> ## Documentation Index
> Fetch the complete documentation index at: https://docs.qwairy.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Topic Performance

> Aggregated metrics by topic (keyword) for topic-level analysis in Looker Studio.

Aggregated metrics by topic — called "keyword" in the underlying data source fields below — for topic-level analysis.

<Tabs>
  <Tab title="Dimensions">
    | Field       | Type | Description                |
    | ----------- | ---- | -------------------------- |
    | `date`      | Date | Analysis date (YYYY-MM-DD) |
    | `provider`  | Text | AI provider name           |
    | `keywordId` | Text | Keyword unique identifier  |
    | `keyword`   | Text | Keyword text               |
  </Tab>

  <Tab title="Metrics">
    | Field                          | Type    | Aggregation | Description                                                                    |
    | ------------------------------ | ------- | ----------- | ------------------------------------------------------------------------------ |
    | `totalPrompts`                 | Number  | SUM         | Number of prompts with this keyword for the provider row                       |
    | `totalAnswers`                 | Number  | SUM         | Total responses analyzed                                                       |
    | `answersWithBrand`             | Number  | SUM         | Responses mentioning your brand                                                |
    | `answersWithAnyBrand`          | Number  | SUM         | Responses mentioning your brand or a direct competitor                         |
    | `totalSelfMentions`            | Number  | SUM         | Total mentions of your brand                                                   |
    | `totalDirectMentions`          | Number  | SUM         | Total mentions of direct competitors                                           |
    | `sentimentSum`                 | Number  | SUM         | Sum of sentiment scores for your brand mentions                                |
    | `sentimentCount`               | Number  | SUM         | Count of scored brand mentions                                                 |
    | `promptsWithSelfBrand`         | Number  | SUM         | Prompts where your brand appears at least once, across all providers           |
    | `totalPromptsAllProviders`     | Number  | SUM         | Total prompts for this keyword/date across all providers                       |
    | `coveragePromptsWithSelfBrand` | Number  | SUM         | Normalized prompt numerator used by `coverage`                                 |
    | `coverageTotalPrompts`         | Number  | SUM         | Normalized prompt denominator used by `coverage`                               |
    | `brandMentionRate`             | Percent | Calculated  | `SUM(answersWithBrand) / SUM(answersWithAnyBrand)`                             |
    | `shareOfVoice`                 | Percent | Calculated  | `SUM(totalSelfMentions) / (SUM(totalSelfMentions) + SUM(totalDirectMentions))` |
    | `avgSentiment`                 | Percent | Calculated  | `SUM(sentimentSum) / SUM(sentimentCount)`                                      |
    | `coverage`                     | Percent | Calculated  | `SUM(coveragePromptsWithSelfBrand) / SUM(coverageTotalPrompts)`                |
  </Tab>
</Tabs>

<Note>
  The percentage metrics are calculated from raw counters in Looker Studio.
  This avoids averaging provider rows and keeps topic-level totals aligned with
  the Qwairy interface.
</Note>

<Note>
  `coverage` is calculated at keyword/date level across all providers, then
  exposed through normalized counters so provider rows do not overweight dates
  when reports aggregate a date range.
</Note>

## Best For

* Keyword performance tracking
* SEO-aligned analysis
* Topic prioritization
* Identifying high-performing topics