Customer engagement and supply chain logistics and management are the two top use cases of Machine Learning in the retail industry.
In the last couple of years, the retail industry has been considerably impacted because of technologies like Artificial Intelligence and Machine Learning. Especially, the companies that rely on online sales are integrating Machine Learning resources to increase sales and reduce costs.
If we go by the books, Machine Learning can be defined as the scientific study of algorithms and statistical models to perform specific tasks by making use of patterns and inference. And interestingly, Artificial Intelligence and Machine Learning go hand in hand because Machine Learning is considered as a subset of Artificial Intelligence.
It's easier said than done to determine which of the industries have altered the most under the influence of Machine Learning and Artificial Intelligence technologies, but the retail sector is definitely one of them. In this article, we talk about how Machine Learning is used in retail, and what all the benefits it provides to the businesses.
There are various companies that are using Machine Learning to enhance their customer’s experience and also to boost sales. Check below some Machine Learning use cases in the retail sector:
A huge amount of data processed in Machine Learning systems allows you to see a holistic picture that unfolds on the market. For example, AI systems allow retailers to track the behavior of their resellers and know for sure if any of them violate the Minimum Advertised Price.
Due to the comprehensive analysis of data on customers and their solvency, it becomes possible to determine the clearest price that they are willing to pay for a particular product. And on this basis, either change your assortment by tailoring the product to a suitable price or earn even more.
In order to offer a customer a truly personalized experience, a business needs to predict demand. Machine Learning will help to make better inventory planning and will also ensure that the product is stocked up according to the demand prediction.
In addition, predictive analytics and Machine Learning makes it possible to predict fluctuations in demand and change the price based on these fluctuations in order not to lose potential profit.
The data on which Machine Learning algorithms are based is also the basis for the formation of routes for the delivery of goods to a particular consumer. Smart systems make logistics more thoughtful, achieving two goals at the same time - the maximum possible improvement of customer service due to rapid delivery, and the maximum reduction of retailer’s costs. Plus, systems can take into account the need to reduce harmful air emissions from road transport.
Personalization is a trend of recent years, and modern buyers no longer want to use mass offers. Adapting to this trend, the Machine Learning system studies the user's behavior, adds information about his last purchases, Google search history, comments and likes on social networks, places the client visits, and solvency, and makes the best suggestions about what kind of product will suit the user and at what point in time he will need it.
Predictive analytics is a powerful weapon that a few years ago seemed to retailers a fantasy. In those days, they could only dream of someone telling them how events would develop, what trends would emerge, and how customers would respond to them with maximum accuracy.
Then, trading strategies were built only on assumptions, conjectures, and common sense. Today, thanks to Machine Learning and artificial intelligence, they are built on common sense and a huge array of historical, current, and alleged data. This is one of the main benefits of predictive analytics.
According to ICECDS research, this is a very important point because when a retail business loses one of its customers, it also loses not only all potential profit but also money that was invested in attracting and building relationships with this buyer.
Plus, now the business will have to pay to attract a new client, and this is five times more expensive than keeping the old one. Machine Learning systems can track situations that are very likely to result in the loss of a client so that the company can take the most urgent measures to retain him.
Businesses can target customers on the basis of their geographical location and can also use the technology to know about the faster and better routes so that an efficient delivery to the customers can be done.
Since the system is capable of self-learning, Machine Learning and AI are very strong in recognizing and preventing fraudulent activity with credit cards when shopping online or offline.
Moreover, having access to an unlimited amount of data, Machine Learning systems can also help prevent fraudulent activities with coupons and discounts by tracking user behavior from a specific IP address.
According to the same principle, the algorithm can determine the user's intentions, for example, if the fraudster is going to buy a product, and then return the fake within the framework of the return conditions.
Machine Learning is also able to analyze your internal data, for example, information about how your company organizes human resources management. On this basis, you, as a retailer, are getting the opportunity to make your employees more flexible, save them from routine tasks, and more competently plan their work schedule so that they remain inspired, efficient, and aimed at customer service.
Machine learning can be used to do visual merchandising, where an online customer will have the same experience as an offline store customer. It has been stated by customers that product images play a vital role in the sales part. Machine learning is now used by businesses to provide a visual effect to the customers.
Machine Learning can offer a more personalized experience to its users by fetching the customers data and use the insights gained from it.
Earlier, there were not many companies could do to compile customers’ data, but today, we have technology like big data that has turned the tables for the businesses. By fetching data, big data defines each new strategic move in relation to the client, product, and market.
Big Data in Business allows you to adapt marketing strategies depending on changing market conditions and predictions derived from them. Thus, retailers insure themselves against surprises, have the opportunity to evaluate which marketing activity gives better results, and to develop individual marketing approaches.
So, for example, having the possibility of suggesting pregnancy with the help of AI, the retailer can make a personalized offer for a particular woman on time, until this is done by competitors.
We have already talked about the possibility of preventing fraud, offering improved delivery, optimizing the price and making personal offers. In addition, interaction with chatbots and virtual fitting rooms also makes interaction with companies more convenient and targeted. All this is about customer service, the level of which is growing.
Considering all of the above advantages, it’s certain that it concludes as an increase in sales, strengthening loyalty and trust, as well as the ability to give people what they need at the moment.
Every organization wants to increase its sales, improve its relationship with the customers, and to stand out in the competition, Machine Learning will make all this possible. Machine Learning has its applications in nearly every industry.
The benefits of Machine Learning for retail are endless, and the cost of introducing this solution into business is reasonably affordable. But to start this process, you first need to understand the advantages of Machine Learning and how exactly Machine Learning can help you meet the end goal.
Next, you need to choose a reliable Machine Learning solution development partner. We recommend that you do thorough research and choose only from the best AI companies like SPD Group so that you can be sure of the result.
Once you have a goal and the Machine Learning setup ready, you are good to deploy this phenomenal technology into your business and experience the potential of Machine Learning.
She is a content marketer and has more than five years of experience in IoT, blockchain, Web, and mobile development. In all these years, she closely followed the app development, and now she writes about the existing and the upcoming mobile app technologies. Her essence is more like a ballet dancer.
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