A product recommendation system is a sophisticated tool that leverages data-driven insights to suggest products or services to users based on their behavior, preferences, and interactions. These systems are a cornerstone of modern eCommerce and digital marketing strategies, enabling businesses to deliver highly relevant and personalized experiences.

By analyzing vast amounts of data, product recommendation systems enhance user engagement, boost conversion rates, and increase customer satisfaction. 

Whether it’s suggesting the next best product to add to a shopping cart or curating a personalized email with recommendations, these systems are integral to creating a seamless shopping journey.

In this article, we’ll explore how to use recommendation systems to drive increased revenue and customer satisfaction. 

Let’s dive in!

At the heart of every product recommendation system is a sophisticated algorithm that processes vast amounts of data to generate accurate, relevant suggestions. The process starts with data collection, capturing information such as browsing habits, purchase history, and product interactions. This data is then analyzed using machine learning techniques to identify patterns and predict what customers may find appealing.

The system doesn’t rely on a single method; instead, it incorporates a variety of data sources to refine its recommendations. Let’s explore how different approaches contribute to its functionality.

Crowdsourced recommendations

Crowdsourced recommendations focus on what’s popular among other shoppers. By identifying trending products, those frequently browsed, carted, or purchased, the system suggests items that resonate with a larger audience. For instance, an online store might recommend “trending now” products to new visitors, leveraging the collective behavior of its user base to drive interest.

These recommendations are particularly effective for showcasing impulse buys or seasonal bestsellers, creating a sense of urgency and curiosity that encourages shoppers to explore further.

AI-powered recommendations

AI-powered recommendations take personalization a step further by analyzing customer behavior patterns and comparing them with those of similar shoppers. For example, the “People like you buy” tactic identifies products that individuals with similar purchase habits have gone on to buy. Machine learning algorithms refine these predictions to ensure the suggestions are both relevant and timely.

Another AI-driven approach is the “Purchased Together” method, which highlights complementary products often bought as a set. If a shopper is viewing a camera, the system might suggest compatible lenses or a tripod, delivering value while increasing average order value.

Personalized recommendations

Personalization is the cornerstone of effective recommendation systems. These suggestions are tailored to the individual shopper, based on their unique history and preferences. For instance, a system might recommend items from a customer’s wishlist, highlight products in their last abandoned cart, or suggest restocks of previously purchased items.

By focusing on the individual rather than the crowd, personalized recommendations create an experience that feels bespoke, fostering customer loyalty and satisfaction.

Imported recommendations

Some recommendation systems rely on pre-defined relationships between products, using imported data to guide their suggestions. For example, if certain items are tagged as “frequently bought together,” the system can draw on this predefined relationship to present targeted suggestions. This approach is especially useful for businesses with niche products or specific promotional strategies.

Benefits of using product recommendation systems

1. Increased revenue

Personalized product recommendations drive higher sales by encouraging customers to purchase complementary or additional items. By analyzing customer data, businesses can identify cross-selling and upselling opportunities, boosting average order value (AOV). For example, showing “frequently bought together” items at checkout increases the likelihood of additional purchases.

2. Enhanced user experience

Tailored recommendations make the shopping experience smoother and more enjoyable. When customers see products relevant to their tastes and needs, they are more likely to engage and convert. This not only improves satisfaction but also encourages brand loyalty. Consider streaming services that recommend personalized playlists or movies, creating a sense of connection and understanding with their users.

3. Improved customer retention

Engaged customers are more likely to return, leading to higher retention rates. By continuously offering relevant and timely suggestions, businesses can keep users interested and coming back for more. For example, online retailers that send follow-up emails with personalized product suggestions after a purchase can deepen the customer’s relationship with the brand.

4. Better inventory management

Product recommendation systems provide businesses with actionable insights into demand trends and customer preferences. This data helps optimize inventory, ensuring popular items are always in stock while avoiding overstocking less popular products. For instance, analyzing seasonal trends can help retailers prepare their inventory for peak shopping periods like holidays.

