What Exactly is Big Data and Why is it Important?
Big data is a term that is used to describe large volumes of data – both in structured and unstructured forms. This large volume of data is collected by businesses on a daily basis but more than the volume of it, what one organization decides to do with this data is what is important. Big data can be analyzed for insights that help in reaching better business-related decisions and allows the professionals in the company to make strategic business moves that lead to the company’s growth.
As mentioned above, the importance of big data is not attached to the quantity of data your company has, but how you act upon that data. This is where big data is important. If analyzed correctly, it can help you in identifying root causes of failures, issues, and defects in real-time, or recalculating risk portfolios at one go, or detecting fraudulent behaviour before it hampers the efficiency of your company.
Amazon and Big Data
You might be thinking why are we talking about Amazon here?
Well, the reason why we are focusing on Amazon use of big data is because this is the e-commerce giant that has more than 2.5 million sellers active currently and selling on the digital marketplace[1]. Its net sales in the US was $280.5 billion in the year 2019.
Furthermore, based on a study by Episerver in 2019, it was found that 23% online shoppers go first on Amazon to find inspiration for a product regarding which they have a little idea in mind but are keen to purchase. Statista in its 2019 report came up with the conclusion that Amazon heads the ranking of the most popular shopping app in the US with 150.6 million mobile users accessing the Amazon app in Sept 2019![2]
All these statistics bring out one question in everyone’s minds – “What is that Amazon is doing with its data retrieved by customer analysis that is making it stand strong in the marketplace?”
This e-commerce giant’s success did not happen by some accident. It was a part of their well thought and executed strategy to leverage data in the best possible manner to derive value for the company.
In this article, we’ll tell you the what and how of it!
How Amazon Collects Customer Data and Uses it
When faced with a huge range of options, customers can feel overwhelmed. They lose perspective of what would be the ideal purchase for them and this is where Amazon steps in to solve the problem. In order to solve the problem stated here, Amazon engineers have written programs on big data gathered from customers while they browse and use it to fine-tune its recommendation engine. The whole idea here is to get this huge volume of data from the customers to build a ‘360-degree view‘ of each customer.
Amazon collects extensive information about how users interact with the various elements of its website. It tracks their journey right from what kind of products they look for on the website and what do they finally purchase. It does not just stop there. It goes a step further and tracks the customer’s journey in terms of the final delivery or return (if any) of the products. The entire process is captured by the website and this data is used to run tests and refine the site for upgraded performance that benefits both customers and sellers on the e-platform.
For customers, Amazon uses this large pool of data to modify the site and underlying algorithms to display the most relevant products that the customer may need. It makes use of this data in creating a personalized recommendation system. For example your last purchase on Amazon was a book based on management principles. Amazon’s comprehensive, collaborative filtering engine (CFE) keeps track of similar books purchased by other customers and recommends the same to you for purchase.
When it comes to the sellers, Amazon provides them with data and other metrics that can help them manage their own operations while improving on how they display the information regarding their product on the site or while advertising. Apart from leveraging data collected from functional interactions with the site and sales process, the company also makes use of the big data generated through customer interactions as well as recordings from Alexa and Echo. The insights are then used to further refine the algorithms, the website, and the overall sales process.
How Amazon Uses Big Data to Boost its Performance
In the last few years, Amazon has shifted gears. From being a pure e-commerce player to a giant that offers more than just products, Amazon has evolved every time a period of transformation in technology has arrived in the tech industry. Now, its main focus is on big data, big data processing, and is changing from an online retailer to a big-data company. It makes use of multiple big data technologies.
But one question that raises one’s curiosity remains how Amazon uses big data to boost its performance?
Here are some of the ways:
- It implements dynamic pricing to stay in the competition: You must’ve noticed that prices for products change frequently on websites. One of the reasons for this is that big data platforms assess a person’s willingness to buy. Amazon is one company that uses this tacting of dynamic pricing aggressively by shifting the cost of an average product every 10 minutes! Dynamic pricing leads to growth in sales and also generates a lot of profitable revenue.
- It performs screening of purchases and returns for possible signs of fraud: Amazon’s stature comes with a double-edged sword as it also makes it a target for retail fraud. Amazon battles this by collecting more than 2000 historical and real-time data points on every order and also makes use of machine learning algorithms to figure out possible fraudulent transactions.
- It depends on information to run fulfillment centers: Amazon’s warehouses have a special name: ‘fulfillment centres.’ The company uses big data there to track which items people buy the most often and if the stock is running low. The brand also has a patent for ‘anticipatory shipping’ too that predicts what people want to buy before they even place their orders.
- It cheers people to buy more with each purchase they make: The Amazon use of big data applications via product recommendations is known to everyday users. Now, with the release of Amazon Personalize, the company has provided developers with an easy to use and scalable platform that provides a recommendation to users across every domain. This allows other companies to tap into Amazon’s technology for their customer base, showing them merchandise options right from clothing to electronics and more.
When Amazon is able to make these successful appeals to customers with personalized picks and pushes them to spend more, company profits rise and people get the perception that it is the company’s website where they can buy virtually anything they might require.
Conclusion
Always adopting the customer-centric approach, Amazon began its journey by providing the best customer experience it could. To achieve this goal, it leveraged big data by collecting, refining, and analyzing it to improve the site’s functionality and the back end process. The Amazon use of big data is the prime example of the power of data science if utilized accurately. This also explains the company’s constant requirement to hire the best data scientists.
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