Estimated reading time: 2 minutes, 26 seconds

franck v 740555 unsplashEvery day, 2.5 quintillion bytes of data is generated by digital devices. This volume is expected to increase with the growth of Internet of Things (IoT). The data has been used by e-commerce websites for marketing, sales prediction and forecasting as well as targeted marketing, and companies take advantage of the digital foot prints that users leave on the internet while browsing. The data includes their location, brands of interest, gender, age, occupation, etc. It comes as no surprise, therefore, that when I perform an Amazon search for a book, I will see advertisements suggesting the same and related books once I log on to my Facebook account. This level of interconnectivity is used by platforms such as Amazon, eBay, and lots more to enhance their marketing efforts via artificial intelligence.

Artificial Intelligence entails computers learning a human’s environment so as to replicate the same activity at higher volumes and efficiency than humans can product.

Machine learning is done using data to study actions and systems so as to make autonomous decisions. In some successful use cases where the use of Artificial Intelligence has been used in retail, it has helped improve customer experience through better communication, inventory management and optimal product pricing.


Retail giants like Amazon use chatbots, conversational robots, and voice shopping to personalize the customer experience as they shop online. These are improving e-commerce by providing shoppers with the information they want to see, by matching the products to the preferences and habits of the customer. Recommendations are based on the customer’s current behavior as well as shopping history. All the data is collected and analyzed to predict a customer’s requirements beforehand.

Example of companies innovating with chatbots and voice assistants include Nordstrom (an industry leader in customer service), the eBay chatbot (most used Chabot), Alexa (Amazon), Lyft, Swelly (For Facebook’s Messenger), and a growing list of others.

Optimal Product Pricing

Historically, merchants didn’t have a way of detecting competitors’ prices and knowing when the prices have changed. Today, companies have vast pricing data and pricing applications that help them respond rapidly to changes in consumer demand and competitor prices. AI startups that are developing applications that adjust prices based on local events such as weather and local activities that can affect pricing.

Inventory Management

Some of the capability of AI powered inventory include identifying gaps based on market trends, forecasting inventory and placing necessary orders, and reducing stock pileup. An inventory optimization suite uses predictive analytics and machine learning based on historical tendencies to provide models of future buying patterns and behavior. It also helps to determine underperforming products so as to set up store space for better performing products.

AI has and will continue to revamp retail with new applications like  virtual fitting rooms and mirrors, visual product search, product categorization, cashier free stores, customer satisfaction surveys,  predictions and, to an extent, influencing customer behavior.

Last modified on Monday, 28 January 2019
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Scott Koegler

Scott Koegler is Executive Editor for PMG360. He is a technology writer and editor with 20+ years experience delivering high value content to readers and publishers. 

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