Understand Product Recommendation Types
Recommending the right products in the cart and during checkout is the way to the profitable e-shop. Let’s look at how to get the most from this feature. There are three main recommendation types that we will be talking about. Firstly the history-based recommendation, secondly the cart-based recommendation, and thirdly the trend-based recommendation. Those three recommendation types work well together, so combining them provides the best results.
The history-based recommendation is the most complex and complicated one, which gives you the greatest opportunity to get the edge over competitors. This type analyses the purchasing history of a customer, including the average amount spent, products purchased, recurrent order, product categories, and the average amount of items in the shopping cart. This represents a lot of data that needs to be processed by a prediction algorithm. The algorithm identifies a few products with the highest likelihood of purchase and recommends it to the customer. The algorithm itself is a black box, and its exact operation is usually a protected secret. But let’s look at the principles the prediction engine uses.
Firstly, the algorithm identifies any recurrent purchases and their frequency. Then it recommends the product once the time for the next purchase is closing.
Secondly, the previously purchased products are used to find look-alike customers with similar tastes. The algorithm is then searching for repeatedly purchased products by the look-alike customer to recommend the most suitable ones.
Lastly, the algorithm assesses whether an upgrade or suitable accessory is available to the previously purchased product and whether the upgrade purchase is likely.
Algorithm development takes a lot of resources and time, but moreover running the algorithm requires high computational power. Therefore, the majority of e-shops use external cloud service to avoid development costs and time delay as well as to keep the server load out of the e-shop itself.
The cart-based recommendation is much simpler, but yet provides significant results keeping e-shops profitable. Its function is similar to finding look-alike customers, but here it looks at look-alike carts. It identifies the most frequent products in the look-alike carts and recommends the best match.
The great advantage of this recommendation type is its simplicity. The algorithm does not need to understand the product or its relation to other products; it only groups them and recommends the missing in the current cart. It is the best choice for new customers, where the first algorithm cannot generate any recommendations.
The trend-based recommendation type is the most rudimentary one. It should not be missing in your recommendation mix, but it should not be the only one. This recommendation’s efficiency rests on 80/20 rule, which says that approximately 20% of your products generate about 80% of your profit.
This is the best approach for new customers with an empty shopping cart. There is no data you can feed to the above-mentioned algorithms, so recommending the bestsellers is your safest bet. This recommendation also performs the best at the homepage or at the top of a category page, but its usefulness diminishes in the cart or checkout. There we recommend using the first two types.
As you can see, the recommendation feature is complex and requires significant effort to be implemented correctly. It is best to choose a strong partner with experience and resources to help you implement the feature. CareCloud provides efficient recommendations tested in many industries and countries. Moreover, experienced consultants will advise the best mix and placement of recommendation types.
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