Did you know? Shoppers who click on recommendations are 4.5x more likely to add items to their cart and complete purchases. Even I was surprised when I heard that 49% of shoppers purchased a product they did not initially intend to buy after watching personalized recommendations. The gist is that more than 75% of customers are likelier to buy based on personalized recommendations. So, it's time to work on product recommendation strategies!

What Are E-Commerce Recommendations?

Ecommerce recommendations are actually what they sound like: products recommended to shoppers based on their interest or purchase history that they may like and want to purchase.

Think about a physical store; recommendations generally come from knowledgeable shopping assistants. For example, a person working in the store may tell you what’s been selling really well or recommend requirements. Like if you took pizza/pasta sauce to your cart, the store person might suggest a pizza base/ a pasta/ cheese, etc., that you might need or want to purchase.

However, in an e-commerce store, the recommendations are based on algorithmic decisions and data. It may be generic or personalized and can be delivered across various platforms and channels, including on-site, via email, or even social media. Generic recommendations are based on things like crowdsourced recommendations and social proof and tend to include trending products, new arrivals, and daily deals, among other things.

While personalized product recommendations are unique to shoppers, they can be based on things like shopper’s attributes, browsing history, situational context, etc.

E-commerce stores show personalized recommendations under various headings, such as "people also like," "you may like," "people also purchase," "complete the look," etc.

What Are The Types of Product Recommendations?

1. Collaborative Filtering

Collaborative filtering suggests products by grouping items that shoppers may like based on preferences by similar groups of users. It collected key data points such as shopper's behavior, activities, and interests.

We can divide collaborative filtering into two sub-categories;

(A) Decide through similar tastes

Product recommendations based on each set of user behaviors that are identical or similar are called user-user collaborative filtering or decided through similar tastes.

Though I clicked the site the first time, they suggested products based on my location, age, gender, and other factors.

(B) Let items be the common factor

Item-item collaborative filtering is more or less similar to its personalized recommendations. An e-commerce website suggests some products based on the item you want to purchase.

2. Content-based filtering

Product recommendations based on content like browsing history, purchases previously made, downloads, items added to the cart, ratings, and the products you like are content-based filtering.

It's a last-minute impulsive purchase that helps you win shoppers, so these are the best suggestions for all e-commerce purchases.

3. Hybrid Recommendation System

Combining collaborative and content-based filtering makes product recommendations more accurate and effective.

In a hybrid system, recommendations are based on:

• Similar behavior with other users and products
• Past purchases and preferences
  

For example, a screaming platform, Netflix, provides a hybrid recommendation system that works really well, using both what other users like and content that matches a user’s preferences.

Where To Place Strategic Product Recommendations?

Now that you know about the product recommendations, you might wonder where to place the recommendations that will easily increase the purchase rate. So, here is the strategic placement of product recommendations.

1. Homepage

Suggesting products on the website’s homepage is often the first point of interest for shoppers to reach their full potential. Recommending popular products, trending brands or categories, new arrivals, and daily deals are commonly used to tempt new customers on whom online retailers may not yet have any data.

When the shoppers return to your site, they expect more personalized recommendations, such as items picked exclusively for them or suggested searches based on their previous browsing history, which can work wonders in encouraging them to purchase.

2. Search Page

To make research easy for shoppers, e-commerce stores recommend products that best match their search query. For example, when customers look for something specific on an e-commerce website, the tech giant shows top-rated items the shoppers pick the most.

3. Category page

Shoppers may not always know what exactly they are looking for, but the product category page tries to solve their dilemma regarding what they want by recommending products they might be looking for. Whether it’s top-rated or popular products, recommendations can help shoppers stop scrolling and complete purchases.

4. Product page

To keep the shopper who already landed on your website page browsing even after they reach a point where they know that the products are not suitable for them. Here, you can give recommendations that show visually similar products or frequently bought items.

For example, a shopper reaches your website through social media to buy a shirt, but he/she finds that the particular product is not available in his/her size. So, if you provide a recommendation, chances are high that they will check any products similar to the shirt he/she wants.

5. Checkout page

The checkout and cart page are the best opportunities for up-selling and cross-selling. Try recommending complementary, top-rated, or products that most shoppers bought together with the selected items. This strategy works really well, especially for grocery stores, fashion websites, or sites engaging in selling items that customers might buy regularly.

