Did you know that product recommendations represent around 31% of eCommerce revenues and that 56% of customers are more likely to return to an eCommerce store that offers product recommendations? (Brilliance, 2023)
Also, did you know that product recommendations increase the time people spend on your eCommerce website? Research shows that customers who click on a product recommendation spend around 12.9 minutes on your website compared to only 2.9 minutes for customers who do not click on any recommendations (Wiser, 2021).
These product recommendation statistics are not to be ignored. They are highly crucial for any eCommerce business that wants to succeed in today’s digital age.
But what do we mean exactly when we say “Product Recommendation”?
Product recommendations are exactly what they sound like. They are products that are recommended to a brand’s customers.
In this article, we are going to start by discussing the benefits of product recommendations for eCommerce. Then, we will walk you through some of the best product recommendation tactics for eCommerce while presenting insightful examples.
Table of content:
Before working on product recommendation strategies for your eCommerce store, you must be fully committed to the idea. For this to happen, you must read about the benefits of product recommendation and how it can revolutionize the customer experience offered by your eCommerce store. The benefits include the following:
This will also lead to a decrease in your cost per acquisition (CPA), and customer acquisition costs (CAC).
Now that you know all the benefits of product recommendations in eCommerce, you must be eager to know how you can effectively implement them. That’s why we will now walk you through some of the most effective product recommendation tactics for eCommerce.
Artificial intelligence (AI) and machine learning are revolutionizing every aspect of eCommerce. That’s why it shouldn’t come as a surprise to you that such technology can be utilized in product recommendations.
Today, we have something called: “A Product Recommendation Engine”. It is an AI-powered system that uses machine learning to offer tailored product recommendations and suggestions to customers. This technology utilizes algorithms that take into account huge amounts of customer data, such as interests, preferences, buying behavior, browsing history, most-viewed products, shopping carts, feedback, and others.
There are three different approaches that product recommendation engines can use to generate product recommendations:
This approach predicts what an individual customer may like based on similarity to what that customer is already buying or using. It does not go beyond that. It often comes in the form of: “If you liked this, then you will also like this …”
For example, let’s say customer (A) is buying a dress from your online store, and you know from her buying history that the last time she bought a dress, she also bought matching shoes. A product recommendation engine using the content-based filtering approach would recommend shoes that match the new dress she’s buying after analyzing the patterns in the buying behavior of the customer (A).
This approach predicts what an individual customer may like based on their similarity to other customers. It often comes in the form of: “Other customers were interested in …”
For example, customer (A) is buying a dress from your online store; however, this time, you have a product recommendation engine that uses the collaborative filtering approach. In this case, the engine will analyze the data of customers (B) and (C) who bought the same dress. Let’s say customers (B) and (C) also bought sunglasses; then your engine would recommend sunglasses to customer (A).
Additionally, product recommendation engines are not the only AI technology that can be leveraged to recommend products to customers. Other available options include AI-powered chatbots, such as ChatGPT.
While it is possible to manually implement product recommendation strategies, we suggest you use a product recommendation engine or another AI tool because it will make the process much easier for you.
Check out this article to learn about the top 10 AI tools you can use for your eCommerce store.
Example: Amazon
Amazon is one of the most popular online retailers that use a product recommendation engine. It uses the hybrid approach and offers the following types of recommendations:
There are different types of product recommendations and many ways you can display them.
Understanding customers is essential for any eCommerce business that wishes to implement product recommendations. It is very crucial to the point that we can imagine “product recommendation” and “understanding customers” as two faces to the same coin.
Therefore, you should not hesitate to invest in understanding your customers. This encompasses the following:
The more you invest in customer segmentation, the more thorough and detailed it will be, which will yield more accurate product recommendations.
Speaking of customer segmentation and customer data, Converted.in offers you a great marketing automation tool that will segment your customers and gather all your data in one place, like a hub, with a 360-degree customer view.
The placement of your product recommendations is crucial. You have to make sure that your recommendations are highly visible. There are many places in which you can place your product recommendations:
Moreover, checkout pages are a great place to remind customers of products that they viewed and forgot about by using the “Recently viewed” type of product recommendation.
You can also use the “Recommended for you” type of product recommendation here to present personalized recommendations of products that you believe your customer might be tempted to add to the cart.
Email marketing never gets old, and it is a marketing channel that can help you implement effective product recommendations. Moreover, research shows that personalized product recommendations in email can increase the click-through rate by 300%.
You can integrate product recommendations into different types of emails within your email marketing campaigns, such as welcome emails, order confirmation emails, and cart abandonment emails.
Examples: Tarte
Tarte is a cosmetics brand with an eCommerce store that sells makeup, skincare, and beauty products. In this example, we are showing an email sent to one of Tarte’s customers. We can see from this example that Tarte used two types of product recommendations in this email:
Speaking of email marketing, the marketing automation tool offered by Converted.in can help you create, customize, personalize, launch, and automate email marketing campaigns.
We’ve already covered how you can incorporate product recommendations across your website pages and how you can integrate them into your email marketing campaigns. However, unfortunately, these options are often not dynamic enough; therefore, you need something additional that keeps your customers engaged.
For this, we are suggesting you use pop-ups. Popups will allow your customers to see your recommendations without having to navigate across pages.
One way to make your product recommendations more effective is to combine them with social proof. There are many ways you can mix social proof with product recommendations. For example, whenever you are recommending a certain product, make sure you display customer testimonials, such as reviews.
Example: Avon
As you can see in this example, Avon, the cosmetics brand, offers product recommendations with customers’ reviews displayed below each product.
We’ve mentioned cross-selling and up-selling in previous parts of this article; however, we wanted to discuss these two strategies in a separate point to highlight their importance when it comes to product recommendation.
First, upselling is the technique of recommending a higher-end, more expensive version of the same product or service that the customer is already interested in buying. The goal is to encourage the customer to spend more money and increase the Average Order Value (AOV).
Second, cross-selling refers to recommending complementary or related products or services to the customer. The goal is to encourage the customer to purchase additional items that complement or enhance the product or service they are already interested in buying.
Tip: The best place to use these types of product recommendations is the “Checkout Page”, and the best customers to use it with are those at the “Acquisition Stage” of their customer journey.
Example: Wayfair
Wayfair is an online retailer that hosts a wide range of products on its website. In this example, we can see how a product recommendation can come in the form of cross-selling.
Notice the phrase: “You might also need” being used as a title for the recommended products.
There are many marketing strategies that, if used, can make your product recommendations 100x more attractive, such as:
Example: Timberland
In this example, Timberland recommends its popular safety boots while implementing FOMO marketing strategies. It appears in the phrase: “Don’t Miss Out On…” and “It may not be here when you come back!”.
All this creates a sense of urgency and a feeling that this product is scarce, which automatically makes this product recommendation more attractive to customers.
Now that we’ve discussed so many types of product recommendations and even more tactics to implement them, you might feel a bit overwhelmed. You might be asking yourself now:
What is the best thing for my eCommerce store?
Well, the only way you’re gonna get an accurate answer to this question is through regular testing. For example, A/B testing can help you test different versions of product recommendations and see which ones work best for you.
Final Thoughts:
To sum up, product recommendations are a valuable tool for eCommerce businesses. They provide customers with personalized suggestions that are relevant to their interests and needs, which creates an overall better shopping experience for them. However, it is important to strike a balance between leveraging product recommendations and overwhelming customers with too many options.