M.Video–Eldorado uses data analytics and machine learning tools extensively to create innovative and personalized shopping experiences, as well as to improve operational efficiency and cut costs.

The Group has created a Data Science Center that develops solutions based on data analysis and machine learning algorithms in numerous areas: communicating with customers, creating personalized offers and working with the product range and promotional plan. In 2020, the center’s team more than tripled in size, from 20 to 75 people.

The center’s key achievements in 2020 were:
  • developing and introducing solutions for customer analytics and recommender services across all touchpoints with customers: the mobile app and website, contact center and seller apps
  • creating a competency center to develop solutions for assortment planning, pricing management and promotions; a newly developed in-house pricing solution was introduced in late 2020, and product assortment planning and promo solutions will be launched in 2021
  • introducing a chatbot for the M.Video website and application, which automates around 30% of customer queries on several different topics
  • implementing pilot solutions for video analytics in stores, which help to assess customer engagement regarding different product categories and improve service quality by analyzing queues and individual shopper behavior

Recommender services and personalization

Creating predictive models for personalization in the consumer electronics segment requires extensive research and experimentation, as well as innovative approaches to working with data and algorithms due to the wide range and low frequency of purchases. Nevertheless, the team at M.Video–Eldorado’s Data Science Center has successfully introduced models that can:

  • predict customers’ susceptibility to different types of promotional tools (accruing bonus rubles, discounts or purchases by installments);
  • determine interest in purchases in certain product categories;
  • recommend accessories, consumables and replacement products.

Using these models across various customer touchpoints (marketing campaigns, the mobile app, website and retail) enables the Group to personalize the shopping experience and interaction, as well as increase conversion rates and the average ticket.

Product category
Promotional mechanics

Speech analytics

In mid–2020, the Data Science Center began focusing on ways to develop speech analytics. Analytical models are primarily applied by creating chatbots that automate standard customer queries, thereby reducing the load on call center operators and boosting service speed.

In addition to chatbots, models have been introduced in speech analytics to analyze customer product reviews and highlight key features from them.

Analyzing customer reviews and queries helps to identify important product features for use in assortment planning, as well as improve scenarios for recommendations and product selection.

Demand forecasting with machine learning

Machine learning algorithms help to make demand forecasting for certain items more accurate and take into account correlations in data that are not obvious to humans.

We are developing two areas in our forecasting models: for regular demand and during promotional periods. Demand forecasting is used for 30,000 items at more than 1,000 M.Video and Eldorado retail stores on a weekly basis. It takes into account store location, traffic, seasonality, the speed of retail sales and potential volume of online orders collected in person.

As such, the introduction of machine learning algorithms can significantly optimize not only forecasting itself but also associated costs, such as for the use of storage facilities or transport.

Cloud infrastructure

In 2020, the Data Science Center team was one of the first in the market to use cloud technologies to manage machine learning infrastructure by creating environments in the cloud for the development and use of their products. By transitioning to the cloud, we managed to triple the amount of resources for machine learning development, which made it possible to simultaneously conduct around 100 pilot projects and experiments, while also significantly reducing costs. In 2020, the cloud was used for machine learning tasks. In 2021, we plan to transfer analytical data storage to the cloud in an effort to gain even greater flexibility and further growth opportunities.

The Data Science Center’s priorities for 2021 are:

  • strengthening the team to prioritize work on product range, pricing and promotions;
  • creating a team of recommender services for the Eldorado brand;
  • optimizing the logistics platform by introducing products based on machine learning algorithms into the main logistics processes: replenishment, transport management and planning delivery quotas, among others;
  • building a data platform that provides Company analysts with an environment and methodology to test hypotheses and create interactive dashboards.