Amazon is a master at leveraging customer-centric innovation to propel its growth. The company has taken its business to the next level by building the organization around artificial intelligence (AI) and machine learning (ML).
Today a trillion-dollar company, Amazon has reorganized itself over the years to integrate AI in every part of the organization.
It won’t be far-fetched to say that AI has played a big part in making Amazon the biggest ecommerce player in the world, and it will continue doing so.
Take the example of product recommendations. These have become so ubiquitous that shoppers don’t even realize that they are AI-powered. Not surprisingly, Amazon was the first company to start offering product recommendations on its platform and has been using these for about 20 years now.
Amazon practically invented personalized recommendations when it started using sophisticated AI working on a ton of data to offer the right recommendation at the right time.
Being an early adopter of AI and automation, Amazon has built a competitive edge over others. The company now uses AI to boost the efficiency of its business operations and more importantly improve customer experience.
Here is this article, we’ll have a look at the various ways Amazon uses AI to dominate ecommerce. We’ll cover not only product recommendations but also other use cases like Alexa, Amazon Go, etc.
So, let’s begin.
Amazon dominates the ecommerce market: key stats
Numbers don’t lie. So, here are a few numbers that showcase how Amazon dominates ecommerce:
- In the US, Amazon is the single biggest player in the ecommerce space with a market share of 37.8%.
- The website Amazon.com consistently averages more than 2 billion monthly visitors.
- It’s the #1 ecommerce website in the US.
- It has over 200 million Prime members worldwide.
- In 2020, Amazon hired 500,000 employees. The company now directly employs more than 1.3 million people around the world.
- More than 1.9 million small and medium-sized businesses sell on Amazon.
- More than 100 million customers use Alexa devices to connect smart home devices.
- Amazon Web Services (AWS) posted a revenue of $62 billion in 2021.
- According to the latest financial reports by Amazon, the company's current revenue is $485.9 billion.
- The B2B ecommerce channel of the company called Amazon Business surpassed $10 billion in global annualized sales.
- According to a Feedvisor consumer survey, 89% of buyers say that they are more likely to buy from Amazon than any other ecommerce site.
Top use cases of how Amazon uses AI
Let's explore some of the top use cases of AI in Amazon.
Personalized product recommendations
When buying something on Amazon you are likely to see options like 'recommended for you', 'products you might like', or 'customers also bought'. This is one of the most popular AI-backed strategies used by Amazon to boost sales and is powered by Amazon’s powerful recommendation engine.
Amazon uses personalized product recommendations as a marketing technique to help it improve sales and continue satisfying customers by anticipating their needs. The idea is to create recommendations that closely align with what the user is likely to buy.
Amazon’s recommendation engine uses big data to analyze:
- The buying behavior of customers
- Products in the cart
- Items viewed
- Most searched items
Based on this user data, Amazon then makes recommendations, predicting exactly what each customer is likely to buy. This is how it nudges the users into buying more items.
AI, therefore, allows Amazon to turn a passive online store into an active sales channel.
Personalized product recommendations have been working well for the ecommerce giant as some reports even went on to claim that these recommendations drive 35% of purchases on Amazon.
How does it work?
Amazon uses item-to-item collaborative filtering to power its recommendation engine. Unlike content-based filtering, collaborative filtering uses the experience of other users to generate recommendations.
Amazon is credited with pioneering this approach after the company published the article, Recommendations: Item-to-Item Collaborative Filtering, in 2003.
The recommendation engine collects the following information:
- General data about products and users
- Data regarding the relationships and dependencies between them
According to researchers who worked on the recommendation engine, the algorithm matches each user's previous purchase to similar products and then compiles these into a recommendation list for each user.
Alexa-enabled voice shopping
Alexa is one of the biggest success stories for Amazon in recent years. The company debuted its Alexa-powered Amazon Echo devices in 2014 to mixed reviews. However, today, this AI assistant is available in 15 languages and 80 countries and boasts 100,000 skills.
While the usage of smart speakers has been increasing globally, the majority of these devices are Alexa-powered devices only. Amazon's Alexa rules the global smart speaker market by accounting for 26.4% market share.
With the growing popularity of Alexa devices, Amazon has been nudging users to start using their Alexa devices for voice shopping. Using voice-enabled ecommerce, customers can find products, add them to the cart, and even complete the purchase, all without ever touching or clicking a screen.
This AI-powered voice shopping not only makes the experience more convenient but also gives users reminders and recommendations about their purchases. Amazon has been leveraging this hands-free checkout experience to further solidify its place in the ecommerce market.
How does it work?
As per Amazon, Alexa devices detect the wake word and then send the users' request to the secure cloud. The cloud then verifies the wake word and processes the user request. After being processed, an answer is sent back to the user.
