Code: EDI-2019-10-MIGROS

Domain: Retail

Summary

Use the gathered IoT data in order to understand the customer behavior

Proposed by

 

Migros is one of the largest FMCG retailers in Turkey. With more than 2000 stores and 30.000 employees, Migros is also the pioneer of organized retailing in Turkey. Migros today offers spacious stores in a wide range of formats and locations whose vast selection of cosmetics, stationery, glass and kitchenware, electronic appliances, book, textiles, and other items along with groceries and other necessities give it the ability to satisfy the shopping needs of its customers. 

The company aims to be always the first choice of customers by providing a unique convenience and trustworthy shopping experience by providing a unique shopping experience through its ultimate service approach, pioneer applications, broad product portfolio and family budget friendly pricing strategy.

Description

For years, Migros has been experimenting with IoT devices as a pilot Project with the objective of measuring several parameters of the stores, such as the temperature, the humidity, the lighting, the number of particles in the air (volatile organic compounds, VOCs), the level of odorous gases (like ammonia and H2S), and whether the doors of refrigerated units are closed or not. The company is now progressing to the next stage, where the IoT devices will be installed in several stores.

 The next step for the company is to use the gathered IoT data in order to understand the customer behavior. The challenge seeks to interpret the effect of the ambient conditions of the stores in customer behavior. We would like answers to questions like:

  • Do the lighting conditions affect the customer basket size?
  • Do the temperature and humidity conditions affect the customer basket size?
  • Is there a relationship between the ambient conditions and sales of various product categories?

Data

The challenge has the following sample datasets available for download

Expected outcomes

As the outcome of the project, we expect a decision support system that can analyse the IoT data along with the transactions in the store, help to derive conclusions and allow Migros to experiment with the ambient conditions of the stores. Migros’ objectives can be found below:

  • Finding revolutionary concepts and/or validation of the hypotheses provided by the company on the impact of the ambient conditions on customers. The findings should be validated with data.
  • Determining thresholds for unfavourable ambient conditions. Based on the findings from the previous results, we would like to set thresholds that would trigger alarms based on the unfavourable ambient conditions.
  • Determining anomalies in the ambient conditions. Anomalies in the sensor data can be a result of a failed device or another issue in the store. Finding anomalies in the data allows the company to detect the issues.

How do we apply?

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