This is good advice for business as well, where, as with government, live, dynamic data, dashboards and seductive analytic tools fuel the illusion of knowing your customer and obviate the need to connect with them in person. The point often missed is that the data and dashboards tend to be supply-sided chronicles of company effort rather than customer outcomes.
A highly tech-savvy chief minister who occupied office at the turn of the millennium was admired for his cutting-edge modernity and his data-driven approach to his job. It was often said, half in jest, that he was the darling of the non-voting elite of Nariman Point. A consultant from one of the many top firms he had engaged, proudly reported that with their help, the CM reviewed data on cases relating to health problems the state faced, one district at a time, every Thursday morning. When he did not get elected for another term, his corporate admirers debated whether Indians were ready for such evolved and efficient governance. Post-election vox pop on TV channels and political analysts reporting from the field said that he would have fared much better had he spent more time among the people. It would have helped him overcome the blind spots in his understanding of their concerns, as well as gain the benefit of being seen by them as someone who listens.
With the excitement about artificial intelligence’s (AI’s) immense capabilities and potential valuations of AI-centred businesses, several startup ideas are sent my way for evaluation around replacing human respondents in market research studies with digital twins or synthetic respondents, saving money spent on market research and reducing time to market. Trained by past company-owned and public data, they propose their use for predicting acceptance of new offers, or ad testing, or price-behaviour modelling, or as the more assertive proposals state, for widespread use so that human respondent-based market research need never be done. It takes a lot of effort and patience to persuade them to read some more and reflect on consumer-intrinsic “whys” and external drivers of consumer behaviour. Then, assess the extent and situations in which a model trained on past data or a certain assumed model of competitive supply is safe to use, and based on that, decide what decision problems may require fresh insight from real people, or what continuous tracking of real people’s minds is needed to make the synthetic respondents more versatile. (ChatGPT agrees with this!)
The critique of algorithm driven performance marketing is also that customer insight gets short shrift and, as the managerial gut on what consumers are thinking and feeling and how they decide is weakened and blunted, the ability to innovate, or disrupt the market, or punt on very nascent trends gets poorer.
Businesses need to walk the talk on their mission statements where it is de rigueur to state their resolve to delight their customers or declare how customer-obsessed they are. The first step is to realise that these statements are all about “we” (the company), rather than about “them” (the customers), and that the missing part is what the company strives to do for their customers to make them delighted, and how it achieves that. Often customer obsession is defined by companies as accessing customer spend 360 degrees, more cross-selling, increasing ticket sizes or frequency of consumption, and improving “stickiness”, and not in terms of solving customer problems or increasing customer-perceived value in their lives.
This is the season of annual operating plans being approved by the board, as required by law. As we all know, most plans rest on the logic of “industry is expected to grow at x per cent and, on that basis , we will grow y per cent”. Perhaps boards also need to do their bit in pushing customer centricity by asking (especially if the company is a market leader) how exactly industry growth happens and what customer-related assumptions underpin this forecast. They must also ask for growth plans to be broken down into components of price-led growth, mix (or portfolio)-led growth, and volume-led growth, and then ask “who”— not to be confused with “where” (geography) and “what" (product) — this growth will come from and why, Socratically drilling it all the way down to management’s foresight about customer behaviour. Yes, it works for all kinds of business, be they business-to-customer, business-to-business or direct-to-consumer!
The writer is the author of Lilliput Land: How Small is Driving India's Mega Consumption Story