Have you ever considered finding out how shopping websites and applications recommend your next purchase? The answer is BIG DATA. Ever seen an item on Amazon and then later seen an ad for that item on another website or been surprised with the recommendation that matches your hobby? Amazon accomplishes these facts by using Big Data.
What can Big Data tell Amazon about its customers?
Today’s consumers know more about that store than ever before, and Amazon knows more about them. With the insights that big data analytics provide, Amazon has the opportunities to engage its consumers with the kinds of products and offers they’re searching for or maybe didn’t even know they wanted. Big Data helps Amazon focus on its consumers by providing them suggestions by carefully examining their browsing and purchase history and then using some predictive models to make relevant suggestions to the customer.
How does big data help the recommendation engine of Amazon?
It involves three stages:
1. Events
2. Ratings
3. Filtering
Let us now understand each of them one by one:
Events
Amazon keeps records of information on all forms of customer behavior and their activity on the website onto the database. For each click made by the user, a record of that event is logged into the database. The entries are stored as User X clicked product Y and therefore the details. Events are logged within the database for every action like users liking a product, adding a product to the cart, and buying a product.
Ratings
Ratings are essential as they indicate what a user feels about a few particular products. Recommendation systems can assign implicit values to different forms of movements. Generally, the utmost rating is 5, but the developers can modify that in step with their needs. Recommendation systems can take into consideration the ratings and feedback the shoppers provide.
Filtering
This stage means filtering the products that supported the ratings and other users’ data. There are three varieties of filtering that are employed by recommendation systems:
1. Collaborative-Filtering
In Collaborative Filtering, all the visitor’s choices are first compared, then they get a recommendation. For example: If user A likes products W, X, Y, and Z and user B likes products W, X, Y, Z, and T. Then, it’s likely that user A will like product T.
2. User-based-Filtering
In User-based Filtering, the browsing history, items bought, likes, and ratings are considered first, then provide recommendations to the user.
3. Hybrid-Filtering
A hybrid approach uses both collaborative and user-based filtering.
For retailers, data is both their asset and challenge. It is the key to understanding and fascinating their consumers. It lets them effectively plan their product assortment mix at the local level. At the shop level, data is at the core of effective store optimization, and when it involves supply chain management, data means visibility and adaptability at a worldwide level. There are many sectors where Amazon implements big data.
Let us observe all the sectors one by one. Retailers competing on low margins have known for a decade that data could be a powerful tool, Big Data takes it and supercharges it for the fashionable multi-channel retail environment.
Retailers who embrace Big Data throughout their organization can unlock hidden keys and values required to create a business successful and profitable. Risk management presents an enormous opportunity for retailers to use their existing data to boost the underside line.
1. Cost Optimization and Performance
Data helps in better decision-making, Amazon makes full use of this data for the value optimization of its products. Pricing is one of the foremost crucial factors of any business, and getting it right is extremely difficult. Using Big Data, Amazon solves this problem.
2. Supply Chain Optimization
The base of retail business is its supply. Efficiently managing the provision chain is directly proportional to growing profits. Amazon uses big data to lock its delivery time by connecting the customer to its nearest inventory or manufacturer.
3. Recommending books using the highlighted words in Kindle
Kindle is quite a book reading application, where the user can highlight words and may make notes which they’ll share among others. Amazon uses this data and recommends Kindle-user books per their reading history and highlighted notes.
There are many other places where Amazon uses and implements Big Data, above mentioned are a number of the few instances.
Conclusion
The ability to predict what the consumers will purchase and in a way is incredibly useful. Big data for Amazon means intelligently measuring, monitoring, and modeling their business in real-time. It creates the power to act on what shoppers are expecting from them, efficiently improving customer acquisition and conversion for lower costs and increased revenues. Additionally, to what data tells Amazon about customers, real-time insight into the whole business creates flexibility in periods of supply volatility or market uncertainty, which makes the retail supply chains more efficient and effective, controlling costs and enabling effective supplier coordination.
