How AI is Helping Amazon become a trillion-dollar Company
Table of Contents
- Dance of the Robots
- Amazon Alexa
- Amazon Recommendations
- Amazon Go
- Creating A Cohesive Customer Experience With Artificial Intelligence
From the beginning, Amazon has used artificial intelligence to make product recommendations based on content that users have already expressed that they like. Over the years, the algorithms behind these systems have been revised several times. Jeff Wilke, CEO of the global consumer unit at Amazon, said that these days, thanks to machine learning, the recommendations are becoming more powerful.
Amazon’s alternative performance and learning capabilities provide Alexa voice assistant, which allows Amazon Web Services users to access cloud-based tools, allowing consumers to pick up items and quickly exit the Amazon Go store, directing the robot to charge. Product Shelves are delivered directly to staff at the fulfillment center and more. While this technology is important to Amazon’s big business, the scope of its use is still astounding.
Dance of the Robots
Deep inside Amazon’s 855,000-square-foot logistics center in Kent, Washington, 18 miles south of Seattle, a group of Amazon orange robots are doing a dance. On top of each orange machine, there is a yellow pod with nine rows of product packaging shelves on each side. With the help of artificial intelligence, when someone in the Pacific Northwest buys on Amazon.com, each robot will automatically start working, and now each robot can automatically bypass other robots to reach the terminal end. A fenced-in robot park, where an employee picks up a problem item and places it on a conveyor belt, and hands it to another employee who packs it in a box.
In the Amazon order processing scale, high efficiency is very important. Millions of orders are increased every year, and even a second or double savings per order can make a huge difference in the bottom line.
For some time, Amazon has used machine learning in its fulfilment centers to “improve our ability to predict what customers order and put them in the right place,” Wilk said. “It also improves efficiency and the speed at which we get the materials to be used.”
Global sales of smart speakers exceeded 30 million units last year, and this number is expected to grow to nearly 60 million units this year. While Amazon is still a leader in the smart speaker industry, with sales of about 20 million units last year, other companies (especially Google) are also growing and starting to hold on. Each has its nuances, but let’s take a look at the “Echo” scenes “and see how Alexa works.
Although the Echo cylinder contains features such as speakers, microphones, and small computers that can wake up the system and turn on its lights to let you know it is activated, if you send it to anything you tell Alexa, its real function is A cloud explained. via Alexa Voice Service (AVS) will appear.
So, when you ask Alexa what the “weather is today”, the device will record your voice. The recordings are sent online to Amazon’s Alexa voice service, which splits the recording into comprehensive commands. Then, the system sends the appropriate output back to your device. If you inquire about the weather, an audio file will be sent, Alexa will tell you the weather forecast, and you will not be able to go any further between programs.
Data and machine learning are the powerful foundations of Alexa, and it will become even stronger as its popularity and the amount of data collected grows. Whenever Alexa makes a mistake while translating your request, this data will be used to make the system smarter in the future. Machine learning is the reason for the rapid development of visual functions.
Amazon actually developed the concept of providing personal product recommendations after online shopping, using an algorithm they call “item-based collaborative filtering”. The algorithm makes the homepage of millions of its customers each different based on their interests and previous purchasing history.
Amazon.com has been building a store for each customer. Everyone who visits Amazon.com thinks about it differently because it is tailored to you in terms of their interests. It’s like entering a store, the shelves are starting to rearrange, things you might want to move forward with, and you are less likely to be interested in moving forward.
Amazon not only uses each customer’s purchase data but also uses the purchase history of other people who have purchased the same product to provide “frequently purchased together” information in their product list. In addition, they will also consider customer feedback and ratings. How so? By providing recommendations such as customer interests and reported customer satisfaction, price, and quality standards.
Amazon continues to improve its integration filtering by linking purchase history with browsing data. For example, if a customer buys socks, Amazon may not only recommend socks in the future. Instead, their algorithm may look at personal browsing history, see them watching hero movies at Prime, and recommend shirts with a Marvel logo. For Amazon, real-time translation of large amounts of different data is key, this recommendation engine calculates 35% of its total revenue.
Amazon’s state-of-the-art retail store that opened last year has opened up new avenues for customers, where they can swipe their smartphones, select the items they want and check out without waiting in long lines. Amazon GO is the first easily accessible store in the history of the tech giant and makes extensive use of computer vision and in-depth learning methods to provide consumers with easy-to-use information. Offering a variety of products, from ready-to-eat foods to groceries, the entire store can be accessed through an Amazon account and an easy-to-install Amazon Go app. The test, which was only open to Amazon employees, is now open to the public in January this year.
With a major plan to open more than 20,000 stores in the coming days, Amazon Go integrates artificial intelligence, computer vision, and data from multiple sensors to ensure a seamless feel for customers.
Creating A Cohesive Customer Experience With Artificial Intelligence
The information from these three main pillars of the company collectively builds a sense of corporate customer solidarity. Customers can visit the Amazon Go store to purchase some dinner items and allow Alexa to find a recipe, and the product recommendation engine may decide that a customer may need to purchase some type of pan. Instead of competing, various departments share their new information to provide customized and integrated customer information.
Amazon has made great strides since its inception in the field of mechanical engineering. The company now sells its electronic learning methods to customers including NASA and the NFL through Amazon Web Services. Through the development and application of artificial intelligence in other areas of the company, it provides personalized artificial intelligence solutions for large and small businesses.
Deepak Wadhwani has over 20 years experience in software/wireless technologies. He has worked with Fortune 500 companies including Intuit, ESRI, Qualcomm, Sprint, Verizon, Vodafone, Nortel, Microsoft and Oracle in over 60 countries. Deepak has worked on Internet marketing projects in San Diego, Los Angeles, Orange Country, Denver, Nashville, Kansas City, New York, San Francisco and Huntsville. Deepak has been a founder of technology Startups for one of the first Cityguides, yellow pages online and web based enterprise solutions. He is an internet marketing and technology expert & co-founder for a San Diego Internet marketing company.