The Cobra Effect in India’s e-commerce startups


In the days of the British Raj, the government of Delhi found that there were plenty of cobras in the capital. In order to prevent the deaths of unsuspecting imperialists from these venomous serpents, they came up with an ingenious idea to curtail the cobra population. They launched a bounty programme wherein citizens would be rewarded with cash when they brought in dead cobras.


The result? Lots of dead cobras. Problem solved… or so the British thought!

Unfortunately for the British, the bounty programme started to backfire. The enterprising locals started breeding cobras to make more money. The British realised their folly and scrapped the scheme. That led to the locals releasing the now-worthless cobras back into the city, leading to a higher population than before the snake bounty was announced.

This came to be known as the Cobra Effect (a type of Perverse Incentive), which is a phenomenon that occurs when an attempted solution to a problem actually makes the problem worse.

This carrots-and-sticks approach has been employed by several startups. More often than not, they lead to unintended consequences with negative effects. The incentive scheme may seem to work for a short period of time. That is usually before the flaw in the logic is exposed, compounding the original problem.

The Cobra Effect of Discounts

E-commerce firms equate unrealised revenue with lost revenue. This means that products that have been added to the cart but not purchased in a long time are considered a ‘loss’.

So, in the heydays of GMV-led startup valuations, the top marketplaces offered 30–50 percent discounts on the products (fashion mostly) in the cart that were over 30 or 60 days old. These were sent as direct push notifications to users.

The premise was that since the buyer had shown strong intent to purchase the product (added to cart), there was high probability that, given a considerable incentive, the user would buy the product at the now heavily discounted price (with the e-commerce firm funding this discount). Moreover, it helped these startups achieve sales targets (read GMV) and unlock more value from each user (thereby increasing the Customer Lifetime Value or CLTV — a key metric for e-commerce performance).

The strategy worked particularly well in the initial days. That is, until the consumers figured out the pattern.

In a highly price-sensitive environment, this became a question of “is this product worth waiting 60 days for?” It surely did for the fashion category at least. That pair of sneakers could wait two months as you already owned at least one pair.

What happened here is that users who would have otherwise bought the product at the regular price (and immediately), were now willing to wait for 60 days or more to receive a massive discount on that product. This strategy initiated by e-commerce firms therefore ended up altering consumer behaviour permanently.

A few of these firms realised what was happening. So they introduced conditional cart discount rules which were based on the category, brand, price band, purchase pattern and history of the user to come up with more optimal discounts in place of the generic rule used earlier. But it was a case of too little, too late. In the minds of consumers, the ‘add to cart and wait’ transaction was now the more economical one.

So even though the firms switched to the more optimal conditional cart discount scheme, waiting would still result in a better deal for users.

Can it be reversed?

Several such newly compounded problems crop up because the incentives drawn up by the party initiating the action (noble or otherwise) do not take into account all the possible outcomes of the reactive party’s actions. Thus leading to negative, unintended consequences. To change human behaviour is quite difficult and requires time and many trials.

Is there a solution to prevent it? Yes. One is by applying Game Theory. One can also apply techniques based on the psychology of motivation, human behaviour and ethics.

More on that in the next article, where other such instances are shared, along with how such scenarios could have been tackled better or probably avoided altogether using these tools.

(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)


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