Shopify Product Recommendations: How to Personalize Your Shopping Experience & Sell More

As a savvy Shopify retailer, you know the importance of creating a seamless and personalized shopping experience for your customers. 

One powerful way to achieve this is through Shopify product recommendations. Seriously, studies have shown that up to 35% of Amazon's revenue is generated through its recommendation engine. 

Thirty. Five. Percent.

Indeed Amazon is a great example to showcase the immense potential of product recommendations in boosting sales and driving profitability. 

But beyond revenue, personalized suggestions also create a better shopping experience for customers.

Done right, you can create a virtuous cycle, where satisfied customers make repeat purchases and become brand advocates, attracting new customers through positive word-of-mouth.

In this article, we will delve into the world of Shopify product recommendations and explore how they can revolutionize your business.

Sounds good? Let’s jump in!

What Are Shopify Product Recommendations?

Shopify product recommendation example

Shopify product recommendations are a sales strategy where you use data-driven algorithms to suggest products that shoppers might be interested in.

On a basic level, you can use simple logic to create product recommendations. So, for example, when a customer adds a pair of shoes to their cart, you might suggest a pair of socks to compliment them.

On a more advanced level, Shopify product recommendation engines can leverage data-driven algorithms. They can suggest relevant and personalized products to individual shoppers based on things like their browsing behavior, purchase history, and preferences.

We’ll discuss more about this later, but for now, let’s cover the reasons why you should add recommendations to your Shopify store:

The Benefits of Shopify Product Recommendations

Adding product recommendations to your Shopify store yields numerous benefits for both you and your customers:

  • Enhanced Customer Experience: Personalized recommendations make the shopping journey more enjoyable and convenient for your customers. They discover products that align with their preferences effortlessly.
  • Increased Sales and Revenue: By suggesting relevant products, the likelihood of cross-selling and upselling increases, leading to higher average order values and increased revenue for your store.
  • Improved Customer Retention: Personalized recommendations foster customer loyalty and satisfaction. Satisfied customers are more likely to return for future purchases, contributing to long-term customer retention.
  • Time and Cost Savings: Automating product recommendations reduces the need for manual merchandising efforts, saving your time and resources.

How Do Shopify Product Recommendations Work?

The process of generating product recommendations on a Shopify store involves several key steps. If you're just getting started this might sound overwhelming - but don’t worry, we’ll talk about the best tool you can leverage later to manage most of the heavy lifting.

  • Data Collection: Shopify stores gather vast amounts of customer data, including click behavior, purchase history, and product interactions. This data is crucial for understanding individual customer preferences and behavior.
  • Data Analysis: Once the data is collected, powerful algorithms come into play. These algorithms analyze the data, looking for patterns and correlations between customers and products. The goal is to identify customers with similar preferences and behaviors, grouping them into segments.
  • Segmentation: Customer segmentation is a fundamental aspect of product recommendations. It categorizes customers into different groups based on their shared attributes and preferences. Segmentation allows for more targeted and relevant recommendations.
  • Personalized Recommendations: Armed with customer segments, the recommendation system generates personalized product suggestions for each individual. These suggestions are tailored to cater to specific customer interests, increasing the likelihood of customer engagement and conversions.

How do Advanced Shopify Product Recommendations Work?

Okay, by now you should understand the basics. But to truly grasp the power of product recommendations, let's take a peek behind the curtain and explore the science that makes them work.

Collaborative Filtering Predicts the Future

Collaborative filtering is a popular technique used in recommendation systems. It leverages the collective behavior of customers to make predictions about an individual's preferences. The underlying assumption is that customers who have exhibited similar behaviors in the past will have similar preferences in the future.

Two primary types of collaborative filtering exist: user-based and item-based. User-based filtering looks at the behavior of similar users to make recommendations. If two users have a history of purchasing similar products, one user will receive product recommendations based on the other's preferences. 

Item-based filtering, on the other hand, compares the similarity between products. If two products have been frequently purchased together, one product will be recommended when the other is viewed.

The Role of Machine Learning in Personalized Suggestions

Machine learning takes product recommendations to a whole new level of sophistication. By leveraging powerful algorithms and vast datasets, machine learning recommendation systems can discern intricate patterns and relationships. 

