Dynamic pricing puts India’s quick-commerce platforms under the consumer lens

64

TL;DR:

Dynamic pricing is moving beyond demand and supply. As platforms gain the ability to tailor prices using behavioural data, India’s quick-commerce sector faces a harder question: when does personalisation become unfair price discrimination?

Article:

India’s quick-commerce platforms could face closer scrutiny over personalised pricing as algorithms become capable of adjusting what consumers pay based not only on demand, but potentially on purchase history, device type and behavioural signals. Dynamic pricing itself is not prohibited in India, but opaque price discrimination could create consumer-protection risks when shoppers cannot understand why prices differ.

The concern matters as AI-led personalisation spreads through Indian e-commerce. India’s 2023 dark-pattern guidelines already prohibit practices including drip pricing, where elements of the final price are withheld or revealed late in the purchase journey. The rules apply to platforms systematically offering goods or services in India.

The financial stakes are significant. A 2026 study of 2,590 consumers across 50 Indian cities estimated that deceptive online design practices, including hidden charges and drip pricing, cost shoppers ₹25,000 crore to ₹28,000 crore annually. It found that 63% of online-payment users encountered hidden charges or drip pricing, up from 52% in 2024.

Consumer authorities have already drawn a clear line on deceptive interfaces. Nidhi Khare, then Secretary at the Department of Consumer Affairs, described the use of dark patterns by e-commerce platforms as an “unfair trade practice” that violates consumer rights.

The challenge now is algorithmic transparency. Jyoti Vij, Director General of FICCI, said policy frameworks would increasingly need to support innovation while “safeguarding consumer interests” as India’s consumer economy becomes more digitally connected.

For quick-commerce companies, the competitive advantage of smarter pricing will increasingly depend on explaining it. Personalisation can improve efficiency; invisible discrimination can quickly become a trust problem.