6. 404 Page

Ensure a 404 page isn’t the reason to end your customer’s journey. Instead, you can turn it into an opportunity by showing product recommendations, whether it’s popular items, things they have viewed before, or similar products. This can inspire customers and keep them engaged rather than leaving them disappointed.

7. Popups

Nobody will look at annoying pop-ups, but the relevant pop-ups are sufficient to help shoppers. If you use this appropriately, pop-ups can turn hesitant customers into loyal brand advocates. But if you use over popups, shoppers will be irritated by it and leave the website without making purchases. So, use popups wisely.

5 Product Recommendation Strategies that Will Improve Conversions

1. Go In-depth into Signals of Interest

Purchase behavior is a generic term for assessing the host of purchase decisions. It is not just about what shoppers buy across many different purchases; it is also broken down into what their immediate last purchase was about, what they bought right before, what they may have paired with what was in their previous purchase, and those right before and so on.

If you dive deeper into users' interest signals, such as their previous purchases, browsing history, likelihood of products, etc., you can mine the data to make even sharper recommendations.

Here is an example of Zalando, which knows its users better and provides the best recommendations. Did you notice how they introduce four different sections to entice shoppers into looking at possibly preferred products across different categories?

Specifically, they induce interest by using copy effectively - “complete the look,” “Similar items,” “more from Lauren Ralph,” and “Better together” are all means to an end - for the customer to think in different ways and then to come to a purchase conclusion.

Quick Actions to Take:

1️⃣ Implement user behavior analytics: Gather detailed insights into how users interact with your site and find out their signals of interest.

2️⃣ Analyze Heatmaps: Identify which products or pages users often hover over or click. This helps pinpoint areas of interest and make personalized product recommendations.

3️⃣ Watch Session Recordings: Watch session recordings to observe user journeys, particularly focusing on what they add to carts, view repeatedly, or abandon. Use this data to refine your targeting and recommendations.

2. Capture the Imagination of Explorative Shoppers

You need to entertain multiple shoppers visiting your e-commerce store; some will know exactly what they are looking for and head right where they are likely to find a preferred product.

Some come over intent on research and are likely taking stock of similar products across various e-commerce stores within the same category. Some others are just browsing out of interest; they may be bored, may have heard about your brand, or may have seen your store coming up as a result of something they searched for. The last of these kinds have a good chance of becoming repeat customers if you know how to keep them engaged on their first few visits.

How you distribute recommendations throughout your site, across different pages and channels, can be key to driving engagement.

Ikea excels at this on their e-store.

They begin on their homepage and seamlessly guide visitors into specific categories as they continue exploring.

For example, while browsing "cafe essentials" and picking any product, we came across recommendation sections like “related products,” “recommended for you,” and “get the look”

The lesson? When presenting a category, make sure to lead customers into relevant sub-categories for a deeper, more personalized experience.

Quick Actions to Take:

1️⃣ Use User Behavior Analytics to Segment Shoppers: Identify patterns of how different types of shoppers navigate your store. Use analytics tools to track new visitors, repeated users, and those who frequently browse without purchasing.

2️⃣ Create Curated Categories for Browsers: Use data insights to build personalized recommendation blocks, such as "pair up with" based on previous interactions, ensuring they stay engaged and feel catered to.

3. Present Categories Similar to the One Being Browsed

Guiding customers through various categories is valuable; there is also an advantage in helping them dive deeper into a specific category.

For example, if a shopper is browsing for bedroom furniture, instead of displaying related items like wall decor or window treatments, focus on offering more options within the bedroom furniture category itself.

Missguided, a fashion brand, effectively employs this approach. They speak their target audience's language, appealing to their preferences and style.

At the same time, they improve the shopping experience by showcasing what other customers are actively searching for, creating a sense of community and validation for new visitors.

Quick Actions to Take:

1️⃣ Use User Behavior Analytics to Track Category Engagement: Monitor which product categories shoppers most explore. Use this data to display more refined and targeted options within the same category.

2️⃣ Analyze Heatmaps to Optimize Category Pages: Use heatmaps to see where customers focus on the category page. Highlight similar products in high-traffic areas, keeping their attention within the same category.

