AI powered product recommendations has been shown to outperform all other methods to recommend relevant products to customers. Even relatively small stores can benefit from smart recommendations and the return on investment is generally good. Most providers offer a free trial period within which you will get a good indication of the impact product recommendations may have on your store revenue. Use it to see for yourself.
Avoid manual recommendations
Unless you have very few products and a static demand for those products, manual product recommendations are time consuming, static and often less relevant for the visitor. It is impossible to combine realtime store information with manual recommendations to deliver up-to-date relevant product recommendations. Displaying relevant alternatives for a product may be viable, but thats about it. It can’t be derived specifically for the individual visitor based on browsing history, prior purchases or items added to cart. And it quickly becomes unviable to manage a large set of products in this way.
But the main argument isn’t that manually selected recommendations tend to perform poor compared to AI powered ones. The main argument is that AI powered recommendations are continuously updated and improved without any manual labor or time spent. Done right it can be an almost autonomous feature that drives increased sales and customer engagement while you spend your time on other things.
The use of rule based recommendations
Rule based recommendations are derived out of rules provided by you (or some default setting). Examples can be to show products in the same product category, best sellers, products on sale, etc. These can be beneficial in cases where there is no or very little information available on the visitor. For example on the front page when the visitor just arrived. The issue with these, apart from being somewhat static, is that they generally drive more of the same sales. E.g. Best seller recommendations tend to be successful for a while because they promote the most popular products on the site, but they rarely introduce a new purchase pattern on the site. Furthermore, these recommendations may not present the most relevant products for the individual buyer. Overtime they also tend to become much less relevant for returning customers, since it is likely they already seen them and neglected them, or bought them.
AI opens a world of opportunities
What if we were to introduce logic and reasoning into the selection of product recommendations to display in any given moment? We could aim to show products based on:
- Purchase behaviour of similar visitor profiles
- Products that are often bought together with the product in question
- Products that have been added to cart, viewed, searched, etc.
Or, we could aim to simplify product discovery for our visitors and display similar products to the one being viewed. Similar either determined by image similarity, or product description similarity, or both.
Suddenly it becomes possible to apply a strategy for recommendations, instead of just filling up the empty space at the bottom of the page. With all these options available, how will you use product recommendations to drive revenue and customer engagement in your store?
We will go deeper into this in subsequent parts of this series, but it’s a good idea to formulate some kind of aim or goal with product recommendations in order to achieve the desired outcome. It could be to:
- Guide the visitor to the product they are looking for
- Make the visitor aware of products that could be of interest to them
Selecting one of the aims above will help you to implement product recommendations that work for your store, strategy and visitors.
Wrapping up this part it is important to consider that the options and possibilities available for product recommendations can help you reach your goals and use the right type of recommendation in every situation to best serve your customers. Continue reading about product recommendations in Part II: Position.