Product recommendations are more than just helpful nudges; they are a powerful way to create a shopping experience that feels intuitive, personal, and relevant. Imagine walking into a store where every item on display is tailored to your tastes and interests. That’s the magic of effective ecommerce product recommendations; they guide each customer to products they’ll love, right when they’re ready to see them, creating a seamless, enjoyable shopping journey.
From increasing average order value to boosting lifetime customer value, the right recommendation approach can drive meaningful growth and keep customers coming back for more. After all, a thoughtfully personalized experience can be the difference between a one-time buyer and a loyal customer.
Whether you’re an experienced ecommerce marketer or just getting started with personalization strategies, this guide has you covered with nine actionable tips to make your recommendations work harder.
Let’s dive in and explore how to make your ecommerce product recommendations as impactful as possible!
What is a product recommendation?
Product recommendations are tailored insights based on a customer’s unique behavior, preferences, and purchase history, aimed at guiding them toward the right products at the right time. Whether a first-time visitor or a repeat buyer, recommendations can create a more engaging shopping experience, leading to greater customer satisfaction and a boost in conversion rates.
How ecommerce product recommendations drive sales
Ecommerce product recommendations directly impact customer experience and buying decisions. When done right, they lead customers to discover new products, make quicker purchasing decisions, and even increase the size of their order.
SEE PRODUCT RECOMMENDATIONS IN ACTION
Key metrics influenced by product recommendations include:
- Increased conversion rates: Personalized recommendations make it easier for customers to find products they’re interested in, resulting in higher conversion rates.
- Higher average order value: Suggestions like “frequently bought together” or “you may also like” encourage customers to add more items to their cart.
- Enhanced customer retention: Relevant recommendations can improve the customer journey, building loyalty and increasing lifetime customer value.
All of these factors combine to drive sustainable sales growth over time, making product recommendations a key driver of ecommerce success.
How do ecommerce product recommendation engines work?
Understanding how recommendation engines work can help ecommerce marketers better leverage their potential. There are three primary models:
Content-based filtering
This approach recommends products based on a customer’s individual browsing and purchasing history. For instance, if a shopper has purchased running shoes, content-based filtering might suggest athletic apparel or accessories. It’s particularly effective for customers with a clear set of preferences.
Collaborative filtering
Collaborative filtering analyzes the behavior of multiple users to identify patterns. For example, if a group of customers frequently buys both a certain book and a specific laptop, this approach will recommend the laptop to others who bought the book. It’s beneficial for discovering cross-category trends and offering novel suggestions.
Hybrid model
By combining content-based and collaborative filtering, a hybrid model can leverage the strengths of both approaches. This model offers the most flexibility, tailoring recommendations based on individual preferences while also incorporating broader trends.
These engines use AI and machine learning to continuously adapt, improving recommendations over time and increasing personalization accuracy.
Types of ecommerce product recommendations
Ecommerce product recommendations serve various purposes throughout the customer journey, guiding shoppers to relevant products and enhancing their overall experience. Each type of recommendation has its unique strengths, aligning with different stages of the buying process and addressing customer needs in diverse ways. Here are some commonly used examples:
- Similar product suggestions
When customers view a specific item, showing alternatives with similar attributes can encourage them to explore additional options. For instance, if a shopper is looking at a specific style of running shoe, ecommerce product recommendations can display similar items with comparable features or designs. This approach keeps customers engaged longer, provides variety, and can help them discover products they might have missed otherwise. - Frequently bought together
“Frequently bought together” recommendations highlight complementary products that are often purchased alongside the current item, which can help boost average order value. For example, if a customer is purchasing a laptop, ecommerce product recommendations might include a compatible case, mouse, or keyboard. This type of recommendation makes it convenient for customers to add relevant items to their cart, enhancing the value of each transaction. - Personalized homepage recommendations
Personalized recommendations on the homepage welcome returning visitors with product suggestions based on their past browsing or purchase history. This could include items they’ve previously viewed, products related to past purchases, or popular items in categories they’ve shown interest in. Personalized homepage ecommerce product recommendations help customers quickly reconnect with relevant items, creating a tailored experience that increases engagement and conversion likelihood. - Cart-based recommendations
Cart-based recommendations, such as “you might also like” or “people also bought,” appear during checkout to encourage additional purchases. These ecommerce product recommendations are based on the customer’s current cart contents and suggest complementary products that enhance the primary purchase. Strategically placed at checkout, these recommendations increase cart size and revenue without disrupting the customer’s path to purchase. - Cross-channel recommendations
Cross-channel recommendations provide a seamless shopping experience across multiple touchpoints, including email, SMS, mobile apps, and web. For instance, if a customer views a product online but doesn’t complete the purchase, a follow-up email with ecommerce product recommendations can feature that item or related products. This cross-channel approach reinforces product interest, keeps customers connected across platforms, and helps bring them closer to conversion.
By strategically implementing these types of ecommerce product recommendations, brands can deliver a more personalized, valuable experience to customers at every stage of the buying journey.
