Artificial intelligence

Machine Learning Applications in Retail: Use-Cases for Marketing, In-Store Management, and Business Processes

Machine Learning

From a scientific point of view, it is just amazing to analyze how consumer behavior, habits, and preferences change year after year. Of course, marketing and the art of selling are also changing under the influence of changing customer values and technology. In the 21st century, retail and marketing have become almost completely digital. What is more, machine learning and artificial intelligence technologies continue to revolutionize this industry. In this article, we decided to put together the best opportunities for adopting AI and ML for retail and e-commerce in 2020 and beyond.

What Are ML and AI for Retail?

Artificial intelligence and machine learning for retail and e-commerce are the set of data-driven technologies that are capable of light-fastening data analysis and pattern-driven predictions. Perhaps, this is all you need to know about ML and AI in retail since all their additional features stem from these two key capabilities. Next, we will analyze retail machine learning use cases and everything will become clear to you.  

How Is Machine Learning Used in E-commerce and Retail?

According to Exchange Solutions White Paper, “For retailers, machine learning is powering an increasing number of technologies that touch every point of the supply chain.”

E-commerce and Retail

Perhaps e-commerce is really the industry whose specificity allows you to apply artificial intelligence and machine learning the most widely – from attracting customers to forecasting supply and demand. Moreover, it is also possible to use AI and ML solutions to make online and offline transactions more secure. Of course, such solutions should be used on both sides – both on the side of the retailer and on the side of the banking / financial organization. For example, a solution developed by the SPD Group to secure online transactions and prevent fraudulent attempts works just in this way.

What Are the Benefits of Artificial Intelligence in Retail?

Cut costs Increase sales Reduce returns Prevent fraud
Machine learning and AI can be applied in almost all business processes, optimizing production, logistics, and human resource management. AI makes it possible to personalize the user experience in the most thorough way. The modern key to sales is maximum compliance with user expectations and anticipation of needs. The introduction of artificial intelligence can be a rather expensive investment, but the ability to develop more competent marketing strategies makes it possible to return investments and earn extra profit.  Thanks to the ability to work with real-time data, analyze patterns, and find anomalies, AI and MO can protect your business from fraud, improving your reputation and saving you from money loss. 

Machine Learning in Retail  – Main Use Cases That Are Suitable For Your Business As Well

Here are some examples of common machine learning applications for e-commerce and retail. 

AI in Retail Marketing

We have already said that it is possible to boost sales with AI and ML introduction. For the main part, it becomes possible thanks to a smarter approach to marketing activities. Here is how does it work. 

AI-driven SEO

Previously, SEO strategies were a real headache for professionals and business owners. Today, with the help of specialized software, it is possible to make your SEO promotion more thoughtful and less costly.

For example, there are many applications that allow you to compose a semantic core for a site, taking into account the specifics of the business, the main types of content, and your SEO goals.

  • Crayon and Bright Edge apps make it possible to quickly find the best ideas for your next blog posts and other materials by quickly analyzing trending topics, user preferences, and even competitors’ actions.
  • The Market Brew allows you to use the best predictive capabilities of artificial intelligence to quickly assess whether a particular change in your content will be effective for search engine promotion.

Content Personalization

Personalized content is already taken for granted. Users want to see a personalized approach and customized content at every stage of interaction with brands. Moreover, it is expected that soon the sites themselves will become fully personalized and will show the user only those products that may suit him in the opinion of AI, plus take into account the purchasing power of each and optimize the price on this basis.

Recommendation Engine

The recommendation engine allowed Amazon to get 35% more revenue. This is the simplest example of using AI and ML in retail. The essence of its work is clear from the title. The algorithm analyzes user behavior on the site and beyond and makes assumptions about similar, or complementary products that could potentially interest a particular client.

Voice and Visual Search Optimization 

As expected, half of the search queries in 2020 are done using voice. This means that retailers need to slightly improve their approaches to SEO optimization in order to get into Google snippet for certain requests. When users use voice search, they most often ask organic questions like “where to buy a Gucci dress near me”.

AI-powered app Answer the Public allows you to research the questions people may ask while looking for your products. As for visual search, it also becomes possible due to AI and ML technologies in retail. Here is the infographic demonstrating what solutions you may use to improve your visual search and drive other marketing efforts as well. 

Retail Store

Artificial Intelligence in a Retail Store 

With artificial intelligence, you can optimize your physical selling point in the following ways.

Robotic Assistance

Shop assistant robots are already a reality. For example, to cope with the challenges of a pandemic, Simple Robotics has developed a robot to help in grocery stores. His task is to help people get goods from the upper shelves to minimize contacts and touches. Under normal circumstances, assistant robots can work to improve the experience and speed up the shopping process, plus minimize the number of human consultants performing routine tasks.

Face and Mood Recognition

With the help of face recognition technology, it is possible to immediately identify the customer at the entrance to the store. With the help of technologies for recognizing emotions and intentions, the machine learning algorithm can immediately make assumptions about what kind of product you need to offer a specific buyer today.

Data-Driven Shop Windows Layout

By analyzing data on the best-selling products (and on products that users cannot quickly find on store shelves), you can optimize your shelves and shop windows to save customers time and improve sales. 

AI for the Retail Industry and Its Business Processes

Key business processes can also be optimized using artificial intelligence and machine learning.

Predict Issues and Develop Smarter Strategies

The simplest example is the ability to predict demand fluctuations and optimize pricing. Smart algorithms are able to catch invisible anomalies leading to fluctuations, and thus ensure your business from unforeseen situations.

Optimize Delivery Chain

This feature will be especially useful for grocery retailers. Very often, goods arrive in the store half-rotten due to delivery delays, for example, due to traffic jams, weather, or a strike. AI makes it possible to predict possible delays and make the delivery process more streamlined to reduce food waste and carbon trace.

Predict and Reduce Churns

An analysis of purchasing behavior using AI and ML can give you inconspicuous insights if a particular buyer intends to stop buying your products. In this case, you can develop an emergency and personalized response strategy and retain the client.

Manage Employees in a Smarter Way

It is also possible to develop smarter human resource management strategies. For example, knowing the time and days of peak load in the store, it is possible to increase the staff of sellers, and vice versa, to reduce it without affecting the customer experience in the “quiet” intervals.

Conclusion

Modern e-commerce trends point out that it is no longer possible to exist without AI and ML. The customers are already getting used to it and expect even more amazing experiences, for example, so that their desire is satisfied even before they feel it. Modern technologies are capable of even more, of course, provided that they are correctly integrated into business processes, and the goals and objectives of AI for retail are also set correctly.

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