When a business tries to study its customers, they often assume that consumers are impacted by similar driving agents that make them decide on buying one product over the other. Convenience, comfort, trust, packaging, brand, quality, quantity, features; these agents and others, depending on the field of product and the type of market associated with it. We go with the convention that consumers will make rational decisions and hence, their decisions can be predicted if we adhere to these the needs of these driving agents. But haven’t we observed an entire generation stop watching television for lack of on-demand entertainment without ads?

Of course, OTTs like Netflix, Hotstar and Amazon Prime (powered by the Internet) have curbed the rise in demand for entertainment in this regard, pretty quickly. Clearly, the ingestion of OTTs did not increase the use of cable TV or even digital TV. Possibly, it might have reduced it further. What OTTs did, was satisfy the consumers’ demand and that’s what my focus is on. Now, this was pretty straight-forward. People had more things to do, so they wanted to spend less time doing things irrelevant to them.

There are other reasons why people prefer one product over the other. Customers often purchase commodities that they can relate to. This statement does not hold for necessary commodities. But it is highly relevant for luxury commodities. This might lead them to buy 10 units of the same material over time, though another product in comparison has better technical features.

The diversity of consumers’ needs are growing exponentially over time. We can no longer stick to a model still ignores the outliers. I feel, today, we have the resources and technology to work towards including these ‘outliers’ into our framework. We can now actually capture these small, but highly relevant natures of market behaviour.

The answer to these questions is the union of Behavioral Economics (BE) and Data Science (DS). Behavioural Economics studies the impact of psychological, emotional and social factors among others on economic decisions. Data Science is the ambit of anything that can be achieved using data; be it problem-solving, decision-making, predictive analytics or artificial intelligence. Use of DS tools can be used to observe the social trend of upcoming generations and detecting a possible drop in demand for a product that otherwise could have never been observed. Research in this area would also improve the predictability of a commodity going out-of-fashion.

Consent-based psychological and social experiments can be conducted by researchers and data can be collected for different locations and strata. These experiments should be carefully developed in a way that it requires an action-based response, rather than a verbal response (So that we do not mistake intent with action). All this data can be compiled and new social groups can be discovered (using clustering techniques) with completely different buying intentions or product mix preferences. There can be studies done to quantify the intensity that can be given to how much a person/group can be influenced (regression techniques). For example, usually, a person who answers ‘Maybe’ on a question is more likely to be influenced and change their opinion after a conversation compared to a person who chose ‘Yes’ or ‘No.’

Who would gain?

Businesses, Investors, Economists, Data Scientists and Consumers!

Businesses would gain as they will be able to make better decisions. Investors would gain as businesses would do well and they’d make greater profits. Economists and data scientists would gain as they’d be able to explore greater areas in their field of expertise. Consumers will gain because businesses would produce as per their needs – both constant and evolving.