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AI tools for e-Commerce – Learn from Amazon to Become the Next Market Leader

AI e-Commerce Tips Recommendations Smart Search
Amazon is currently dominating the e-Commerce landscape. The company succeeded, most of all, due to the smart and timely implementation of innovative AI solutions, and it keeps advancing and investing in more tools. 
It will be extremely hard for the competition to outplay Amazon. But the good news is, you don’t need to be a corporate giant to benefit from smart solutions for e-Commerce.
More and more large and small retailers are entering the AI game. In 2020, every store can easily install AI tools that will drive revenue (see how in our article How AI Is Transforming eCommerce), optimize purchases, and increase conversions. 
So how can you use AI to drive your e-Commerce efforts? Here is a selection of tools that helped Amazon become a market leader and that are considered must-haves for an online store in 2020. 

Product Recommendations

Amazon’s recommendation system that is up-selling and cross-selling products has brought the company 35% of its overall revenues and has transformed the whole e-Commerce industry.  
Amazon’s recommendations are based on the user data: purchase history, cart items, viewed, liked and rated products. Not every shop has such a broad offering and amount of user data to take advantage of. 

There is other data you can use to recommend users the most relevant products:
  • product specifications (function, color, brand, etc)
  • user behavior patterns (users that bought it also viewed)
  • compatibility parameters (bike + bike lock)
The product matching algorithm combines the analysis of user behavior with the power of the items comparison algorithm. 
More good reads about recommendations: 

Dynamic pricing

Amazon is constantly monitoring competitors’ pricing to make sure it always stays below the lowest level to win the customers. To stay competitive, Target, Walmart and Kohl’s regularly adjust product costs as well, both online and in mobile apps. 
Besides competition monitoring, with AI tools for e-Commerce you can adjust dynamic pricing based on:
  • supply and demand
  • seasonality
  • user data (e.g. geography) 
  • custom shop criteria (e.g. happy hours). 

You can also dynamically change the discount amount according to: 
  • users’ purchase history
  • competitive pricing 
  • customer data and other factors
Keep in mind that you can’t charge different prices to customers based on a protected class, such as race, gender, or sexual orientation.

You can implement dynamic pricing into your store by using WooCommerce plugins, for example, by AlgolPlusORIONAcowebs, or dynamic pricing services like Omnia or Competera.
More good reads on dynamic pricing:


Chatbots are working 24/7 and require no waiting time for the customer. 
They also lead to 70% reduction in call, chat and/or email inquiries, according to Gartner research. 
Not only can they help you save time and expenses on customer service, but also set you apart from competitors and increase conversions. 
Many retailers, such as Staples, H&M, Sephora and Nordstrom, are successfully implementing customized chatbot solutions for product discovery and purchase.

Chatbots often go together with customized product recommendations as one of the most important AI tools for e-Commerce. If your bot greets your customer with a question “What are you needs?”, it can start showing them your existing selections based on the previously mapped data from product recommendations. 
There are different chatbot development services providing customized experience and machine learning, such as UbisendBotsifyCollect.chat and many others. 
More good reads on chatbots: 

Intelligent Search

Natural language processing (NLP) allows your smart search to understand human speech as it is spoken. Processing typos, synonyms and all kinds of wording variations along with noticing what isn’t said, is just a part of what a smart search can do. 
Together with up-selling and cross-selling AI tools for e-Commerce, it allows you to recommend similar items to the user on the fly. 
Dynamic filtering that differs from one search request to another, can help with customizing the search results, while filters are set uniquely for each product based on the most essential criteria (color, manufacturer, size, voltage, etc).