> For the complete documentation index, see [llms.txt](https://support.happydemics.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://support.happydemics.com/methodology/brand-lifts-by-happydemics/why-do-we-prioritize-ad-recall.md).

# Why do we prioritize ad recall?

## Exposure alone is not enough

Even with advanced tracking methods such as pixels, relying solely on exposure data to assess campaign performance has limitations:

* A user might visit a website but never scroll down to see the ad.
* An app might run in the background, delivering a campaign without it being actively viewed.
* Ad blockers can prevent ads from being displayed, skewing exposure data.

As these examples illustrate, it is difficult to define truly "exposed" and "non-exposed" groups in traditional brand lift studies.

Additionally, if you rely only on exposure data, comparing performance across different media channels becomes impossible. Exposure levels vary by channel, and in some cases—particularly with offline media—exposure data may not be available at all.

## Ad recall as the core metric

To overcome the previously mentioned limitations, we’ve made **ad recall** the cornerstone of our methodology.

All our short questionnaires include an **ad recall question**: we display the ad and specify the environment in which it appeared. Depending on the media channel, we adapt the wording to maximize the accuracy of performance measurement on the correct channel or platform.

<figure><img src="/files/vrRdv0Qg0gLjP59L8SLP" alt=""><figcaption><p>Examples of ad recall questions</p></figcaption></figure>

By measuring whether respondents remember seeing your ad, we focus on **actual engagement** rather than just **potential exposure.** We assign them to the **ad recall or control group** depending on their ad recall status. This approach ensures consistency across all media types, making cross-channel comparisons possible.

<br>


---

# 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 dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://support.happydemics.com/methodology/brand-lifts-by-happydemics/why-do-we-prioritize-ad-recall.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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.