What are Product Recommendations? Types & Strategies

Product recommendations enhance the shopping experience, so they lead to higher ecommerce revenues and better customer loyalty.

I created this guide for those looking to get a quick overview of product recommendations, including their types, examples, and quick ways to implement them properly.

Some good news for you right away—you can add recommendations to your store in minutes and have them work on autopilot.

In this post:

• What are product recommendations?

• Types of product recommendations

• How are product recommendations made?

• Product recommendation strategies

• How to get started with recommendations

How are product recommendations created?

Product recommendation systems suggest items by analyzing site data, such as visitor activity, sales, and product features.

They are categorized into two types:

• traditional (rule-based)

• AI-powered (using self-learning algorithms)

Traditional product recommendation systems use three techniques (collaborative filtering, content-based filtering, and hybrid) to generate suggestions based on rules developed from the known preferences of either an individual customer or a customer group.

AI product recommender systems analyze both historical and real-time customer data to generate highly personalized and contextual suggestions without manual input.

Product recommendations: strategies

Use these ideas to increase your sales with product recommendations:

• Display related, popular, and personalized recommendations through popups and forms

• Re-engage visitors by showcasing their recently viewed products

• Promote best-sellers and featured products in a website feed

• Highlight discounted items with website popups

• Use social proof to showcase your products in action

• Combine best-selling items from multiple categories

• Recommend items that complement the viewed product

1. Display related, popular, and personalized recommendations through popups and embedded forms

Popups are an effective way to recommend products, capturing customers' attention by presenting items directly and prominently.

But it's not just about the display—popup tools make recommendations contextual and relevant thanks to visitor targeting and/or AI algorithms.

For example—

Allbirds suggests checking out socks for shoppers who have added shoes to their cart:

That was a "You might also like" type of recommendation, but you can add many others with popup software, including these in Wisepops:

Let me show you how you can easily implement this product recommendation strategy with Wisepops, our own tool.

In the template library, you choose to see the options containing product recommendation "blocks" with campaigns, like this one with Others also viewed:

The process takes a few steps:

• customize the design of the campaign

• choose how many products you'd like to recommend in one popup

• add products ids for the software to choose from/ignore automatically

The last two steps are done in this window, for example:

If you'd like to try creating a similar campaign, let me recommend Wisepops—it's rated 4.9 stars on Shopify and Capterra.

You'll be able to use three onsite channels—popups, forms, and the onsite feed—to display recommendations and track results in a detailed dashboard:

Here are the resources you need:

• How to create and customize product recommendation campaign

• How to choose which product recommendations to include or exclude

Grab a free account to get started:

2. Re-engage visitors by showcasing their recently viewed products

Product recommendations based on browsing history are highly relevant as they let shoppers revisit viewed items, reducing "decision fatigue" that comes from being overwhelmed by too many choices.

You can make those items accessible from any page on your store in two clicks.

One way to use this tactic is AI Wishlist.

AI Wishlist uses browsing data and sales performance to not only display recently viewed items but also do so based on purchase intention analysis.

Example:

In OddBalls, shoppers can return to the items they viewed from any page. The onsite feed (that animated bell) lets them know, so they click on it to see the recommendations:

AI Wishlist displays these messages on autopilot (there's an AI algorithm running and analyzing data at all times), so it doesn't require any input or coding.

This feature helps ecommerce stores increase sales by 5%.

Get a free account to get started:

3. Promote best-sellers and featured products in a website feed

Recently viewed items aren't the only product recommendation type you can use in the feed. You can also use best-selling and individual products.

For example, a luxury fashion brand A.P.C. shares their best-sellers (check out the first message in the feed):

When visitors click that notification, they'll get a reminder about free shipping and a list of best-sellers they can check out with one click:

If your Shopify store features a smaller product range or requires frequent promotion of individual items, you can create dedicated promotions for each.

Black Ember, a California-based lifestyle Shopify store, offers a small selection of products—just a couple dozen or so. Their product recommendations often focus on the unique fabrics and innovative laser-cut manufacturing techniques behind each item.

See how they showcased FORGE, their popular backpack, in the feed:

With this simple product recommendation tactic, the brand managed to grab customers' attention—which was a smart move since each product had some great selling points.

Thanks to this approach, customers could learn a bit about the quality and craftsmanship behind each piece right away, making them more likely to want to learn more.

And it worked—

A notification promoting a Multicam Black backpack, one of Black Ember's most popular items, was seen by 23,121 visitors, and 2,059 of them clicked through to the product page to check it out:

This message appeared on the homepage about three seconds after I landed there, meaning that most visitors will see it.

Considering that about one million visit Patagonia's website monthly, this popup can easily lead tens of thousands of shoppers to discounted items.

If you'd like to share similar product recommendations, get a popup software. Let me recommend Wisepops—it's rated 4.9 stars on Shopify and Capterra.

7. Recommend items that complement the viewed product

Suggesting items that go well with the product being viewed is a way to help customers imagine having them. That results in a positive and easier shopping experience.

Let's see some examples:

BYLT shows these suggestions to complement a simple white t-shirt (the section located at the bottom of the product page):

How to get started with product recommendations

You can add product recommendations to your website in two ways: by using a third-party tool or by developing a recommender system in-house.

Option #1: Get a product recommendation tool

This is the best way for small and mid-sized businesses.

You can get an app developed specifically for your ecommerce platform (Shopify, BigCommerce, etc.) and add product recommendations to your site with almost no coding in a matter of hours.

Here are some great ones:

• Wisepops. This app allows adding as well as generating product recommendations in popups, embedded boxes, the feed, and AI Wishlist. It uses browsing activity, visitor profiles, and sales data to predict purchase intentions, automatically showcasing products visitors are most likely to buy.

• Optimizely. An advanced suggestion system for generating web and email product recommendations, allowing you to personalize customer experiences both on your website and in their email inboxes.

• Nosto. A platform for creating enterprise-grade personalization, which includes product recommendations based on AI analysis of historical and real-time customer data.

Option #2: Develop your own recommendation algorithm

This option is the best for large businesses and retailers.

Developing a recommendation system in-house may require more time and effort, but it gives you full control over the algorithm, data, and process.

Amazon offers its recommendation engine through Amazon Personalize if you'd like to check it—it's one of the best in the business.

But if you want to build one yourself, check out the white papers on the basics of Amazon’s item-to-item collaborative filtering recommendation engine—it’s easy to implement (this one from Greg Linden should be a good start).

Also, Surprise is a great open-source Python library for building collaborative filtering models on a budget.

Summary

Product recommendations are a powerful tool that can make all the difference in converting visitors into customers. Whether you choose to use a third-party tool or develop your own recommendation algorithm, incorporating product recommendations into your website is an effective way to drive traffic and boost sales.