We’ve all been there. Browsing an online store, maybe looking for a new pair of shoes or a gift for a friend, and suddenly you see it: a “You might also like…” section with products that seem to be tailored made for you.
It’s like the website is reading your mind!
That’s the magic of product recommendations and it’s getting more and more important in ecommerce by understanding customer preferences.
In this guide we’ll go deep into product recommendations.
We’ll look at how they work, see some amazing real world examples and uncover the secrets to using them to boost sales and create a truly personal experience for your customers.
Let’s get started!
What is a Product Recommendation?
At its simplest a product recommendation is a suggestion to a customer about what they might like to buy next.
These can appear anywhere in your online store, from product pages and the checkout page to email campaigns and even social media ads.
Think of it like a helpful salesperson in a physical store.
They might see you browsing a particular product category and suggest a related or popular item that other customers tend to buy with it.
Product recommendations in the online world do the same – guide customers to relevant products and improve the shopping experience.
For ecommerce store owners, effective product recommendations are crucial for enhancing customer engagement and driving sales.
How Product Recommendation Systems Work
Ever wondered how these recommendations seem so tailored to you? It’s all thanks to machine learning and some clever algorithms.
Here’s a look under the hood:
1. Collaborative Filtering
This method looks at the purchase history and browsing behavior of many customers to find patterns and similarities.
If you and another customer bought the same book, the system might recommend other books the other customer also bought.
It’s like saying, “People who bought this also bought that.
2. Content-Based Filtering
This method looks at the product itself.
By looking at product attributes, descriptions and even customer reviews the system can suggest similar products or something that complements what the customer is looking at.
3. Hybrid Systems for Complementary Products
Many ecommerce platforms use a combination of collaborative and content-based filtering to provide the best recommendations.
This allows them to use the strengths of both and deliver a truly personal experience.
11 Remarkable Product Recommendation Examples
Now that we know the basics let’s see some real world examples of product recommendations in action!
1. Kylieskin
Kylie Cosmetics uses the cart page to cross-sell a lip oil to customers who’ve added a matte lipstick to their order.
The “Don’t Forget” section along with product ratings encourages larger purchases by suggesting a product that complements the customer’s initial purchase.
It’s simple but effective to boost sales and improve the shopping experience.
2. Wandering Bear
This coffee company puts a “The Pack Membership” offer directly on the cart page, with subscription pricing, exclusive access and more.
This is a smart upsell, encouraging customers to become recurring customers and increase their lifetime value. The limited time offer adds a sense of urgency to take action now.
3. Tarte
Tarte Cosmetics sends personalized email recommendations based on a customer’s browsing history.
They remind the customer of a recently viewed product and suggest other products they “may also like” to increase the chances of a sale.
This targeted approach keeps the brand front of mind and encourages product discovery.
4. Soi
This fashion brand uses product recommendations to encourage customers to “complete the look.”
After adding a dress to their cart customers are shown related products like a matching belt or shoes to create a full outfit.
This increases average order value and enhances the customer experience by providing styling suggestions.
5. Colourpop
Colourpop uses “cart specials” to encourage customers to add more to their order.
By offering discounts on specific products like the liquid liner and cheek palette here they encourage impulse buys and increase average order value.
The limited availability (“limit 1 per order”) adds to the sense of urgency to buy now.
6. My tote
This brand suggests additional products on the cart page with a “You might love these too” section.
They show a serum and mascara alongside the customer’s current product to encourage product discovery and add to cart.
The free gift with min purchase encourages customers to add more to their cart.
7. Cottages
This travel site uses social proof (“42 people have recently viewed it”) to create urgency and get customers to book now.
By showing similar properties the customer “might like” they provide alternative options and increase the chances of a sale.
8. Frankbody
Frank body uses beautiful product displays to encourage browsing and discovery.
They show a range of products with images and short descriptions so customers can find what they need.
The icons to highlight key benefits help with decision making.
9. Gymshark
Gymshark has a “People Also Bought” section on their product pages to show items that are frequently purchased with the product being viewed.
This is collaborative filtering, suggesting products based on the purchase history of other customers.
By showing these popular combinations Gymshark encourages customers to browse related products and add more to their cart to increase average order value.
10. Bag
This brand has a “Goes Great With” section on their product pages to suggest related products.
