> 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/event-propensity.md).

# Event Propensity Scoring

Estimate the likelihood that a profile or subscription will perform a defined business event within a future window. Use the scores to prioritize eligible audiences for conversion, engagement, or retention.

## Choose a scoring use case

| Business need                                | Mode and scoring grain                                                    | Example target                              |
| -------------------------------------------- | ------------------------------------------------------------------------- | ------------------------------------------- |
| Prioritize likely purchasers                 | Generic event propensity; one score per profile                           | `order_completed`                           |
| Identify registration or trial opportunities | Generic event propensity; one score per profile                           | `registration_completed` or `trial_started` |
| Prioritize content engagement                | Generic event propensity; one score per profile                           | `premium_content_viewed`                    |
| Anticipate subscription cancellation         | Subscription lifecycle propensity; one score per profile and subscription | `cancellation_requested`                    |

Generic mode learns from the timing and mix of behavioral events. Subscription mode also reconstructs subscription state and history, with optional behavioral signals such as usage or content consumption.

For retention, choose a target that leaves time to act. A cancellation request can be more actionable than the effective subscription end. Subscription mode requires mappings for starts, cancellation requests, and ends even when only one movement is the prediction target.

## How it works

Training uses historically observable subjects from the configured event streams. The current User model defines who can receive a score; it does not filter that historical training population.

When you create an Event Propensity intelligence, DinMo starts training on your event history. Once training succeeds, DinMo calculates the first scores for your selected population. You do not need to create a separate prediction. You can change how often scores are refreshed from the intelligence overview.

Outputs include a score, a training-reference percentile, a score band, up to three signed local reason families, and scoring history. Interpret a score as a probability only when its score semantics confirm calibration. Percentiles and reasons support prioritization and interpretation; they do not prove that a campaign will change the outcome.

If you need to choose which individual product or offer to show, use [Product Recommendations](/intelligence/product-recommendations.md). Event Propensity predicts an event, not an item ranking.

## Documentation guide

| Start here                                                                                           | What you will learn                                                                                        |
| ---------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
| [Prepare your data](/intelligence/event-propensity/prepare-data.md)                                  | Technical prerequisites, raw event mappings, subscription history, input limits, and readiness thresholds. |
| [Configure and refresh](/intelligence/event-propensity/configure-and-train.md)                       | Target and horizon choices, training, scoring populations, and refresh schedules.                          |
| [Understand performance and scores](/intelligence/event-propensity/understand-scores-and-reasons.md) | Temporal evaluation, probability, percentile, signed reasons, and warehouse outputs.                       |
| [Use results and best practices](/intelligence/event-propensity/limits-and-best-practices.md)        | Actionable targets, leakage risks, audience selection, experiments, and monitoring.                        |

Event Propensity Scoring must be enabled for your workspace. Confirm access with your DinMo administrator or customer success contact.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.dinmo.io/intelligence/event-propensity.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.