Geographic Analysis

Author: Robin Jia，Tommy Lin

Last Update：2024/06/14

 

This model is only applicable to AMC Instances that include DSP advertisers

1. Model Introduction

The Geographic Analysis model is mainly used to analyze the differences in ad reach, ad effect, and other aspects among users in different regions. By comparing key data indicators of different geographical dimensions such as countries and regions, this model can help discover the penetration gap of ads between regions, evaluate the rationality of regional delivery strategies, explore regional growth potential, and optimize regional delivery strategies such as budget allocation and bid adjustment accordingly. This model supports selecting different countries and flexibly adjusting metric combinations, which can meet the personalized analysis needs of different business scenarios.

Through this model, the following business questions can be analyzed:

• Overall, what is the situation of ad attention and purchase conversion in various regions? Does the regional distribution match the target market?
• What are the differences in user reactions to brand ads in different regions? How is the ad ROAS performance in each region?
• Within a few specific regions, what behavior trends do users exhibit across the four seasons of a year?

 

2. Model Interpretation

This model mainly consists of four parts. The data cards at the top represent the ad data currently being viewed and an overview of each data. The geographic distribution map shows the distribution of the selected data across regions. The bar chart is the specific ranking of user ad data performance in each region. The trend chart shows the ad data performance on the time dimension. These modules can all be interacted with and filtered, and analysis can be performed on any region, time, and data by combining these modules.

In the model's top menu, the predefined model is for fixed Amazon users, DSP ad type (allowing free selection of DSP advertisers associated with the Instance), and all ASINs under the store. The time range selector can select data by month, but it is fixed at a one-year data cycle. In addition, you can also select the countries/regions to view and switch between total data or monthly data.

Customers shopping on Amazon sites may come from all over the world. The model selects the countries/regions with the most customers by default. By using the switch country and region option, you can see customers from countries/regions outside the main site.

The data cards at the top by default display data for 5 commonly used metrics. Clicking on a card can switch the ad data observed in the current geographic distribution map, bar chart, and trend chart. Click the edit button to expand the sidebar, where you can drag more data metrics into the cards. The filtering condition supports range filtering of ad data in up to three data cards. Clicking this filter will affect the regions in the geographic distribution map. Regions that do not meet the current filter will be grayed out. This setting is mainly used to exclude some regions with too small or too large data volumes.

In the geographic distribution map, the color depth represents the size of the currently observed data metric. The darker the color of a region, the better the performance of the data metric.

When hovering the mouse over a certain region in the map, the specific data of the current region will be displayed. Clicking on that region at this time will highlight it and enter the analysis mode for a specific region. The data in the data cards, bar chart, and trend chart will all switch to the data of the selected region. A maximum of 5 regions can be selected simultaneously. Dragging the legend in the lower right corner of the map can also quickly exclude some regions with too large or too small data volumes.

The bar chart on the right shows the specific ranking of each region under the current filtering conditions. Dragging the scroll bar on the right can display more regions.

If you are not familiar with the regions in the current map, you can click on the bar chart on the right, and that region will be highlighted in the map. This method can also be used to quickly locate the best and worst performing regions in the map.

The trend chart shows the data performance trend of the selected regions in the past 12 months. If no specific region is selected in the distribution map or bar chart, the overall data of the current country/region will be displayed. If specific regions are selected, you can see the trend of specific regions, and the overall data will also be retained for comparative analysis.

If a specific month time point is selected in the top menu, the data in the data cards, geographic distribution map, and bar chart below will all change to the data of that month. In the trend chart, the currently selected specific month will be marked, and the data cards will display the comparison data of the previous month.

 

3. Model Customization

The custom model conditions of this model provide the function of filtering by advertiser/ad campaign and support customizing the monitored ASINs. After setting, only the conversion data of the set ASINs will be counted.

