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Recommendations let you show each customer the products, content, or offers they are most likely to act on — automatically, and at scale. Instead of promoting the same items to everyone, you can surface the right item at the right moment, whether that is a personalized pick, a relevant alternative, or a timely price-drop alert. MoEngage Recommendations engine overview The Recommendations engine covers the full range, from promoting the same curated items to everyone, to identifying relevant items tailored to each individual, inspired by similar users’ behavior or item similarities.

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How Recommendations Work

Setting up a recommendation follows four steps. You complete the first once, then reuse it across every recommendation you build.
1

Prepare your data

Connect an item catalog (your products, content, or listings) and map your user-action events (views, cart adds, wishlist saves, purchases) to MoEngage’s standard events. See Prerequisites.
2

Choose a recommendation type

Decide between Basic (rules you define) and Advanced (machine-learning models). See Choosing a Model below.
3

Create and test

Configure the recommendation, then preview results for sample users before you launch.
4

Use it in a campaign

Insert the recommendation into a Push, Email, In-App, or other campaign to deliver personalized content to your users.

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Basic vs Advanced Recommendations

MoEngage offers two families of recommendations. Most accounts use both, depending on the campaign.
Advanced models need enough historical data to train — as a baseline, roughly 1,000 interaction events across at least 25 users. More data produces better results. If a model has too little data, MoEngage falls back to Popular Items. See Prerequisites.

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Choosing a Model

Start from your goal and work back to the model.

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Benefits

  • Higher engagement and conversion — relevant suggestions keep users active and increase the likelihood of purchase.
  • Better cross-sell and upsell — contextual bundles and add-ons lift average order value.
  • Reduced churn — the right item at the right touchpoint keeps users coming back.
  • Stronger loyalty — personalized experiences make customers feel understood.

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Recommendations Across Industries

Recommendations apply to almost any catalog-driven business. A few representative use cases:

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Next Steps

Before you build your first recommendation, confirm your data is ready: Then choose Basic or Advanced recommendations.