How customised can customisation be allowed to be, and when does it stop being a benefit for the consumer, and become a tool for discrimination?

If you’re looking to leave a particular telecom operator, or there is sufficient data on your usage patterns that suggests that you might leave, a telecom operator could give you free Internet, as a means of retaining you as a customer. But if your balance runs out in the middle of a call, a recharge might be made available to you at a significant mark-up. In the same way, post 10 pm at night, a female passenger might end up being charged more for a taxi ride than a male passenger.

All of this depends on the individualised estimation of consumer surplus, which is the difference between how much are you, as a customer, willing to pay for something at a particular time, and the amount you are charged. Businesses relying on a large amount of data about you might be in a position to reduce the consumer surplus in order to maximise profits. More on how companies such as Uber are uniquely placed to estimate demand (price versus quality) curve, which isn’t easy to estimate.

This doesn’t necessarily need to be a consumer-side situation: large Internet businesses are built on the idea of being platforms. Their business model is to create infinite fragmentation and monetize its aggregation. Flipkart and Amazon (in India) benefit from more sellers coming on board, because it leaves them with lower bargaining power versus the platform. YouTube benefits from their being more content creators, because any single player leaving doesn’t make that significant a difference. If Uber and Ola drivers each get a customised incentive plan, the variance in individual experiences may end up depriving struggling drivers of a common grounds for a protest. If YouTube content creators get paid for advertising based on estimation of their individual comfort with earnings, it could help the business reduce payouts to some, and increase payouts to others.

One of the sectors which appears to have caught the eye of Venture Capitalists in India over the past year is the lending sector: online lending services have raised seed and Series A rounds of funding. The lending ecosystem is likely to shift to flow based lending, a concept which is on iSpirt’s agenda for pushing the government on in 2017, and is the idea that money can be lent based on credit scoring, based on data points such as the persons monthly cash-flow data, and additional data points such as their mobile balance. Shivani Siroya’s Tala is a great example of this being used for inclusion. But what stops a company from using the same data and patterns for doing something like what the infamous payday loan companies do: target people who are desperate and vulnerable, and use that estimation of consumer surplus to maximise profits. This is predatory, and a problem in the making, and I won’t be surprised if it hits Indian users in a few years.

What we have right now in terms of consumer, is pricing on the basis of specific products/models (with additional limits for transparency, such as the disclosure of Maximum Retail Price), or pricing on the basis of class of consumer, like in telecom. At a regulatory discussion a couple of weeks ago, Rajan Mathews, the Director General of the COAI, a lobbying organisation comprising of almost all of India’s leading telecom operators, suggested that Big Data can be used to offer customised services, and that “classes of consumers” is no longer an adequate way of pricing. The TRAI Chairman RS Sharma pointed out to Mathews that telecom is a regulated business using public resources, they have to publish rules (of pricing) for the sake of transparency, so that the regulator can ensure fair play. Then telecom operators started pushing for transparency in published tariffs, but non-disclosure in “retention tariffs”.

Where we have some dynamic pricing right now, is in case of travel: on the basis of an unclear mix of time and availability: as more tickets get booked for a flight, and one gets closer to the date of the flight, the prices go up. On the positive note, initial pricing of tickets is lower. This allows airlines to ensure a minimum number of passengers, and get cash in early in the booking cycle. It’s not clear whether different consumers are being charged differently, though there has been controversy in the past about using IP addresses to identify a returning customer and raise prices for them.

The debate on dynamic pricing and issues of predatory behaviour hasn’t begun yet in India, but I have little doubt that it is one we will need to have a few of years from now, as the ecosystem involving Aadhaar, digital payments, and the collection of data on various parameters of an individual – including health records – grows. My sense is that it will begin once issues around the lending ecosystem emerge. The challenge for regulators will be to deal with this when pricing is based on proprietary algorithms analysing large troves of data, and isn’t determined by the management of a company.

The role of the government and regulators will be to prevent exploitation of consumers and ensure fairness. That’s a precondition for capitalism to thrive.