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I search for Attribution app and click on the first non-ad link.\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/573b808-Screen_Shot_2021-01-21_at_2.08.40_PM.png\",\n        \"Screen Shot 2021-01-21 at 2.08.40 PM.png\",\n        1440,\n        862,\n        \"#333\"\n      ]\n    }\n  ]\n}\n[/block]\nI would then arrive to the page below and record a visit on the dashboard. You can learn more about [how visits are recorded here.](https://docs.attributionapp.com/docs/visits-visitors) \n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/847e649-Screen_Shot_2021-01-21_at_2.07.22_PM.png\",\n        \"Screen Shot 2021-01-21 at 2.07.22 PM.png\",\n        1440,\n        858,\n        \"#333\"\n      ]\n    }\n  ]\n}\n[/block]\nWhen the visit is recorded, Attribution will capture the referring domain and destination URL. In this case, the referring domain would be https://www.google.com and the destination URL would be https://www.attributionapp.com/.\n\nSince this is a visit with the referring domain captured as https://google.com and the destination captured without any utm parameters we are filtering for this visit would then fall into the Google Organic filter. Let's see below what happens when one of our paid ads are clicked. \n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/634c87a-Screen_Shot_2021-01-21_at_2.10.49_PM.png\",\n        \"Screen Shot 2021-01-21 at 2.10.49 PM.png\",\n        1440,\n        857,\n        \"#333\"\n      ]\n    }\n  ]\n}\n[/block]\n\n[block:api-header]\n{\n  \"title\": \"How Attribution ties paid ad clicks to visits\"\n}\n[/block]\nAttribution uses two methods for providing credit to specific paid ads. \n\n1. Attribution will look for a unique parameter in the destination URL of the visit I.e. 'gclid' or 'fbaid' \n2. Attribution automatically add a unique parameter to your ads, then later look for that unique parameter in the destination URL of visitors. I.e. 'fbaid' or 'liaid'\n\nFor example, with Attribution's Google Ads integration Attribution will automatically look for specific 'gclid' parameters in the destination URL of visitors to filter Google paid ad clicks properly. [You can learn more about this here.](https://docs.attributionapp.com/docs/google-adwords) \n\nIn contrast, with Facebook Ads, Attribution will automatically add a unique 'fbaid' parameter to your ads. Attribution will then look for these parameters in the destination URL of visits to filter by. [You can learn more about this here.](https://docs.attributionapp.com/docs/facebook-autotagging) \n[block:api-header]\n{\n  \"title\": \"A paid ad example to conversion\"\n}\n[/block]\nIn this example you can again imagine I am a potential Attribution app customer browsing Google. 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In reality there are often multiple ad touches, model types, and settings to consider. 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A visit to conversion example

See how Attribution collects information and builds your model

Here we will follow a visitor from ad click to the 'register demo' conversion event with the goal of demonstrating how each piece of the Attribution tracking apparatus & model interact to derive return on ad spend for your model. [block:api-header] { "title": "Recording the first visit (Google Organic example)" } [/block] In this example you can imagine I am a potential Attribution app customer browsing Google. I search for Attribution app and click on the first non-ad link. [block:image] { "images": [ { "image": [ "https://files.readme.io/573b808-Screen_Shot_2021-01-21_at_2.08.40_PM.png", "Screen Shot 2021-01-21 at 2.08.40 PM.png", 1440, 862, "#333" ] } ] } [/block] I would then arrive to the page below and record a visit on the dashboard. You can learn more about [how visits are recorded here.](https://docs.attributionapp.com/docs/visits-visitors) [block:image] { "images": [ { "image": [ "https://files.readme.io/847e649-Screen_Shot_2021-01-21_at_2.07.22_PM.png", "Screen Shot 2021-01-21 at 2.07.22 PM.png", 1440, 858, "#333" ] } ] } [/block] When the visit is recorded, Attribution will capture the referring domain and destination URL. In this case, the referring domain would be https://www.google.com and the destination URL would be https://www.attributionapp.com/. Since this is a visit with the referring domain captured as https://google.com and the destination captured without any utm parameters we are filtering for this visit would then fall into the Google Organic filter. Let's see below what happens when one of our paid ads are clicked. [block:image] { "images": [ { "image": [ "https://files.readme.io/634c87a-Screen_Shot_2021-01-21_at_2.10.49_PM.png", "Screen Shot 2021-01-21 at 2.10.49 PM.png", 1440, 857, "#333" ] } ] } [/block] [block:api-header] { "title": "How Attribution ties paid ad clicks to visits" } [/block] Attribution uses two methods for providing credit to specific paid ads. 1. Attribution will look for a unique parameter in the destination URL of the visit I.e. 'gclid' or 'fbaid' 2. Attribution automatically add a unique parameter to your ads, then later look for that unique parameter in the destination URL of visitors. I.e. 'fbaid' or 'liaid' For example, with Attribution's Google Ads integration Attribution will automatically look for specific 'gclid' parameters in the destination URL of visitors to filter Google paid ad clicks properly. [You can learn more about this here.](https://docs.attributionapp.com/docs/google-adwords) In contrast, with Facebook Ads, Attribution will automatically add a unique 'fbaid' parameter to your ads. Attribution will then look for these parameters in the destination URL of visits to filter by. [You can learn more about this here.](https://docs.attributionapp.com/docs/facebook-autotagging) [block:api-header] { "title": "A paid ad example to conversion" } [/block] In this example you can again imagine I am a potential Attribution app customer browsing Google. However, this time I click on a paid google ad with a unique 'gclid' parameter [block:image] { "images": [ { "image": [ "https://files.readme.io/1c1591e-Screen_Shot_2021-01-21_at_2.27.21_PM.png", "Screen Shot 2021-01-21 at 2.27.21 PM.png", 1440, 860, "#333" ] } ] } [/block] In this scenario my destination URL would include a 'gclid' parameter that then would tie back to a specific Google Ad you are running and Attribution is looking to filter for. [block:image] { "images": [ { "image": [ "https://files.readme.io/3fb3f6e-Screen_Shot_2021-01-21_at_2.29.03_PM.png", "Screen Shot 2021-01-21 at 2.29.03 PM.png", 1440, 789, "#333" ] } ] } [/block] This would then appear as a visit on your dashboard in your Google channel associated with the specific campaign's filter, in this case 'request demo campaign.' If the visitor then submitted a demo request form this would register a 'registered demo' conversion event and assuming there are no other visits for this visitor, the 'request demo campaign' filter would get credit for this conversion. [block:image] { "images": [ { "image": [ "https://files.readme.io/d18789d-Screen_Shot_2021-01-21_at_2.40.32_PM.png", "Screen Shot 2021-01-21 at 2.40.32 PM.png", 1440, 791, "#333" ] } ] } [/block] [block:image] { "images": [ { "image": [ "https://files.readme.io/6f58d62-Screen_Shot_2021-01-21_at_2.33.09_PM.png", "Screen Shot 2021-01-21 at 2.33.09 PM.png", 1440, 789, "#333" ] } ] } [/block] You can drill down further into each of these conversions. [block:api-header] { "title": "In conclusion (a basic example)" } [/block] To close, this is a fairly basic example of the path to conversion. In reality there are often multiple ad touches, model types, and settings to consider. Attribution will collect all this data and model it out depending on your adjustments in a real world multi-touch environment even in scenarios where you could have had 100s of ad touches prior to a conversion. If you have any questions on this please feel free to contact [email protected]