SNG: AI personalised pricing: the impact on consumer welfare

Dr Mythili Kolluru

AI now acts as a super analyst and it can tweak prices instantly across millions of data points. The result is that two people searching for the same product or service could be offered a higher or lower price. This is the latest trend: AI-powered dynamic pricing across multiple industries. For instance, airlines have been using dynamic pricing for years, where the fares fluctuate based on demand, booking time, availability and holiday seasons. However, AI has taken dynamic pricing a step further, creating personalised prices tailored for each individual. Some AI systems can analyse purchase history, time of day, device used, the lifetime value of customers, as well as whether the customer is booking at home or work. AI scans historical and behavioural data to predict the likelihood of price acceptance by each customer. Though the trend is not widespread, it is being used by many companies.

For example, Delta Airlines is implementing dynamic AI pricing for 3% of its domestic tickets, with plans to expand it to 20% by the end of 2025. This model is so customised that two passengers sitting adjacent to each other might have paid vastly different amounts due to differences in price sensitivity.

In retail, prices are being adjusted in real-time to match customer profiles by analysing shoppers' loyalty, purchase time and location. E-commerce giants like Amazon adjust prices multiple times a day and it has boosted their revenue by 25%. Walmart adjusts prices at the store level by using AI to make demand forecasts. And they have cut inventory costs by 10% and improved availability by 15%. Some retailers use digital price tags that can also be adjusted based on local conditions, resulting in a 20% increase in sales during holidays. AI dynamic pricing is also evident in the transportation sector, with companies like Uber and Lyft utilising AI models to analyse real-time data, such as weather and driver availability, to adjust prices during peak times. In the logistics sector, companies are tweaking delivery rates based on fuel cost, weather conditions and capacity. Even in the travel and hospitality sector, rooms and meals are not fixed, as hotels and vacation platforms have adopted AI-powered, personalised, dynamic pricing. Airbnb has a sophisticated pricing system that adjusts the room price based on a customer's booking history, local events and demand. Prices of items on the menu fluctuate with the cost of ingredients or changes in competitor pricing. Even in manufacturing and wholesale, AI is working to handle complex, real-time pricing for raw materials and bulk goods. Steel manufacturers utilise AI to monitor and adjust prices, thereby maintaining their margins. It impacts everyday prices, including the cost of groceries.

This approach is driven by machine learning, which allows the company to predict what consumers are willing to pay. This trend demonstrates the role of technology in creating inequality and raising serious concerns about consumer rights. This practice leads to a breach of trust and indiscriminate use of personal data. If the AI personalised dynamic pricing trend continues, it would add trillions to global profits and companies could enjoy a 30% increase in profits. Still, it raises serious questions about a lack of transparency, ethical issues, potential discrimination, especially against vulnerable groups, privacy violations and poses a threat to consumer welfare.

In conclusion, the increased use of AI-powered dynamic pricing could amplify social biases in training data and widen socioeconomic gaps. The inability to predict and comprehend AI algorithm functionality erodes trust and accountability, posing a serious threat to consumer welfare and awareness. Despite regulations, data protection and transparency norms, there are gaps in global enforcement that hinder assessment of fairness in profitability.

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