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The following table lists all tables that are materialized within this package by default.
> TIP: See more details about these tables in the package's [dbt docs site](https://fivetran.github.io/dbt_amazon_ads/#!/overview?g_v=1&g_e=seeds).
-| **Table** | **Description** |
-| ------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------- |
-| [amazon_ads__account_report](https://fivetran.github.io/dbt_amazon_ads/#!/model/model.amazon_ads.amazon_ads__account_report) | Each record in this table represents the daily performance at the account level. |
-| [amazon_ads__portfolio_report](https://fivetran.github.io/dbt_amazon_ads/#!/model/model.amazon_ads.amazon_ads__portfolio_report) | Each record in this table represents the daily performance at the portfolio level. |
-| [amazon_ads__campaign_report](https://fivetran.github.io/dbt_amazon_ads/#!/model/model.amazon_ads.amazon_ads__campaign_report) | Each record in this table represents the daily performance at the campaign level. |
-| [amazon_ads__ad_group_report](https://fivetran.github.io/dbt_amazon_ads/#!/model/model.amazon_ads.amazon_ads__ad_group_report) | Each record in this table represents the daily performance at the ad group level. |
-| [amazon_ads__search_report](https://fivetran.github.io/dbt_amazon_ads/#!/model/model.amazon_ads.amazon_ads__search_report) | Each record in this table represents the daily performance at the search term level. |
-| [amazon_ads__keyword_report](https://fivetran.github.io/dbt_amazon_ads/#!/model/model.amazon_ads.amazon_ads__keyword_report) | Each record in this table represents the daily performance at the keyword level. |
-| [amazon_ads__ad_report](https://fivetran.github.io/dbt_amazon_ads/#!/model/model.amazon_ads.amazon_ads__ad_report) | Each record in this table represents the daily performance at the ad level.
+| **Table** | **Details** |
+|-----------|-------------|
+| [`amazon_ads__account_report`](https://fivetran.github.io/dbt_amazon_ads/#!/model/model.amazon_ads.amazon_ads__account_report) | Represents daily performance aggregated at the account level, including `spend`, `clicks`, `impressions`, and `conversions`.
**Example Analytics Questions:**
- How does performance compare across different accounts by account manager?
- Are currency fluctuations affecting results across markets?
|
+| [`amazon_ads__ad_group_report`](https://fivetran.github.io/dbt_amazon_ads/#!/model/model.amazon_ads.amazon_ads__ad_group_report) | Represents daily performance at the ad group level, including `spend`, `clicks`, `impressions`, and `conversions`.
**Example Analytics Questions:**- Which ad groups have the strongest engagement relative to their budget?
- Do certain ad groups dominate impressions within a campaign?
- Are new ad groups ramping up as expected after launch?
|
+| [`amazon_ads__ad_report`](https://fivetran.github.io/dbt_amazon_ads/#!/model/model.amazon_ads.amazon_ads__ad_report) | Represents daily performance at the individual ad level, including `spend`, `clicks`, `impressions`, and `conversions`.
**Example Analytics Questions:**- Which ad creatives are driving the lowest cost per click?
- Do expanded text ads perform better than responsive search ads?
- How do performance trends change after refreshing ad copy?
|
+| [`amazon_ads__campaign_report`](https://fivetran.github.io/dbt_amazon_ads/#!/model/model.amazon_ads.amazon_ads__campaign_report) | Represents daily performance aggregated at the campaign level, including `spend`, `clicks`, `impressions`, and `conversions`.
**Example Analytics Questions:**- Which campaigns are most efficient in terms of cost per conversion?
- Are paused or limited-status campaigns still accruing impressions?
- Which campaigns contribute most to overall spend or conversions??
|
+| [`amazon_ads__keyword_report`](https://fivetran.github.io/dbt_amazon_ads/#!/model/model.amazon_ads.amazon_ads__keyword_report) | Represents daily performance at the keyword level, enriched with account, campaign, ad group, and criterion context. Includes metrics such as `spend`, `clicks`, `impressions`, and `conversions`.
**Example Analytics Questions:**- Which keywords are driving the highest quality traffic at the lowest cost?
- Are branded vs. non-branded keywords performing differently?
- Should underperforming keywords be reallocated to different match types?
|
+| [`amazon_ads__portfolio_report`](https://fivetran.github.io/dbt_amazon_ads/#!/model/model.amazon_ads.amazon_ads__portfolio_report) | Represents daily performance at the portfolio level, including `spend`, `clicks`, `impressions`, and `conversions`.
**Example Analytics Questions:**- Which portfolios are delivering the best return on ad spend?
- How do different portfolio strategies compare in terms of performance?
- What are the spending trends across my portfolio segments?
|
+| [`amazon_ads__search_report`](https://fivetran.github.io/dbt_amazon_ads/#!/model/model.amazon_ads.amazon_ads__search_report) | Represents daily performance at the search term level, enriched with account, campaign, and ad group context. Includes metrics such as `spend`, `clicks`, `impressions`, and `conversions`.
**Example Analytics Questions:**- What new search terms are emerging that I should add as keywords?
- Which irrelevant search terms should be added as negatives to reduce wasted spend?
- Are there seasonal shifts in search terms driving conversions?
|
+
+Many of the above reports are now configurable for [visualization via Streamlit](https://github.com/fivetran/streamlit_ad_reporting). Check out some [sample reports here](https://fivetran-ad-reporting.streamlit.app/ad_performance).
+
+### Example Visualizations
+Curious what these tables can do? The Amazon Ads models provide advertising performance data that can be visualized to track key metrics like spend, impressions, click-through rates, conversion rates, and return on ad spend across different campaign structures and time periods. Check out example visualizations in the [Fivetran Ad Reporting Streamlit App](https://fivetran-ad-reporting.streamlit.app/ad_performance), and see how you can use these tables in your own reporting. Below is a screenshot of an example dashboard; explore the app for more.
+
+
+
+
+
+
### Materialized Models
Each Quickstart transformation job run materializes 30 models if all components of this data model are enabled. This count includes all staging, intermediate, and final models materialized as `view`, `table`, or `incremental`.
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