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All too often companies have only the vaguest idea about what kind of data they’re holding; because such data is very often hidden deeply away in a variety of databases and fragmented across different departments. We identify this data and bring it to light, making it visible, cohesive, comparable and easy to understand so that it really does support YOU in making the right decisions. And if need be, we can also identify any lacking data and define a concept to fill in the gap.

Urban Indian Consumers – Their Personal Finance Landscape

Posted by on Nov 5, 2015 in Case Studies | No Comments

Client: The Economic Times (ET)
Time: April 2015 – April 2016 (Ongoing)



To understand the investment needs, behaviour and preferences of urban Indians in the fields of

  • income (household and personal)
  • expenditure
  • savings
  • investment
  • credit and debt
  • risk coverage
  • taxation


12 online surveys are planned over the period of one year and each survey will be focused on particular personal financial aspects (like risk cover, taxation, investment, banking, insurance, real estate etc.). The starting point is a large baseline exercise that maps the population in terms of the financial products they currently using and intend to acquire in future. The sample source the network websites and social media pages of The Times. Each survey is conducted amongst 10,000+ urban Internet users in India and other countries. The findings are representative for India.




Three of our ways of looking at the data:

  • Correspondence Mapping is a classical visualization tool which is used for understanding differentiation between four geographical zones, age and gender groups.
  • TURF Analysis (Total Unduplicated Reach and Frequency) helps to figure out which product combination has maximum reach. We used it to identify which financial product combination reaches out to what proportion of the population.
  • Cluster Analysis is a method to break-up consumers into smaller groups, clusters or segments of homogeneous people – meaning: similar to each other on one or more of the parameters and clearly distinguishable from the rest.


Such an analysis allows us to “slice and dice” the findings not just by demographics but risk appetite and affluence as well. It helps the Banking, Financial Services and Insurance (BFSI) segment to gain a consolidated understanding of the investor and thus to fine tune and customize its offerings.

In addition to that the is creating a kit for ad sales through deep profiling of a very large number of its users /readers providing the advertisers better intelligence and targeting of ET readers. The editorial team of The Economic Times is gaining insights to create more relevant content, and also precisely pick the personas for featured stories. Finally, this study helps our client to continue their thought leadership journey.

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