Low conversion rates remain one of the biggest challenges for online businesses. Even with increasing website traffic, convincing users to complete a purchase is still difficult. This is especially true when your site offers hundreds of different products or services, and customers aren’t sure exactly what they want.
Recommendation systems have proven to be an effective solution to this problem, with a solid track record of success. Just look at Amazon, which generates over 35% of its revenue through recommendations, or Netflix, which saves a billion dollars annually in customer retention.
Wondering how to implement this method in your business successfully? This article will help answer that question. We’ll analyze key statistics and showcase real-world examples of recommendation systems that could inspire your approach.
Why Should You Use Recommendation Systems?
In the introduction, we mentioned the success of recommendation systems at tech giants, but how does this translate to other businesses? Let’s look at key global data highlighting the main benefits of implementing a recommendation system.
Key advantages of recommendation systems:
- Sales Growth: McKinsey research shows that personalized communication significantly impacts purchasing decisions – 76% of consumers cite it as a key factor when considering a brand, and 78% say it increases their likelihood of making repeat purchases.
- Meeting Customer Expectations: According to Statista, personalized product recommendations have become a standard expectation across major markets. For instance, 54% of online shoppers in Portugal and 49% in the USA expect personalized suggestions.
- Staying Competitive: Implementing recommendation systems is no longer about getting ahead but staying ahead. 89% of decision-makers believe personalization will be crucial for their company’s success in the next three years.
- Higher Shopping Cart Value: Accurate recommendations encourage customers to purchase related products and services, leading to higher cart values. This is confirmed by 54% of retailers identifying recommendation systems as a key factor in increasing average order value.
- Reaching Younger Generations: 85% of companies plan to adapt their marketing strategies to meet the unique needs of Generation Z, for whom personalized experiences are the standard, not an extra feature.
Most Popular Types of Recommendations
1. Similar Product Recommendations
One of the simplest yet most effective personalization methods is making recommendations based on product similarity. The system analyzes product features such as name, description, and tags to present users with similar items. This allows customers to quickly see alternative products and choose the one that best suits their needs.
Example Applications:
- Displaying similar products in the same price range
- Showing alternative product versions (e.g., different colors, sizes)
- Suggesting products with comparable technical specifications
This method is particularly effective for stores that frequently expand their inventory or offer products with complex technical specifications, where comparing features is crucial for purchase decisions. Moreover, it’s worth noting that this type of recommendation doesn’t require historical user behavior data – recommendations can be implemented immediately after adding a product to the catalog.
2. Trending Products / Best Sellers
These recommendations show users items that are most popular with other customers. This could include best-selling products, most-viewed items, or highest-rated products within a specific period.
Why Does It Work? People are more likely to buy products that others have found successful. This is especially important in online shopping, where customers can’t physically examine products before purchase.
This popular method is especially useful for new users when we don’t yet have data about their preferences.
To maximize the effectiveness of these recommendations, consider:
- Adding labels like “Trending Now” or “Best Sellers”
- Displaying ratings and reviews alongside recommended products
- Updating bestseller lists in real-time
3. You Might Also Like” (Related Products)
Cross-selling is a sales strategy that recommends complementary (supplementary) products to what the customer is viewing or buying. For example, when a customer looks at a camera, the system shows them matching lenses, memory cards, and camera bags. If they’ve just purchased a smartphone, they’re offered products like cases, screen protectors, chargers, or headphones.
This strategy works at different stages of the buying journey:
- On the product page – where customers can immediately see the necessary accessories
- In the shopping cart – as a reminder about complementary products
- After purchase – suggesting useful add-ons for items already bought
It’s worth noting that well-chosen related product recommendations aren’t just a way to increase cart value. They’re also part of building a positive shopping experience – customers appreciate comprehensive service and accurate suggestions for products they need.
4. “Customers Who Bought This Also Bought”
This simple message is one of the most effective ways to recommend products. It uses the behavior patterns of similar users to predict what might interest the next customer.
This approach has distinct advantages:
- Based on real purchasing behaviors, not just product features
- Reveals unexpected connections between products
- Automatically adapts to changing trends
These recommendations can take various forms:
- “Frequently Bought Together”
- “Others Also Bought”
- “Similar Customers Chose”
The effectiveness of this method comes from its authenticity – instead of relying on predetermined rules, the system learns directly from customers’ purchasing decisions. That’s why these recommendations can often surprise users with their accuracy and help them discover products they wouldn’t have found on their own.
