8 Product Recommendation Strategies for eCommerce Success
A smart product recommendation strategy can turn casual browsers into loyal customers. From personalized suggestions to dynamic product page recommendations, these tactics boost conversions and keep shoppers engaged.
Updated July 22, 2025
According to the Customer Data Platform Institute, around 85% of online shoppers report being influenced by product recommendations. This presents a golden opportunity for online store owners to turn online window shoppers into loyal buyers.
To help you make the most of online product recommendations, we’ve consulted with an expert, Arjel Vajvoda, Head of Product at Motomtech, who leverages her extensive experience in customer support to craft user-focused SaaS products with innovative documentation solutions. We have created a list of the 8 best product recommendation strategies for you to try.
» Learn how AI can personalize your product recommendation strategies
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, and it also helps to improve 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.
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.
To improve predictive search, you need strong data collection and active customer feedback to make results more accurate and relevant. Without this, recommendations can miss the mark—like suggesting dog food to someone who just bought a running watch on Amazon.
Example: AmerCareRoyal improved its B2B search and product recommendations with Fast Simon’s eCommerce Search. By personalizing suggestions and using a continuously updating search engine with adaptive results, the company boosted conversion rates by up to 50% on certain products.
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2. Contextual Recommendations at Checkout
This online product recommendation 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.
This can lead to:
- Increased average order value through relevant upsells and cross-sells.
- Reduced cart abandonment by presenting useful, timely suggestions.
- Better customer satisfaction due to well-matched product recommendations.
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.
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 bad example would be recommending kitchen appliances to someone buying a fitness book—completely unrelated and out of context. A better approach would be showing relevant suggestions, like offering gloves or purses to a customer who just bought glasses and a scarf.
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:
- Increased cross-selling opportunities and average order value.
- Improved product discovery and exploration.
- Enhanced customer satisfaction through relevant options.
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 important to make sure recommendations are directly related to the product being viewed. Rely on best practices, such as analyzing customer data, to deliver product recommendations that are truly relevant and effective.
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.
» Learn why product recommendations also need merchandising rules
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, making it a powerful tactic in eCommerce product recommendation strategies.
This can lead to:
- Higher engagement rates as returning visitors see products they’re most interested in.
- Increased repeat purchases.
- Stronger brand loyalty.
Who Is It Best For?
This strategy works well for eCommerce stores with large and frequently updated inventories, such as retail, fashion, and electronics, where customers often return.
It is most impactful at the start of return visits, as this initial engagement can significantly influence visitors' decisions 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.
» Understand how to overcome eCommerce challenges for fashion brands
5. Seasonal and Trending Products on Main Pages
This online product recommendation strategy aims to leverage trending products to capture customer interest and seasonal demand by featuring popular and relevant items on the main pages. Fast Simon’s Smart Collections feature helps automate this by dynamically displaying the most relevant products and keeping pages consistently updated.
This can lead 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
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.
This can lead to:
- Increased trust as shoppers are drawn to trending and highly rated items.
- Higher conversion rates by featuring products already validated by other buyers.
- Faster product discovery for new visitors.
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.
» Ready to captivate new visitors with bestsellers and fresh arrivals? Let Fast Simon’s Merchandising AI showcase your top trending products
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 can lead 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 from the same brand.
» Fast Simon’s AI-powered dynamic product filter for WooCommerce shows the right in-stock items at the right time
8. Recommendations on 404 and Zero-Result Search Pages
This approach aims 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 can lead 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.
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 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.
» Need more help? Here are more product recommendations to boost your sales
Embrace Data-Driven Personalization
To master the best practices for product recommendations, leverage AI-powered analytics like Fast Simon’s intelligent site search and machine learning. These tools analyze shopper behavior, predict intent, and deliver personalized, relevant results instantly.
Remember, product recommendations aren’t one-size-fits-all. Continuous testing and tailoring to your unique audience using data-driven insights are key to staying ahead and maximizing impact.
» Explore Fast Simon’s AI capabilities for smarter personalization and better product recommendations
FAQs
How do product recommendations improve eCommerce sales?
Relevant product recommendations guide customers to items they’re likely to buy, increasing average order value and conversions. Personalized suggestions based on browsing behavior or cart contents help turn casual visitors into loyal buyers.
How can AI enhance product recommendations?
AI analyzes customer behavior, search history, and purchase patterns to deliver highly relevant product suggestions. Fast Simon, for instance, combines AI-powered search with smart merchandising to recommend in-stock, trending, or complementary products automatically.
Should I show recommendations even when products are out of stock?
Yes. Suggesting alternatives keeps customers engaged and reduces abandonment. Smart merchandising tools can automatically show similar or upgraded products when an item isn’t available, ensuring shoppers still find something they want.
