Case study

Predicting the optimal discount for users.

CHALLENGE

Development of an algorithm (in the absence of historical data) that will select an individual discount to encourage the user to make purchases. An additional challenge is the algorithm’s operating time – the user is to receive a discount in less than a second.

ORGANISATION PROFILE

Our client is one of the largest drugstore chains in Europe with around 56,200 employees and more than 4000 stores across Europe. 

In 2019, our client had more than €10 billion turnover in Germany, Poland, Hungary, the Czech Republic, Turkey, Albania, Kosovo and Spain.

SOLUTION

We built a model that predicted the probability of buying items depending on the discount received. Initially, to train the model, we used historical data on sales of all items in our client’s store network.

The algorithm developed by our team has been implemented and is currently being used.

In addition, our client organises many promotions and offers its clients various discounts. In order to optimise the number of ordered products and better plan marketing campaigns, we have developed a model predicting how a given article will sell in the event of various promotions. As various promotional campaigns often overlap, it was a big challenge to isolate the impact of individual promotional campaigns on sales.

Another challenge was identifying problems on the basis of consumer surveys.

Sometimes customers, after visiting the store, leave comments on various aspects of shopping (prices, promotion, quality of service, etc.). It is not easy for network employees to find out what customers most often complain about – each customer enters a different message.

Therefore, using deep neural networks, we have developed an algorithm that divides comments by topic to facilitate this task.

Results

The algorithm developed by our team has been implemented and is currently being used.

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