• Ai Huynh

Machine Learning and Its Roles In Retail

Updated: Nov 26, 2019

Whether it’s used to give product recommendations or to optimize the price of the product, machine learning creates a unique shopping experience and its important role in the retail industry is undeniable.

October 4, 2018


Guest Post by Pauline Farris, The Wordpoint

Whether it’s used to give product recommendations or to optimize the price of the product, machine learning creates a unique shopping experience and its important role in the retail industry is undeniable.

Machine learning is an approach to data analysis that makes the process of analytical model building fully automated. It’s a specific subarea of Artificial Intelligence that is used to train a machine how to learn. Thus, it is based on certain types of software, design specifically for certain fields, like retail.

How does marketing and retail use machine learning for their benefit? Exploring your previous purchases to recommend you certain items, analyzing your buying history to study your consumer behavior and create personalized shopping experience — that’s how machine learning helps retail industry function successfully. Retail industry also uses machine learning for their financial activity, using it to ensure cybersecurity and prevent fraud.

Due to machine learning, over the last 5 years, U.S. companies have seen a 19% increase in operating margin. Machine learning is data-driven and completely automated, which creates a number of benefits for the retail industry.

Let’s take a look at these benefits.

#1 It draws the attention of lagging customers

For many retail businesses, attracting more customers is a key focus. Besides, it’s a lot cheaper to sell to the existing customers than to target new ones. With the help of machine learning, the retail business is able to retain information on customer transaction, engagement data, and personal data.

Machine learning has made retail customer database sensitive to various kinds of customer activity. It helped the retailers surface the customers, who are about to leave, and uses a variety of methods to re-engage them. Machine learning mostly uses a personalized approach to reconnect with the lagging customers.

How does machine learning identify lagging customers? It analyzes the first and the last purchase, the total number of orders of all customers from the database. This thorough analysis helps retailers identify, which customers are unlikely to return. This information often remains unused, but thanks to machine learning, retailers can easily benefit from it.

#2 It makes stock and inventory organization easier

Inventory and stock optimization is the most time-consuming job aspect in the retail industry. This aspect also involves working out any logistics concerns.

How does machine learning help retailers work these things out?

Retail industry uses machine learning to use online and offline purchase data to predict inventory needs of a customer. Retailers are able to do it by analyzing the time of the purchase (seasonal or not, the day of the week) as well as geo activity data. With the help of machine learning, the system is able to send prompts to a sales manager to fill in the orders, or it can do it automatically.

Retail industry can use machine learning to supervise video surveillance systems in order to observe unusual activity in inventory data. It can also detect and analyze any fluctuations in sales data of a particular product.

Thus, machine learning does a great deal in terms of stock and inventory organization activity for the retail industry. It makes the whole process automated with minimum supervision from the staff, which allows the personnel to focus on other important tasks.

#3 It offers effective methods of customer behavior analysis

86% of customers claim that personalized approach has influenced their purchasing decisions. With the help of AI and machine learning, retailers are able to customize personalized product recommendations.

But these recommendations aren’t intuitive: based on a customer’s purchase history and activity, retailers can now predict which product the customer will want to buy and when. This helps them predict customer behavior and create a unique shopping experience for a particular item.

What aspects contribute to the ability of machine learning to predict and analyze customer behavior?

Real-time data — with the help of machine learning, retailers have a chance to collect updated real-time information, which helps them make decisions on the spot. This feature helps the customers see content that changes in real time based on their current preferences.

Creating dynamic content — online content is the modern currency of the digital world. By analyzing the shopper’s activity, machine learning is able to identify, which content he or she wants to see at this very moment.

Ad personalization — by analyzing the shopper’s activity, machine learning is able to define, which kinds of ads he or she prefers. It also makes it possible to geotarget the shoppers and send them ads based on their location. To do this, a system analyzes online browser behavior as well as the previous shopping experience.

As you can see, machine learning in the retail industry has a great potential. But how do actual retail companies use machine learning for their benefit on practice?

Target’s experience

Target is one of the biggest U.S. retailers that sells everything — from groceries to shoes and dresses. However, Target also has a lot of competitors who have much lower prices. Thus, they needed a strategy to encourage shoppers to buy a wider variety of goods from them, and not from their competitors.

They analyzed the behavior of their consumers, which led them to the results that their customers buy a wider variety of goods from them when they have an important lifetime event coming up. And while such events like graduation or marriage looked too clichéd for them, they decided to try and predict pregnancy.

Andrew Pole, Target’s statistician and customer behavior expert, figured out a way to analyze the shopper data and predict, which shoppers are likely to be pregnant, with the help of machine learning. For instance, if a woman suddenly started to buy certain pregnancy vitamins and supplements, machine learning allowed even to identify the current trimester of the woman’s pregnancy!

After identifying potentially pregnant shoppers, target sent them coupons to buy maternity clothes and pregnancy-related goods at more attractive prices, than their competitors had. Target later added other offers to make their customers even more satisfied.

Target’s experience with using machine learning to analyze consumer behavior and predict pregnancy is a perfect example of the importance of machine learning in retail.

Machine learning is already an integral part of the retail industry

With the current development of AI, retail industry invests a lot of money in machine learning in order to make its functions wider. The retail industry is interested in having the systems that will target potential theft (identifying individuals that are likely to steak and detecting suspicious behavior).

Retailers also plan to expand the functions of machine learning to use it for personalization and consumer behavior analysis. As of now, machine learning is very helpful, when it comes to price optimization and personalized product recommendations. There’s no doubt that it will get even more helpful in the future.

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