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From Amazon to Zappos, online shopping is has become most people’s preferred method for buying products they need and do not need, along with items that we probably would not have encountered on a trip to the local Target, such as garden gnomes riding dogs

Whatever you might be putting in your cart (we will not judge any reader that buys that gnome), you may be interested to know the role that artificial intelligence plays in the process of you going to Amazon and purchasing a pretty unique lawn ornament. 


One of the biggest differences between online shopping and in-store is that online shopping is much more personalized for any given user. While the items you are interested in, and regularly buy, in a store like Target would be spread out according to the will of the store managers, Amazon employs algorithms that can track your purchases, searches, and clicks to predict which items you would be most interested in seeing. 

You may be looking for a certain brand of raisin bran—you eat a box a week—when you log on to Amazon, and, what do you know, it is right there on the front page under your suggested items, without you having to search for it. 

In the world of AI, most algorithms, which are the step-by-step instructions that guide an AI agent’s processing of data, are concerned with prediction. You might not realize it, but so many human tasks, and the solutions to those tasks, are primarily based in prediction, and suggesting products for customers certainly involves prediction. 

The difference between a human retail worker trying to figure out what pair of headphones is the best fit for you, and an AI algorithm, is the amount of data that the algorithm has access to. The aggregate of your searches and clicks and purchases gives an algorithm a solid idea as to what your interests are, your possible budget, and other important data. 

You may recognize personalization in product suggestions already, because that is probably the most obvious example to be found in the online retail experience. However, there are other, more subtle aspects of online retails’ personalization experience. 

For instance, not everyone reads the same product descriptions. Rather, the texts that you have been observed by the algorithm to most likely respond to (products you add to your cart, or favorite/save for later, or buy), and tailor the product descriptions accordingly. 

Emails from online retailers are not one-size-fits-all, but will often feature products you are likely to be interested in based on activity on that retailers’ site. 

Natural Language Processing Technology

There are two major areas of online retail that involve natural language processing, which is a subfield of AI dedicated to teaching computers how to understand the complexities and ambiguities of natural languages like English. 

Obviously, language plays a big part in online shopping, as your typical site does not just include images with prices that you one-click buy. 

Natural language processing, or NLP, is used in many aspects of online shopping, but two are especially important, which are conversational computing and semantic search. 

Conversational computing, like suggested searches, is one of the more obvious examples of AI in online retail, meaning that you have probably encountered a conversational computing platform before. You know those little chat boxes that are (typically) in the bottom right corner of a website, where you can ask questions about the company and its products or services? More often than not, that is a “chatbot” and not a human being, 

These chatbots have been trained to recognize the most common questions, and the many forms that they may be asked in, and are able to answer these questions, or perform searches across the website to direct the customers to product pages they may be looking for. 

Oftentimes, these chatbots perform the role that retail workers typically do in stores, which is to help curious customers find products based on vague criteria. 

Semantic search, on the other hand, concerns what customers type in the search bar. A lot of AI agents with NLP tasks are trained to search for keywords in interactions with humans. Semantic search, on the other hand, is more ambitious, training search algorithms to understand the entire context of an entry, which is crucial for finding the appropriate products for customers.

If you search for a “$40 set of skateboard wheels,” then the search algorithm cannot just focus on one keyword in that search, but the entire phrase, so that it knows that the customer is searching, precisely, for a $40 set of skateboard wheels.

Hopefully, this article has enlightened you about the aspects of online retail that use AI. If you are a business of any size, you may want to implement these AI features into your own website, if you haven’t already, to improve customer experience and outreach.