Learn more about DSP Campaign Performance by Geo Location query and how you can utilize it on Intentwise Explore (AMC)
What are instructional queries?
Amazon Marketing Cloud's (AMC) instructional queries provide pre-written SQL code that AMC users can use as is or modify for common measurement and analytics tasks.
DSP Campaign Performance by Geo Location
The DSP Campaign Performance by Geo Location query provides insights into the performance of DSP campaigns across different geographical locations, including unique reach, total conversions, and conversion rates at the campaign level. This data breakdown by country, state, and DMA (Designated Market Area) can be instrumental in understanding the effectiveness of campaigns in various regions and optimizing future marketing strategies.
The query utilizes tables such as dsp_impressions and amazon_attributed_events_by_traffic_time to gather data on impressions and attributed events over time. You can customize the query to filter results based on specific campaign IDs and geographical levels (country, state, DMA, or postal code). The query returns metrics such as unique users reached, total purchases, total product sales, total add-to-cart events, and total detail page views attributed to each campaign in different geographical areas. These metrics offer a comprehensive view of campaign performance across diverse locations.
The example query results showcase campaign performance metrics across different geographical areas in the United States. These results can be used to calculate KPIs such as purchase rate, detail page view rate, and add-to-cart rate for each location, enabling advertisers to assess the effectiveness of their campaigns in specific regions. By analyzing the query results and calculated KPIs, you can gain insights into which geographical areas are most responsive to your campaigns (New York in this example). This information can be used to optimize targeting, allocate resources effectively, and plan future campaigns based on regional performance.
For more information, refer to our data model.