Optimise the prices of services aimed at the general public.

Challenge:

People who manage services aimed at the general public have the option of designing a demand-driven pricing policy, but this design is usually based on the personal experience of each manager. Data analysis gives managers a design tool that takes account of the history of the service and other related data, ranging from weather forecasts to public holiday information.

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Solution:

GCD@UB and the company TÜV Rheinland worked together to design a tool for forecasting the optimum prices for the provision of services in the field of vehicle roadworthiness testing in markets where this service is liberalised. The algorithms were developed based on a regression system to allow managers of local services to optimise their pricing policy based on their historical results and the results of vehicle inspection stations across the country.

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Impact:

Pricing policy decisions should focus on optimising the number of customers who opt for our service, but this optimisation cannot be achieved if the factors that influence the customer’s decision are overlooked. Analysing mass historical data allows these factors to be taken into account automatically, instead of relying on managers’ intuition.

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