expenditure and savings habits. Processing the database included the validation of characteristics to ensure they match the proportion at the district level with parameters from other databases, viz. the Census, surveys conducted by the Indian Institute of Population Studies, National Account Statistics, etc.
The problems of under- and over-reporting, missing values, inappropriate weighting, etc., were all dealt with through econometric techniques to create a final dataset of 1.6 million households, which ensures a geographical and demographic representativeness of India’s population. Finally, neural networking techniques were applied to create homogenous clusters of consumers across income, occupation and life stage.
We end up with groups that are not only distinctly different from each other, they also have much less variation within them compared with the broad SEC categorization.
So how does finer segmentation help? To take just one example, if we look at the broad segment SEC A, a group that is very easy to market to because it has more spending power and greater exposure to media, we see it comprises chief wage earners across all age groups.
All these households clearly would not have similar needs or wants—for selling a new hi-tech gadget for instance, such a broad SEC segment would give a false impression of the true market size. It would be better to focus on the younger households—for instance, focus on segments A1 and B1 instead, where more than 50% of the chief wage earners fall in the age group of 25-34 years. This set of households would be more receptive to new technology than the SEC A in general.
What about a product for children? Here again, more than one-third of SEC A households have no minors; it will be segments A2 and B2 that would be of interest now—households with young children. There are also regional dimensions that can be important while targeting certain products or services. For instance, estimates of finer segments will give a good idea of whether a particular target market is bigger in Delhi or in Mumbai. Estimates at the city or district level can therefore be extremely important.
There are interesting insights when it comes to media habits—the Internet is quite clearly the pr