Over the past two decades, two unique forces operating in tandem have had a profound influence on the shaping of the consumer landscape. First, the phenomenal growth we have witnessed since the liberalisation era has led to spectacular income gains across the entire distribution, significantly increasing household disposable incomes. Secondly, rising connectivity (in various forms) has ensured that information is much more easily disseminated and accessible, implying that trends and patterns of consumption can just as easily be emulated or discarded by various segments of consumers across the length and breadth of the country.
The pace at which tastes, preferences and patterns of consumption are changing is quite bewildering to everyone, so much so that marketers are having a hard time understanding the underlying dynamics of the ever-evolving consumption landscape. To be fair to them, they have over the years experimented with various innovative approaches, albeit with limited success, to effectively segment consumers and make sense of the multi-headed Indian consumer. However the question remains: Can one, in the land mass that is India, home to countless cultures, subcultures, traditions and tastes, really rely on a single segmentation model or matrix? If not, how does one then effectively segment consumers?
However, before addressing this question, one must first understand the underlying rationale for segmentation. Companies need to understand several features about their consumers. Who are their consumers and where do they reside? What are their tastes and preferences?
How much are they willing to pay? Such questions can be effectively answered only if the eventual segmentation results in successfully categorising consumers with similar purchasing power, almost homogeneous tastes, preferences, lifestyle habits and consumption patterns.
A central problem facing us is the absence of reliable data on various facets that would aid in a reliable and effective segmentation process. Income or purchasing power, which is of foremost importance, is extremely difficult to measure. Lack of reliable datasets projecting income at the national level complicates matters. In order to circumvent this problem, marketers have relied on various other parameters such as education and occupation to serve as proxies of income.
This led to the construction of the socio-economic classification (SEC), which has been widely used by industry over the years. This method of classification essentially relies on variables such as education, occupation, type of dwelling and (more recently) assets of segment households. Although these variables are relatively easier to collect as compared to income, needless to say