How to reveal the secret of Amazon suggestions?
If you have ever purchased something on Amazon, you should notice the recommendation it provides. For instance, best selling products or the best price of the month. You should realize that these Amazon suggestions are not coincidences. They are based on a range of metrics and statistical data.
This global retail system recommendation system is based on the number of the simple elements - what a user has recently bought on Amazon, which products a user has in its virtual shopping cart, his or her wish list, the most well-ranked product in a particular market niche, and other factors. This recommendation algorithm is called “item to item collaborative filtering.” Generally speaking, this suggestions mechanism serves to customize the browsing experience for returning customers heavily.
Amazon sales growth directly depends on the way Amazon has integrated suggestions into all aspects of the purchasing process from product discovery to checkout.
As the Amazon representative said: “Our mission is to delight our customers by allowing them to discover great products serendipitously. We believe this happens every single day and that’s our biggest metric of success.”
More to say, Amazon provides recommendations via email. The best of it is these emails look personal despite the number of possible recipients. Amazon emails are based on user’s purchasing and browsing behavior.
Except refining the accuracy of recommendations themselves, Amazon continuously explores the new ways to reach regular shoppers. Amazon has already begun selling the products previously sold in bulk that were too cost-prohibitive to ship individually. Customers may order these items but only if their total purchase price is higher than $25. Such products can be actively recommended if an order crosses that pricing threshold.
Subsequently, Amazon searchers order more products bringing more profit to Amazon.
How does Amazon's suggestion engine work?
There are a lot of discussion around this topic on the web, and all merchants think differently. Let us also say some words toward this question.
Current Amazon item-to-item collaborative filtering scales hundred of thousands customers and products. It produces high-quality recommendation in real time.
Amazon filtering matches each of the user’s purchased and rated products to similar products. On the second stage, it matches identical products into a recommendation list for the user.
Amazon suggestion system is an efficient way of creating a personalized shopping experience for each customer which helps Amazon increase average order value and the profit generated from each customer.
As the official Amazon statistics show, the suggestions system works. As the company reported, the average sales number has increased to $12,83 billion during the second fiscal quarter, up from $9.9 billion during the same last year. Amazon high revenue for the previous year shows the high benefit of integrated recommendations in all stages of the purchasing process.
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