TL;DR — ‘Affluent’ Customers — individuals with distinctly apex lifestyles, are BFSI’s’ most coveted cohort. Inarguably — they have the purchase dispensability to be consumers to the finest products. For Banking and Insurance companies, it translates to one thing only — minimal risk. But there’s another challenge — how do you know if a consumer is ‘affluent’ unless you’ve seen ‘where’ and ‘how’ they live?
‘Affluent’ consumers are a coveted breed of consumers. According to a 2019 BCG-RAI report, you could define this group variably — stocking a net asset worth of at least 2 Cr to 150 Cr, or earning about 10L to 20L annually as of 2022, though it depends on who you ask. Businesses, however, generally know an ‘affluent’ consumer when they see one.
Why are they so important for businesses, though?
Well, a primary reason is that the ‘affluent’ cohort displays a very unique set of characteristics not particularly seen in any other group — determined to consistently upgrade their standard of living, and stay in pursuit of better things in life.
Generally, it would sound like a pretty foriegn idea, some even believing that most affluent consumers are from developed nations. And rightfully so, by a fair margin. But things are rapidly changing — a report from Euromonitor states that the pool of affluent consumers, about 85.1% of those hailing from developed nations, is likely to see an incoming spike from developing countries, amounting to nearly 29% by the year 2030.
This development is already bringing a new class of consumers to the forefront of core business challenges. It could be supposed that anyone would want such class-apart goods and services, ‘those things’, given the premium they’re available at, arguably so. But that’s about ‘affluence’ — they are the only group of customers who are able and willing to offer a premium to receive those services.
A particular such case exists for the Fintech industry. If we are to assume that ‘expensive’ equals ‘better’, or at least ‘more satisfaction’, it could be stated that affluent customers lead ‘less riskier’ lives. Less risky — because such a lifestyle significantly reduces the control external variables have on their lives.
That is exactly the kind of customer cohort the most financial instruments are modeled for. Less risk directly correlates to less claim propensity or default rate, and therefore resulting in significant profit margins while cutting down chances of fraud. That brings insurance and lending companies to another problem —
How do you identify these customers at scale, and in real-time?
It requires an enormous amount of resources and time to identify low-risk, ‘affluent’ segments — nearly impossible to send personnels to gather and collate such data and report quantifiably without bias. Moreover, further customer segmentation would always remain necessary — targeting just the ‘affluent’ segments would not stop ‘less’ or ‘least’ affluent customers from joining the pool. It’s microphone feedback — ‘poor’ affluence amounts to higher claims and defaults, and higher claims and defaults amount to higher premiums or interest rates. And at last, higher premiums amount to dropping ‘high affluence’ customers.
BFSI would happily serve customers in this area but would be known only to people who are familiar with such locations.
Identifying customer ‘affluence’ is not only necessary, for the BFSI industry, it is inevitable.
There has been an incoming flood of data that wasn’t available previously — with the development of mobile, connected devices, and the introduction of transaction identity through part removal of cash. It has become revealing to us how and where these customers are, but the data is scattered and expensive and requires heavy processing before it can add value.
Where ‘affluent’ customers live, the surrounding areas generally depict a story. In nearly all affluent neighbourhoods, you’re likely to see a relatively high density of branded retail outlets, hospitals, schools, gated societies, higher rentals — variables that are generally missing from low affluent areas. If this information is offered at granularity, such data points can be used in location intelligence to describe the ‘conditions’ a neighbourhood is in, and therefore the people living in it. This is especially critical for markets such as hyper-cosmopolitan societies, where the quality of neighbourhoods change every 200m radius. By deploying location intelligence tools, which are as simple as 0–0.99 index via ready to plug APIs, BFSIs can check check, either at first contact or at lead capture level, the most critical challenge of all business operations:
Is this a location we should serve?
Our solutions help businesses predict customers upfront. GeoIQ’s affluence index, derived from parameters like income segments, rental rates, commercial activity, population density and infrastructure quality, ranks locations in terms of their affluence. To know more about what we do, check out our console, or to reach out directly on the email below.
Ankita Thakur is the Co-Founder and Head of Data at GeoIQ.io. She has previously helped bring data to life at OYO Rooms, Cognilytics, HSBC and Mu Sigma. She’s reachable at ankita@geoiq.io