AI radically impacts e-commerce. It’s a mandatory staple for businesses today.
Scientific research tanks like Gartner say up to 80 percent of customer interactions are managed by AI today.
In 2020, Statista stated that AI handled 54 percent of customers’ daily interactions with their favorite organizations or stores.
These AI-enabled features include biometric scanners, chatbots, digital assistants, facial recognition scanners, etc.
More of this will help you predict customers’ preferences, hook them, turn visitors into customers and make their shopping experiences more accessible.
Servion Global Solutions predicted that AI would empower 95% of customer interactions by 2025 back in 2017.
Since the COVID-19 pandemic happened, this figure is more certain. And here is Amazon, making the best use of the technology.
How Amazon Uses Deep Learning AI
Amazon seeks to provide a high-quality shopping experience, and artificial intelligence helps with that.
Amazon uses AI technology in many ways, from Alexa voice assistant to image search to recommendation systems. The company also uses AI at its fulfillment centers, for fraud detection, product tagging, A/B tests, and pricing.
Amazon sees its retail business as a means of delivering value to customers. T
The firm sees that by using data science and machine learning techniques, it can provide better products at lower prices while maintaining consistently high customer service standards.
The company has been expanding its use of deep learning AI through these means:
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Speech recognition, synthesis of text-to-speech, and natural language processing (NLP).
All these are used to empower Alexa and related devices.
Alexa, the AI-powered voice assistant from Amazon, has become integral to the company’s strategy to dominate the e-commerce and smart home market.
Alexa plays a crucial role in many of the devices that Amazon sells, and it serves as a platform for third-party developers.
Speech recognition is one way Alexa can understand spoken language and answer questions. Text-to-speech synthesis is another way it converts text into speech.
The third way is natural language processing (NLP) which enables it to understand natural language and respond to queries you could ask off the top of your head (like “What’s going on with my calendar today?” or “what is today’s date?”).
To achieve these capabilities, Amazon uses artificial intelligence (AI) and deep learning technologies under development at its Lab126 research facility in Cupertino, California. This makes it easy for customers to use their voices to do things like making a grocery list.
Using the Alexa app, a customer can say, “Alexa, add a frying pan to my shopping list” or “Alexa, check out my shopping list.”
You can also track the location of your order by saying, “Alexa, where’s my stuff?” and many other things, as shown here. Customers want ease; here is ease. If you can’t offer that as an e-commerce or retail business.
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Deep learning AI helps Amazon’s recommendation systems work better.
To recommend the right products to customers and increase sales, Amazon achieves greater accuracy through customer behavior analysis.
This leads to more relevant product recommendations, resulting in higher conversion rates, lower returns, and less money spent on marketing campaigns.
It also uses deep learning algorithms for:
- Product categorization: Assigning products into categories such as “books,” “electronics,” or “home improvement.” You wouldn’t expect humans to classify all products manually, did you?
There are millions of products on Amazon. That would consume both time, energy, and tons of money.
- User feedback: This is used as training data for machine learning algorithms like deep belief networks that can learn from millions of examples with high accuracy over time. You’ll get to know more about this later in the article.
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Product search: Helping customers find what they want as quickly as possible by searching through millions of products at once instead of having them scroll through pages on each page individually (or type keywords into the search box).
This is done by providing users with suggestions based on their previous searches and variables such as location or past purchases.
This way, customers don’t have far too many choices to overwhelm them while looking for items online.
There is an emphasis on “providing users with suggestions based on their previous searches and variables such as location or past purchases.”
You will need to invest in deep learning AI to maximize results for your business.
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Product description generation: Amazon generates descriptions automatically so customers can get more information about any given item before purchasing one to keep costs low while increasing revenue.
Those descriptions, you’d think humans wrote them. Nah, not all of them.
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Personalized recommendations: Amazon also utilizes personalized suggestions based on user preferences/history data collected during past purchases.
It also considers demographic factors such as age, others’ tastes, similar interests, etc.
In other words, Amazon doesn’t rely on customer history when providing recommendations alone. Through AI, the preferences of people of that same age, location, and similar experiences are provided to the shopper.
This way, when shoppers don’t know what they want exactly, Amazon is like: “Hey, people of your age/location, etc., buy these products, wanna check them out?”
The major lesson here is that shoppers are not seeing everything you have on sale on their landing pages; they’re seeing what has been customized to be what they’ll like.
If you’d like to experiment, tell someone in the UK and someone in Texas to log into their Amazon website. These are two different people with different interests— They’d find different offers on those pages.
Wondering how I found out? You’d find it here.
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Machine learning and deep learning help Amazon improve delivery time frames.
This may not come as a surprise, given that electric vehicle brands are also utilizing it.
However, what makes this third area unique is that it significantly impacts Amazon’s business in helping determine the best route for delivery agents to take.
Before using machine learning and deep learning, Amazon’s delivery vehicles would travel randomly between stops. Now, like Amazon, businesses can use __these algorithms __to calculate the most efficient way for each driver to navigate through traffic. Amazon also uses these algorithms to predict which orders will most likely be late based on weather conditions and other traffic patterns throughout the day.