9 tips for effective ecommerce product recommendations
- Leverage customer data and behavior
Accurate recommendations depend on a well-rounded view of each customer’s interests, preferences, and purchasing behaviors. By integrating data from multiple sources, such as browsing history, past purchases, and interactions on various platforms, you can build a more complete customer profile. This approach allows you to spot patterns and predict future behaviors, creating tailored product suggestions that resonate, whether for a returning shopper or a new visitor exploring your site. - Segment your audience
Not all customers are alike, which is why segmentation is key to relevant recommendations. By dividing your audience into groups based on factors like purchase history, browsing behavior, or loyalty status, you can offer each segment tailored recommendations. High-value customers, for instance, might receive exclusive product bundles or early access to new products, while new customers are introduced to trending items or best-sellers. This targeted approach improves engagement and conversion rates by ensuring customers see products they’re more likely to appreciate. - Use product recommendation engines to scale 1:1 experiences
Personalization at scale becomes feasible with the help of machine learning and AI-powered recommendation engines. These engines analyze real-time user data to create 1:1 experiences for every visitor, delivering accurate, context-aware product suggestions without manual input. This scalability is particularly advantageous for ecommerce businesses with extensive product catalogs, as it enables dynamic, individualized recommendations for a diverse customer base. - Incorporate social proof
Social proof can make recommendations more compelling by providing validation from other customers. User-generated content like star ratings, reviews, and real-time popularity indicators builds trust and encourages conversions. Highlighting a product’s popularity or showing that it’s frequently bought by other shoppers offers reassurance and encourages buyers to proceed with confidence. - Optimize placement and timing
Effective product recommendations rely on thoughtful placement and timing. Recommendations should appear in high-impact areas, such as product pages, cart pages, and follow-up emails. For example, showing complementary items just before checkout can encourage last-minute additions to the cart, while personalized suggestions in post-purchase emails can prompt repeat visits. Timing recommendations to appear when a customer is actively browsing or about to make a purchase can lead to more conversions. - Create a seamless customer journey with cross-channel recommendations
Consistency across channels, whether email, web, mobile app, or SMS, ensures customers have a cohesive experience with your brand. If a customer shows interest in a product on your website, reinforcing that interest with follow-up recommendations across other channels, like personalized email reminders, helps create a fluid journey. Cross-channel recommendations build familiarity and increase the likelihood of conversions by gently guiding customers back to the products they viewed. - Leverage visual and contextual cues
The design of product recommendations matters. Use high-quality images, concise descriptions, and icons to make recommendations visually appealing and easy to understand. Contextual cues like “top picks for you” or “recommended based on your search” clarify the reason behind each suggestion, adding a personal touch that can increase engagement. - Implement A/B testing
A/B testing allows you to identify which recommendation strategies resonate most with your audience. Test variables like recommendation placement, product types, and messaging to determine which combinations drive the best results. This iterative process helps you continually improve your recommendation strategy and optimize for a more impactful customer experience. - Monitor and analyze performance
Regular performance tracking is essential for refining recommendation tactics over time. Metrics like click-through rate, conversion rate, and order value provide insights into which recommendations are most effective. By analyzing this data, you can make data-driven adjustments to improve engagement, revenue, and the overall impact of your recommendations.
Boosting revenue and customer trust with Fresh Relevance
Product recommendations play a crucial role in the ecommerce customer journey, guiding shoppers toward relevant products, enhancing their experience, and driving conversions. Effective recommendations not only boost immediate sales but also build long-term customer loyalty by catering to individual preferences. Würth UK, a leader in fastening and assembly materials, demonstrates the transformative impact of well-implemented product recommendations.
After evaluating multiple personalization tools, Würth UK chose Fresh Relevance for its powerful recommendation and personalization features, quickly achieving remarkable results. With Fresh Relevance, Würth UK implemented dynamic product recommendations, triggered emails, social proof, and dynamic banners, leading to an impressive 1303% ROI on product recommendations.
Specific enhancements included new “frequently bought together” suggestions and personalized product displays on their homepage and product pages, which increased revenue by 72.4% in the UK and 89.1% in Ireland, with respective increases in average order value.
Additionally, Fresh Relevance’s user-friendly tools enabled Würth UK to include popular items based on real-time browsing data, and ‘add to basket’ options, boosting engagement. Through social proof integrations, Würth displayed star ratings and popularity indicators to validate quality and improve customer trust. The platform also facilitated data capture popovers, helping grow Würth’s marketing database effortlessly. With Fresh Relevance’s support team, Würth UK experienced a smooth transition, effectively leveraging personalization to create a seamless and optimized customer journey.
Watch a quick demo to see just how easy it is to set-up AI-powered product recommendations with Fresh Relevance by Dotdigital.
Conclusion
Product recommendations are a powerful tool in ecommerce, helping brands drive revenue and foster long-term customer relationships. By implementing these nine strategies, ecommerce leaders can optimize their recommendation approach, creating a more engaging and profitable shopping experience. As customer expectations continue to evolve, personalized recommendations will remain essential to building loyalty and maximizing revenue.