5. Data-driven decision making

Recommendation systems generate valuable data about customer behavior, preferences, and purchasing patterns. Businesses can use these insights to refine marketing strategies, improve product offerings, and make informed decisions about pricing and promotions. By understanding what drives customer purchases, companies can create more effective campaigns and enhance overall profitability.

Types of product recommendation systems

To meet diverse business needs, recommendation systems employ three main approaches:

Collaborative filtering

Collaborative filtering analyzes user behavior, such as browsing history or purchase activity, and compares it with other users to identify shared preferences. This approach is especially effective for predicting products that customers with similar habits are likely to enjoy.

Content-based filtering

Content-based filtering focuses on product attributes, recommending items similar to those a shopper has shown interest in. For instance, if a customer views a book about graphic design, the system might suggest other titles with similar themes or genres.

Hybrid systems

Hybrid systems combine collaborative and content-based filtering to offer more accurate and comprehensive recommendations. By drawing on multiple data sources, these systems can tailor suggestions to both individual preferences and broader trends.

Key components of a product recommendation system

Data collection and analysis

Data is the foundation of every recommendation system. Effective systems rely on two main data sources:

User data

Access to diverse types of user data enables smarter and more personalized recommendations:

• First-party data: Is a combination of all the data a brand can record through the users interactions with them across their owned channels these include:
  • Location data: Information about the user’s geographic location, including country, region, or city.
  • Technical data: Details about the device, browser, and operating system used to access the site.
  • Behavioral data: Actions users take on the site, such as clicks, cart additions, page views, and hover interactions.
  • Affinity-based data: Information about the interests and preferences users display during their site visits.
  • Purchase data: Records of products purchased online or offline, offering a holistic view of shopping habits.
  • Traffic source data: The origin of site visits, whether from direct traffic, social media, paid ads, or referrals.

• Zero-party data: Information directly shared with a brand via setting preferences or simply signing up to a newsletter with an email. This could include:
  • Demographic data: Insights into a user’s age, gender, marital status, and other defining traits.
  • Survey data: feedback and desires submitted directly via forms

• Third-party data: External data, onboarded through systems like a Data Management Platform (DMP), to enrich user profiles.

What is product data?

Product data is the foundation of effective recommendation systems, providing the necessary context for aligning user preferences with available items. By organizing and analyzing detailed product information, recommendation engines can deliver accurate, relevant suggestions to shoppers. Below are the key types of product data that power these systems:

1. Basic product attributes
   These include essential details about a product, such as:

• • Name: The product title or identifier.
  • Description: A detailed overview of the product’s features and benefits.
  • Tags: Keywords or categories that help group similar products, such as “eco-friendly” or “seasonal.”

These attributes are critical for matching products to user preferences, especially in content-based recommendation systems that focus on similarities.

2. Visual content
   High-quality images and thumbnails are vital for enhancing the shopper’s experience and ensuring consistency across recommendations. For example:

• • A product with clear, engaging visuals is more likely to convert.
  • Recommendation systems can filter products to include only those with images, ensuring visually appealing suggestions.

3. Pricing information
   Accurate pricing data allows recommendation systems to tailor suggestions based on a user’s price sensitivity or budget preferences. Key pricing elements include:

• • Price range: Useful for targeting bargain hunters or high-value shoppers.
  • Price changes: Discounts or recent price drops can highlight deals, appealing to value-conscious users.
  • Average product price: Used to predict and match price affinity for individual shoppers.

4. Availability and stock levels
   Stock information ensures that the system only recommends products that are currently available for purchase. This prevents shopper frustration and optimizes conversion opportunities. Key data includes:

• • Stock level: Highlighting in-stock items or prioritizing overstocked products.
  • Back-in-stock alerts: Recommending products that were previously unavailable but have returned to inventory.

