Even despite the fact that artificial intelligence is not an invention of yesterday, 2019 was quite hyped when it came to this topic. Experts from all industries made predictions about how artificial intelligence and machine learning could change all industries without exception, and developers made global plans on how AI would help defeat environmental, political and economic problems around the world.
In the coming 2020, we can only compare expectations with reality. Therefore, in this article, the SPD Group development company proposes to narrow the research field to the retail industry only and see how AI in retail will be used in this area this year. Let’s get started.
What Can AI Do for Retail
Before we begin our research, let’s recall the main goal of creating artificial intelligence. The main goal is to get mega data-processing abilities that, when used correctly, can change the life of humanity for the better.
As part of retail and e-commerce, this statement is also true – artificial intelligence makes life easier for customers, helps to make the right choice with minimal time, look for the best price and the most suitable store, and even more – predicts the wishes of consumers and ensures their safety both online and at physical points of sale. Let’s see how this is possible.
Improve Customer Experience
Source: futuredirectors.com
User experience is another hype word of the previous and current year. However, it has a strong meaning – since the wishes of buyers are often ahead of the opportunities of traders. Nevertheless, artificial intelligence makes it possible to realize most of them.
-
Voice and Visual Search
Traditional text search is becoming a part of the past – and this means that approaches to content management and search engine optimization are changing as well. It is predicted that in 2020 more than half of the search queries will be voiced, that is, made using programs that recognize and process human speech.
For retailers, this means the need to revise their content strategies and adapt to this form of search results – after all, when a user makes a voice request, he receives the result in the form of a short response, rather than a potentially suitable link to a web page.
As for visual search, this is a real chance for retailers to increase sales due to the fact that users will find what they need with only an image of the product. This is an ideal option for stores selling clothes and accessories – sometimes it’s really difficult to identify a brand at a glance.
-
Robot Assistants and Chatbots
Chatbots have already become a fairly familiar tool for interacting with brands. For example, H&M chatbot has long been able to communicate with users and help them in choosing clothes based on current requests and data on purchases made. However, it is believed that in 2020, service in physical stores will also become robotic – that is, each buyer can ask the robot for help in simple tasks and interact with him until he gets the desired result.
-
Personalized Content
Already, we receive a lot of personalized content in email newsletters and on sites that we visit on an ongoing basis. However, with the help of artificial intelligence and behavioral analysis, personalization will become deeper and deeper. And it is very likely that soon we will see not only personalized content but also entire personalized sites that will instantly adapt to our preferences and wishes.
-
Virtual Try-on Rooms
When two powerful technologies meet within the same industry, the wow effect is guaranteed. Virtual fitting rooms are about augmented reality technologies combined with artificial intelligence. And many leading sellers of clothing, accessories, and jewelry have already implemented this feature in their applications – with the goal of saving customers time, simplifying choices and further improving the user experience.
-
Improved Product Layout in Physical Stores
Data is fuel for artificial intelligence, and data on which products users choose and buy on an ongoing basis is fuel for the retail business. If you combine these two factors into one, then the retail will have the opportunity to sell more – due to the improved layout of goods in the shop windows in full accordance with the preferences of customers. As they say, data knows better.
Also Read: 7 Business Applications Offering Of Artificial Intelligence
Predictive Analytics in Retail
Source: digitalvidya.com
Predictive analytics in retail is the opportunity in which the business is really ready to invest. The predictive capabilities that data analysis and artificial intelligence provides are akin to riding a time machine — and accurate knowledge of what awaits you around the next corner. Here are a few ways retailers can use predictive analytics in 2020.
-
Price Prediction and Optimization
The price is quite controversial, and even an individual factor in retail. And before, companies did not have such strong opportunities to analyze the market situation. However, artificial intelligence can see the situation on the market as a whole, as well as compare it with previous periods, add data on customer sentiment and develop recommendations on what price is optimal taking into account the product, season, customer, store location, weather, and other factors.
-
Churn Prediction
By analyzing the totality of data about the user, it becomes possible to make some predictions about his intentions, including the intention to stop being a client of the company. This gives retailers the opportunity to always overtake their customer by one step – and in advance to block his path along which he was going to move to competitors.
-
Demand and Supply Prediction
However, in retail, not everything depends only on user behavior. There are many other factors that determine demand and, accordingly, supply – however, they are not always obvious. For example, it is obvious that people will buy gifts before Christmas, however, it is not always possible to predict what will be at the top of sales. AI analyzes current and previous trends and provides the most accurate data on products that have a chance to be in great demand in a certain period of time.
Credit Card Fraud Detection and Prevention
Source: martechseries.com
Retail is one of the most dynamic and richest industries – perhaps after the oil industry. The purchase of each product is money, the movement of which does not stop even for a second. That is why this area is most susceptible to fraudulent attacks – moreover, regardless of whether the purchase is online or offline. However, AI can track potentially fraudulent patterns – and guarantee greater security for customers and business owners.
-
RTO and Promo Code Thefts
RTO means a return to origin – this is a fraudulent trick when a person buys a product in a completely legal way, but with the intention of returning the fake according to the conditions of return and exchange. Artificial intelligence systems are able to capture patterns of behavior (and some of them are even able to read moods from facial expressions) and signal that this action can be potentially fraudulent.
As for the promo codes, this is another type of fraud when a person using the guise of a respectable buyer uses several promotional codes from different IP addresses. In this case, the system catches the address data and marks it as potentially suspicious.
-
Face Recognition in Physical Stores
According to, POSQuote if the physical point of sale is equipped with a camera capable of recognizing faces, then it is almost impossible to make a purchase using a stolen credit card – but only on the condition that the legal owner of the card is also a customer of the store, the store has all the necessary information about him, and a photo of the person, as well. In this case, the system recognizes the face of the person in whose hands the card is and compares it with the face of the rightful owner. In the case of coincidence, the operation is considered legal, and vice versa.
-
Behavioral Analysis
As for the analysis of behavior, this feature is very widely used by banks to ensure the safety of users. And it is possible to integrate the behavioral analysis function for security purposes in retail as well. For example, if a user makes purchases in one chain of stores with an interval of several hours, while the stores are hundreds of kilometers apart, this is clearly an alarming signal that the user’s financial data has been compromised. And it is very possible that the user himself does not even know about it.
Conclusion
As you can see, the correct use of AI can increase the level of sales due to the improved service and 100 percent hitting the expectations of users. However, business owners should also remember that AI is also able to optimize processes, make the business more ethical and environmentally friendly, reduce costs, waste and emissions for the benefit of its main goal – to make human life better.