5. “Based on Your Purchase History”
A customer’s purchase history is the most reliable source of information about their preferences. By tracking users’ previous choices, the system can suggest products likely to match their taste and needs.
This is a particularly effective method because it:
- uses complex data about what the customer was willing to pay for
- helps build long-term relationships through accurate suggestions
- works well for repeat purchases or replenishment items
Example applications include:
- reminders for products that may be running low
- suggestions for new versions of previously purchased products
- recommendations from categories where the customer has already made purchases
This type of recommendation works exceptionally well with regular customers. The more we know about their shopping preferences, the more accurate our suggestions can be. It’s like having a good salesperson who remembers what the customer bought and can recommend something new that matches their taste.
6. “New Arrivals” (New Products or Services Recommendations)
Showcasing new products is a great way to maintain customer interest and encourage repeat visits. However, newness alone isn’t enough – the key is presenting new products that match the user’s interests.
To make new product recommendations effective:
- Personalize them based on browsing or purchase history
- Emphasize the uniqueness and limited availability of new products
- Combine them with other types of recommendations (e.g., “New Items Similar to Your Previous Purchases”)
This strategy is particularly effective in industries where novelty matters – like fashion, electronics, or cosmetics. New destinations or hotels can be equally appealing in the travel industry.
7. “Near You” (Location-Based Recommendations)
While most recommendation systems are primarily associated with physical products, they also work excellently in the travel industry. Recommendation systems using geolocation data are particularly effective here. They can significantly help users find the perfect option in their area of interest.
Here are some examples:
- Showing similar properties in the selected area
- Suggesting alternatives in nearby, attractive locations
- Recommending places with similar standards and amenities in the region
- Proposing places to visit in the immediate vicinity
This solution is handy for undecided users who already know their preferred location but are still looking for accommodation or attractions nearby. These types of recommendations also make it easier to find alternatives when the originally chosen property is unavailable.
8. Text-Based Recommendations
Text-based recommendation systems are innovative tools that help users discover content that best matches their interests. For example, when browsing “Harry Potter” on Amazon’s website, the system automatically suggests other fantasy novels or children’s books.
The methodology behind these recommendations relies on advanced algorithms that can understand text context and meaning, not just simple keywords. This ensures that suggestions are genuinely relevant and valuable.
Recommendation Systems – Industry Applications
Recommendation systems are now crucial to digital strategies across almost every business sector. Their primary purpose is personalizing user experiences by intelligently matching content, products, and services.
E-commerce
Recommendation systems are particularly advanced in this industry. Algorithms analyze purchase history, preferences, and consumer behavior to suggest products most likely to interest the customer. Amazon is a prime example of a platform that effectively uses such solutions.
Entertainment and Media
Netflix, mentioned in our introduction, is a model example of a recommendation system. The platform’s algorithms analyze user preferences to create personalized content lists. Similar solutions are used by Disney+, Amazon Prime Video, and HBO Max, tailoring suggestions to individual tastes.
Travel
In the travel industry, recommendations have become a key decision-making tool. Companies like Booking.com and Airbnb don’t just show available options; they prioritize those that best match individual preferences and previous traveler choices. An interesting example is Qtravel Search, which uses artificial intelligence, allowing tourists to search using natural language quickly. Moreover, it also remembers user choices along with their queries. This makes each subsequent search and recommended suggestion increasingly accurate.
Social Media
Platforms like Instagram and TikTok have built their business models on content hyper-personalization. Their algorithms analyze user behavior in real-time, creating an almost endless stream of tailored content.
Online Education
Educational platforms like Udemy use recommendation systems to suggest courses, training, and educational materials perfectly matched to the user’s knowledge level, interests, and development goals.
Recommendation Systems – The Key to Digital Business Success
A recommendation system is no longer just a marketing tool—it’s become a market necessity. Companies that don’t implement effective recommendation systems risk falling behind their competition. Personalized experiences should be the standard, especially if you want to reach younger generations.
It’s important to note that a significant change is happening right now. With third-party cookies being phased out, companies face the challenge of redefining their approach to personalization. The “The State of Personalization 2024” report shows that 55% of business leaders see the future in advanced AI-powered recommendations.
These upcoming changes are forcing companies to rethink their strategies. Instead of invasive tracking, there’s a need to build trust and transparency and offer real value to users. Artificial intelligence is becoming a key tool that will enable the creation of intelligent, contextual recommendations that understand individual user needs.
Want to learn how to use AI to improve user experiences and stay up-to-date with this breakthrough technology? Sign up for our newsletter!