Practice: Online Advertising Platform

Here's a high-level dimensional model for the online advertising platform:

Fact Tables:

• Ad_Event_Fact
• Keyword_Performance_Fact
• Advertiser_Spend_Fact

Dimension Tables:

• Date_Dim
• Time_Dim
• Ad_Dim
• Campaign_Dim
• Advertiser_Dim
• Keyword_Dim
• Platform_Dim
• User_Dim

Fact Table Schemas:

Ad_Event_Fact (
    date_key (FK),
    time_key (FK),
    ad_key (FK),
    campaign_key (FK),
    advertiser_key (FK),
    platform_key (FK),
    user_key (FK),
    impressions,
    clicks,
    conversions,
    cost
)

Keyword_Performance_Fact (
    date_key (FK),
    keyword_key (FK),
    campaign_key (FK),
    advertiser_key (FK),
    platform_key (FK),
    impressions,
    clicks,
    average_position,
    cost
)

Advertiser_Spend_Fact (
    date_key (FK),
    advertiser_key (FK),
    platform_key (FK),
    total_spend,
    total_impressions,
    total_clicks,
    total_conversions
)

Explanation

Core Business Processes:

1. We've identified three main processes: ad events (impressions, clicks, conversions), keyword performance, and advertiser spending. Each of these corresponds to a fact table in our model.
2. Granularity:
   • Ad_Event_Fact: Individual ad event grain. This allows for detailed ad performance analysis.
   • Keyword_Performance_Fact: Daily grain per keyword, campaign, advertiser, and platform. This supports keyword performance analysis.
   • Advertiser_Spend_Fact: Daily grain per advertiser and platform. This provides a high-level view of advertiser behavior.
3. Dimensions:
   • Date_Dim and Time_Dim: Support time-based analysis.
   • Ad_Dim: Contains ad attributes including ad type and format.
   • Campaign_Dim: Stores campaign information.
   • Advertiser_Dim: Contains advertiser attributes for segmentation.
   • Keyword_Dim: Stores keyword information for search ads.
   • Platform_Dim: Represents different ad platforms (search, display, video).
   • User_Dim: Captures user demographics for engagement analysis.
4. Facts:
   • Ad_Event_Fact: impressions, clicks, conversions, and cost for detailed ad performance analysis.
   • Keyword_Performance_Fact: impressions, clicks, average_position, and cost for keyword analysis.
   • Advertiser_Spend_Fact: total_spend, total_impressions, total_clicks, and total_conversions for advertiser behavior analysis.
5. Slowly Changing Dimensions:
   • Ad_Dim could be a Type 2 SCD to track changes in ad content or targeting.
   • Campaign_Dim might be Type 2 to track changes in campaign settings.
6. Additional Considerations:
   • ROI can be calculated by joining Ad_Event_Fact with Advertiser_Spend_Fact.
   • Bidding patterns can be analyzed using the Keyword_Performance_Fact table.
   • User engagement with different ad formats can be analyzed by joining Ad_Event_Fact with Ad_Dim.

This model supports all the required analyses:

• Ad performance (impressions, clicks, conversions) can be analyzed using Ad_Event_Fact.
• Advertiser spending patterns and ROI can be derived from Advertiser_Spend_Fact and Ad_Event_Fact.
• Keyword performance and bidding patterns are captured in Keyword_Performance_Fact.
• User engagement with different ad formats can be analyzed using Ad_Event_Fact joined with Ad_Dim.
• Ad performance across platforms can be analyzed using the Platform_Dim in all fact tables.

The model balances the need for detailed event data with the ability to perform high-level advertiser analysis. It's also flexible enough to accommodate future requirements, such as new ad formats or bidding strategies.