How many times have you visited an online store with a plan to buy a certain item, only to add several other items to your online shopping cart? This may be due to random chance, but it’s more likely the ecommerce website made relevant recommendations and product suggestions while you were browsing.
As an ecommerce merchant, you too can boost shopping cart totals by suggesting complementary products based on a customer’s behavioral data. The secret is making personalized recommendations that align with a shopper’s original purchase intent. Here’s a guide to ecommerce product recommendations, with tips for using them on your own ecommerce site.
What are ecommerce product recommendations?
An ecommerce product recommendation is a personalized suggestion or prompt displayed to online shoppers, guiding them toward products they might be interested in purchasing. These personalized product recommendations are based on factors like browsing history, purchase history, user behavior, customer segmentation, and the purchasing history of similar users.
Ecommerce companies use software tools called product recommendation engines to suggest relevant products to shoppers. These engines use machine learning algorithms and data points to generate product suggestions. While incorporating a product recommendation engine cannot guarantee bigger cart totals, it can inspire customers to consider products they might have otherwise overlooked.
Benefits of ecommerce product recommendations
- Increased sales and revenue
- Enhanced user experience
- Customer loyalty
- Optimized marketing spend
- Data insights for continuous improvement
Product recommendations offer several advantages for an ecommerce store:
Increased sales and revenue
Personalized product recommendations significantly impact average order value on an ecommerce platform. A 2023 study by Barilliance found product recommendations account for an average of 31% of ecommerce site revenues. In a McKinsey report, 35% of what consumers bought on Amazon came from its recommendation engine.
Enhanced user experience
Tailored suggestions can improve the overall shopping experience for site visitors, leading them to product pages that are relevant to their needs. A report by Moengage indicates 49% of consumers have purchased items they didn’t intend to buy initially due to personalized product recommendations.
Customer loyalty
Effective product recommendations contribute to customer satisfaction and retention. When shoppers find relevant and appealing suggestions, they are inclined to return to the site and become loyal customers. A study by McKinsey found personalization can produce a 10% to 15% increase in sales-conversion rates, indicating customer satisfaction and brand loyalty.
Optimized marketing spend
By understanding customer behavior and preferences, ecommerce businesses can optimize their inventory and marketing strategies. This can improve ad targeting, as online retailers can suggest products users may have viewed on previous visits to their sites. Online stores can also improve outreach to new customers by applying previous data from shoppers and using it to suggest relevant items.
Data insights for continuous improvement
Product recommendation engines generate valuable data about customer behavior, preferences, and trends. Ecommerce stores can use this customer data to refine their selection, improve recommendation strategies, and even develop new products based on customer interests.
Types of product recommendation engines
Product recommendation engines utilize different algorithms and techniques to generate suggestions for users. Here are three popular types:
Collaborative filtering
This recommendation strategy takes two forms: user-based filtering and item-based filtering.
User-based collaborative filtering
This method suggests products based on the preferences or behavior of similar users. It identifies similarities between users’ past behaviors (e.g., purchases, likes, or ratings) and recommends items that similar users have interacted with.
Item-based collaborative filtering
Instead of comparing users, this technique focuses on similarities between items themselves. It recommends products similar to those a user has interacted with previously.
Content-based filtering
This approach suggests items based on their attributes or characteristics. It analyzes the properties or product descriptions of items a user has shown interest in and recommends similar items. For instance, if a user has viewed or purchased a specific brand of shoes, the content-based filtering system might recommend other shoes with similar styles, colors, or materials.
Hybrid recommender systems
Hybrid systems combine collaborative filtering and content-based filtering to overcome the limitations in each method. For example, a hybrid system might use collaborative filtering to identify users with similar tastes and then utilize content-based filtering to offer personalized recommendations based on item attributes.
Tips for using ecommerce product recommendations
- Tap into returning customers’ previous purchases
- Optimize category pages
- Cross-sell on product pages
- Personalize recommendations
- Use social proof
- Blend online and offline shopping
- Continually optimize
- Study other brands
You can maximize a customer’s online shopping journey and push them toward additional purchases by embracing a product recommendation system. Here’s how to leverage ecommerce product recommendations to enhance the customer experience and boost sales:
Tap into returning customers’ previous purchases
Use data from past purchases, browsing history, and interactions to offer relevant suggestions. Incorporate “Frequently purchased together” or “Recommended for you” sections on product pages or the cart page to encourage additional purchases.
Optimize category pages
Implement recommendations to guide customers through a product category page. Showcase “Bestselling products,” “Highest customer reviews,” or “Recommended for you” sections to assist customers in their buying journey.
Cross-sell on product pages
Recommend complementary products on a shopping cart page or individual product pages. Use “Customers also bought” or “Frequently bought together” sections to encourage cross-selling to increase the average order value.
Personalize recommendations
Tailor suggestions based on the individual’s preferences and behavior. Implement personalized search results and peer-generated recommendations to improve recommendations and keep customers coming back.
Use social proof
Incorporate social proof by highlighting products with the best customer reviews or ratings on your brand’s site. Testimonials or endorsements from a fellow website visitor can build trust and influence a user’s purchasing decisions.
Blend online and offline shopping
If your business has brick-and-mortar stores as well as an ecommerce store, use the information gleaned from customer behavior in one space to inform your approach on the other. For instance, if a customer has purchased a sofa (and has shared their data with you at the time of purchase) at your furniture showroom, you can recommend throw pillows and cushions to them on your website.
Continually optimize
Use A/B testing (where different customers are shown different variants of the same content) to refine suggestions, improve accuracy, and track the impact of recommendation placements on sales and customer engagement. This optimization can help you reduce operational costs while maximizing the effectiveness of the recommendations shown.
Study other brands
Visit competitor websites and observe how they use product recommendation engines. You may glean insights you can apply to your own ecommerce store.
Ecommerce product recommendation FAQ
What is an example of a product recommendation system?
An example of a product recommendation system is using a website module titled “Customers who bought this item also bought,” which suggests products based on users’ purchasing behavior and preferences.
What should a product recommendation be based on?
Product recommendations should offer relevant and personalized suggestions based on a user’s preferences, behavior, past purchases, and browsing history.
Does Shopify offer product recommendations?
Yes, Shopify offers tools and tutorials for ecommerce merchants who wish to incorporate product recommendations into their online stores.