5. Relationships between products
   Product relationships enable cross-selling and upselling strategies. Examples include:

• • Frequently bought together: Highlighting complementary products that enhance the value of a primary purchase.
  • Similar products: Recommending items with shared attributes, such as design, functionality, or category.
  • Pre-defined associations: Imported lists that link related products for specific promotions or collections.

6. Performance metrics
   Ratings, reviews, and popularity indicators add an element of social proof to recommendations. Examples include:

• • Rating and review count: Displaying highly rated items to build shopper confidence.
  • Popularity metrics: Recommending trending or frequently purchased products based on current performance.

This diverse range of data helps marketers not only refine recommendations but also develop robust buyer personas and segmentation strategies. For instance, understanding traffic sources can guide marketing campaigns, while device information can help optimize the user experience.

Continuous learning from user interactions

The more frequently users visit and interact with a site, the richer the dataset becomes. With every click, cart addition, and purchase, the recommendation system refines its understanding of user preferences. This continuous influx of data empowers the system to make smarter segmentation decisions and deliver increasingly relevant recommendations over time.

By combining advanced data analysis with predictive modeling, recommendation engines can anticipate what users are most likely to purchase next, driving higher engagement and conversions while enhancing the overall shopping experience.

Cross-channel dynamic content

In order to display product recommendations on web, email or in-app, a recommendation system will need to provide a mechanism to inject product recommendation into an existing user experience. This is typically achieved through dynamic content blocks that can be placed on the most important ecommerce channels. Rules can then be set to determine who will see those recommendation and when.

Where to use product recommendations

Product recommendations are versatile, enhancing various touch points across the customer journey:

On your website

Web-based recommendations, like “Trending Now” or “Customers Also Bought,” cater to different audience segments. For new visitors, crowdsourced suggestions create immediate engagement, while personalized options guide returning shoppers toward their next purchase.

Product recommendations are most effective on:

• Homepage – convert new visitors and help existing customers find more that they love
• Product listing pages – reduce ecommerce friction and prevent shoppers from getting overwhelmed by choice
• Product detail pages – create upsell and cross-sell opportunities with complementary product recommendations
• Cart pages – grant the shopper a last chance to discover more that they love

In email campaigns

Email is an ideal channel for product recommendations, offering opportunities to re-engage customers. Tactics like “People also bought” or “Still thinking about this?” nudge shoppers to complete their purchases or explore similar items. Integrating product recommendation into cart or browse abandon is also an effective way to rescue revenue by suggesting something potentially more suitable to a shopper if they are hesitant to buy. Email campaigns with crowdsourced or AI-powered recommendations can be used as a tool to reengage lapsed customers by predicting the type of product that might bring them back.

Within apps

In-app recommendations deliver real-time personalization, creating a dynamic experience for users. For instance, a fitness app might suggest workout gear based on recent purchases or browsing habits, keeping customers engaged and converting interest into action.

How and when to use recommendation filtering

Recommendation filtering refines product suggestions, making them more precise and relevant. By applying filters like price range, stock levels, or tag values, businesses can align recommendations with customer needs. For instance, excluding recently purchased items prevents redundancy, while location-based filters ensure availability.

Filtering is particularly useful for targeting specific customer segments, such as bargain hunters or high-value shoppers. The flexibility to adjust filters dynamically allows businesses to optimize their strategies and stay responsive to customer behavior.

Try the Fresh Relevance by Dotdigital Recommendation System

Fresh Relevance’s product recommendation system, powered by Dotdigital, is a comprehensive solution for businesses looking to deliver tailored shopping experiences. By leveraging robust data collection, dynamic filtering, and AI-driven insights, Fresh Relevance empowers businesses to:

• Personalize every touchpoint, from website interactions to email campaigns.
• Automate recommendations to enhance user engagement and boost conversions.
• Gain actionable insights into user behavior and product performance.

Ready to revolutionize your product recommendation strategy? 

Try Fresh Relevance today and discover how it can transform your eCommerce performance.

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By Dominic Carelse

Marketing Manager