Today, our lives are tailored to our preferences. Think about it consider the meanderings of our daily lives. Online news agencies and blog sites recommend related articles based on those we’ve already read, what friends are reading and what’s popular around the net. E-commerce stores predict additional items we should purchase based on past purchases and in-cart items. Video streaming platforms know pretty much exactly what we want to watch before we do. And, as if that weren’t enough, social media makes it easy to follow exactly who and what we want with a simple click or tap.
Unarguably, this has the potential to create a harmful, world-shrinking tunnel of existence that shows you only what you want to see, surrounding you only with specialized information you want to believe. But, alas, as consumers, getting what we want (and I use “want” purposely) has never been easier.
The algorithms used by each online company from Facebook, Twitter and Amazon to Netflix and The New York Times are constantly shifting and improving in order to make your reading, shopping and social experiences even easier, even more intuitive than before. Let’s keep it simple and stick with Amazon to understand algorithm changes and use them wisely.
Amazon’s E-Commerce Personalization
When it comes to tracking consumer buying behaviors and product recommendations, specifically Amazon holds hordes and hordes of data. On one hand, the data is supremely valuable and reflective of consumer behavior and, on the other, somewhat tainted and disillusioned.
But, even with the occasional irrelevant data sets, the e-commerce giant’s recommendation system is super effective. In fact, according to Rohini Srihari, in his article entitled Amazon and the Age of Personalized Marketing, “The response to buying suggestions’ that Amazon offers its customers is said to generate an additional 10% to 30% in revenue for the business.”
Like the strategic placement of the Buy’ button, or starred reviews, Amazon is able to capitalize on this advancement because their algorithm makes consumer’s lives easier. No fuss, no muss. What you’re looking for is exactly where you’d expect to find it. And that’s key.
So, How Do They Do It?
Brace yourself, now, for an over-simplified explanation of an incredibly complicated system one that scholars actually refer to as ‘personalized collaborative recommendation systems.’
Here goes nothing: the Amazon algorithm groups together products that multiple like-minded users have viewed in succession of one another. For instance, if George, John, Paul and Ringo all look at yoga mats individually of one another, and then all proceed to navigate from a yoga mat page to a water bottle page, Amazon knows that these two items correspond.
Thusly, if you visit the yoga mat page, you’ll notice a buying suggestion for the water bottle. Of course, there are other factors that go into your recommendations: user feedback, product popularity, etc. Regardless, with this studied recommendation, as Srihari mentions, revenue inevitably increases. And, honestly, the shopping process is just too convenient for it not to garner profits.
Include It in Your Design
With a little time and effort, you, too, can implement a recommendation engine onto your product pages. There are multiple platforms out there that have developed and engineered these systems in order to mesh with your layout and code. You simply implement it.
For instance, companies like Barilliance offer “True Personalization” and a sophisticated system that spans across multiple channels: mobile, desktop and tablet. On the other hand, if you have the time and the know-how, you can build your own.
Whether or not you choose to integrate something like this onto your e-commerce site, the significance of recommendation engines – within the scope of today’s larger digital landscape – should be understood. That is, now more than ever, it’s all about convenience.