3️⃣ Offer Personalized Recommendations Within the Same Category: Use user behavior data to present more options within the category they are currently viewing, such as "Top-rated in bedroom furniture" or "Trending in this category."

3️⃣ Showcase Popular Searches from Other Shoppers: Highlight products within the same category that other customers are actively searching for, reinforcing a sense of community and encouraging deeper exploration.

4. Focus on Affinity-based Suggestions

One of the most personalized ways to show recommendations to your shoppers is Affinity-based suggestions. Interactions can span across the kinds of products they are viewing, what they are reviewing, what questions they are asking, and what they are adding to the cart.

One of the most personalized ways to showcase eCommerce recommendations is to consider user affinities.

Affinity-based suggestions can be especially fruitful for those already at the “awareness” stage of the purchase funnel.

Shoppers at the awareness stage are clear about what they want and why.

Interactions can span across the kind of products they’re viewing, what they are reviewing, what questions they are asking, and what they are adding to the cart.

Let’s take an example of One,

When shoppers show interest in a refreshing overnight balm, they suggest products like eye cream, a vitamin C serum, and an antioxidant PM serum.

They recognized that someone searching for an overnight balm is likely aiming to improve their night skincare routine.

This insight drove their additional recommendations.

Quick Actions to Take:

1️⃣ Create Affinity-based Product Bundles: Offer related products as bundles based on typical customer behavior. For example, suggest an eye cream, vitamin C serum, and antioxidant PM serum alongside an overnight balm for a cohesive skincare routine.

2️⃣ Highlight Complementary Items: Product pages should prominently display complementary or frequently paired items based on the customer’s current interests. For example, they should show skincare products that align with their night routine preferences.

3️⃣ Use Purchase History for Recommendations: If repeat customers browse, suggest products that align with their previous purchases. Tailor recommendations to their shopping history to create a personalized experience.

4️⃣ Offer Customized Product Categories: Organize related items under personalized categories like "Complete Your Night Routine" or "Pairup with" to guide customers deeper into relevant products based on their affinities.

5. Create More Engagement with Wishlist Recommendations

One often-overlooked yet essential feature for e-commerce sales and engagement is the wishlist.

It’s easy to ignore, as it sits quietly next to the “add to cart” button and is usually overlooked by those not immediately ready to buy.

However, we tend to differ, and an e-commerce wishlist holds great potential.

Here are some key reasons why using wishlist recommendations can be powerful:

• Notify users of price drops
• Share brand-specific sales and discounts
• Highlight fast-selling items from their wishlist to create urgency
• Announce promos related to wishlist products
• Inform users when a wishlist item is low or back in stock
  

A brand that excels at following up with wishlist recommendations via email is Missguided. Their emails are nurturing, relevant, and packed with specific, easy-to-understand content.

Myntra and almost all big e-commerce stores provide wishlist recommendations.

Quick Actions to Take:

1️⃣ Leverage Wishlist Data for Personalized Offers: Regularly review customers' wishlists to send targeted emails with personalized product offers, such as discounts or promos on wishlist items.

2️⃣ Notify Users: For stock changes, highlight popular wishlist items, promote related products based on the wishlist, and send price drop notifications.

Providing your shoppers with relevant product recommendations is an essential part of part of running an e-commerce store. These related recommendations help your shoppers find products they love, and they also help to increase conversion rates and average order value. So, now that you know the best 5 product recommendation strategies, it’s time to start applying them to your business. Give them a try, and let us know how it works for you!

FAQs

(1) How do you write a product recommendation?

To write a product recommendation, focus on the customer’s needs and explain how the product solves their problem. Mention key features and benefits clearly. Use simple, persuasive language highlighting why the product is a good fit for them.

(2) How does behavior analytics help to plan product recommendations?

Behavior analytics helps you understand what customers are interested in by tracking their clicks, browsing history, and purchase patterns. With this data, you can make more accurate product recommendations based on their preferences and actions.

(3) How can I improve my product recommendations?

You can improve your product recommendations by using personalized data like previous purchases, browsing habits, and items frequently bought together. Tools like user behavior analytics help refine these suggestions, making them more relevant to each customer.

(4) How do you recommend someone to buy something?

To recommend someone to buy something, highlight how the product meets their specific needs or solves a problem. Focus on the benefits, use relatable language, and add social proof like customer reviews to build trust. Keep the recommendation clear and to the point.