Personalized recommendations in the app

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Example of popular products
Example of displaying popular products on the site
Viewed. These are products that the user has seen but has not purchased. The most recently viewed products are usually displayed here, even if they are from different categories.

Viewed products in pop-up on the site
Pop-up offers to send products by mail
Packaged. Items that are sold as a set. Buying them as a set is cheaper than buying them separately. Like related items, they often complement each other.

Personalized. These recommendations are based on the preferences and entire purchase history of a specific customer. Algorithms analyze not only recent purchases, but also predict which products will be needed again soon.

An example of a personalized selection in an application based on the user's interests
In fact, there are many more such types, and they are often combined.

How to recommend products on your website and in newsletters
Three steps before launching recommendations:

Determine the type of recommendations . Understand what list of netherlands cell phone number kind of recommendations you will make: personalized or general, based on data. Each type requires its own data set and IT solution. Typically, fully personalized offers require working with large volumes of data and machine learning.
Develop recommendation options. Determine what exactly will appear in the recommendations: related products, more expensive alternatives, or bundled offers. These recommendations should be part of your marketing strategy and target the user's cjm.
Choose how you want to display your recommendations: on your website and/or in email newsletters. Product newsletters are especially effective in an omnichannel strategy, where actions on your website are synchronized with sending messages.
Explore our e-commerce trigger mechanics map to gather even more ideas for communicating with users.

Recommendations on the site
To recommend a product on a website, it is important to take into account several features:

Consider collecting data for personalization. Lead the user to registration or use cookies. Usually, gender, geography, order history and activity on the site are tracked.
Choose a place to display recommendations and test hypotheses through A/B testing .
Where you can show recommendations: