8 Product Recommendation Strategies to Leverage for Your Online Store

Implementing best practices that align with your business objectives and resonate with your target audience is crucial for effectively leveraging product recommendations.

a woman with glasses sitting in front of a wall
By Arjel Vajvoda
a woman wearing a suit and a black shirt
Edited by Nelsy Mtsweni
Oli Kashti - Writer and Fact-Checker for Fast Simon
Fact-check by Oli Kashti

Published June 5, 2024.

A customer clicking on a recommended product to make a purchase

Did you know that around 85% of online shoppers report being influenced by product recommendations? This presents a golden opportunity for online store owners to turn turn online window-shoppers into loyal buyers that keep coming back. That's why we've created this list of the 8 best product recommendation strategies to leverage for your online store.

Meet the Expert

Arjel Vajvoda, Head of Product at Motomtech, leverages her extensive experience in customer support to craft user-focused SaaS products with innovative documentation solutions.

Fast Simon infographic showing the eight product recommendation strategies to leverage for your online store

1. Personalized Recommendations Based on User Behavior

This strategy uses artificial intelligence (AI) and machine learning (ML) systems to analyze a wealth of data, including users' browsing habits, purchase history, and product preferences.

Over time, the system leverages this data to refine its approach, achieving a deeper understanding of personalization. This continuous learning process allows it to adapt to each user's unique interests, making its suggestions more accurate and personal, thereby improving the overall shopping experience.

This can lead to:

  • Improved customer experience through relevant product showcasing

  • Increased average order value and conversions

  • Enhanced customer loyalty and repeat business

Who Is It Best For?

The strategy is well-suited for sectors with frequent customer interactions like fashion, technology, and beauty where frequent customer interactions occur based on personal taste and fast-changing trends.

Personalized recommendations are especially powerful during browsing, checkout, and return visits, as customers are more open to recommendations when actively seeking products or considering purchases.

Challenges and Solutions

Insufficient data for accurate recommendations is a challenge because it leads to inaccurate recommendations, hindering the ability to offer personalized customer experiences.

Addressing this requires robust data collection and promoting customer feedback for a predictive search that is more understandable. Without this, customers might receive irrelevant recommendations, like dog food after buying a running watch on Amazon:

Amazon store's website showing poor product recommendations

Example: AmerCareRoyal enhanced its B2B search and product suggestions using Fast Simon's eCommerce Search, personalizing recommendations and increasing conversion rates by 50% in some offerings through a continuously updating search engine with adaptive suggestions.

a web page with an  example of personalized recommendations based on user behavior

2. Contextual Recommendations at Checkout

This strategy's goal is to enhance the customer experience by offering complementary products to those already in the customer's cart. This uses real-time cart data analysis to suggest relevant add-ons aligned with the current purchase intent, more commonly known as upselling and cross-selling.

Who Is It Best For?

This strategy is particularly effective for fashion, electronics, and groceries, where complementary products are easily suggested. It maximizes additional sales by reaching customers right before checkout, making it convenient for them to add more items with little to no friction.

Challenges and Solutions

Ensuring recommendations truly complement purchases is key. You can do this by leveraging algorithms and data analysis to identify strong product correlations and purchase patterns for relevant suggestions.

Example: A poor example is recommending kitchen appliances for a fitness book purchase—unrelated and lacking context. The optimal approach suggests relevant items like a customer purchasing glasses and a scarf might be shown gloves and purses as contextual recommendations.

Contextual Recommendations at Checkout as a Product Recommendation Strategy to Leverage for Your Online Store

3. Dynamic Recommendations on Product Pages

This strategy's main objective is to present products similar to or complementary to the currently viewed item. These dynamic recommendations are produced by analyzing user behavior, preferences, and real-time product interactions to offer the most relevant options.

This can lead to:

Who Is It Best For?

This strategy is well-suited for diverse product ranges and cross-selling opportunities like fashion, electronics, and home goods. It is most effective on product detail pages during consideration.

Interestingly, nearly half of the consumers (49%) report making unplanned purchases after receiving personalized product recommendations.

Challenges and Solutions

It's crucial to ensure that recommendations relate to the product viewed. Use algorithms and data analysis for effective product recommendations based on customer data.

Example: A poor example is recommending unrelated sports equipment on a camera product page, lacking context. A good example of this strategy is when a customer views a T-shirt, and the site dynamically suggests compatible accessories and garments such as hats, jeans, and sneakers.

An example of dynamic recommendations on product pages

4. Homepage Personalization for Returning Visitors

The core aim of this approach is to enhance the website experience for returning visitors by leveraging their past interactions to streamline your merchandising. The system intelligently customizes the homepage to align with each visitor's preferences and interests.

Who Is It Best For?

This strategy is effective for eCommerce with frequently updated, broad inventories like retail, fashion, and electronics, where repeat visits are common.

It is most impactful at the start of return visits, as this initial engagement can significantly influence visitors' decision to stay on the site and explore further.

