Personalized Email Recommendations

This app shows how to leverage Algolia Personalization to display product recommendations in emails

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This sample app shows how to leverage Algolia Search and Algolia Recommend to display product recommendations in emails. Showing recommendations is a great way to engage your customers at various stages of their journey before and after visiting your site.

This sample app comes with the following recommendation models:

  • A customer browsed a category: recommend best rated products from that category. This recommendation model uses Algolia's Faceting feature.
  • A customer just bought a product: recommend products that are frequently bought together. This recommendation model uses Algolia Recommend to train a machine-learning algorithm based on which products users often buy together.
  • A customer just bought a product: recommend related products. This model also leverages Algolia Recommend to train a machine-learning algorithm to find products that are similar.
  • A customer has an existing user profile: recommend products that match their affinities. This model uses Algolia Recommend and Personalization.

This sample app comes with the following features:

  • A rock-solid base email template with Cerberus
  • A rich and powerful templating language with Nunjucks
  • Three different email templates for different moments of the customer's journey (pre-order, post-order, re-engagement)
  • Four different models for recommended products with Algolia Recommend and faceting

This sample app integrates with:

  • Twilio SendGrid
  • Adobe Marketo Engage


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Built by Algolia's developer community, not supported by Algolia


Algolia Labs

Algolia Labs

Experimental apps and tools built by Algolia engineers.

Clément Denoix

Clément Denoix

Software Engineer @algolia

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