They recommend a matching wallet and laptop sleeve with the featured tote bag to encourage customers to buy the whole set and increase average order value and convenience..
11. Olive
This brand uses a pop up to offer a limited time offer with a “mystery discount”. The countdown timer creates urgency to buy now before it expires.
By bundling two related products they encourage a bigger purchase and introduce customers to new products they may not have considered otherwise.
Personalization in Product Recommendations
In today’s world of online shopping, personalization is everything. Customers have too many choices and generic product recommendations just won’t do.
Personalized recommendations can make all the difference.
Why is personalization important?
More Customer Engagement: When customers see relevant products they’ll stay on your site longer and explore more. This leads to more customer engagement and higher conversion rate.
Better Customer Experience: Personalized suggestions show customers you understand their needs and preferences. This is a more enjoyable shopping experience and customer loyalty.
More Sales: By suggesting products customers will love you’ll increase average order value and sales. Win win for you and your customers!
3 Product Recommendation Tools to Use
Ready to implement product recommendations in your own online store?
Here are a few tools to get you started:
1. Recommendify (Shopify)
This Shopify app uses collaborative filtering to analyze your customer data and provide personalized product suggestions.
It’s easy to set up and can be customized to match your brand.
2. Nosto
Nosto is a powerful personalization platform that offers a range of features, including product recommendations, personalized content, and email marketing.
It uses AI and machine learning to deliver a truly tailored experience.
3. Barilliance
This platform offers a suite of personalization tools, including product recommendations, triggered emails, and personalized search.
It’s designed to help ecommerce businesses increase conversions and foster customer loyalty.
Here is a more detailed guide on it: 15 Product Recommendation Software That Actually Work
3 Product Recommendation Mistakes to Avoid
Even with good intentions it’s easy to go wrong with product recommendations.
Here are three common mistakes and how to avoid them:
1. Ignoring Customer Preferences
A customer is browsing winter coats on your site. They click on a parka to learn more. But what do they see in the “You might also like” section? Swimsuits. Not very helpful right?
This is ignoring context. Your recommendations need to be relevant to the customer’s current browsing behaviour and the product they are looking at.
Here’s how to get it right:
Page Specific Recommendations: Recommend products specific to the page the customer is on. On a product page recommend complementary products, similar products in a different colour or size, or popular products that are often bought with the main product.
Category Awareness: If a customer is browsing a specific category like “running shoes” your recommendations should stay within that category or suggest related products like running apparel or accessories.
Consider the Customer Journey:
- Where is the customer in their buying journey?
- Are they just starting to explore or are they ready to buy?
Adjust your recommendations accordingly. For example if they’re early in the journey suggest more options. If they’re closer to buying suggest cross-sell or upsell relevant products.
2. Too Much
While it’s tempting to show lots of products, too many can be counterproductive. It’s called “choice overload” and leads to confusion and indecision.
Think of it like being given a menu with hundreds of options. It’s overwhelming! A curated selection of a few options is much more appealing.
Here’s how to get the balance right:
Less is More: Quality over quantity. Instead of a long list of random products, curate a few relevant recommendations.
Prioritise and Rank: Use your product recommendation tool’s features to prioritise the most relevant recommendations. This will show the best options first.
Visual Appeal: Show your recommendations in a visually appealing and easy to navigate format. Use high quality images and clear descriptions to make it easy to browse.
3. Not Measuring
Implementing product recommendations without measuring the results is like flying without a map.
You need data to know what’s working and what’s not.
Here’s how to make data driven decisions:
Track Key Metrics: Monitor click through rates (CTR) on your recommended products. This will tell you how good your recommendations are. Also track conversion rates and average order value (AOV) to see how recommendations are impacting your bottom line.
A/B Testing: Try different recommendation strategies, placements and algorithms. A/B testing allows you to compare results and see what works best for your audience.
Analyse and Adjust: Review your data regularly and use the insights to optimise your recommendations.
- Are certain product categories performing better than others?
- Are there any patterns in customer behaviour?
Use this to refine your approach.
Avoid these common mistakes and be data driven and you’ll unlock the full potential of product recommendations and give your customers a truly personal and engaging shopping experience.
Conclusion
Product recommendations are a must have for any ecommerce business looking to increase sales, customer engagement and customer loyalty.
By knowing how they work and doing them right you’ll give your customers a truly personal shopping experience.
