Imagine stepping in a physical store, like Marks & Spencers. A customer goes to buy a pair of trousers. After the selection, the sales guy recommends the best shirt as well as a pair of shoes to go with. That can improve user experience as well as satisfaction with the store. This kind of experience has been missing with eCommerce stores.
But with product recommendations through AI data-driven algorithms, all that can change. Techniques such as collaborative filtering, content-based filtering, and hybrid approaches can be of help. In fact, it is one of the key innovations in eCommerce at present.
In this blog, I’ll tell you how the eCommerce experts go about implementing product recommendations and the kind of benefits it offers. So let’s begin.
What are Product Recommendations?
Product recommendations are a powerful tool that can significantly enhance the online shopping experience. By suggesting products that are likely to appeal to a customer based on their past behavior, preferences, or other relevant data, these recommendations can drive sales, improve customer satisfaction, and foster loyalty.
Key Features & Benefits of Product Recommendations in eCommerce
- Tailored suggestions: Recommendations should be based on individual customer preferences, purchase history, and browsing behavior.
- Real-time updates: Recommendations should adapt as customer preferences and behavior change.
- Accurate suggestions: Recommended products should be relevant to the customer’s interests and needs.
- Contextual awareness: Recommendations should consider the customer’s current context, such as the product they are viewing or their recent search history.
- Seamless integration: Product recommendations should be seamlessly integrated into the eCommerce website or app.
- Easy to understand: Recommendations should be presented in a clear and understandable manner.
- Visual appeal: Recommendations should be visually appealing and encourage customers to explore further.
- Measurable results: Metrics like increased sales, higher average order values, and improved customer satisfaction help measure the effectiveness.
- Continuous optimization: The recommendation system should be continuously monitored and optimized to ensure it is delivering the best possible results.
Product recommendations are a key part of the eCommerce Ai personalization. But how do they work? And what kind of benefits do they offer?
How Do eCommerce Product Recommendations Work?
Simply put, a customer buys (or tries to) a particular item from your eStore. The Ai-based, data-driven algorithms explore other relevant items in your inventory and recommend them to the customer. These items may be similar with some peculiar differences (like color or brand) or items that can be used in complement with the one they have bought.
That typically involves a combination of algorithms and techniques:
Collaborative Filtering
Collaborative filtering is a method that analyzes the purchase history and preferences of other customers with similar tastes to suggest products that they might also enjoy. It works by identifying patterns in customer behavior and recommending products that were popular among similar customers.
Example: If a customer frequently buys running shoes and yoga mats, a collaborative filtering system might recommend a new workout outfit or a sports drink.
Content-based Filtering
Content-based filtering recommends products based on their attributes, such as brand, color, or material. This approach analyzes the characteristics of products that a customer has previously purchased or expressed interest in, and suggests similar items.
Example: If a customer has recently purchased a red dress, a content-based filtering system might recommend other red dresses or similar items, such as a red skirt or a red handbag.
Hybrid Approaches
Hybrid approaches combine collaborative and content-based filtering to provide more accurate recommendations. This approach can be particularly effective when dealing with sparse data, such as when a customer has a limited purchase history.
Example: A hybrid system might use collaborative and content-based filtering in conjunction. Collaborative to identify similar customers, and then use content-based to suggest products that are similar to those purchased by those customers.
How to Implement eCommerce Product Recommendations?
Implementing effective product recommendations in your eCommerce store can significantly enhance the customer experience and boost sales. Here’s how it goes:
Step 1: Define Your Goals
Clearly outline what you hope to achieve with product recommendations, such as increasing sales, improving customer satisfaction, or reducing cart abandonment. Establish metrics to track the success of your recommendations, such as increased average order value, higher conversion rates, or repeat purchases.
Step 2: Choose a Recommendation Engine
Consider both in-house development and third-party solutions. Factors to evaluate include cost, scalability, customization options, and integration capabilities. Ensure the chosen engine can handle the volume and variety of data your store generates.
