Lookalikes - predictive product recommendation


Lookalikes is a predictive recommendation type. It uses a type of machine learning called content-based filtering to find types of product a customer has shown an affinity for. When creating this recommendation, it will duplicate and contain the name of the primary recommendation followed by '_fallback' at the end. The fallback contains a best seller and is editable in the product recommendation builder.

How it works

When a lookalikes recommendation is created, your catalog is analysed. The process groups similar products together. If you were a winter sports retailer, products could be grouped by tags such as 'snowboard', 'female', 'burton', 'red', and so on.


Next, a contact’s purchase and browsing history are analysed. Products with a suitable similarity score are chosen. Each contact is then given a ranked product set unique to them.

Accessing predictive recommendations

Go to Campaign > Product recommendations, click Create recommendation, mouse over the recommendation type you're after under the 'Predictive' section and click on Learn more.


The sidebar will allow you to request the feature is enabled on your account (for an additional monthly charge). We'll first review your data and then confirm with you that the feature is ready to use, or if there are data dependencies to be resolved first.

Predictive recommendations are available on a 14-day free trial.

Getting the best results

Before getting started, please review the data dependencies for this product recommendation type.

This model is most effective when there's good quality and depth of product data available. Product detail can be improved by using product data enrichment. Catalogs with a high number of products but with low orders will not give optimal results.

Order data is one indicator of customer preference. By using Web Behavior Tracking, the machine learning model's accuracy will be improved.

Why use this recommendation type?

Lookalikes (content-based filtering) has a narrower scope than best next (collaborative filtering). It can make very accurate recommendations, in particular for niche purchasers.

Have more questions? Submit a request