What are product recommendations?

Product recommendations are suggestions of items that a shopper might be interested in purchasing, based on their past behavior, preferences, or the behavior of similar shoppers. They are a powerful ecommerce merchandising technique that uses algorithms and behavioral data to display relevant products to shoppers. Think of them as your online store's helpful, sales-savvy sidekick, enhancing the customer journey and increasing sales.

Product recommendations go beyond simply showing random items. They are carefully curated suggestions designed to guide customers toward products they are likely to buy, ultimately improving their shopping experience and boosting your bottom line.

In essence, product recommendation engines analyze browsing behavior, purchase history, and other behavioral data to predict what a customer might want to buy next. They then display products in strategic locations like the homepage, product page, category pages, and checkout. These engines are the brains behind the operation, constantly learning and adapting to provide the most relevant recommendations.

5 reasons product recommendations are so important

Boost average order value (AOV): By suggesting complementary products and related products, you encourage customers to add more to their cart. Think "Frequently Bought Together" on Amazon. This is a classic example of how cross-selling can significantly increase AOV.
Increase conversion rates: Relevant recommendations guide first-time visitors and loyal customers alike toward items they're likely to purchase, optimizing conversion rates. By showing customers exactly what they're looking for (or didn't know they were looking for!), you can turn browsers into buyers.
Enhance product discovery: Help customers find new products and best sellers they might otherwise miss, improving product discovery and overall customer experience. Think of it as a virtual treasure hunt, where customers uncover hidden gems they'll love.
Personalize the experience: Personalized product recommendations, driven by machine learning, create a more engaging and personalized experience, fostering customer loyalty. In today's world, customers expect a tailored experience, and product recommendations are a key way to deliver that.
Optimize merchandising: Use templates and functionality to strategically display products, optimize your online store, and drive increase sales. It's about putting the right products in front of the right people at the right time.

Technical aspects of recommendation engines

Algorithms

The heart of any product recommendation system is its algorithm. Here are a few common types:

Collaborative filtering: This algorithm recommends products based on the preferences of similar users. For example, if two customers have similar purchase histories, the algorithm might recommend products that one customer has bought to the other.
Content-based filtering: This algorithm recommends products that are similar to those a user has liked in the past. It analyzes product attributes (e.g., category, features, price) to find matches.
Hybrid approaches: These algorithms combine collaborative filtering and content-based filtering to provide more accurate and diverse recommendations.
Machine learning algorithms: More advanced systems use machine learning algorithms like neural networks and decision trees to learn complex patterns in user behavior and predict future purchases.

Data Sources

Recommendation engines rely on various data sources to understand customer preferences:

Explicit data: This includes data that customers directly provide, such as ratings, reviews, and feedback.
Implicit data: This includes data that is collected passively, such as browsing behavior, purchase history, add-to-carts, and dwell time on a web page.
Demographic data: This includes information about a customer's age, gender, location, and income.
Contextual data: This includes information about the customer's current context, such as location, time of day, and device.

Real-time vs. Batch Processing:

Real-time processing: This involves analyzing data and generating recommendations in real-time, based on the customer's current behavior. This is ideal for providing highly personalized and responsive recommendations.
Batch processing: This involves analyzing data and generating recommendations in batches, typically on a daily or weekly basis. This is suitable for less time-sensitive recommendations, such as email marketing campaigns.

Ecommerce strategies for product recommendations

Homepage

The homepage is prime real estate for product recommendations.

Personalized recommendations: For returning visitors, showcase products based on their past browsing behavior and purchase history.
Trending products and best sellers: For new visitors, highlight popular products and trending items to pique their interest.

Product page

The product page is a crucial point for influencing purchase decisions.

"Customers who bought this item also bought": This classic recommendation highlights complementary products that other customers have purchased alongside the current item.
"Frequently bought together": Similar to the above, this showcases items that are often purchased as a set.
"Similar products": This displays similar products to the one the customer is viewing, providing alternatives and options.

Category pages

Category pages offer an opportunity to showcase top-performing products within a specific category.

Top-selling products in the category: Highlight the most popular products to guide customers toward popular choices.
Personalized recommendations based on browsing history: Tailor recommendations based on the customer's past interactions within the category.

Cart and checkout

The cart and checkout process is the final opportunity to influence the purchase.

Cross-selling recommendations: Suggest complementary products that the customer might need or want (e.g., "You might also need...").
Upselling recommendations: Offer an upgraded version of the product or a related service (e.g., "Upgrade your order with...").

Post-purchase

The relationship doesn't end after the purchase.

Email recommendations based on past purchases: Send personalized email recommendations based on the customer's purchase history.
Loyalty program recommendations: Offer exclusive recommendations to loyalty program members.

A/B testing methodologies

Even with advanced mechanisms to determine the best product recommendations, continuous a/b testing will ensure you're continuously refining and optimizing the process.

Algorithms: Compare the performance of different algorithms to see which one generates the most effective relevant recommendations.
Placement: Experiment with different placements of product recommendations on the web page to see which ones drive the most engagement.
Design and layout: Test different designs and layouts to see which ones are most visually appealing and effective.
Types of recommendations: Try different types of recommendations (e.g., upsell, cross-selling, similar products) to see which ones resonate best with customers.
Number of recommendations: Experiment with the number of recommendations displayed to find the optimal balance between providing options and overwhelming customers.

For more A/B testing ideas, check out this article: 101 A/B testing ideas to improve conversions in 2024

Key metrics

Conversion rate: The percentage of visitors who make a purchase.
Average order value (AOV): The average amount spent per order.
Click-rhrough rate (CTR): The percentage of visitors who click on a recommended product.
Add-to-cart rate: The percentage of visitors who add a recommended product to their cart.
Revenue per session: The average revenue generated per website session.
Statistical significance: It's crucial to ensure that your A/B testing results are statistically significant, meaning that the observed differences are not due to random chance.

Advanced personalization techniques

At the heart of every product recommendation is a personalization engine. Yea, you want to keep users on site and interested, but creating a personalized experience that makes the customer feel like you really know them is what will keep them coming back.

Behavioral segmentation: Divide your customers into segments based on their browsing behavior, purchase history, and demographics. This allows you to tailor recommendations to specific groups of users.
Personalized content: Go beyond just recommending products and personalize the entire content experience based on individual preferences.
Dynamic pricing: Adjust prices based on demand and customer behavior to maximize revenue.
AI-powered personalization: Use machine learning to predict customer behavior and deliver highly personalized product recommendations in real-time.

Check out this article on how it supercharges ROAS: How personalization supercharges ROAS (without the creep factor...)

Conclusion

Product recommendations are more than just a "nice-to-have" feature; they're a critical component of any successful ecommerce strategy. By leveraging algorithms, data, and A/B testing, you can create a personalized experience that delights shoppers, boosts AOV, and drives increase sales. As technology evolves, expect to see even more sophisticated and AI-powered product recommendation engines that further blur the lines between the physical and digital shopping worlds.