> For the complete documentation index, see [llms.txt](https://docs.dinmo.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.dinmo.io/intelligence/overview.md).

# Overview

DinMo Intelligence uses your modeled data to generate predictions and recommendations that you can use in segments, campaigns, and analysis.

## Choose an intelligence

| Intelligence                                                        | Use it to                                                                                                        |
| ------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------- |
| [Event Propensity Scoring](/intelligence/event-propensity.md)       | Estimate the likelihood that a profile or subscription will perform a defined event within a future time window. |
| [Product Recommendations](/intelligence/product-recommendations.md) | Rank products, offers, brands, or categories; find audiences; distribute a campaign audience between targets.    |
| [LTV and Churn](/intelligence/ltv-and-churn.md)                     | Estimate customer value and identify customers at risk of becoming inactive.                                     |

Requirements and output formats vary by intelligence. Start with its data prerequisites rather than assuming that every engine accepts the same models or source.

## From data to activation

1. **Prepare the data:** connect a supported source and define models, identifiers, relationships, and relevant history.
2. **Configure and train:** choose the learning inputs and inspect data quality and evaluation results.
3. **Generate outputs:** define the business population, constraints, and refresh schedule.
4. **Activate and measure:** use published results, check current eligibility, and measure the business outcome.

For Product Recommendations, one intelligence can support several recommendation scenarios. Training learns the model; each scenario defines what to recommend, to whom, and when to refresh. Outputs are not recomputed after every individual customer action.

## Start with an intelligence

* [Event Propensity Scoring](/intelligence/event-propensity.md): predict a business event for profiles or subscriptions and use the output safely in an activation policy.
* [Prepare your data for Product Recommendations](/intelligence/product-recommendations/prepare-your-data.md): supported warehouses, required models, mappings, and data checks.
* [Choose a recommendation use case](/intelligence/product-recommendations/recommendation-use-cases.md): personalized lists, related items, preferences, and audiences.
* [Build and allocate audiences](/intelligence/product-recommendations/audiences-and-allocation.md): overlapping lists versus one assignment per customer.
* [Understand performance](/intelligence/product-recommendations/understand-performance.md): distinguish delivery, offline relevance, and measured commercial impact.

Intelligence access depends on your workspace subscription and permissions. Contact your DinMo administrator or customer success contact if it is not available.


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