Campaign pROAS Output

Introduction

AlgoLift's Probabilistic Attribution solution for iOS14 provides app developers the tools to measure the performance of their advertising campaigns where ad networks don't have access to the IDFA. Utilizing anonymous user data, the Algolift user-level predicted LTV's, and the ConversionValue data from Apple's SKAdnetwork, our attribution model reports ROAS performance at the campaign level on a daily basis.

Raw Data

A raw output of the Probabilistic Attribution solution will be available daily for your processes to ingest and use in any other systems. The data includes a row for each campaign ID and install date and these projections show the projected revenue for advertising campaigns at day 30, 60, 90, 180 and 365, along with cost data that can be used to calculate pROAS.

We will create the S3 bucket to hold this data and provision access for your systems (please make sure you've provided your AWS IAM profile to us to ensure access). It is partitioned by projection date (same as output date) and may be split into multiple files based on size. We encode output as compressed CSV for maximum compatibility with downstream systems.

The key structure of our output looks like the following:

s3://algolift-//daily_output/campaign_proas/2001-01-01/campaign_proas_output_0000.csv.gz

Campaign pROAS Output Schema

Field	Data Type	Example	Description	Column Type
report_date	date	2020-01-06	Date of activity (date that the ads were served)	Dimension
source	varchar(255)	apple search ads	Source / Channel / Ad Network Name	Dimension
campaign_name	varchar(255)	FB_LAL10p	Campaign name	Dimension
campaign_id	varchar(255)	9b06c7532a	Campaign ID	Dimension
spend	double	1343.32	Total spend reported by the network for this date range	Metric
cpi	double	3.23	Estimated cost per install	Metric
clicks	integer	6240	Total reported clicks	Metric
impressions	integer	617760	Total reported impressions	Metric
installs	integer	80	Estimated app installs	Metric
actual_revenue	double	300.34	Estimated actual revenue	Metric
projected_revenue_30	double	2334.39	Projected gross revenue in dollars for horizon of 30 days made on projection_date. This value will represent the actual revenue once the cohort is 30+ days past acquisition_date_end	Metric
projected_revenue_60	double	3940.01	Projected gross revenue in dollars for horizon of 60 days made on projection_date. This value will represent the actual revenue once the cohort is 60+ days past acquisition_date_end	Metric
projected_revenue_90	double	6380.67	Projected gross revenue in dollars for horizon of 90 days made on projection_date. This value will represent the actual revenue once the cohort is 90+ days past acquisition_date_end	Metric
projected_revenue_180	double	18973.13	Projected gross revenue in dollars for horizon of 180 days made on projection_date. This value will represent the actual revenue once the cohort is 180+ days past acquisition_date_end	Metric
projected_revenue_365	double	42544.61	Projected gross revenue in dollars for horizon of 365 days made on projection_date. This value will represent the actual revenue once the cohort is 365+ days past acquisition_date_end	Metric
projected_organic_lift_30	double	2334.39	Projected organic incremental revenue for horizon of 30 days that was driven by this marketing activity	Metric
projected_organic_lift_60	double	3940.01	Projected organic incremental revenue for horizon of 60 days that was driven by this marketing activity	Metric
projected_organic_lift_90	double	6380.67	Projected organic incremental revenue for horizon of 90 days that was driven by this marketing activity	Metric
projected_organic_lift_180	double	18973.13	Projected organic incremental revenue for horizon of 180 days that was driven by this marketing activity	Metric
projected_organic_lift_365	double	42544.61	Projected organic incremental revenue for horizon of 365 days that was driven by this marketing activity	Metric
projection_date	date	2020-02-01	The date when the projection was made	Dimension