Must-Have Product Recommendations For Your Shopify Store

PRODUCT RECOMMENDATION KICKSTART: a short list of simple, ready-to-go product recommendations that will effortlessly unlock new revenue potential for your Shopify store.

Why product recommendations?

Short answer: if shoppers can’t discover a product of interest – they can’t buy it.

Ecommerce sites use product recommendations to help shoppers discover products of interest and persuade them to buy. Some market stats to get your attention:

  • 91% of consumers say they are more likely to buy products from brands that provide relevant recommendations – Accenture Survey
  • 47% of consumers are willing to check Amazon if the brand they want to buy from does not provide product recommendations – SmarterHQ
  • 56% of online shoppers are more likely to return to a website that recommends products – Invesp.

PARADOX: Product recommendations are incredibly valuable and yet, are still massively under-utilized. Based on our empirical experience, less than 10% of all Shopify Plus merchants use a professional grade recommendation app of any kind.

Should you use Shopify’s own product recommendations?

Absolutely, yes. Sooner you act and implement any type of product recommendations the better.

Shopify’s recommendations are designed to generate a list of similar products based on product purchase data. You, or your developer can easily add them to your pages.

Adding Shopify’s recommendations to your site will help you validate the impact of product recommendations. You’ll quickly realize, however, that you’ve only scratched the surface of the revenue growth potential product recommendations can bring, and you’ll hopefully be inspired to start using a professional grade product recommendation app.

How do product recommendations work?

In a nutshell, product recommendations use machine learning algorithms to process transaction, product, web, and customer data and generate product suggestions and offers.

Professional grade product recommendations aren’t a ‘one trick pony.’ Connecting different audiences with different needs and preferences, at different stages of the buying journey, is a multi-dimensional problem.

That’s why advanced algorithms are needed to crunch product and customer data, including:

  • Visitor browsing history
  • Buying patterns
  • Product indexes
  • Description meta data
  • Sales history
  • Reviews
  • Visitor engagements
  • Preferences
  • Customer purchase history
  • Recently viewed items
  • What’s in the shopping cart
  • What’s on wish lists

Recommendations are continually updated as new customer data is collected and processed.

INFINITE POSSIBILITIES. To illustrate why Obviyo offers so many recommendation options, let’s use product similarity strategy as an example.

By using rich customer data one can model product similarity based on items viewed, items added to the cart, items abandoned, purchased, re-purchased, etc. One can also model product similarity using product descriptions, Shopify tag information, meta-fields, etc. Permutations of similarity models quickly escalate to a very large number of possible product similarity options.

So, where do you start?

To establish a reference point, let’s take Amazon’s home page as an example.

It may not be obvious at first glance, but if you pay close attention you’ll quickly realize that Amazon’s home page has dozens of dynamic content blocks that change as you’re engaging with the site.

AMAZON HOME PAGE SCREENSHOT: Almost every content block (marked by dotted lines) is both a data and AI algorithm driven content block introduced to help Amazon’s shoppers discover products of interest in the most efficient and delightful way.

Unlike Shopify which only provides similarity type recommendations, Amazon uses many different recommendation strategies, powered by many different types of algorithms, and displays many of them all at once on its pages.

In general, it feels as if every component of every Amazon’s web page is a variation of personalized product recommendations.

Realizing the scale and complexity of what Amazon is doing, it’s easy to throw in the towel and say, “We’re not Amazon, and we don’t have the people, technology, or know-how to even do this”.

This is where Obviyo comes in.

Growth Bots

To enable every Shopify merchant to bridge the gap between their current site state and the high bar set by, we built a revenue growth automation platform that provides the following:

  1. Algorithms: the solution is powered by the same machine learning algorithms as Amazon.
  2. Growth Bots: to minimize technical and operational complexity we built a long list of Growth Bots, or micro-apps. Each micro-app is designed for a specific use case, driven by real-time data, and powered by an algorithm designed for each scenario.
  3. Playbooks: to minimize a need for expert know-how we grouped Growth Bots into playbooks that act as a blueprint for achieving specific business objectives.

This blog post introduces you to our ‘Must-have product recommendations’ playbook.

Note: Obviyo has developed a whole library of Growth Bots – visit our Shopify demo store to learn more about bots mentioned in this post and many others.

The story behind “Must-Have Playbook”

We curated this playbook with the goal of helping you develop a revenue growth automation (‘hacking’) mindset.

You should view it as ‘silver mining’ – breaking the overall online shopping experience into many micro interactions and then using data and artificial intelligence to improve the revenue outcome of those interactions.

To help you easily understand the role of each Growth Bot, the playbook is centered around the different stages of the buyer journey.

Must-have recommendations

The following describes the context behind what happens at each stage of the buyer journey and the specific logic applied to each of the Growth Bots below.

Phase 1: Discover

Discover phase in the buyer journey is a critical step for all web visitors, especially so for new visitors that represent more than 90% of your web traffic.

These are people who are not familiar with your offering or your brand, who are coming in search of items that will meet their fundamental need for comfort, connection, variety, or uniqueness.

If your store does not connect with them quickly the opportunity is lost – often forever.

Popularity by items sold

PLAY: Boost revenue by showing what other customers love to buy from you.

WHY: In ecommerce, shoppers have no ability to try on, touch and see products in the flesh, making them more likely to be swayed by other customers’ opinions.

Product popularity is a great marketing technique that makes use of social proof, the concept that people will follow the actions of the masses. Featuring your most popular products reinforces what’s brilliant about your brand.