The machine learning process that powers Alexa-enabled voice shopping works in a similar way. The user first activates the device and then lists items they want to search, buy, or add to the cart.
AI-powered search relevancy
When a customer uses the search bar on Amazon, there is a 42% chance of them clicking through to a potential purchase. In comparison, the number is 16% for Walmart and 13% for Etsy.
So, how does Amazon manage to be so good at search? The answer is AI.
Amazon's search bar is another great example of smart AI implementation.
The company has a large team of engineers that have expertise in search relevance. The team of these AI-savvy engineers spends its day making Amazon search as relevant as possible. All this hard work pays off since Amazon uses AI to convert more searches into actual sales as compared to their competitors.
This case study highlights how Amazon's conversions average 2.17% when someone visits the site. However, if a visitor runs a search, the conversion jumps to 12.29%. So nearly a 6x increase.
Moreover, Amazon's proprietary algorithm called the A9 algorithm (now called A10) puts a strong emphasis on sales conversions.
AI optimization in the Amazon warehouses
Integrating AI in its supply chain has helped Amazon optimize its warehouses and inventories. Amazon uses AI to predict consumer demand and accordingly manage the inventory.
Here's how Amazon uses AI in its warehouses:
- Predict consumer demand
- Evaluate product availability
- Optimize delivery routes
- Track the supply chain
- Personalize customer communications
This helps Amazon ease the delivery process and give customers a delightful shopping experience.
The company is also a market leader in using AI to optimize product delivery by determining the most effective route for a package to reach from point A to point B.
Amazon was the first company that introduced one-day shipping. This has only been possible because the company has continuously adapted and evolved to be more automated and streamlined. AI has had a big role in achieving that.
Through robotics and AI-related innovations, Amazon continues to expand and streamline its global operations model.
More recently, Amazon has also started using automated drones for quicker delivery.
‘Just walk out’ tech in Amazon Go stores
Recently, Amazon launched a new kind of retail store in the US called the Amazon Go stores. These stores are based on a revolutionary tech that Amazon calls the 'just walk out' tech.
Shopping at these 'just walk out' stores means that customers don't need to wait in line. They can enter the store using their Amazon app, take the products that they want, and simply leave. The billing will be done automatically through the Amazon app.
The first Amazon Go store was a small convenience store in Seattle with roughly 1,800 square feet of retail space. As of March 2021, now there are 32 Amazon Go stores in the US and 15 in the UK.
Amazon has also started offering its technology to other retailers to help them automate their retail stores as well.
How does it work?
This 'just walk out' tech works in the same way self-driving cars work - by relying on computer vision, sensor fusion, and deep learning.
The technology automatically detects when a product has been taken from the shelf or put back. It keeps a track of items in your virtual cart and when you leave the store, your Amazon account is charged, and you are sent a receipt.
This 'just walk out' shopping experience is another way Amazon is using AI to cement its place as a market leader. Just as the company set a precedent for one-day delivery, now with these Amazon Go stores, the company has set a precedent that customers shouldn't have to wait in line in physical stores.
Amazon’s secret - The flywheel approach
Amazon is particularly adept at integrating AI from top to bottom. The secret to this is something called the 'Flywheel' approach that Amazon utilizes as an AI management strategy.
In engineering terms, a flywheel is a tool designed to efficiently store rotational energy. It works by storing energy when a machine isn't working at a constant level. Instead of wasting energy by turning it on and off, the flywheel keeps the energy constant and spreads it to other areas of the machine.
Similar to a flywheel, using AI takes a lot of effort to get started. However, once the wheel begins turning, it's far easier to keep it going by giving it continuous smaller boosts.
At Amazon, using the flywheel approach means keeping AI innovation humming along while encouraging energy and knowledge to spread to other areas of the company.
Thanks to this approach, innovation around AI and machine learning in one department helps the efforts of other teams as well. This essentially ensures innovation throughout the entire organization.
What is therefore created in one part of Amazon can act as a catalyst for AI growth in other parts of the company.
This approach has proven to be extremely successful in making Amazon a major AI adopter as well as a market leader.
From starting off as a book-selling company to becoming the household name that it is today; Amazon has come a long way. AI has definitely played a big role in helping Amazon dominate the eCommerce space.
The company is betting big on an AI-powered future as it pushes its AI expertise to just about every layer of the business including warehouse robots, its next-gen cashier-less retail stores, and of course, Alexa.
The 5 use cases we discussed here are just the tip of the iceberg. As a consumer, soon, you are likely to see many more interesting applications of AI.
While Amazon is not the only tech company pouring resources into AI (Microsoft, Google, Apple, and Facebook have also been investing billions in AI research), it definitely has an edge over its competitors.
The seeming AI expertise that Amazon has built over the years along with its general push towards more AI-driven applications makes it likely that Amazon will continue on its trajectory to dominate ecommerce for years to come.