These algorithms continuously learn from customer interactions, enabling them to adapt and improve their suggestions over time.

One of the key advantages of machine learning-based recommendations is their ability to handle more complex scenarios. They can identify subtle correlations between products, offer real-time recommendations, and even predict customer preferences for products they have not interacted with previously. 

This level of personalization contributes significantly to enhancing customer engagement and driving conversions.

Types of Shopify Product Recommendations (With Examples)

Ok, now that we know the ‘behind the scenes’ of product recommendations let’s talk about types of recommendations.

Each type serves a different purpose to enhance the shopping journey. Here are some of the most common types:

  • Thank you page product recommendations: Thank you page recommendations are personalized product suggestions that your customer sees on the order confirmation page after they’ve completed an order.
Shopify product recommendations on the order confirmation page
  • Post-purchase product recommendations: One-click upsells are potent product recommendations that you display immediately after a customer has completed their purchase - between checkout and the thank you page.
one click upsells are a powerful strategy for recommending products on shopify
  • Checkout product recommendations: Checkout recommendations are products that you suggest at checkout. Here you can offer non-physical products (e.g. shipping insurance) that effortlessly boost cart value. Or you can also offer cross-sells as Taylor & Stitch do:
Adding product recommendations to your checkout page is a great way to boost AOV
  • Related Products: These recommendations suggest products that are related or complementary to the item currently viewed by the customer. For example, if a customer is looking at a watch, related product recommendations may include bracelets or additional straps.
  • Recently Viewed Items: This type of recommendation reminds customers of products they have recently viewed, encouraging them to consider those items again. It's a helpful feature for customers who want to revisit products before making a decision.
  • Frequently Bought Together: These recommendations showcase products that are often purchased together by other customers. Offering bundles or package deals can increase the average order value and customer satisfaction.
  • Personalized Product Recommendations: Utilizing customer behavior data, these recommendations tailor suggestions based on an individual's browsing and buying history. The system identifies customers' preferences and suggests products they are likely to be interested in. Here's an example from Israeli candle brand Ner4U.

How to Add Product Recommendations on Shopify with ReConvert

Now that we understand the science behind product recommendations, let's explore how you can seamlessly integrate them into your Shopify store with ReConvert. 

ReConvert Upsell is an app that allows you to trigger personalized recommendations at checkout, between checkout and order confirmation and on your thank you page.

This is by far the easiest (and probably most profitable) way to add product recommendations to your shopify store in under 10 minutes.

  • Step 1: Go to ReConvert’s listing on the Shopify App Store & add the app.
  • Step 2: Install the app and follow the prompts to approve billing (ReConvert has a free trial for 30-days so you can test out your product recommendation strategy)
  • Step 3: Define ReConvert as your ‘post-purchase page app’. 1. Go to Settings -> Checkout -> Post purchase page. Select ReConvert Upsell & Cross Sell. Click the 'Save' button.

Step 4: Enable ReConvert’s conversion monster funnel, which will generate a branded post-purchase product recommendation funnel for your store. Simply choose the maximum discount you wish to offer & the app takes care of the rest.

Step 5: Congratulations, post-purchase product recommendations are live on your store! From here you can review your analytics to test different types of recommendations. You can also use the editor to easily build custom product recommendations. Or use the segments and triggers feature to build more personalized offers.

Shopify Product Recommendations Best Practices

Once you’ve installed ReConvert, it’s time to optimize them for best results. Here’s how:

Put Your recommendations where shoppers want them

Carefully consider where to place the product recommendation widget on your store. Common locations include the homepage, product pages, cart page, and checkout page. However, we’ve seen the highest conversion rates with:

  • Checkout product recommendations.
  • One-click upsells displayed immediately post-purchase.
  • Thank you page recommendations. 

See post-purchase product recommendations don’t interrupt the initial buyer’s journey. Introducing too many products pre-purchase runs the risk of overwhelming customers and risks losing the initial conversion. So, as a general rule, the later along the journey you offer additional products, the more conversions you’ll get.

For example, ReConvert user Tushy moved most of their product recommendations from the pre-purchase journey to the thank you page. They were able to achieve a 2.6% conversion rate and add almost $200,000/mo in additional revenue. 

