AI for customized dynamic pricing

Using AI for dynamic pricing is an opportunity to turn a complex business environment into an advantage, while retaining appropriate profit margins on all products. This is especially true for products with complex pricing models and high levels of individual customization such as insurance.

Can AI help to gain competitive advantage in pricing?

02/ What can be achieved?

Using deep learning techniques to automatically set prices for complicated, individualized products can help reduce risk and increase profit. The ability to automatically change prices based on real-time data can provide a competitive advantage in fast-moving business environments.

03/ The problem

Traditional pricing models often set fixed prices or similar prices for a variety of situations. This means that some potential customers are missed out on even though a lower price would still be a profitable sale. Meanwhile, other customers are paying less than they should and would be willing to pay.

04/ Opportunity for deep learning

Deep learning models are able to pick up on complicated, underlying patterns in large amounts of varied data, in a way that is beyond the scope of traditional statistical techniques. This creates an opportunity for pricing systems to have far greater insight.

05/ How is the AI model implemented?

Vehicle insurance is an example of where AI-based dynamic pricing is likely to have a large impact in the near future. A willing consumer installs an IoT sensor in their car that tracks the specific patterns of how the car is used. This data can then be combined with traditional information like the details of the car, the driver’s profile, insurance history, and coverage requirements to better understand risk and suggest a price.

Dynamic pricing is also a large opportunity for more simple products such as fast-moving consumer goods (FMCGs) where profit can be increased by tailoring prices based on stock keeping, product portfolio, channel, point of sale, and even individual customers.

06/ Data requirements

The data requirements depend largely on the specific industry and product. In general, the model needs data about the consumer and product requirements as well as historical information from other customers on the type of risk and opportunity that each type of user represents to the company. Deep Learning is especially useful when dealing with information from natural language and images to help make better predictions.

The Peltarion Platform can combine images and tabulated data to suggest prices. Follow the tutorial to learn all the steps!

07/ Where to learn more?

To learn how to use the Peltarion platform to build a regression model and combine a variety of input data types try our Predicting real estate prices tutorial.