Explore Amazon’s remarkable journey from its inception to becoming a global giant. Discover how technological innovations like virtual shopping carts and AI-driven recommendations have redefined the customer experience, setting new industry standards.
Jeff Bezos, The wealthiest man today, had impulsively decided to start a company in the dot-com boom in 1994. He never regretted it. He might have regretted the then-contemplated company name, Cadabra, which weirdly sounds like a ‘cadaver.’ Settling with the name Amazon, today Bezos is the CEO of a globally recognized brand.
Revolutionizing ecommerce, Amazon is one of the innovative leaders on several technological fronts, from online shopping and video streaming to cloud computing. Consumers expect better and more significant experiences, and Amazon has delivered consistently.
Suppose you wonder why Amazon’s ecommerce platform has survived over the years, having revolutionized shopping experiences. In that case, it is mainly because of their technological innovations and focus on personalization and recommendations for a fabulous customer experience.
Technological Innovations
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Virtual Shopping Cart
Online grocery shopping is not a recent digital advancement. Dating back to the 1990s, HomeGrocer.com was one of the first players, later sold to Webvan in 2000. Bankrupted in 2001, Webvan was bought by Amazon.
Experts reckon that Amazon is the only brand with a successful reign over the years. Amazon Fresh, launched in 2007, built a successful business model, including a two-hour delivery system for its members.
Adding technology to the mix, Amazon recommends using its cloud-based voice service, Alexa, to help with orders. Amazon claims that the more Alexa places orders in the Fresh cart, the more the virtual assistant learns about customer preferences.
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Walk-in, Walk Out
Would you put products into your shopping bag and walk out of the store without paying guilt or fear of being caught? The innovators took their e-commerce service offline with ‘Just Walk Out’ stores. With cashier-less exits, customers only need a mobile phone with a personalized QR code in their Amazon app to enter the store and buy products. While cashier-less models are also adopted by retailers like Tesco and Marks & Spencer, the technology used by Amazon Go is unique.
The products that customers pick up are automatically detected by depth sensors, computer vision, and machine learning. The Amazon attendants clad in fluorescent green T-shirts are only there to help out with a quick, seamless customer experience.
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The Dash Button that failed but succeeded
Imagine you are looking into your pantry for flour, and there isn’t any. One slight push of a button on a small device and your order is placed. Such was the promise sold by Amazon to its customers back in April 2015. Customers were so bewildered by the concept that they decided it was an April Fools’ prank. Amazon had to release an official press release confirming the actual existence of the single-function controller Dash Button.
Being named the tech inventor, Amazon’s launch of Alexa and Echo devices digitized the shopping list concept, and in 2019, the Dash button was discontinued. Yet, both Amazon and industry experts do not call it a technology casualty.
The Dash Button served its purpose as a marketing and customer experience prop. The concept tickled customers as the brand took an e-commerce website offline. Amazon used the quirky product to promote itself and began to drive more innovations into the market.
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Incoming Amazon Drone Delivery
A Hunger Games or a PUBG fan will be the most excited about mystery boxes dropping from the air. The only difference is that Amazon’s vision is to associate drones as a delivery system rather than a technology used for spying or deploying weaponry.
Currently in development, the brand hopes to achieve a 30-minute or less delivery service with drones. With a target of fast delivery by leveraging automation technology, Amazon hopes to launch Prime Air soon.
Amazon has Prime Air development centers in many countries, including the US, UK, and Israel. Amazon is leveraging the ‘sense and avoid’ technology to withhold its promise of safety. Moreover, their experimentation and testing data are also analyzed to improve their systems and operations.
The Back-end Algorithms and Analytics
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The End Game: Personalize & Recommend
Amazon customers are the essential key to growth, but the challenging aspect is personalization and recommendation algorithms.
Their personalization journey began in 2010 with the ‘Customers who bought’ widgets that worked wonders. Even today, the widgets catch the customer’s attention for a few seconds. Over 35% of sales are credited to these widgets.
Amazon Personalize, an in-house machine learning (ML) service, helps the e-commerce leader develop personalization experiences with its static rule-based recommendation system. Managing the entire process of data processing, training data, optimizing, and using algorithms, it launches custom-made machine learning models to deliver customized experiences to its customers.
A highly researched process, collaborative filtering is Amazon’s most common process of product recommendations. Amazon researchers Greg Linden, Brent Smith, and Jeremy York were also recognized by the journal IEEE Internet Computing for their paper on “Amazon.com Recommendations: Item-to-Item Collaborative Filtering.”
The e-commerce giant utilized a relatedness metric to streamline the recommendations process for its customers, whose purchase histories can drastically change in a day. In 2019, during Amazon’s AI conference, re: MARS, consumer division CEO Jeff Wilke explained a “once in a decade leap,” which was an algorithmic advancement for recommending movies to Prime Video customers.
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Prime Video Factor
Some years ago, Amazon researchers brainstormed for a unique movie recommendation method for their Prime Video customers. Calling recommendation a ‘matrix completion problem,’ the researchers decided to leverage deep neural networks, which consist of millions of simple processing nodes.
Data would be fed to these nodes, and the results would pass on to the next layer. After long research and testing, they had a winning recommendation combination with an item-to-item collaborative-filtering algorithm and an autoencoder. Wilke commented, “We had a winner,” with their final results pointing towards autoencoders.
With a little help from Artificial Intelligence (AI), ML, and Predictive Analytics, Amazon also used the Deep Learning technique to broaden their recommendations algorithms. They also added a recommendation AI-driven engine to show customers what they want to buy. Amazon continues researching and experimenting with recommendation algorithms for an even more extraordinary customer experience.
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In-house Tools & Services
Initially, Amazon used third-party providers to help them with their backend operations. For instance, the global brand used Akamai, a content delivery network, and XHTML Strict, the markup language. They also used third-party services like Embedded CSS and HttpOnly Cookies for their site elements.
They soon began to build their tools and algorithms, including Amazon Personalise, Amazon Associates, Amazon CloudFront, and their e-mail service provider and DNS service provider. What’s more, Amazon launched a search engine, A9.com, in 2004 that demonstrated Block View, a street view map, much before Google launched theirs.
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Always Hustlin’
Enough said the ecommerce giant has put immense pressure on their competitors. While technology is responsible for its unmeasurable success, the seamless customer experience is credited to Amazon’s well-researched personalization and recommendation models.
With several technology-based projects in their pipeline and continued work on better personalization, will the industry giant achieve another revolutionary CX success?
