By Kiran Patil
Did you know that people who click on product recommendations are two times more likely to convert? Nowadays, nearly every online business is leveraging product recommendations to boost revenues, increase customer satisfaction, and provide users with a more personalized shopping experience.
There’s no doubt if a product recommendation engine is leveraged correctly, it can improve click-through rates, conversions, average sales orders, and other important metrics for your business.
What is a product recommendation engine?
A product recommendation engine is a system which collects data and uses algorithms to make product recommendations that benefits both customers and businesses. Data is collected for every user and analyzed by criteria such as users’ past purchases, search history, or demographics. It can help customers make decisions by showing them a narrow selection of products that are recommended specifically for them.
According to a report by Salesforce, visits where a shopper clicked on a recommendation accounted for a small percentage (7%) of visits, but brought in a considerable chunk of revenue (26%).
How to leverage product recommendations on websites
Shoppers today expect a seamless and customized experience while they browse or shop online. Although most customers are looking for an enhanced personalized experience, only 22% are satisfied with the level of personalization they currently receive, according to a report by Segment. You can improve on this by having a sophisticated product recommendation engine which will improve your customers’ shopping experience, and thus increase conversions and sales.
The types of recommendations you offer customers will depend on where each customer is in his or her journey. The following are the best practices to keep in mind while designing effective recommendations for your ecommerce site.
1. Home page
Your website’s home page is the first point of interaction for any user coming to your website. For first-time visitors who may not have a specific item they’re looking for, you will not have enough data about them to recommend relevant products. Hence, recommendations on the home page for first-time visitors aim to help them discover and explore your products.
For returning visitors, since you already have data about their recent purchases and browsing history, you can offer personalized product recommendations. For example, if a customer has previously bought a camera, you can show new arrivals of camera accessories.
Product recommendations on a home page may include:
Most popular products—different rules can be set for popularity
Best-selling products
Newly launched products
Special deals on products or products that are on sale
Items related to previous purchases or discounts on recently viewed products
2. Category pages
Recommendations on category pages work similar to recommendations on the home page, but differ in one way: They showcase products which are specific to the category or subcategory that a customer is looking at. Here new and returning visitors will see different products based on their interactions with your website.
Product recommendations on a category page may include:
Best-selling products of the category
Popular products of the category
Newly added products in the category
Special deals on products in the category or products that are on sale
For the returning visitors, products related to past purchases from this category
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3. Product pages
Product pages provides detailed information about the items you are selling. When customers visit a product page, you can determine whether they are viewing their first product or if they have been looking at other products. Based on that information, you can then offer them tailored suggestions of other items on your site. Recommendations offered on product pages aim to increase conversion rates and average order value.
Product recommendations on a product page include:
Related or complementary products (cross-sell)
Accessories (cross-sell)
Similar products
Frequently bought together items (up-sell)
4. Shopping cart page
When customers reach the last step of their buying journey, it is important to let them complete their transaction without any distractions. However, you can still leverage this step as an opportunity to recommend additional products. The main purpose of product recommendations on the shopping cart page is to increase the value of an order.
Product recommendations on a cart page may include:
Add-ons or accessories (cross-sell)
Recently viewed products
Higher value versions of products already in the cart (up-sell)
Add-ons to get free shipping or other offers
Frequently bought together products for items already in the cart (up-sell)
5. Order confirmation page
When customers have made their purchase, most businesses think that the deal is done. However, that is not necessarily true. Online stores should still recommend products based on user interaction. The main aim of this recommendation is to give people another hook so they can continue their journey on your website and restart the loop again. Recommendations here should be more personalized, as you now have significant data at this stage.
Product recommendations that can be used here include:
Cross-category recommendation
Bestsellers (related to the purchased items)
Trending products
New arrivals
6. Error or “out-of-stock” pages
Getting to a “404 error” page or an “out-of-stock” page often result in the user abandoning their purchase. These error pages have a very high exit rate, which is a potential loss of conversion. Businesses, however, can turn these pages into opportunity by displaying bestsellers (to keep users engaged), items based on browsing history, etc. This can be a great catalyst in resuming an interrupted experience.
Product recommendations that can be used here include:
Products similar to the sold out or searched item
Recently viewed items
Bestsellers
A sophisticated product recommendation engine offers many benefits
Users will arrive on your website from various channels. Whether you are looking to upscale your current recommendation engine or build one from scratch, it is important that you provide users with intelligent guidance. Once a basic rule-based framework for product recommendations has been set, you can implement detailed analytics and a data capturing system, and thereafter, refine recommendations through machine learning.
There are numerous product recommendations that your business can use to increase revenues, customer engagement, and user experience. But the key here is to keep your short-term and long-term goals in mind, and then start to implement them in a phased manner.
With a robust product recommendation engine, you will help users make more informed choices with greater ease and thus boost your top and bottom line. Implementing a sophisticated product recommendation engine with cutting-edge technology is key to staying ahead of your competition.
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About the Author
Post by: Kiran Patil
Kiran Patil is the founder and CEO of Growisto, an e-commerce marketing and technology company based in Navi Mumbai. He has 16+ years of experience in e-commerce, phone commerce, and digital marketing. Growisto helps brands and private labels to grow their businesses on platforms such as Amazon and their own website. Kiran is an alumnus of IIT Bombay and has worked with companies like Evalueserve and Future Group before starting his entrepreneurial venture. His personal interests include traveling, cycling, and trekking. His work has been featured in Entrepreneur, Business2Community, YourStory, Inc42, Indian Retailer, and many others.
Company: Growisto
Website: www.growisto.com
Connect with me on Twitter and LinkedIn.
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