How can a product recommendation system help your e-commerce store?
(Why) Do you need a product recommendation system?
Product recommendation systems have been around for a while. They have become more relevant in recent years with the rise of Amazon and machine-learning/AI technologies.
For example, Amazon’s famous “Customers also bought” product recommendation.
Below are some general reasons why you could use a product recommendation system if you are not Amazon.
Reason #1: If you have a large product catalog
A product recommendation system is useful when your shop has a large number of products. There is no general rule to this, however, if your store has at least 100 products it can be suitable.
This mostly plays into the “comparable shopper” psychology. The product recommendation can help capture a potential sale if the shopper sees a similar or comparable item in your shop. This can also help reduce abandonment as the recommendation can help the shopper to find alternative products from your shop.
Reason #2: If your store has high/sufficient traffic volume
The product recommendation system sits in the bottom half of the conversion funnel. This means it is only helpful if your shop already receives sufficient traffic from both organic and paid channels.
Reason #3: If you are looking to bundle products
If you sell products that are complementary to each other, product recommendations can help make additional sales.
The classic example of this includes the “Shop this look” or “Wear with” where an apparel store can recommend items from a specific outfit set to the shopper.
Other reasons
There are other reasons to use a product recommendation system including promoting discounted/overstocked items, sponsored/affiliated product listings, etc.
How can product recommendations help your bottom line?
The best way to test whether a product recommendation system can help your shop is through:
- Identify the Key Performance Metrics(KPM)
- Analytics tracking of the KPMs
- Extensive A/B testing
Identify the key performance metrics (KPM)
For example, some common KPM to track for e-commerce include average order value, average basket size, cart abandonment rate, and others.
Analytics tracking of the KPMs
The above KPMs should be tracked through analytics software such as Google Analytics or as part of your e-commerce shop platform. Additionally, the recommendation engine should offer conversion analytics such as click rate of recommended items, added to cart, and checkout statistics.
A/B Testing
To truly know if a recommendation system is working or not, extensive A/B testing and multivariate testing are required. For example, the simplest A/B testing will be to split your site traffic so that half of the shoppers will see product recommendations and the other half will not.
The resulting KPMs from the two sets of audiences can give you insight into if the recommendation system is working well for your e-commerce shop.
Common types of product recommendation systems
Curated Recommendations
This type of recommendation system allows you to manually add products to be recommended to the visitor. For example, adding accessories related to an outfit.
Taxonomy-based (Tagging) Recommendations
This type of recommendation system relies on the tags entered for the product and it displays other products that have the same or similar tags.
Machine-learning/AI-based (Data-driven) Recommendations
This type of recommendation system uses various data sources to present recommendations to shoppers. The most common data sources include product catalog data, user visits, product ratings, reviews, order information, and others.
Siggy is a Machine-learning/AI-based product recommender for Shopify. If you run a Shopify Store, you can Try Siggy Free
