The Pricing, trade-off between Efficiency and Fairness

In theory, the process AI based Pricing Process, may provide a technical framework for building an economically efficient Dynamic and Personalised Pricing, capturing the perceived value of the product for every single customer.

In practice, such Pricing tactics might cause some unwanted serious side-effects, and raise huge concerns among customers regarding the fairness of such decisions.

Indeed, the experience of Pricing Optimisation and Revenue Management,  developed in the Airline, since the 90s, showed that customers are not radically against  the principle of price discrimination but are rather concerned with the discrimination criteria.

The time based Pricing discrimination, where Airline companies change prices for all the customers based on remaining time to flight are today relatively well-acceptable, but other Pricing tactics based on the specific OS version of the customer (Mac vs Microsoft) and the use of cookies were rejected.

The decision process in the modern AI based on Machine Learning models, such as Neural Networks  and Ensemble Models are difficult to interpret and to understand.

The Massive use of such models, with a unique objective of capturing the maximum value from each  single customer, may be badly perceived by the customers and considered as an ambiguous, arbitrary and unfair decisions.

In order to make to make this Personalised AI based Pricing more acceptable and reduce the it’s perceived unfairness, companies need to put constraint to those models, such as avoiding too  frequent Pricing updates, keeping the Published Prices the same for all the customers, and proposing instead targeted discounts, and above all share and communicate in a transparent manner the Pricing rules to its customers.

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