Online price discrimination exists. Here’s how it’s evolving.

Photo credit: Pike Place Market

Recently the awareness of price discrimination rose as a result of a phenomenon called “Pink tax”. This phenomenon refers to the price difference of similar products for men and women. A recent study by a data-mining company, ParseHub, unveiled that Canadian women pay 43 percent more for their personal care products than men pay for comparable items. In addition, in 2015, New York City’s Department of Consumer Affairs research found that women pay about 7 percent more than men for similar products. The Telegraph also reported that ticket prices to see Calvin Harris in Las Vegas are 150 percent more expensive for men than for women.

The economic term which captures these cases is “third degree price discrimination”. This discrimination occurs when sellers charge different prices for similar products to different groups of consumers. This price discrimination exists across different industries and products. For example, movie tickets are generally cheaper for seniors and students due to their cost-sensitivity.

A more advanced price discrimination is called “first degree price discrimination”. In this pricing method, the seller is able to identify the customers’ willingness to pay and can therefore personalize the price of the product to the elasticity of the demand.

Just try to imagine a merchant in a bazaar who sells a product for a higher price tag to a person that is dressed nicer and looks “richer”. The computer indeed can’t see what we are wearing, but it is nonetheless capable of extracting a much deeper understanding of the consumer.

Technological readiness

As targeting and clustering capabilities are progressing, personalized pricing will become more and more evident and common among online sellers. Building a profile of a client based on variables such as: location, device type, gender, age and interest will enable sellers to provide to the customer the most relevant price according to the customers’ willingness to pay.

Sellers now could provide the customer a price that will fit his preferences and willingness to pay.

If in the past sellers were capable of differentiating between different groups, now, they could potentially differentiate between individuals and thus provide the customer a price that will fit his preferences and willingness to pay. The same mechanism that works in online advertising will now be adjusted to personalized pricing.

The most advanced implementation of personalized pricing is evident in the travel sector, where prices of flights are dynamic and changing constantly according to your intent to fly to a certain location. Currently the flight change in pricing is being affected by components such as number of searches and the day of the search (weekends can be more expensive). But imagine that in the future, flight providers will be able to provide the most relevant price for the client according to their “customer profile”. For example, the system will identify that the customer is a student in his twenties and will provide him a lower price than a high net income individual in his forties for the same flight.

Another form of personalized pricing could be personalized discounts. Machine learning algorithms that can identify clients that have a high probability of leaving the seller’s website or app without purchase are not science fiction anymore. Marketing automation technology companies fill this void, and they now offer personalized discounts and messages according to the user’s profile. A new report by Coupons.com and research firm Bovitz supports this claim. They have found that millennials long for personalized coupons and are willing to provide information about themselves in exchange for it.

Obstacles ahead and consumers counter attack

The public opinion towards price discrimination is usually negative and can harm the company’s reputation. Hence, a company that will use this method might increase their revenue and profits in the short term, but in the long term, a viral shaming campaign might harm the company’s reputation and future sales. The fact that the media was vexed with gender price discrimination is an example of the potential downsides that may loom in the future for personalized pricing.

In addition, crunching massive datasets into real time insights is a quite a challenge. A company that wants to use first degree price discrimination will need to invest high amounts of resources in order to make it happen. Moreover, gathering the relevant data for building the profile of the user is a challenging task by itself.

There are few ways how consumers can fight against their “profile building”.

First, consumers can use a VPN service that will camouflage their online activities. Second, comparing the price of products with your friends and family could help the consumers to come up with a solid benchmark. Third, erasing cookies can disrupt the user profiling building.