But We Are All Targets, Aren't We ?????
I travel to Mumbai very often, a place where I inevitably end up taking several Ubers. A friend of mine had just travelled to Mumbai for work, and we planned to meet. He was trying to book an Uber to come to my house but couldn’t believe his wits when he saw that a 30-minute journey was asking for Rs. 603. I, finding it hard to believe that one ride could be that costly, checked my app only to see that the price quoted on his screen was twice that of mine! Eventually, the price reduced slowly, but it never matched mine. I observed the same with a friend who had just started using the service and consistently showed higher rates than me.
We have all heard of price discrimination, the idea that different groups of people are sold the same services at different rates. One can imagine why that would sound unfair; thus, no company would admit to using such practices. Whether Uber with its price discrimination or Amazon with its personalised recommendations, regular users often notice such things. Behavioural economics is a field of study that evaluates human decision-making and, unlikely so, finds itself collaborating with data science. Classical economics bases its ideas on human rationality and self-serving motives. But the flaw lies in the principle that individuals are not rational. Their decisions are guided by their biases and emotions, and irrationality. Imagine a real-time measure of each individual’s irrationality, thus personalising any options given to them. That’s the middle point of data science and behavioural economics.
What is the user’s propensity to spend? Are some people more likely to spend on particular products? What contexts are significant in situations (circumstances)? Data science can observe social and online interaction trends, observing patterns within individuals and grouping them. The winning combination is. Therefore, data science responds to online activity, defining group characteristics and their thinking patterns in people, and behavioural economics selling them products based on their motives and spending capacities.
To illustrate an example, let’s analyse online shoppers. In the pandemic, people were seen behaving differently. Some were bored at home and decided to shop, some were seen browsing the net but never buying things, and some looked at specific types of things, such as students looking at college room supplies, etc. Some also spent hours and hours making up their mind, while others immediately made up their minds. Data science can thus, segment this population of online shoppers with respect to their propensity to save or spend. Depending on their characteristics, some people are offered discounts while others are not, or some people are given a push, “2 left in stock, shop now!” while others are not. Businesses have realised that they need to appeal to individuals, not just broadly cater to everyone.
The same thing can be seen in many ways. Someone who is observed buying products that are “in” right now could be first shown the most wanted items, will be given the “last chance” notice, will be granted more discounts on those types of products, anything to make the user keep up their patterns. Suppose data science algorithms identify you as someone who makes a lot of big purchases. In that case, you could be given lesser discounts, be shown higher-cost products, and be provided more secondary offers on the probability that you would pay the full price. Someone who is using a credit card vs a debit card or cash could be shown different items too.
It intuitively makes sense to give every person the incentive they need, something that could be different for every person based on their circumstances, likes and dislikes, and the likelihood of paying. The Uber example thus explains why, say, a person travelling from a more affluent neighbourhood is given a costlier ride (that they probably can afford). From the user’s side, this mixture of subjects provides the highest levels of personalisation possible, designed to simplify your everyday needs!