Personalized Pricing and Price Discrimination
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NEW Major online companies like Google process a large amount of users' data, which is commonly known as "Big Data." This includes a lot of information, like past purchase behavior, list of websites visited, geolocation, emails and search results among many other things. With improved technology, these companies can process millions of data points, build individual profiles, and predict online consumer behaviour. And they can use this information to advertise a specific product to a consumer or to predict customer behaviour online.
Furthermore, the platform can rely on the gathered data to persuade customers to buy products on special discounts or buying conditions; this special price might not be available to other consumers.
Personalized Pricing is a consequence of the availability of an increasing amount of data collected by companies. In today's times, online platforms might not always determine the willingness to pay of an individual correctly. But, big data does allow online platforms to divide their customers into smaller segments and makes it easier for them to identify the expected willingness to pay for every group for a given product and then adjust the price accordingly. A special form of
dynamic pricing.
So even if personalized pricing might not be a reality today, there are already forms of price discrimination in digital markets. These use different degrees of price discrimination, which can be combined with various forms of personalization:
- STEERING: A search engine can show different forms of results for different consumers even though the search queries are the same. This practice is referred to as "search discrimination." For example, Google can assign a higher search ranking to a cheaper product for the consumers who are budget-conscious compared to the list of products to a more rich set of consumers.
- DECOYS: A platform can differentiate the product "decoys" presented to various categories of consumers. For example, Apple can show a bigger range of products to the more affluent customers when they enter the online Apple store as compared to a more basic version of their services to the customers who are unlikely to buy additional services.
- DRIP PRICING: The platform could easily mislead the customers by showing a low "starting price" for the product; further, the platform adds more charges to the final price before the purchase is finalized. A classic example of having this trend is the airline prices. The whole idea is to attract the budget-conscious buyer but later add additional charges during the processing of the request (e.g., fuel charges, airport taxes, food options, priority services, etc.)
- RE-OFFERS: Platforms often exploit time constraints and test the willpower of the consumers, personalizing their treatment. For example, if a consumer looks up a product on Amazon but does not complete the purchase, the platform could send the buyer a discount coupon encouraging them to complete the purchase. A more patient customer gets better deals than the less patient who purchases the product as soon as they look it up.
- FAKE SPECIAL OFFERS: The platform could frame fake offers for a certain type of customers. For example, the limited time offers on many online platforms, which often are individualized, while in reality, this special offer is the regular price charged by the platform for the product. Less sophisticated consumers are more likely to fall for this special offer trap.
Some of these price discrimination can at times be a good business practice. But when that is not the case, consumers can only appeal to the business leader's sense of social responsibility or hope the government will intervene when needed. And there is also a possibility that a few enlightened businesses could start a virtual trend which forces better behavior from online market players or a new online player arrives.
⇒ Do you know of other forms of online price discrimination?
Sources:
B. Marco, W.Klaus (2019), "To Discriminate or not to Discriminate? Personalized Pricing in Online Markets as Exploitative Abuse of Dominance," European Journal of Law and Economics.
F. Ray, L. Micheal (2016), "Fixing Discrimination in Online Marketplaces," Harvard Business Review.
