Is it price discrimination? Or price personalization?

How does technology affect prices?

The price of a commodity/service has its elbow room, the lower limit is the cost of the commodity/service, and the upper limit is the highest price the customer is willing to pay, which is called the reservation price in economics. In short, the price of a good/service is determined by the cost and what the customer is willing to pay.

In terms of cost, through the accumulation of information technology and data, Industry 4.0 can make production more intelligent, reduce the cost of unnecessary waste, and even introduce robots and automated production equipment to reduce the cost of goods/services. A considerable amount of data and introductions have been accumulated in this regard, and companies are also working hard to introduce Industry 4.0.

However, the price ceiling, how to determine the amount customers are willing to spend? The digital age will also generate price personalization. Through the accumulation of big data, customers can be informed from their location, search records, preferences, online behavior, and performance on social media, etc. Analyzed material. From the analysis results, not only put personalized advertisements, but also personalized price arrangements. This is just like the traditional commodity sales model. The sales staff negotiate back and forth with the customer through mediation skills, and finally coordinate the purchase price.

I myself have heard from a friend in human resources that as long as you unsubscribe from LinkedIn's service once, and fill in the official questionnaire is too expensive, you can get a discount plan immediately.

Definition of Price Discrimination

Price discrimination is not a product of the era of big data, which was proposed by scholars a century ago (Pigou, 1920). Firms determine the price of goods/services not based on cost, but based on different customer backgrounds. Economics textbooks distinguish three different types of price discrimination.

• Qualitative discrimination companies offer different sales prices per unit for different customers. For example, when foreign tourists buy goods in local markets, they will be several times more expensive than local people. It usually occurs most often in sales models where prices are not transparent. However, through big data analysis, it is possible to provide different prices according to different people's purchasing desires.
• Quantity discrimination The company provides different prices for different quantities, such as 30% off for three pieces, 40% off for four pieces, etc. Giving feedback to more loyal customers (those who buy more) also boosts sales.
• Multi-level discrimination companies offer different prices for different customer classes, such as student plan prices, etc., and offer different prices to customers of different classes. The usual reason is to provide discounts for price-sensitive groups.

actual case

Uber ride prices are personalized prices that take into account the user's geographic location, ride time, traffic conditions, weather conditions, and what the customer is willing to pay.

Airbnb housing prices, he claims that through the real-time, dynamic prices are based on different market conditions, which consider a lot of dynamic information, and not only about objective and external information, colleagues will also use human feelings as the basis for pricing. (Hill, 2015)

The capabilities of big data and artificial intelligence have gradually enabled price strategists to change their prices through customer buying habits and history. And through the data provided by the customer himself, as well as the customer's location, climate, etc.

Current regulations?

The most relevant provision for personalised prices is Article 22(3) GDPR. Through the automation of personal data that has legal effect or significant impact on individuals, personal data subjects can request fairness, human participation, and express their opinions ( or refuse to be an automated decision, but this right may be excluded by an exception to the same article).

Does it sound painless?

It is true that there is currently no major regulation in Europe and the United States that regulates personalized prices. Although scholars have put forward various policy suggestions, such as prohibiting personalized prices in principle, allowing personalized prices but requiring full disclosure, allowing personalized prices and requiring partial disclosure. (Personalised Pricing and Disclosure, BEIS Research Paper Number 2021/008). But each measure has obvious flaws.

How personalised pricing will develop, will it disappear due to consumer exclusion, or will it be due to the expansion of big data to more services, it is worth continuing to pay attention.