To further improve efficiency, AI can tell drivers where they should park so they can pick up packages more quickly. This feature has __reduced wait times __by 20 percent since its introduction.
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Amazon uses deep learning for fraud detection.
This shouldn’t be surprising too. Big banks, Fintechs, and many other institutions use it. But Amazon knows that the more data you have, the better your AI at detecting fraudulent transactions and other harmful behaviors.
Amazon uses deep learning for all these purposes:
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Detecting fraudulent transactions.
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Detecting fraudulent reviews (both buyers and sellers).
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Detecting fraudulent buyers or sellers (when someone has multiple accounts or hacks into another account).
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Detecting fraudulent account sign-ups and returns.
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Detecting credit card use (this is done through a third-party service called machine learning)
All of this work leads to fewer things going wrong with Amazon’s business operations while providing the highest possible customer experience.
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Deep learning AI is used to tag products in photos and improve customer experience.
Amazon improves the customer experience by making it easier for customers to find what they want.
For example, if you want a pair of Gucci sunglasses but don’t know what they look like, you would have had to search through thousands of photos before finding one that matches your criteria.
With Amazon’s tagging system, you can upload a photo of what you want and use AI to identify products that best match what you’re searching for. Amazon has been able to do this through Amazon Rekognition.
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Deep learning algorithms are used in Amazon Robotics, AWS, and fulfillment centers.
In addition to the company’s cloud computing division, AWS, which uses deep learning to improve storage solutions and predict customer behavior, other divisions of Amazon are using deep learning algorithms.
For example, Amazon Robotics is a division within the company that focuses on developing robots for use in e-commerce fulfillment centers. These robots automatically move products around a warehouse and use computer vision technologies to detect specific items needed by humans working near their workstations.
The Fulfillment Center (FC) Division uses machine learning for part of its forecasting process. That is, they forecast demand so that they can fill orders more efficiently. How?
Deep learning algorithms help locate items inside an FC quickly and efficiently during peak times like the Christmas shopping season or Black Friday weekend sales events. This helps keep up with customer demand at those particular moments when customers want what they have ordered faster than usual.
If you are the customer and you know your store runs cheap sales events like Black Friday weekend, would you be comfortable waiting for over a week for an order? It’s free, yeah, but you won it. How would you feel?— You’d perhaps want to quit Amazon for some other place. You’d think they have so many customers they can’t keep up with their services.
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Predicting Product Prices with Neural Networks
“Customers always have choices”
Amazon uses deep learning to predict the price of products in its marketplace. The model, called Deep Price Predictor, uses an MLP (multilayer perceptron) architecture with a single hidden layer.
It’s trained using optimization techniques like stochastic gradient descent and Adam to find the best parameters of the model so it can be used in production.
If you don’t understand those terms, that’s okay. You’d hire an expert to help you improve your eCommerce website.
As a result of the models Amazon has implemented, those models you don’t understand, it has seen great results in:
- Better prices for customers: Deep Price Predictor makes sure that prices are more accurate by accounting for factors such as sales tax and shipping costs when determining them. This ensures that customers aren’t paying too much or too little for an item based on their location or what they’re buying.
- Higher customer satisfaction: With accurate pricing information provided by Deep Price Predictor, customers are happy because they know exactly how much they’ll pay before they buy anything at all; this means no surprises or extra fees!
And you know how it works in the business world. Assume you’re the customer and checking out only to see a crazy surprise fee; how would you feel?
Many people would abandon the cart. Your business will contribute to the statistics of those whose carts were abandoned. In 2021, there were 69.57% of abandoned carts. This doesn’t make you happy, nor will it make customers happy.
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The Use of A/B Tests
This is one fascinating experiment some e-commerce companies don’t do enough of. Amazon uses A/B testing to optimize its products. This method of experimentation is used to compare two versions of a product to determine which experiment performs better.
For instance, Amazon may want to know whether showing a list of related products on the right side of their homepage or at the bottom has higher conversion rates. Using A/B testing, they can determine how much more effective one variation is over another.
You can use A/B tests to optimize the customer experience too.
For example, if Amazon wants customers who have just placed an order to see an image of their package as soon as it’s shipped to let them know what’s coming, Amazon will send out two emails.
One email will be without tracking information, while the other one will have actual tracking information sent to different groups of shoppers. These groups will receive emails at different points in time.
The results from these tests allow Amazon engineers and other stakeholders involved in product development projects to decide how to serve their customers’ needs best.
They get to improve existing codebases or create new ones based on findings from various experiments. Without these experiments, Amazon wouldn’t dream of leading e-commerce as long as it has.
Conclusion
Amazon has achieved great results using AI, but it’s also improving research to achieve optimal performance.
All these are just examples of how Amazon uses deep learning and other types of artificial intelligence in the real world.
And while you may not have heard about all these projects before now, they’re examples of how companies are embracing these technologies—and why they’ll continue to do so in the coming years.
Software companies and brands are offering these services to businesses. All you’d need to do is find them and maximize the potential of your business.