Step 3: Gather & Prepare Data
Gather customer purchase history, browsing behavior, product attributes, and other relevant information. Ensure data quality and consistency for accurate recommendations.
Step 4: Train the recommendation engine
Input your prepared data into the recommendation engine. Select the appropriate algorithm (collaborative filtering, content-based filtering, or hybrid) based on your goals and data. Allow the engine to learn patterns and relationships within the data.
Step 5: Integrate Recommendations into Your Store
Determine where recommendations will appear on your website or app (e.g., product pages, homepage, checkout). Ensure recommendations align with your store’s design and branding.
Step 6: Test and Optimize
Experiment with different recommendation strategies and placements to identify what works best. Track key metrics to assess the effectiveness of your recommendations. Make adjustments based on insights and feedback to improve recommendations over time.
Through the process, use the customer information to ensure more tailored recommendations. You can use factors like customer location, device, and recent search history.
You can hire our eCommerce development company to implement the product recommendations the best way possible.
Top 13 Ways to Do eCommerce Product Recommendation
Let’s look at the best ways to recommend suitable products to the customers to entice the sales.
Based on Browsing History
Suggests products similar to those a customer has viewed recently, even if they haven’t purchased them. This approach leverages the customer’s browsing behavior to provide personalized recommendations that are more likely to be relevant and engaging.
Example: If a customer has been looking at blue jeans, the website might recommend other blue jeans in similar styles, colors, or materials, such as dark wash skinny jeans or ripped boyfriend jeans.
Frequently Bought Together
Suggests products that are often purchased together with the item a customer is currently viewing. This method taps into the collective wisdom of other customers to provide valuable recommendations.
Example: If a customer is looking at a new laptop, the website might recommend a laptop case, mouse, and keyboard, as these items are frequently purchased together.
Personalized Email Campaigns
Sends targeted email campaigns to customers based on their purchase history, browsing behavior, or other relevant data. This allows businesses to reach out to customers with highly relevant and timely offers.
Example: A customer who recently purchased a new smartphone might receive an email campaign promoting smartphone accessories, such as cases, chargers, or headphones.
Introduce New Items
Highlights new products that may be of interest to customers. This can help to increase product visibility and drive sales.
Example: A website might feature a banner promoting new arrivals in the clothing section, or send out a newsletter highlighting the latest products.
Recently Viewed Items
Suggests products that a customer has recently viewed, even if they didn’t purchase them. This can help to remind customers of products they may have been interested in but forgot about.
Example: If a customer has viewed a product but didn’t purchase it, the website might display it again in a “Recently Viewed” section or send a reminder email.
Related to Items You’ve Viewed
Suggests products that are similar to the ones a customer has viewed, even if they haven’t purchased them. This approach can help to introduce customers to new products that they might not have considered otherwise.
Example: If a customer has viewed a red dress, the website might recommend other red dresses or similar styles, such as a red skirt or a red blouse.
Customers Also Bought
Suggests products that other customers have purchased along with the item a customer is currently viewing. This method leverages the collective wisdom of other customers to provide valuable recommendations.
Example: If a customer is looking at a new gaming console, the website might recommend popular games for that console, such as the latest bestsellers or critically acclaimed titles.
Feature Best-selling Items
Highlights the most popular products on the website. This can help to draw attention to high-demand products and increase sales.
Example: A website might feature a “Bestsellers” section on the homepage, or include a “Most Popular” tab on product category pages.
Product Bundles
Offers discounted bundles of related products. This can encourage customers to purchase multiple items at once and increase the average order value.
Example: A website might offer a bundle of a laptop, mouse, and keyboard at a discounted price.
High-rated Items
Suggests products with high customer ratings. This approach can help to build trust and credibility with customers and encourage them to purchase products that are likely to be of high quality.
Example: A website might display a “Top-Rated” section featuring products with the highest customer reviews.