📚Suggested Reading: How Tushy used thank you page cross-sells to generate an extra $191,786/mo.

Tailor your recommendations 

Different customers have different preferences, so avoid a one-size-fits-all approach. Customize the recommendation settings to target specific customer segments based on demographics, purchase history, and behavior patterns. Tailor recommendations to showcase trending products, bestsellers, or personalized suggestions.

Display social proof

Social proof such as customer reviews and ratings instill confidence in potential buyers. Social proof reinforces the quality and popularity of recommended products, encouraging customers to make informed purchasing decisions.

Add subtle scarcity & urgency

When you limit your product recommendation offers to the transaction, you’ll give customers more impetus to take action. Use exclusive offers that customers must claim in the moment. For example, a triggered pop-up with a timer is a powerful product recommendation tool that’ll drive your conversion rate even higher.

[fs-toc-omit]Common Product Shopify Recommendation Challenges

Adding Shopify product recommendations may come with occasional challenges. Being aware of potential hurdles and knowing how to address them will ensure a smooth customer experience.

  • Challenge 1: Data Accuracy and Completeness Product recommendations rely heavily on accurate and comprehensive data. Ensure that your store's inventory data is up-to-date, and product information is well-organized. Additionally, address any data gaps or inconsistencies that may affect the quality of recommendations.
  • Challenge 2: Over-Personalization While personalized recommendations are valuable, over-personalization can be counterproductive. Strive to strike a balance between personalized and diverse recommendations to avoid appearing intrusive or repetitive.
  • Challenge 3: Seasonal Variations and Trends Keep in mind that customer preferences may change with seasons and trends. Stay updated on industry shifts and adjust your recommendation strategies accordingly.

[fs-toc-omit]How to Leverage Customer Data for Enhanced Shopify Product Recommendations

Customer data holds the key to unlocking the true potential of product recommendations. Let's explore how to harness this data to deliver even more accurate and relevant product suggestions.

Collect and Analyze Customer Behavior Data

Accumulating customer behavior data is the foundation of an effective recommendation system. The data can be collected through various touchpoints, such as website interactions, purchase history, wishlists, and abandoned carts.

  • Data Collection Tools: Implement tools like Google Analytics, Shopify Analytics, and customer behavior tracking apps to gather data on customer interactions.
  • Data Analysis for Segmentation: Analyze customer data to segment your audience based on demographics, buying behavior, and preferences. This segmentation allows for more targeted and personalized recommendations.
  • Automated Data Collection: Utilize automation to streamline data collection and analysis processes, enabling real-time insights and more dynamic recommendations.

Strategies for Data-Driven Product Recommendations

Once you have gathered customer data, it's time to turn that data into actionable insights that power your recommendation engine.

  • Machine Learning Models: Integrate machine learning algorithms into your recommendation system to leverage the full potential of customer data. These algorithms can process large datasets and uncover hidden patterns, generating more accurate and sophisticated recommendations.
  • Collaborative Filtering Refinements: Continuously refine your collaborative filtering techniques to keep pace with changes in customer behavior and preferences. Implement user feedback loops and rating systems to improve collaborative filtering suggestions.
  • Personalization Tiers: Offer customers multiple levels of personalization, allowing them to adjust the recommendation intensity according to their preferences. Some customers may prefer broader recommendations, while others want hyper-personalized suggestions.

Always Respect Customer Privacy With Product Recommendations

As personalization becomes more sophisticated, customer privacy becomes a paramount concern. Learn how to balance personalization with respecting customer data privacy to build trust and maintain loyal customers.

  • Transparency and Consent: Clearly communicate your data collection and usage practices to customers and seek their consent before gathering any personal information. Be transparent about the purpose of data collection and how it will be used.
  • Anonymization and Encryption: Implement anonymization techniques to protect customer identities when analyzing data. Use encryption to safeguard sensitive customer data from potential security breaches.
  • Data Retention Policies: Establish data retention policies to delete or anonymize customer data after a certain period. This ensures that you do not retain unnecessary personal information for extended periods.

