How Amazon Uses Deep Learning to Improve Buying Experience

AI radically impacts e-commerce and It’s a mandatory staple for businesses today. The world has changed and you know it. Scientific research tanks like Gartner say up to 80% of customer interactions are managed by AI today.

In 2020, AI handled 54% 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 spice up their shopping experiences.

Companies have become smarter about AI since the COVID-19 pandemic happened. And here is Amazon, making the best use of the technology.

How Amazon Uses Deep Learning AI

Amazon provides a high-quality shopping experience and artificial intelligence helps with it. From Alexa voice assistant to image search to recommendation systems, Amazon uses AI technology in many ways. AI is used at its fulfillment centers, for fraud detection, product tagging, A/B tests, and pricing.

Amazon wants to deliver consistent value to customers and it has been using deep learning AI through these means:

1. 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.

Speech recognition is big deal in today's world as it provides ease to consumers who want to use voice commands rather than use their hands on their keyboards. It’s one of the ways Alexa understands spoken language and answers questions.

Text-to-speech synthesis is another way it converts text into speech.

The third way is natural language processing (NLP). It enables Alexa 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?").

Amazon uses AI and deep learning technologies — which undergo consistent improvement at its Lab126 research facility in Cupertino, California — to achieve these capabilities. This makes creating a grocery list for customers effortless as they could simply command Alexa. Rather than manually surf through the list and add stuff to their cart.

Using the Alexa app, a customer can say, "Alexa, add a frying pan to my shopping list" or "Alexa, check out my shopping list."

Customers can also track the location of their order by saying, "Alexa, where's my stuff?" or other commands revealed here. Customers want ease; here is ease. You'll slowly be worn out of business if you can't offer ease.

2. Deep learning AI helps Amazon's recommendation systems work better.

   
   

Amazon achieves greater accuracy in recommending the right products and increasing sales through customer behavior analysis.

This leads to more relevant product recommendations, resulting in higher conversion rates, lower returns, and nominal 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?

• User feedback: This is used as training data for machine learning algorithms like deep belief networks. The algorithm can learn from millions of examples with high accuracy over time. You'll get to know more about this later in the article.

• Product Search: This helps customers find what they want as quickly as possible. It scans through millions of products at once instead of having customers scroll through pages on each page (or type keywords into the search box). Amazon's algorithm also provides 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.

• 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.

• Personalized recommendations: Amazon uses personalized suggestions based on user preferences/history data collected during past purchases.

  It also considers demographic factors such as age, others' tastes, similar interests with other people, etc. to provide recommendations.

In other words, Amazon doesn't rely on customer history when providing recommendations alone. The preferences of people of that same age, location, and similar experiences are provided to the shopper through AI.

This way, when shoppers don't know what they want, Amazon is like: "Hey, people of your age/location, etc., buy these products, wanna check them out?"

The major lesson here is that shoppers don't see everything you have on sale on their landing pages; they see what your algorithm has customized for them.

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.

3. Machine learning and deep learning help Amazon improve delivery time frames.

   
   

This may not come as a surprise. Electric vehicle and autonomous car 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.

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% since its introduction.

4. 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:

• Detecting fraudulent transactions.

• Detecting fraudulent reviews (both buyers and sellers).

• Detecting fraudulent buyers or sellers (when someone has multiple accounts or hacks into another account).

• Detecting fraudulent account sign-ups and returns.

• Detecting credit card use (this is done through a third-party service called machine learning)

  
  

All of this work ensures that fewer things go wrong with Amazon's business operations while providing an outstanding customer experience.

5. 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.

You can now upload a photo of what you want and use Amazon's tagging system to identify products that best match the items you're searching for. The company did this with its Amazon Rekognition.

6. 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 special moments when customers want what they have ordered faster than usual.

If you are the customer, 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.

7. Predicting Product Prices with Neural Networks

   
   

"Customers always have choices". Don't forget that.

And this is why Amazon uses deep learning to predict the price of products. 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 e-commerce 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.
• 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.

8. 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. They can determine how much more effective one variation is over another through A/B testing.

The results from these tests take Amazon engineers and other stakeholders involved in product development projects back to the drawing board. They then 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 variations 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.