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Artificial Intelligence
for customized
dynamic prizing

The use of AI for dynamic pricing provides the opportunity to turn a complex business environment into an advantage while maintaining reasonable margins for all products. This is especially true for products with complex pricing models and a high degree of customization, such as insurance.

The Challenge

Conventional price models frequently establish fixed prices or prices similar to fixed prices for a wide range of situations. That means that some potential customers are passed over, even though a lower price would still represent a profitable sale. Meanwhile, other customers pay less than they should and would be willing to pay.

The opportunity for deep learning

Deep learning models can identify complicated underlying patterns in large amounts of disparate data in ways that go beyond the scope of traditional statistical techniques. This provides the opportunity for pricing systems to gain far greater insight.

How is the AI model implemented ?

Car insurance is an example of how AI-powered pricing is likely to have a big impact in the very near future. One willing consumer installs an IoT sensor in their car that records the specific usage patterns of the car. The data can then be combined with traditional information such as the vehicle's details, driver profile, insurance history and coverage requirements to better understand the risk and suggest a price. 


Dynamic pricing is also a great opportunity for simpler products such as fast-moving consumer goods (FMCG), where profits can be increased by adjusting prices based on inventory, product portfolio, distribution channels, points of sale, and even individual customers.

What can be archieved

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

Data requirements

Data needs depend largely on the industry and product. In general, the model needs data about consumer and product requirements, as well as historical information from other customers about the type of risk and opportunity each type of user represents for the company. Deep Learning is particularly useful in processing information from natural language and images to make better predictions.

Where to learn more?

To learn how to use to build a regression model and combine a variety of input data types try out here our tutorial.

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