Upsell Recommendations
Suggests a more expensive or higher-quality version of the product a customer is considering. This approach can help to increase the average order value and introduce customers to premium products that they may not have considered otherwise.
Example: If a customer is looking at a basic smartphone, the website might recommend a premium model with additional features, such as a better camera or more storage.
Seasonal Recommendations
Suggests products that are appropriate for the current season. This can help to drive sales of seasonal products and keep customers engaged throughout the year.
Example: In the summer, a website might recommend swimwear and sunglasses. In the winter, they might recommend winter coats and boots.
Product Pairings
Suggests products that complement each other. This approach can help to create more complete and satisfying customer experiences and increase the average order value.
Example: A website might recommend a wine pairing with a specific cheese or a matching accessory with a particular outfit.
So, want to implement these AI-based recommendations on your eCommerce website? Then hire our eCommerce development experts. We use the best tools and tactics to ensure the most accurate recommendations possible.
Best Tools for eCommerce Product Recommendations
Now that you know of the best recommendation ideas, let’s look at the best tools to implement them.
Bloomreach is a digital experience platform (DXP) that offers a suite of tools for eCommerce businesses, including product recommendations. Bloomreach’s product recommendation engine uses AI to personalize recommendations for individual customers based on their purchase history, browsing behavior, and other relevant data.
Pricing: Available upon request.
Klevu is a product search and recommendation platform that helps eCommerce businesses improve their search relevance and conversion rates. Klevu’s product recommendations are powered by AI and machine learning, and can be personalized to individual customers or groups of customers.
Pricing: Starts from €449 per month.
Algolia is a search and API platform that can be used to power product recommendations on eCommerce websites and apps. Algolia’s platform is highly scalable and can handle large amounts of data, making it a good option for businesses with a large product catalog.
Pricing: Starts free, but pay-as-you-go available.
Emarys is a customer engagement platform that offers a variety of features, including product recommendations. Emarys’ product recommendations can be personalized to individual customers based on their purchase history, browsing behavior, and other relevant data.
Pricing: Available upon request.
Clerk.io is a product recommendation platform that is designed to be easy to use and integrate with eCommerce websites. Clerk.io’s product recommendations are powered by AI and machine learning, and can be personalized to individual customers or groups of customers.
Pricing: Free plan available. Paid plan starts from $119 per month (for 1K usage).
Nosto is a personalization platform that offers a variety of features, including product recommendations. Nosto’s product recommendations can be personalized to individual customers based on their purchase history, browsing behavior, and other relevant data.
Pricing: Available upon request.
Wisepops is a popup and notification platform that can be used to display product recommendations to customers on eCommerce websites. Wisepops offers a variety of features for customizing the appearance of product recommendations, and can be integrated with a variety of eCommerce platforms.
Pricing: Starts from $449 per month.
FAQs on eCommerce Product Recommendations
Q1. What data is needed to implement product recommendations?
To implement product recommendations, you will need to collect and analyze customer data, including:
- Purchase history
- Browsing behavior
- Product attributes
- Demographic information
Q2. How can I measure the effectiveness of my product recommendations?
Track key metrics such as:
- Increased sales
- Higher average order values
- Reduced cart abandonment
- Improved customer satisfaction
- Increased customer lifetime value
Q3. How often should I update my product recommendations?
Regularly update your recommendations to ensure they remain relevant and effective. Consider factors such as product changes, customer behavior shifts, and seasonal trends.
To Summarize
Product recommendations offer a powerful tool to achieve these goals by suggesting relevant and appealing products to customers. Effective implementation requires careful planning, data collection, and ongoing optimization. The right tools can simplify the process and enhance the accuracy of recommendations.
The key to successful product recommendations is to focus on personalization, relevance, and user experience. Common tactics include “Frequently Bought Together”, “Introduce New Items”, “Recently Viewed Items”, “Customers Also Bought”, etc. Do you want product recommendations on your eStore? Then contact our experts today!