 

4. Model Data

The Geographic Analysis model is mainly based on users' geographic location information and associates it with behavior data such as ad impressions, clicks, and conversions to count various ad metrics within different geographic ranges. However, due to the complexity of geographic location data, the following two points need to be noted when interpreting regional data:

1. Representativeness of data within a region. Affected by factors such as sample size and statistical period within a region, the metric performance of a specific region may fluctuate greatly. Therefore, when interpreting regional data, it is necessary to fully consider the representativeness of the sample in that region to avoid drawing biased conclusions. The filtering function can also be used to exclude some regions with smaller sample sizes before analysis.
2. Complex causes of regional differences. The differences in ad effects between regions may be due to the differences in preference characteristics of users in different regions, or may be influenced by factors such as the regional market environment and delivery strategies. When interpreting regional differences through comparison, it is necessary to carefully evaluate the impact of various factors to more comprehensively guide the formulation of optimization strategies.

Glossary

Type	Term	Description
Dimension	State	State or province of country
	Advertiser	DSP advertiser or entity of Sponsored Ads
	Marketplace	The marketplace where ads are displayed
	Country	Country of advertisement
Metrics	Time Node	Month
	UV	Unique Viewer
	Total Cost	Total Cost of ads
	Impressions	Number of impression events
	Click-throughs	Number of click events
	Branded Search	Number of branded search events
	Reach UV	Deduplicated number of impression users
	Click-throughs UV	Deduplicated number of click users
	Branded Search UV	Deduplicated number of branded search users
	Total ATC	Number of add-to-cart events
	Total ATC UV	Deduplicated number of add-to-cart users
	Total DPV	Number of detailed page view events
	Total DPV UV	Deduplicated number of detailed page view users
	Total NTB Product Sales	Total New-to-Brand Product Sales
	Total NTB Purchase	Total New-to-Brand Purchase
	Total NTB Purchase UV	Deduplicated number of new-to-brand purchase users
	Total Product Sales	Total Product Sales
	Total Purchase	Total Purchase
	Total Purchase UV	Deduplicated number of purchase users
	CTR	Click-through Rate (Click-throughs/Impressions)
	CTR UV	Unique Click-through Rate (Click-throughs UV/Reach UV)
	eCPC	Effective Cost per Click (Total Cost/Click-throughs)
	eCPC UV	Effective Cost per Unique Click (Total Cost/Click-throughs UV)
	Branded Search Rate	Branded Search Rate (Branded Search/Impressions)
	Branded Search Rate UV	Unique Branded Search Rate (Branded Search UV/Reach UV)
	CPBS	Cost per Branded Search (Total Cost/Branded Search)
	CPBS UV	Cost per Unique Branded Search (Total Cost/Branded Search UV)
	eCPM	Effective Cost per Thousand Impressions (Total Cost/Impressions*1000)
	Total ATCR	Add-to-Cart Rate (Total ATC/Impressions)
	Total ATCR UV	Unique Add-to-Cart Rate (Total ATC UV/Reach UV)
	Total ATV	Average Transaction Value (Total Product Sales/Total Purchase UV)
	Total CPATC	Cost per Add-to-Cart (Total Cost/Total ATC)
	Total CPATC UV	Cost per Unique Add-to-Cart (Total Cost/Total ATC UV)
	Total CPDPV	Cost per Detailed Page View (Total Cost/Total DPV)
	Total CPDPV UV	Cost per Unique Detailed Page View (Total Cost/Total DPV UV)
	Total CPP	Cost per Purchase (Total Cost/Total Purchase)
	Total CPP UV	Cost per Unique Viewer Purchase (Total Cost/Total Purchase UV)
	Total DPVR	Detailed Page View Rate (Total DPV/Impressions)
	Total DPVR UV	Unique Detailed Page View Rate (Total DPV UV/Reach UV)
	Total NTB CPP	Cost per New-to-Brand Purchase (Total Cost/Total NTB Purchase)
	Total NTB CPP UV	Cost per Unique Viewer New-to-Brand Purchase (Total Cost/Total NTB Purchase UV)
	Total NTB PR	New-to-Brand Purchase Rate (Total NTB Purchase/Impressions)
	Total NTB PR UV	Unique New-to-Brand Purchase Rate (Total NTB Purchase UV/Reach UV)
	Total NTB ROAS	New-to-Brand Return on Ad Spend (Total NTB Product Sales/Total Cost)
	Total PR	Purchase Rate (Total Purchase/Impressions)
	Total PR UV	Total Unique Viewer Purchase Rate (Total Purchase UV/Reach UV)
	Total ROAS	Return on Ad Spend (Total Product Sales/Total Cost)