Challenges and Solutions

Displaying irrelevant products like winter gear to tropical visitors is ineffective and detracts from the experience. Optimal solutions leverage location, climate, and preference data to showcase regionally and seasonally appropriate content.

Example: Hillberg & Berk enhanced their shopping experience by directly aligning with customer interests. A returning visitor who previously browsed pendants might see a personalized banner showcasing new arrivals in that category or related accessories.

An example of homepage personalization for returning visitors

The strategy aims to leverage trending products to capture customer interest and seasonal demand by featuring popular and relevant items on main pages.

This leads to:

  • Increased visibility and sales for relevant products

  • Alignment with customer interests and seasonal needs

  • Perception of being an on-trend, relevant brand

Who Is It Best For?

The strategy is particularly effective for consumer goods like fashion, home decor, and beauty where trends and seasons influence purchases.

It works best during the awareness and consideration stages because showcasing products prominently on your site captures visitor interest, leading to exploration and purchases.

Challenges and Solutions

Identifying and prioritizing the right featured products based on trends and seasons is crucial for successful implementation. To further optimize customer experience, use data analytics and monitor social media to make sure your competitors aren't beating you to it. To stay ahead of emerging trends and demands, industry insights are key.

» Check out the upcoming personalization technology trends in 2024

6. Bestsellers and New Arrivals for Fresh Visitors

This strategy aims to capture interest by showcasing proven popular and latest products. It leverages the allure of trending and top-selling items to engage new customers and simplify their initial product discovery.

Who Is It Best For?

This strategy is effective for consumer-driven markets like fashion, technology, and home goods where trends influence purchases. It is most impactful in the discovery phase for new visitors, providing a curated introduction to appealing products.

Challenges and Solutions

Identifying true bestsellers and prioritizing compelling new arrivals can be challenging due to the need for market trend analysis and sales data monitoring. Utilizing sales data (like conversion rates, units sold, and sales by product/category), customer product reviews, and trend analysis helps strategically curate and feature these selections.

Example: A poor execution of this strategy is featuring heavy winter gear as "new arrivals" during the warm summer season as this could come across as out of touch. The optimal approach is featured by Princess Polly effectively showcasing a "Shop New Arrivals" banner on their homepage. This highlights the latest trends in women’s fashion, such as seasonal jackets and accessories.

An example of bestsellers and new arrivals strategy for new visitors

7. Out-of-Stock Alternatives on Product Pages

The strategy's goal is to retain potential customers by guiding them to similar products when their desired item is out of stock. This involves presenting alternatives like comparable products, upgraded versions, or related items directly on the unavailable product's page.

This leads to:

  • Reduced customer abandonment and lost sales

  • Increased cross-selling opportunities

Who Is It Best For?

This is particularly effective in consumer goods sectors like electronics, fashion, and home goods, where item similarity allows for suitable alternatives. It is most beneficial during the consideration phase when products are unavailable.

Challenges and Solutions

Handling out-of-stock products can pose challenges when no suitable alternatives that meet the original customer are found.

Example: A poor example is recommending a yoga ball when a yoga mat is out-of-stock as this is not an adequate substitute. The optimal solution uses data on product attributes, preferences, and purchase patterns to recommend the most relevant alternatives. For instance, a customer looking to buy a specific type of bread finds it out of stock; the site suggests similar types of bread of the same brand.

An  example of out-of-stock alternatives on product pages

8. Recommendations on 404 and Zero-Result Search Pages

The aim of this approach is to salvage user experience on error pages with no direct results instead of having your potential customers leave. It does this by providing alternative suggestions, related content, or strategies to retain customers.

This leads to:

  • Reduced bounce rates and customer abandonment
  • Improved site search navigation and user experience
  • Increased opportunities for product discovery

Who Is It Best For?

The strategy is universally applicable across eCommerce platforms and crucial for sites with extensive inventories or frequently out-of-stock items. It is most effective in the search and discovery phase, preventing user frustration and exits by offering alternative paths, potentially leading to a sale.

Challenges and Solutions

Providing relevant recommendations on error pages can be challenging, as poor approaches may result in completely unrelated suggestions that confuse users. Instead, offer targeted suggestions that resonate with users' needs and turn potential exits into discovery opportunities by carefully utilizing (and treating with privacy) customer data such as behavior, preferences, and search history.

Example: When a user searches for a specific type of coffee mill that is out of stock, the zero-result page can suggest other coffee products from the same brand or popular alternatives.

An example of recommendations on 404 and zero-result search pages

Embrace Data-Driven Personalization

To improve product recommendations in eCommerce, businesses should leverage AI technologies to analyze user behavior and preferences for personalized recommendations. Robust data collection and analytics frameworks are crucial for relevance.

It's essential to remember that by embracing emerging trends and data-driven personalization, businesses can create a superior shopping experience that drives customer loyalty and long-term success.

Also, product recommendations are not one-size-fits-all. Each business should carefully assess its unique needs and target audience to determine the most effective strategies. Continuous testing, iterating, and optimizing based on performance data is key to maximizing impact.