Artificial Intelligence and Retail. How Machine Learning Helps to Increase Sales and Reduce Expenses Artificial Intelligence and Retail. How Machine Learning Helps to Increase Sales and Reduce Expenses

Companies inevitably face the automation in the international business arena. If you have not seen the majority of your employees and managers cannot do without a CRM system, you will likely meet such a monster.

The article reveals how machine learning can help retailers who no longer manage to process data of their clients.

What Retail Goals Does Machine Learning Solve?

The world’s largest retailers and other corporations are extensively applying a data-driven approach. It is a comprehensive strategy of the company where all of its further solutions are based on customer data.

There are a lot of analytics tools allowing to track user behavior, demand dynamics, and other significant parameters. However, data collection is just the first stage. Afterwards, these data should be interpreted in order to correct marketing according to the research results.

Machine learning will be useful if one has too much client/purchase data. Therefore, artificial intelligence will deal with the following:

  • forecasting. AI can process sales data of previous years and help to determine what results the company will gain as of today. Sometimes analysts should define a class or a status of the object and realize whether the shop will close or not. Similar tasks can be solved using machine learning;
  • assessment of retail facility attendance. Spatial data (population size, revenues, popular professions/interests) as well as data on competitors and outlet chains allow to build a mathematical model showing the amount of customers or sale receipts per day/month in the store, etc.;
  • assessment and forecasting of client behavior based on geolocation. If retailers need to know the approach of people in a certain region or city, machine learning will help to collect data on the price of housing rent and purchases in the given place or other profit margins. Thus, one will be able to see the rate of average income of consumers from a certain district.

By the way, experiencing the great thing about the customized approach, clients will be hardly surprised by email newsletters.

Besides, machine learning will help to establish efficient marketing communications. On what gadgets users open a letter, how many times they enter the website, what promotional message they follow: all of these things will supplement the pattern of consumers’ interests in order to find an individual approach to each of them.

AI algorithms are able to analyze the equipment deterioration, significantly reducing a work schedule, expenditures for implementation, and the volume of consumable materials.

In 2023, the South Korean electronics manufacturer – LG – is going to open a factory where all the processes, from acquisition of C&E materials to control of output products and their delivery, will be monitored by artificial intelligence. AI will be responsible for equipment deterioration, accomplishment of plans, and other factors commonly checked by people.

The company is planning to start partially shifting producing operations from old factories in 2021.

AI Conference Kyiv: Artificial Intelligence and Retail. How Machine Learning Helps to Increase Sales and Reduce Expenses 1

How to Integrate ML into Company Operations

Artificial intelligence is not a comprehensive off-the-shelf product. To apply the technology, the company has to conduct a range of transformations. Vladimir Kuchkanov, Data Scientist at Competera, believes that to adopt machine learning, the company should:

  • collect the pure structured data at least for three years in order to train the neural network;
  • be ready for changes: it refers to both chief executives and the team. Otherwise, one of them will slow down the process;
  • have its own development department consisting of 6–10 specialists or form a partnership with a technological company in order to keep the system in working order.

Competera took such an approach to cooperate with the Ukrainian retailer – Foxtrot. As the result, proceeds in AI-driven shops increased by 16% and sales grew by 14% during testing of the neural network.


New technologies allow to cut expenses, minimize risks, customize service, assess client financial solvency, and prepare forecasts. AI makes company operations faster and more efficient, toughening the competition.

You will be able to discover more about machine learning in the retail sector on June 4 at AI Conference Kyiv.

The conference will explain what tools and approaches should be used to integrate AI into your projects, as well as how to manage the millionth consumer base and marketing activities.

Details about presentations and speakers can be found in the vent program.

Registration is already available ►►►

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