Applications of Machine Learning in the Retail Industry

The use of machine learning in retail can enhance sales and customer engagement by enabling personalized recommendations, optimized pricing, and streamlined supply.

The retail industry serves as a middleman between manufacturers and consumers. Retailers purchase finished products either directly from manufacturers or wholesalers and sell them to end consumers. Hence, the role of the retail industry plays a pivotal role in the supply chain. According to a study ofthe retail industry, global retail sales accounted for $22.97 trillion in 2017. And the same study shows that they are expected to reach $29.76 trillion in 2023. Various factors are influencing the growth of the retail industry. For instance, showing dedication towards customers and appreciating their loyalty by giving rewards is currently a growing trend in the retail market. And, digital startups are using emerging technologies like AI, ML, IoT, and blockchain to provide enhanced 24-hour service to customers.

One of the most important technologies that can be used by retailers is machine learning. ML uses several learning algorithms to detect patterns and provide insights from data. Further, it can predict the future based on the insights gained from data. Thus, by implementing the use of machine learning in retail, retailers can leverage its benefits and take customer service to a whole new level.

How Machine Learning in Retail is Reinventing the Industry

Technologies like IoT are helping retailers to collect an abundance of data. But, this data is of no use if they cannot get useful insights from it. And, with the help of various data science techniques, ML algorithms can generate tangible insights from data that can be used by retailers.

Personalizing Product Recommendations

Almost every retailer provides product suggestions and recommendations to their customers. But, if customers keep on receiving wrong suggestions from retailers, then they can become frustrated at some point. Hence, it becomes a necessity to provide only the best recommendations to customers, which can be done with the help of ML. ML algorithms can create various correlations among products and customers to give product recommendations. For instance, they can create a user-product relationship based on users’ preferred products like shirts or jeans. ML algorithms can also create a user-user relationship by segmenting customers into different groups like people with similar age groups or likes and dislikes. Another relation that ML algorithms can create is a product-product relationship based on similar or related products like printers and ink cartridges.

Thus ML algorithms can help retailers to provide accurate recommendations to customers and bring them across various related products that they might not have come across otherwise. Providing personalized recommendations makes customers feel that they are known and understood by the retailers, which eventually leads to retaining customers. Thus, retailers will not have to fear losing customers to competitors with the use of ML.

Optimizing Price

Setting the right price for a product is always challenging for retailers, as there are many pricing strategies based on various contextual factors. For instance, pricing strategies are chosen based on factors like competition, weather, local demand, and the company’s objective. And, pricing is an important factor that can impact consumers’ product choices. For example, if competitors are providing a similar product at a lower price, then retailers can lose potential customers. ML algorithms can constantly monitor web content to gather information about the pricing of competitors. They can also gather information on discounts and promotions. Then, based on the data gathered, ML algorithms can determine the initial, best, discounted, and promotional prices of products.

ML algorithms can also monitor the local demand for any product. And if the demand is high and product supply is low, then retailers can raise the price to gain more profits from that product.

Providing Customer Service

Chatbots have become common for providing customer service across all industries. Retail businesses can use chatbots for similar purposes. Customers ask several questions to retailers regarding the specifications or availability of a product. For instance, sales assistants may come across questions like what color variations are available for a product or whether a product is in stock. But, sales agents may not be able to constantly monitor the inventory and also might not be able to provide detailed information about a product to customers. But, chatbots can provide answers to all such questions.

Chatbots, with the help of ML algorithms, can analyze sentiments of customers and provide appropriate responses. Thus, they can enhance customer engagement by interacting according to the mood of customers. Also, in the case of online retail, chatbots can help find what consumers are looking for without the need for applying many filters to find specific products.

Optimizing Inventory

Managing inventory has always been a challenging activity for the retail industry. It is challenging because it involves monitoring various uncertain factors. For instance, it involves monitoring the purchasing behavior of the customer, changing climate, changing prices, and the increasing/decreasing popularity of a product that can change at any point in time. ML can perform predictive analysis on all these factors to predict how they will change in the upcoming time. For instance, ML can detect patterns in customers’ behavior based on historical purchase data to predict an increase or decrease in the demand for a product. Further, if it predicts an increase in demand, then ML can also provide information on how much stock will be required to meet the demand. For instance, ML can determine the level of carbon gases in the environment, and it can also constantly monitor the changes in the ecosystem. Thus, ML can predict the upcoming climate changes and the consequences it will bring in context to the retail industry. For instance, if the climate is going to become hot, then demand for summer wear is likely to increase.

Predicting Customer Behavior

Predicting customers’ behavior with the help of ML algorithms can assist sales representatives to increase sales. When trained with historical purchase data, ML algorithms can predict which customers will take how long to purchase a product. For example, ML can provide an answer to questions like how many variations of a product a customer will go through before buying that product. Thus using ML assistance, the sales representative can know which are the potential customers and provide enhanced service to them.

ML algorithms can constantly monitor customers’ social media accounts to determine major events going on in their life. Based on this analysis, they can provide product recommendations with custom offers. For instance, if a lady posts images of her pregnancy, then ML algorithms can provide custom offers for baby products to her.

Preventing Theft

Shoplifting is a major crime in the retail industry that costs businesses billions of dollars each year. As per a statistic, more than $13 billion worth of goods are stolen from retailers each year. But, ML can aid retailers to identify shoplifters and prevent theft. When images of known shoplifters are embedded into a computer vision system, it can spot them as soon as they enter the shop. Further, ML algorithms can provide real-time notification when a shoplifter enters the shop.

ML algorithms can also detect new shoplifters based on their behavior. For instance, ML algorithms can match the behavior of people within the shop with the behavior of shoplifters from previous incidents. And, with the help of real-time notifications, shop administrators can keep a close watch on potential suspects to prevent shoplifting. There’s no denying that ML is changing the business nature of the retail industry. And the future of the industry is with more automated tools. But, for accurate functioning, ML requires a tremendous amount of data. Hence, before rushing towards the use of machine learning in retail, businesses must know the fact that a huge volume of data will be required. Businesses can gather the requisite data by synergizing ML technology with other technologies like IoT and big data analytics. Thus, by combining ML with other technologies, retailers can achieve unprecedented breakthroughs in the retail industry.