Published on
October 6, 2020
Why is Netflix the most popular streaming service? There are so many similar services out there.
Well, there’s one thing that makes Netflix different from the others. It’s the personalized experience it offers.
The Netflix home screen is tailored for each user, showing them content they will most likely want to play.
Netflix is successful because it fulfills the expectations of the modern consumer.
According to Broadridge, 77% of users have purchased, recommended, or paid more for a product that offers them a personalized experience.
E-commerce websites can do this by offering their website visitors AI-powered product recommendations.
Data discovers that 56% of users are more likely to come back to a website that offers them personalized product recommendations.
Although the term product recommendations sounds like something simple and easy to incorporate, things are a bit more complicated.
In order to achieve the most successful outcomes, the key lies in intelligently implementing this engine.
Product recommendation engines are mechanisms that provide consumers with appropriate product details in order to enable them to take steps that favor both the customer and the business.
This makes them discover new products they are likely to purchase, extending the time they spend on the e-commerce store.
From landing pages to newsletters, these suggestions can appear at any point in the customer journey.
The product recommendations section can represent a simple list of your bestsellers. However, they can also be based on user data, displaying a customized list of products that the particular user can be interested in.
In this case, they are powered by AI algorithms that leverage data to determine the products they are most likely to purchase.
After every new interaction that the user has with the website, these user profiles are then recorded and continuously updated.
For most online stores, a solution for product recommendations is considered a must.
Although online stores use them widely, due to slow data collection, constraints on large datasets, and the failure of certain technologies to deliver recommendations through all consumer touchpoints, product recommendations can be difficult to implement with the necessary versatility.
AI helps overcome those hurdles.
Retailers can give more accurate and meaningful suggestions to the consumer thanks to the ability to gather real-time behavioral background from all platforms and use it to guide current decisions.
Depending on the company and the clients, the sort of product recommendation you choose can vary, so here are the best product recommendation activities to bear in mind no matter what you offer.
Suggested read: The What, Why, and How of Product Recommendation Engines
You probably know your bestsellers.
So, why not bragging a bit? This isn’t anything difficult, just identify the products that sell the most and show them to your visitors. Chances are, these products are something most of them can use.
Focusing on the products that bring you the largest income is a safe option.
What is more, when the user hasn’t made their decision, seeing that other people really like a certain product can impact their decision.
It’s in human nature to follow the tribe.
For example, Amazon highlights its bestsellers with a label:
Whenever you’re looking at a certain product, you’ve probably noticed the “you might also like” section on the bottom of the site.
These recommendations are based on your behavior on the website, it could be browsing or purchasing history.
Research has shown that this kind of filtering can increase sales by 20%.
Here’s how Nike does it:
Amazon’s personalized recommendations engine also uses the browsing history data to show you items you’re likely to purchase:
The segment where you show the kinds of brands and items available in your online retail store is the featured products section.
You can use this section to showcase your newest arrivals or the highest-rated products of your e-commerce store.
This is how Nike features its products:
Whenever someone’s purchasing an item, it’s likely that they will want to buy something that goes together with that item.
For example, if someone’s looking at an evening dress, they’ll probably want to get a clutch that goes with that dress too.
For example, ASOS is offering the “Buy the look” section that shows you the complementary products used on the picture of the product you’re looking at.
You can show product recommendations on various pages of your e-commerce store, including:
E-commerce visitors come from all sorts of channels.
Some of them know exactly what they want and have come to get it, with no intention of remaining more.
However, some are just looking around and can easily be converted into customers with precise and relevant product recommendations.
If you have a comprehensive product recommendation engine with insightful advice, you can help your customers make more enjoyable decisions while also improving your revenue.
Pixyle’s product recommendation engine can help you personalize the entire customer journey.
You will boost the exposure of your products, increase the relevance of your offering, and increase your sales rate by integrating this feature.
Never miss a conversion!
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How to develop solutions that help shoppers find what they are looking for.
How to develop solutions that help shoppers find what they are looking for.
How to develop solutions that help shoppers find what they are looking for.
How to develop solutions that help shoppers find what they are looking for.
Pixyle AI generates e-commerce product data that enables brands, retailers and marketplaces to deliver exceptional product discovery experiences.