Viewed similar products

PLAY: Decrease bounce rate of product pages and increase engagement by showing products other visitors most often viewed next.

WHY: This recommendation play exposes shoppers to a range of alternative products similar to the one they’re browsing, allowing them to quickly find the one that best suits their needs. The recommendations are engaging and catchy and encourage visitors to view more products. They also give the sense to a shopper of being part of a crowd of similar shoppers.

PLACEMENT: Product Detail Pages

Popular in {Collection}

PLAY: Motivate visitors to view more products in a collection being browsed by promoting popular products in that collection.

WHY: Collection pages dominated by a product gallery grid are increasingly ineffective. Today’s shoppers are less and less likely to use product grid filtering and sorting features. They simply scroll down the product grid until they discover a product of interest or give up.

Featuring most popular products above the product grid helps shoppers find a product of interest more easily and quickly.

PLACEMENT: Collection Pages

Phase 2: Explore

Explore phase is about shoppers spending time investigating available options that will meet their needs, product details, and value considerations.

The rise of the mobile shopper has made this phase more complex. Buying sessions are shorter and highly fragmented. These shoppers now expect to have all information instantly available, in context, and when needed.

New arrivals

PLAY: Create a perception of exclusivity while making visitors more curious about your products.

WHY: We have a built-in expectation that something new must be better than what came before. Displaying the latest products will encourage people to purchase before anyone else. It’s a great way of making the customer feel as if they’ve got their hands on an exclusive treat!


Re-engage with returning visitors

PLAY: Re-engage with returning visitors by presenting products they viewed during prior visits.

WHY: It’s pretty rare that people buy something when they visit a site for the first time. Usually, they browse, compare and check out competitors before making a final decision. Reminding shoppers of products they browsed during prior visits to your store makes a fragmented buying journey more seamless.


Trending in {Collection}

PLAY: Spotlight products belonging to a collection that are trending with other online shoppers.

WHY: Using recent sales activity as social proof of trending products, you will more effectively influence buying decisions. Displaying trending products will encourage people to purchase with more confidence.

PLACEMENT: Collection Pages

Step 3: Buy

Success of the buy phase depends on the overall buying experience and effort needed to complete a transaction, from product availability to checkout.

The buy phase is also influenced by the actual price paid and the perceived value received from the seller.

Products viewed cross-sell

PLAY: Help shoppers find the right products by showing them products other shoppers purchased after the product they are currently browsing.

WHY: It’s nice to know what other people, who followed the same browsing path, ultimately purchased. It takes the pressure off your shoppers knowing they can rely on the fact others did the research for them.

By putting these options in front them it’s much more likely that they’ll follow suit!

PLACEMENT: Product Detail Pages

Cross-sell of complementary items

PLAY: Combination of product discovery and product cross-sell technique driven by product browsing patterns.

WHY: Shoppers are unaware of the breadth of your product catalog.  Recommending items that are frequently bought together will alert shoppers to products they didn’t previously know you offered, further earning their confidence as the best retailer to satisfy a particular need.

PLACEMENT: Product Detail Pages

Cart cross-sell

PLAY: Inspire customers to buy related or complementary items to those they’ve already added to their cart.

WHY: Cross-selling is a highly-effective tactic for increasing cart value and demonstrating the breadth of a catalog to customers. Cross-selling of less expensive items stimulates impulse buying.


Checkout cross-sell

PLAY: Reduce cart abandonment by offering impulse buy items.

WHY: Cross-selling is a highly-effective tactic for increasing cart value along the entire checkout funnel. By offering complementary impulse buy items you will create a more ‘sticky’ checkout process and reduce cart abandonment.

PLACEMENT: Checkout Pages

Step 4: Re-engage

Post-purchase engagements are critical for increasing lifetime value and brand loyalty.

Most importantly, a successful re-engage phase drives existing customers to discover additional products, and accelerates the next buying cycle.

Related to order

PLAY: Re-engage your buyers immediately after they have completed a purchase with products related to those they have just purchased. You have nothing to lose!

WHY: Purchase confirmation page is too often treated as a ‘dead’ page. Instead, you should view it as an opportunity to start a new buying lifecycle with product cross-selling, upselling, and other type of re-engagement. This is your opportunity to alert your buyers to products they didn’t previously know you offered.

PLACEMENT: Confirmation Page

Related to items viewed

PLAY: Showcase the depth of your product catalog with product recommendations based on browsing history.

WHY: Related product recommendations are an effective way to guide shoppers through their product discovery steps. By exposing them to the depth of your product catalog you will help your shoppers uncover items they did not even expect to find in your store.

PLACEMENT: Confirmation Page


Revenue Growth Automation is an emerging ecommerce strategy successfully practiced by Amazon. Our goal with this blog is to provide context behind a short list of product recommendations that each Shopify site should have.

At first glance implementing a Revenue Growth Automation strategy might seem too complex and out of reach for the majority of Shopify merchants. In reality, with Obviyo’s Growth Bots – ready-to-go site modules – almost every Shopify merchant can easily begin their own product recommendation journey.

Author: Zee Aganovic

Founder & CEO – serial software technology entrepreneur whose prior startups were acquired by Microsoft and Ricoh. Focused on use of AI in ecommerce. Spending most of his time listening to and learning from founders, online marketers, and ecommerce professionals, and channeling their input into Obviyo’s product development.