AI Recommendation systems play a crucial role in enhancing the shopping experience for e-commerce customers. E-commerce platforms (example: Woocommerce) can provide personalized product recommendations tailored to each individual shopper’s preferences, browsing history, and purchasing behavior. They are a major driver for achieving higher conversion rates and selling more products.
Here are the reasons why it could help your bottom-line :
Your own dedicated salesman in a digital landscape
Trust your recommender to recommend the most relevant content to potential customers. Like an ordinary salesman would, the recommender system will try to learn more about visitors.
Instead of blindly suggesting random items, the recommender will collect user events/interactions (page views, add to cart, etc…) and use this data to recommend the most relevant visitors and customers. If you use an AI-powered recommender, the model will be trained on the stored user events so the AI will truly know your users (not dependant on tags and categories) and recommend the most relevant content with far less fine-tuning required.
This will in turn increase the odds of your visitors buying the products they viewed since the recommender predicted their needs. According to Barilliance : “shoppers that clicked on recommendations are 4.5x more likely to add items to cart, and 4.5x more likely to complete their purchase“.
Greatly increase user engagement
User engagement is a metric that illustrates your visitors’s experience on your e-commerce website. If they frequently interact with the components on your website you have a good user engagement. If not, if they bounce immediately and never come back, your user engagement needs work. Some of these user engagement metrics are : click-through rates, conversion rates, bounce rates, etc…
Recommender systems can help with maintaining a high user engagement . The more the visitor’s click, view and browse, the more they are likely to make a purchase. All this contributes to a higher Revenue Per Visitor (RPV) and Average Order Value (AOV). So recommending them relevant items to click on and to keep them interested (in turn boosting time on website) is a tremendously helpful.
High user engagement is also crucial for increasing traffic to your e-commerce website since they are a major factor in determining your Google (or any other search engine) rankings. A higher rank increases a website’s visibility on search engines so recommendations contribute to greatly to your SEO. By providing valuable content and suggesting it to the right items to visitors (using personalization), you can attract and retain more of them, leading to improved search engine rankings and ultimately driving more organic traffic to your website. Having more traffic has a direct consequence of converting more users and increasing profitability.
Site identity
Expectations play a vital role in e-commerce, where customers often anticipate and rely on personalized recommendations to guide their purchasing decisions.
AI recommendations systems can be more than just a tool to increase conversion rate, oftentimes they are the core part of an e-commerce website’s experience. For Amazon, the world’s most profitable e-commerce website they are a core part of the website’s identity. If you look at the Amazon dashboard, it is filled with recommendations of many types and contains only that.
So equip your e-commerce with worthwhile recommendations and impress your visitors, otherwise 47% of them may head directly to Amazon instead.
Reduce cart abandonment
According to Baymard institute, the average cart abandonment rate is 70.19%. However if you send personalized emails to your customers right after they abandoned their cart, 50% of them who clicked on the email completed the purchase (according to Moosend). So as you can see, personalization can be used outside of your e-commerce website aswell to increase it’s profitability.
10 types of recommendations to boost your sales
If you wish to boost your sales, you should consider implementing the following types of recommendations into your e-commerce website (you could learn which plugins could integrate recommendations into your Woocommerce) :
Screenshot of Amazon related products at the bottom of a product detailed view
1. Related products
This is the most simple form of recommendation, often being displayed as a slider full of similar products at the bottom of the detailed view of a product. ‘Related products’ recommendation will try to recommend similar products (based on different criteria like categories, tags, name, etc…) to the one you are currently viewing. This gives the user as many options as possible so that they can find the perfect one for their needs without hassle.
For example, if you are viewing a Samsung Galaxy (is a phone) you could also be interested in an IPhone (also a phone).
Screenshot of Amazon ‘Cross-sell’ recommendation in product detailed view
2. Cross-sell
Cross-selling is used to promote complementary items (to a product that a customer has viewed or bought) that your customers may want.
For example, if a customer is buying a laptop, cross-selling might involve suggesting a laptop bag, mouse, or antivirus software as additional purchases.
3. Up-sell
Up-selling is suggesting the same type of product that the customers are interested in but at a higher price point.
For example, when a customer adds a laptop to their shopping cart, the website suggests upgrading to a model with higher storage capacity or faster processing speed.
Screenshot of Amazon ‘Frequently bought together’ recommendation in detailed product view
4. Frequently bought together
This form of recommendation is quite different since the products to display aren’t necessarily related based on the attributes that define them but on what people who bought this product also purchased around the same time. This could inspire your customers to buy a product they didn’t even consider they needed.
For example, if a customer buys skis (in category ‘sports’), the recommender could suggest gloves (in category ‘clothing’) despite having little attributes in common. This is because this recommendation is based on all the users’s purchase history as a group and, since most of the people that went skiing needed to buy protective clothing, gloves become a relevant product.
Screenshot of Amazon recommendations based on user history in personalized dashboard
5. User history
Another common type of recommendation is the one based on user history. Mostly appearing on a customer’s personalized dashboard, this recommendation will display items related to the customer’s previous views, clicks and purchases.
For example, if a customer has been viewing a great number of DVDs, a smart recommender would suggest other DVDs of the same genre.
Screenshot of Amazon ‘Trending’ recommendation in user personalized dashboard
6. Trending
If you want to sell more, you could recommend items that you know customers already love and value. This is where trending recommendations come in. Since customers as a group have collectively shown interest in a product, you could speculate that others could want it aswell. So you should recommend it to them using the trending recommendation.
Screenshot of Amazon recommendations after adding item to cart
7. Cart
Shopping cart recommendations are personalized product suggestions that are displayed to shoppers while they are viewing their shopping cart or during the checkout process on an e-commerce website. These recommendations are based on the items already added to the shopper’s cart and are designed to encourage additional purchases or upgrades.
8. Email
This is an interesting one since it is used outside of the website technically. Email recommendations are personalized product or content suggestions that are delivered to subscribers via email. These recommendations are based on the recipient’s past behavior, preferences, purchase history, and other relevant data. This is powerful for engaging customers and driving sales.
9. Upcoming events
Your recommendations could also use upcoming real-world events or holidays.
For example, if Christmas is approaching, you could want to sugggest christmas-themed items.
10. Geolocation
Geolocation-based recommendations are more situational than the other types of recommendations but brings incredible value to specific sectors. These sectors could be real-estate agencies (find a house in your general area), nearby points of interest for tourism websites (restaurants near you, hotels, etc…) or localized offers (discounts at physical stores). Some AI recommenders like Recombee do this.
