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The New Reality of Retail (Machine Learning & AI)
AI & Machine Learning in Retail

The retail industry is going through an exciting yet challenging phase. Competition is fierce and consumers have more options to choose from. This has caused the industry to grow at a rapid rate and it is expected to hit the $27 trillion mark by 2020.Some experts agree that machine learning and artificial intelligence will play a vital role in the future. In fact, the latest technology has already found a foothold in the industry. Big players like Amazon, Alibaba, and eBay have successfully integrated several AI technologies across sales cycles.


In this article, we’ll talk about how machine learning is moving the industry forward and have a look at some real life cases.

Improved Customer Service

Contrary to popular belief, AI and machine learning technologies do not only improve the supply chain but can also play an important role in reducing customer churn. According to Forbes, the use of AI and machine learning can improve customer satisfaction by 10%. This is why about 75% of businesses appear to be using AI and machine learning to improve customer support.


A very good example of this is chatbots you see on a large number of e-commerce platforms. They can handle customer questions and solve issues in seconds. This helps retain customers since buyers are more likely to use a platform that offers better customer support.It can improve loyalty, help retain and win more customers, and have a positive impact on the bottom line.

More Data For Better Accuracy

Every time a buyer or a potential buyer lands on your site, they leave tonnes of data you can use to improve your marketing tactics and win more customers.The problem is that there is too much data and not all of it is valuable. This is why companies use AI and ML tools to analyze data so they can find actionable information. Data helps businesses know which product to produce, where to sell it, and at what price. This information helps businesses take the right staffing and production decisions by correctly forecasting demand. As a result, there is no shortage or wastage, which has been a major problem in the retail industry.

More Sales With Recommendations

Recommendation Engines play a vital role in selling more. When you land on a webpage to make a purchase, you will often see a list of ‘similar products’ or ‘recommended products’ based on your search or purchase history.
These engines are generally powered by product or sales data in addition to customer activity. Special algorithms analyze the data and make connections to determine what to show to a customer. The job of a recommendation engine is to introduce new but relevant products to a customer to increase the size of the bucket.


Many big and small retailers are using this feature now but Amazon is the only one that appears to be doing a decent job. Netflix also comes close but it doesn’t qualify as a retailer. A major reason why Amazon keeps on growing is due to this engine since it keeps the customer journey going with no dead end.

Defeating Competition with Price Optimization

This may come as a surprise to some but price optimization is one of the most important features of machine learning but very few retailers appearing to be using this technology at the present.


Retailers need to pay attention to a number of factors when deciding prices. These include product availability, season, competition, etc. It can be difficult to manually incorporate all associated costs including non-monetary factors to reach a price.


Machine learning can be used to study different elements and suggest a viable price. It can even be used for dynamic pricing, a popular strategy where prices change in real-time based on variable factors. Amazon once again tops as an example since it’s known to change prices more than 2.5 million times per day.


Competitors like Target, Walmart appears to be following the same technique now. In fact, Target received immense backlash earlier this year for inflating prices when a customer entered the store.

How Are Retailers Using the Technology in Retail?

According to a study by Juniper Research, the use of AI tools in the retail industry is expected to grow by about 400% percent to reach the $7.3 billion mark by 2022.Machine learning is key to several industry trends, especially regarding personalization and product discovery. It also plays a key role behind the scenes through optimization and analysis and forecasting.


It’s not only changing digital businesses but brick-and-mortar stores as well thanks to technologies like facial recognition and smart mirrors. Facial recognition is making it easier and more secure to shop and smart mirrors are helping boost sales.


Here are some companies using machine learning:


Walmart: It uses machine learning to improve delivery, sync product catalog, recommend products, and improve checkout. The company also uses targeted advertising to push buyers to save more.- ASOS: This online fashion store uses image recognition technology to explain products. It also uses AI and machine learning to gauge the worth of a customer and assign each buyer a Customer Lifetime Value.- Macy’s: The company joined hands with IBM to use Watson for the company’s newly launched feature, Macy’s On Call. It allows customers to use the Macy’s mobile version to ask questions and receive information that they need.


If you’re a retailer then it’s important to know about machine learning and how it can help your business stay afloat, especially when others around you are moving the needle. I will be writing more in depth articles on this subject so stay tuned for more in the coming weeks.

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