product recommendation amazon

How to Ace Product Recommendation like Amazon

The best way for an eCommerce venture to succeed is by providing relevant products and content to the audience. Making the shopping experience as personal and tailored as possible for each individual consumer. Product recommendation has become the driving force for any eCommerce business.

Recommendation and personalization improves sales, conversion rates and revenue by a vast margin. And the leading example in this sector is Amazon.

Amazon is an eCommerce industry pioneer. And, when it comes to recommending products and personalizing the whole customer experience in relevance to an individual, no one does it better than Amazon.

A few examples of recommendation from Amazon (On and off their website/app) –

  • What Other Customers are Looking at Now

  •  Customers Who Bought Items in Your Recent History Also Bought

  •  Your Recently Viewed Items and Featured Recommendations

  •  Customers Who Bought [the chosen product], Also Bought –

And, now here are a few juicy bits about how a recommendation engine impacted Amazon’s sales and profit number –

  • 35% of’s revenue is generated by its recommendation engine. (Source)

  • This has contributed to a 29% increase in Amazon’s earning in the second fiscal quarter from the previous quarter. Learn more about it here.

These figures  lead us to only one statement and one question. Amazon has mastered the art of selling tens and hundreds of product to every individual customers with the help of its AI based recommendation engine. It’s considered as one of the best recommendation engine on the planet.

So now how do you emulate Amazon? How can you also cross-sell or upsell more products to your existing customer-base? Let’s find out.

Building your own AI recommendation engine

Yes, this is the perfect way to go, but only if you have a lot of capital and time to spare. Building your own AI engine might take years to happen, implementation on the other part, would take another 6-10 months time.

Ask yourself do you really have enough time and money that you can wait so much. Build an entire team from scratch to build an AI engine from scratch? No, right?

Building your own AI recommendation engine, is the ideal thing to do in a perfect world. Anything home brewed is as good as it can get, but one has to grasp the magnitude of the situation at hands. Development of your engine would take years and by that time you and your so called competition would be miles ahead of you.

So what do we do?

Well, there is an alternative. Read on to know more.

Using AI based product recommendation engine

AI based recommendation

As I said above,  problem at hands when it comes to emulating a recommendation model similar to Amazon is the capital and time investment. Developing such an in-house engine could cost millions of dollars as well as devoting thousands of hours to build it.

But with the advent of AI marketing brands like Boomtrain, the tech has become accessible to brands all across the globe.

Today an AI engine does the heavy lifting, otherwise done by marketers and engineers manually.  You don’t have to worry about spending hours and hours implementing a recommendation engine on your website. We’ve got you covered.

How does this work?

AI engines sits on top of your website and starts monitoring all user activities. Using technologies such as machine learning and deep learning, AI learns all user habits and what sort of products they are interested in. It analyzes all the key signals a that may emanate from a customer’s journey, like products bought, products browsed, pages visited, time spent of the website, etc.

Using these data-points AI stitches together cohorts as well as targets users on an individual level. As soon as the cohorts are ready and every user has been individually analyzed, the AI engine kicks into top-gear. It dynamically starts suggesting products on-site, as well as on other platforms such as emails and mobile based Push. Your work here is to just designate target pages and sections where such recommendation would take place.

And we are yet to discuss what’s the best part of AI based recommendation engines.

AI recommendation engines runs on your system all the time (yes, 24 X 7), forever learning and looking for new data-points based on user-behavior. It will constantly keep optimizing recommendations in search of higher conversion ratios and user-engagement rates.

Now, here is a brief of how AI recommendation engine will help your brand similarly to what Amazon has done –

  1. Reduce cart abandonment rates,

  2. Boost cross selling of products (if X bought this, he might also be interested in Y),

  3. Recommendation of  new products that a user might find useful but has failed to discover,

  4. Increased customer loyalty. You can learn more about it here.

  5. Address omnichannel brand communication to boost user engagement,

  6. Implement a 1:1 Brand to User personalization model.

In conclusion, using a recommendation engine that could emulate something that Amazon does, can be really beneficial for your venture. It will help you understand your customer’s journey better by seeing how they behave on your website, the time when they are most likely to make a sale, the time when they are most likely to open your emails, etc.

And hence, a recommendation engine would use this data to its advantage and target users with precise products that are likely to be bought by the user.


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