[fs-toc-omit]Measuring and Optimizing Shopify Recommendation Effectiveness

You've implemented product recommendations, but how do you know if they are truly effective? Let's explore the metrics and strategies to measure their impact and continuously optimize them for better results.

Key Performance Indicators (KPIs) for Measuring Recommendation Impact

Effectively measuring the impact of your recommendation system requires the use of key performance indicators (KPIs). You can get an overview of your product recommendation performance in ReConvert’s dashboard. Here’s what to keep an eye on:

  • Impressions: This metric measures how many shoppers viewed your product recommendations. If this is too low, consider creating less strict conditions for your offers.
  • Conversion Rate/Accepted offers: Calculate the percentage of visitors who make a purchase after viewing a recommended product. This metric directly reflects the effectiveness of your recommendations in driving sales.
  • Click-Through Rate (CTR): Measure the percentage of users who click on a recommended product out of the total number of recommendations displayed. A high CTR indicates that customers find your suggestions relevant and enticing.
  • Average Order Value (AOV): Monitor changes in AOV after implementing product recommendations. If customers are purchasing more items or higher-priced products, it demonstrates the success of your cross-selling and upselling efforts.

Analyze Conversion Rates and Customer Feedback

Quantitative data from KPIs provides valuable insights into recommendation performance, but it is equally crucial to gather qualitative feedback from customers.

  • Customer Surveys and Feedback Forms: Create post-purchase surveys and feedback forms to gauge customer satisfaction with your product recommendations. Ask specific questions about the relevancy and usefulness of the suggestions.
  • User Testing and Focus Groups: Conduct user testing sessions and gather feedback from focus groups to understand how customers interact with your recommendation widgets and whether they find them valuable.

Use A/B Testing for Continuous Optimization

No recommendation system is perfect from the start. Learn how A/B testing can help you identify areas for improvement and refine your recommendations over time, ensuring they deliver the best possible results.

  • A/B Testing Recommendations: Perform A/B tests by displaying different recommendation strategies to two separate groups of customers. Compare the performance of the two groups to identify which approach yields better results.
  • Continuous Refinement: Use data-driven insights from A/B testing, customer feedback, and performance metrics to continuously optimize your recommendation system. Make iterative changes to enhance relevancy and customer satisfaction.

[fs-toc-omit]Use ReConvert to Add Shopify Product Recommendations Today

In conclusion, Shopify product recommendations are a game-changer for online businesses with digital products. 

By implementing intelligent algorithms and leveraging customer data, you can offer personalized suggestions that boost sales and foster customer loyalty. Remember, success lies not only in the technology itself but also in how you use it to cater to your audience's needs.

Embrace the power of product recommendations, and watch your Shopify store thrive like never before. Use Reconvert to begin personalizing your customers' shopping experience today, and witness the incredible transformation in your business.

[fs-toc-omit]Shopify Product Recommendations FAQ

How to add product recommendations on Shopify?

To add product recommendations on Shopify, you can utilize various Shopify apps such as ReConvert Upsell. ReConvert allows you to create triggers and segments based on customer behavior and display relevant product suggestions at checkout and post-purchase.

How to add product recommendations in cart page Shopify?

To include product recommendations on the cart or checkout page of your Shopify store, you can use a recommendation app such as ReConvert that allows you to customize the placement and design of the widget. The app will automatically display personalized product suggestions based on the items in the customer's cart or other shopping behaviors.

How to add product recommendations on Shopify invoice email?

To include product recommendations in Shopify invoice emails, you can integrate a recommendation app that supports email customization such as Wiser. Wiser will analyze the customer's purchase history and include relevant product suggestions in the order confirmation email.

How to remove product recommendations on Shopify?

To remove product recommendations from your Shopify store, you can either disable the recommendation app you've installed or customize its settings to stop displaying recommendations on specific pages or sections.

What is the Shopify product recommendations API?

The Shopify product recommendations API allows developers to access and utilize recommendation data programmatically. This API enables customization and integration of product recommendations into Shopify themes and apps, offering more flexibility in tailoring the recommendation experience for customers.

Start your Free Trial

ReConvert empowers you to instantly boost revenue by 15% with one-click upsells, customized thank you pages, and more.
Find it on the
Shopify App Store