March 6, 2022
The power of product recommendation and why your online business should implement it in 2022
But don’t just take our word for it. Everyone has heard of Amazon, right? Did you know that one of their most useful engines for improving their sales is their product recommendations?
According to an article by McKinsey, their product recommendation engine generates 35% of Amazon.com’s revenue. And that’s not all. They reported a 29% increase in sales from $9.9 billion to $12.83 billion during the same year. Amazon’s recommendations cause more than a third of its product sales.
Amazon developed automated, real-time product recommendations based on customer behaviors such as onsite browsing and making a purchase. By doing so, they created a personalized shopping experience for each user.
Most of that growth can be attributed to the integrated recommendations which are a part of almost every part of the purchasing process from a shopper’s first click to checkout.
What do product recommendations look like?
If a shopper was looking for a specific product such as a pan, they may be interested in purchasing other kitchen-related products as well. Knowing this information, how can you apply product recommendations?
Through the use of AI and Machine learning in crafting product recommendations, the shopper’s interest in the specific product they are viewing will automatically be detected and generate tailored recommendations that would best match their interests.
For instance, back to our pan example, that shopper would see similar kitchen products that they may find interesting below the image of the item they are viewing. Similar items such as a saucepan or grill skillet would be displayed as product recommendations.
How are product recommendations so powerful?
- Product recommendations make your customers feel a sense of emotional attachment to a brand that truly understands them.
- Customers want to know what’s available to them, and are ready to make a purchase if they’re directed to the right products.
- When products are recommended based on customers’ needs, they will be inclined to spend more because they feel the brand personalizes their experience.
- Undoubtedly, the higher the product’s relevance is to the shopper, the greater the chance that he or she will purchase it.
- 56% of online shoppers are more likely to return to sites that provide them with product recommendations.
- Personalization ultimately enhances the shopping experience for shoppers and potential customers which leads to better conversion and greater sales.
What are the benefits of product recommendations?
The benefits of product recommendations are quite obvious. Customers are likely to shop more which leads to higher conversions. Why? Because when shoppers are offered personalized and customized products they are more likely to look at what is recommended to them and spend a longer amount of time browsing on your site.
Overall, the customer experience is improved and it is also more efficient and enjoyable for both the buyer and the seller. Furthermore, it increases sales and enhances customer loyalty. This creates a win-win situation for both parties.
Thanks to powerful computing technologies and more and more data available and easily accessible, creating product recommendations has now become very sophisticated.
What are the results of implementing product recommendations?
- The CTR will be increased- shoppers will continue to shop and click through the similar products provided by the product recommendations.
- Average order value will grow- customers will end up adding more items to their carts which will increase the total order value.
- Revenues will improve- shoppers will be spending more time and more funds on your site, which will ultimately cause improvement in your revenue.
Let Exposebox help you increase your online business sales by implementing our AI-powered and highly sophisticated product recommendation engine. We give you the ability to provide your customers with dynamic and personalized product recommendations based on visitor data, behavior, and history.