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

# Getting started

Understand the impact of your data: measure campaign performance, run A/B tests, and optimize with funnels and attribution.

This section covers proving what your campaigns actually did: tracking what they delivered, deciding which conversions to credit to them, following the path your users take from exposure to conversion, and isolating the campaign's true incremental effect with a controlled test.

## The jobs

<table data-view="cards" data-search="false"><thead><tr><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Track your campaign</strong></td><td>Set up a campaign, watch its delivery, and choose how to attribute the conversions it drives.</td><td><a href="/measurement-and-attribution/measure-and-optimize/track-your-campaign.md">Track your campaign</a></td></tr><tr><td><strong>Analyze conversions</strong></td><td>Read the volumes and revenue your goals and transactions produced.</td><td><a href="/measurement-and-attribution/measure-and-optimize/analyze-conversions.md">Analyze conversions</a></td></tr><tr><td><strong>Build funnels</strong></td><td>See how far your audience gets through an ordered sequence of steps.</td><td><a href="/measurement-and-attribution/measure-and-optimize/build-funnels.md">Build funnels</a></td></tr><tr><td><strong>Set up an A/B test</strong></td><td>Split a segment into test and control groups to measure a campaign's real, incremental impact.</td><td><a href="/measurement-and-attribution/measure-and-optimize/set-up-an-a-b-test.md">Set up an A/B test</a></td></tr></tbody></table>

## How the pieces fit together

```mermaid
flowchart LR
    C[Campaign] --> E[Exposure: ad view or ad click]
    E --> G[Goal or transaction]
    G --> A[Attribution model]
    A --> R[Attributed conversions]
    E --> F[Funnel steps]
    G --> F
    S[Segment] --> X[Experiment]
    X --> T[Test group]
    X --> K[Control group]
    T --> D[Compare conversion rates]
    K --> D
```

A tracked campaign produces exposures. A goal or a transaction event turns an exposure into a conversion, and an attribution model credits that conversion back to the campaign. A funnel reads the same underlying activities as an ordered sequence instead of a single credit.

An experiment sits beside all of this. It does not track a campaign: it isolates whether the campaign *caused* the difference between a group that saw it and a control group that never did.

## Before you start

The jobs in this section read data produced elsewhere in the platform:

* A **goal** turns a user action into a conversion. You set it up once, in the **Setup & Monitor** section under *Collect & manage your data > Collect data in real time > Conversions*, and reuse it here for attribution.
* Funnels and conversion analysis both read **activities analytics**, the platform's optimised copy of your predefined events. It is documented in **Setup & Monitor**, under *Collect & manage your data > Collect data in real time > Activities analytics*. Only predefined events reach it, which is the single most common reason a measurement page shows zeros.
* An A/B test is always created **from a segment**, built as described in the **Audience & Activation** section under *Build your audiences*.

{% hint style="info" %}
Some measurement features are configured once per datamart before anyone can use them, in the Computing Console: the permanent control group, and the representativeness criteria available to stratified randomisation. Where that is the case, the page says so.
{% endhint %}


---

# 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.mediarithmics.io/measurement-and-attribution/readme.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.