Andersen

How AI and ML Can Be Used for Efficiency Improvement and Customer Engagement in Retail

Nov 05, 2020
Blog

A few years ago, the clothing company Burberry destroyed around 28 million pounds of surplus goods. This provoked a huge scandal and resulted in a loss of loyalty from part of the target audience. This case is still an example of an irrational and quite archaic approach to stock management – the company made the mistake at the production stage.

AI and ML help to avoid such mistakes by calculating the volume of required products with an accuracy of one item and adjusting the figures in real time. What is more, control over production/procurement volumes is just one of the opportunities that AI and ML open up for manufacturers/retailers. In this article, we’re going to consider five of the most popular and powerful methods to improve efficiency and attract customers using AI and ML.

1. Personalization of featured products

The personalization of recommendations not only helps to sell more goods but also gives a clear picture of the volume of supplies that need to be produced. Nowadays, there are many different algorithms for analyzing customer actions and making the subsequent decisions on personalized offers.

One of the most successful recommendation engines is a technology developed by Amazon. When forming recommendations, it evaluates not only the purchases and interests of a particular buyer but also the purchases of other users with similar interests. In order to implement this engine, Amazon developed item-to-item collaborative filtering. One of its main features is the focus not on the user but on the element, which opens up additional possibilities for scaling.

2. Supply chain planning

People in charge of supply planning and inventory management usually deal with a lot of unknown variables. ML makes it possible to forecast the level of demand, its dynamics, and even the likelihood of emergency situations.

First of all, ML will help to analyze the history of supplies and identify vulnerabilities. Such an analysis is independent of the human factor and hence more objective. Based on the initial analysis, it is possible to identify more popular products and more efficient sales channels.

With the emergence of SKUs, companies have much more data to analyze. Along with the ability to work with large volumes of data, ML has the following advantages:

  • greater segmentation for more detailed analysis;
  • coverage of factors such as advertising rhythm, market cannibalization, etc. by considering multiple SKUs in a single study;
  • a combination of historical and real-time data;
  • automation of filling in databases using computer vision.

3. Planning delivery routes

When planning routes, real-time data collection is much more efficient and allows for making adjustments based on current conditions. The ML model designs the optimal route after each turn made, based on several variables such as weather conditions or the traffic level.

For example, the brewery Anheuser-Busch has launched an ML-based pilot route-mapping program. The performance was so positive that Anheuser-Busch soon transitioned all of its American and part of its Canadian logistics to this model. Their model takes into account real-time variables and is based on the experience of drivers with the best driving history.

ML also helps to solve the “last mile” problem. The final stage of delivery is where most problems arise, and analyzing the data with ML can help to understand their causes and take action.

4. Electronic consultants

ML can help to integrate all information about a product into a virtual consultant and update this information in real time. A human consultant may not know the answers to some questions or may not have up-to-date information; a virtual one is free of such shortcomings.

One of the companies that launched such a consultant is Macy’s. The virtual assistant Macy’s On Call is AI-powered and uses Watson’s Natural Language Classifier API by IBM Watson.

In order to improve the performance of virtual consultants, neural networks and Deep Learning are used. By constantly processing new data, such assistants become “smarter” over time.

5. Price optimization and promotions

The advantage of ML in pricing is the ability to create multiple decision trees based on different subgroups. Then, a complex model that can calculate the maximum price acceptable for a certain number of users is created.

Several variables are taken into account in the analysis:

  • discount rate;
  • original product price;
  • competitors’ prices;
  • level of competition, etc.

Dynamic pricing allows us not only to set the best price but also to effectively scale sales.

With the increasing use of AI and ML, retailers can improve their efficiency and productivity by actively interacting with consumers via digital and mobile platforms. The development of such platforms, as well as AI and ML solutions, are tasks that should only be trusted to professionals.

Large businesses have enough resources to build a powerful IT department, but even these businesses turn to outsourcing companies like Andersen. For representatives of small and medium-sized firms, fully outsourcing the development of IT solutions is much more profitable. We have experience with not only large corporations but also small businesses and startups. You can view all our case studies on the corresponding page of our official website.

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