Attribution is the method to give credit to sources that help lead to customer conversion. There are different methods to give weight to marketing activities. We aim to give you a full picture of customer journeys so you can decide which model best suits your business.
Attribution window is an important factor. It determines how long time we look back into marketing activities. Since we are able to build full customer journeys, by default we set a 180 days attribution window. You may also adjust attribution window by yourself.
Be aware that all our attribution models will ignore any direct traffic. Meaning we will not put any credit to direct traffic. Since direct traffic is valueless to optimize our marketing activities.
Last Click attribution gives 100% credit to the last click-through & non-direct in customer's journey.
Example: If a customer clicked on a Facebook ad, then a Google search ad, and finally made a purchase. In last click model, Google ad will get 100% credit.
Google Analytics uses the last click model by default.
First Click attribution gives 100% credit to the first click-through & non-direct in the customer's journey.
Example: If a customer clicked on Facebook ad #1, then a Google search ad, and finally made a purchase. In first click model, Facebook ad will get 100% credit.
Linear gives credit equally to all the click touch points(except direct traffic) in customer journey. Ignoring organic sources is also available.
Linear is useful when analyzing entire marketing activities. The conversion contribution index will help you determine each touchpoint's value and optimize your budget.
Example: If a customer clicked on Facebook ad #1, then a Google search ad, and finally made a purchase. In Linear model, Facebook ad & google search ad will get 50% credit each.
💡Direct traffic will be categorized into "Others" . And only give credit when all touch points are direct access.
Position based model puts 40% contribution to the first & last touch point, and divides the rest 20% to all the touch points in the middle.
Example: If a customer clicked on Facebook ad #1, TikTok ad #2, Youtube video #3, then a Google search ad, and finally made a purchase. In Position based model, Facebook ad & google search ad will get 40% credit each. TikTok & Youtube will get 10% each.
Discrepancies in report data
When you view attribution report, you might notice some differences between the Attribuly reports and related platforms. This is because of the differences in how Attribuly and third parties attribute interactions and sales, and also because of delays in syncing data.
For example, if you create a Facebook ad, and then view the conversion report for the activity in Attribuly as well the data in Facebook for the same ad, then the two reports might show different results.
Discrepancies in attribution
You might note discrepancies in sales and order data between your Attribuly and the reports available from the marketing app or the third-party platform where you created the marketing activity.
For example, if you're running an email campaign and a Facebook campaign simultaneously, in cases where a customer clicks on both your email and your Google Shopping ad, they can each record a separate conversion. But the Attribuly attributes the sale to all the touchpoints happening last 180 days, with Linear attribution model by default.
Discrepancies in ad spend
You might note discrepancies between the ad spend data in your attribution reports and reports available externally for the same service.
For example, your Attribuly might display a 10 USD ad spend for a Facebook campaign, but the same campaign in Facebook Ad Manager displays 13 USD in ad spend. The Facebook ad spend amount for marketing apps can take up to 30 minutes to sync to Attribuly, and 24 hours for Google ads, so the delay causes the discrepancy.