Consider product suggestions on your eCommerce website.

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Did you know that companies that personalize their web experience through product suggestions experience a significant increase in conversion?
According to a report by Mckinsey & Co, 35% of purchases on Amazon are the result of its recommendation engine. These days, nearly every online business is leveraging product recommendations to improve customer satisfaction, increase revenue, and solidify personalization. Obviously, if done correctly, they can increase click-through rates, average order value, conversions, and other important business metrics.
What is a product recommendation engine?

It is a system that collects information and uses algorithms to make recommendations for the benefit of the client and the business. Information is accumulated for each user and analyzed based on categories such as past purchases, demographics, or search history. On this basis, the system delivers content that is tailored to the customer’s needs. Showing limited options of highly relevant products makes decision making easier. According to a Salesforce analyst, this system may represent a small percentage (7%) of visits, but a significant part of revenue (22%).

How to take advantage of product recommendations on websites?

When browsing or shopping online, shoppers expect a seamless and personalized experience. To meet this expectation and increase conversions, an advanced recommendation engine is more necessary than ever.

A Business Insider report reveals that while most customers are looking for an improved personalized experience, only 22% are satisfied with the level of personalization they receive. Improving the shopping experience through product recommendations gives your business a boost. The type of recommendation to show depends on where the buyer is on their trip.

Here are the best practices to consider when developing effective recommendations:

1. Home page

For any user visiting the website for the first time, the homepage is the main point of interaction. New visitors don’t have a specific intention in mind, and as a result, stores can’t collect enough data about them to recommend relevant products. Therefore, the recommendations for first-time home visitors go to great lengths to help them explore and discover products. Since you already have enough data for loyal customers, personalized product recommendations can be easily used. For example, if the consumer has bought a television before, it can display the latest television accessories.

Product recommendations that can be used include:

a. Most popular products: various rules can be based on popularity.
b. Best selling products.
c. Recently Released Items.
d. Products that have discounts or offers.
e. For returning visitors, products related to past purchases or discounts on recently viewed items.

2. Category pages

Category pages need to be differentiated accurately and quickly. Recommendations on category pages are almost similar to those on the homepage, although there is only one difference. The items displayed are specific to the category or subcategory that users see. Here, the products displayed are based on the interaction between the customers and the website.

Product recommendations that can be used include:

a. Top selling items in category.
b. Popular products in category.
c. Recently added products in category.
d. Products with offer or sale in the category.
e. For returning visitors, the items associated with their previous purchase in this category.

3. Product pages

Detailed information about the product you are selling is displayed on the product information page. When consumers visit a product page, we collect data to determine if this is their first product or if they have seen other products. Personalized suggestions can be provided using this available data. The recommendations on the product pages aim to increase the average order value and the conversion rate.

Product recommendations that can be used include:

a. Associated or complementary products (cross-selling).
b. Similar products.
c. Often bought together (top sellers).

4. Cart pages

During the last stage of the customer journey, it is essential to allow them to complete the transaction without distraction. However, companies can take this opportunity to drive sales by recommending products effectively. The main purpose of product recommendations on the shopping cart page is to increase the order value.

Product recommendations that can be used include:

a. Complements or accessories (cross-selling).
b. Recently Viewed Products.
c. Higher value alternative of products added to cart (best selling).
d. Add-ons to benefit from free shipping or other offers.
e. Products that are frequently purchased together for items added to the cart (best selling).

5. Order confirmation page

Most businesses believe the deal is complete after customers make the purchase. However, this is not always correct. Still, they should recommend items based on user interaction. The main purpose of this recommendation is to provide users with another hook so that they can continue their journey through the website and thus repeat the cycle. Knowing that we have important data at this point, these recommendations should be more personalized.

Product recommendations that can be used include:

a. Cross-category recommendation
b. Best sellers (related to items purchased)
c. Trendy items
d. New Arrivals

6. Error or exhausted pages

Reaching an “out of stock” or “404 error” page increases the chances that users will abandon the purchase. The exit rate is usually very high for these error pages, leading to possible loss of conversion. These pages can be turned into potential business opportunities by displaying top sellers (to keep users engaged), items based on browsing history, etc. This can act as an excellent catalyst to repeat the experience that would otherwise be interrupted.

Product recommendations that can be used include:

a. Products similar to out-of-stock or wanted items
b. Recently viewed articles
c. Best sale


Consumers access a website from different channels. Whether you’re developing your current recommendation engine or building one from scratch, you need to provide users with smart advice. Once a basic structure for product recommendations has been established, a detailed system of data entry and analysis should be implemented adding another layer of data. Therefore, applying machine learning can improve recommendations.

Companies can use a variety of product recommendations to increase revenue, customer experience, and engagement. But the key here is to consider the short and long term objects and implement them in a structured way. Therefore, a robust product recommendation engine will help your customers to make more informed decisions very easily, thus increasing your revenue and profits. Advanced product recommendation with cutting edge technology is the way to stay ahead of the competition.

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