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

# Data Overview

Churnkey gives you two main data entities in Coupler.io: raw session records and pre-aggregated session summaries. Together they cover the full spectrum from granular interaction logs to rolled-up retention metrics.

## Entities

| Entity               | What it contains                                                                                              | Best for                                                      |
| -------------------- | ------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------- |
| Sessions             | One row per churn prevention session — customer ID, session outcome, timing, offers shown, engagement signals | Cohort analysis, individual customer review, funnel debugging |
| Session aggregations | Rolled-up counts and rates across sessions — saves, cancellations, deflection rates by time period or segment | Dashboards, KPI tracking, trend reporting                     |

## Sessions data

#### Session fields

| Field            | Type      | Description                                            |
| ---------------- | --------- | ------------------------------------------------------ |
| Session ID       | String    | Unique identifier for the session                      |
| Customer ID      | String    | Identifier linking back to your customer record        |
| Created at       | Timestamp | When the session was initiated                         |
| Outcome          | String    | Result of the session (e.g., saved, cancelled, paused) |
| Offer shown      | String    | Which retention offer was presented                    |
| Offer accepted   | Boolean   | Whether the customer accepted the offer                |
| Session duration | Number    | Time spent in the cancel flow (seconds)                |
| Cancel reason    | String    | Reason selected or submitted by the customer           |
| Revenue at risk  | Number    | MRR associated with the customer at session time       |

#### Session metadata

| Field            | Type   | Description                           |
| ---------------- | ------ | ------------------------------------- |
| Plan name        | String | Subscription plan the customer was on |
| Billing interval | String | Monthly, annual, etc.                 |
| Country          | String | Customer's billing country            |
| Source           | String | How the session was triggered         |

## Session aggregations data

#### Aggregation metrics

| Field           | Type   | Description                                            |
| --------------- | ------ | ------------------------------------------------------ |
| Total sessions  | Number | Count of all sessions in the period                    |
| Saves           | Number | Sessions that resulted in a retained customer          |
| Cancellations   | Number | Sessions that ended in cancellation                    |
| Save rate       | Number | Percentage of sessions resulting in a save             |
| Revenue saved   | Number | MRR recovered through successful saves                 |
| Deflection rate | Number | Rate at which customers were deflected from cancelling |

#### Aggregation dimensions

| Field         | Type   | Description                           |
| ------------- | ------ | ------------------------------------- |
| Period        | Date   | Time bucket for the aggregation       |
| Plan name     | String | Grouped by subscription plan          |
| Cancel reason | String | Grouped by stated cancellation reason |
| Offer type    | String | Grouped by the offer presented        |

## Common metric combinations

* **Save rate by cancel reason** — identify which objections are hardest to overcome
* **Revenue saved by plan** — prioritize retention efforts on high-value segments
* **Session volume vs. save rate over time** — spot whether your cancel flow performance is improving
* **Offer acceptance rate by offer type** — determine which offers actually work

## Use cases by role

{% tabs %}
{% tab title="Retention teams" %}

* Track weekly save rates and revenue recovered without manually exporting from Churnkey
* Compare offer performance to refine which discounts or pauses to show first
* Build a Looker Studio dashboard that updates automatically each day
  {% endtab %}

{% tab title="Finance teams" %}

* Pull revenue-at-risk and revenue-saved data into spreadsheets for MRR reconciliation
* Join Churnkey session data with billing records in BigQuery for a complete churn impact report
* Use Aggregate transformation in Coupler.io to roll up saves by billing period
  {% endtab %}

{% tab title="Product teams" %}

* Analyze cancel reasons by plan to inform roadmap prioritization
* Send session outcome data to ChatGPT or Claude for automated pattern detection and recommendations
* Append data from multiple Churnkey environments (test and production) to compare behavior
  {% endtab %}
  {% endtabs %}

## Platform-specific notes

* Session aggregations are pre-computed by Churnkey — they are not the same as applying Coupler.io's Aggregate transformation to the Sessions entity, though you can use both approaches
* The Sessions entity can grow large for high-traffic cancel flows; consider using Coupler.io's Aggregate transformation to summarize before loading into spreadsheet destinations
* Revenue fields reflect MRR at the time of the session and are not updated retroactively if a customer's plan changes later
* Cancel reasons depend on what options you've configured in your Churnkey cancel flow — custom reasons will appear as-is


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