Overview of Data available in Adloop
A small synthetic diagram to understand
Let's try to explain this simply.
A. For starters we import data from Data Sources
You set the API connectors in Data Sources that will retrieve information from the Ad Platforms and Analytics.
First we retrieve 1 year of data and then every day we add fresh data.
The 'raw' data retrieved from the platforms is stored and identified by the small logo of the Data Source and a name. An example for the metrics :
Impressions: these are the Impressions retrieved from Google Ads
Conversions: the conversions indicated by Facebook Ads
It's the same for the Dimensions from the Data Sources :
Campaign name: identifies the name of the campaigns Google Ads
Name of the ad: the ads from Facebook Ads
noteAt this point, we have just retrieved the information from the data sources without changing anything. A kind of copy and paste of what is in the platforms (but without manual intervention).
At this point, we have just retrieved the information from the data sources without changing anything. A kind of copy and paste of what is in the platforms (but without manual intervention).
B. We then make a Normalization to simplify the reading
As you operate many marketing channels, you will import a lot of data. Now it happens that some Dimensions or Metrics are common to several Data Sources .
For example, we have Impressions in Google Ads and Facebook Ads, as well as Clicks, for the distribution costs (called Ad spend in Adloop). The same goes for campaign names, ad names, etc.
We therefore operate a Normalization to arrange Dimensions or Metrics of the same nature in a common place.
Source variable | Normalized Variable |
The metrics :
|
Impressions
Impressions
| Normalized (copied) into :
Impressions
| | Dimensions :
Campaign name
Campaign name
| Normalize (copy) to:
Campaign name - SEA
|
This operation allows you to have several Variables of the same nature gathered in a single Variable, which considerably simplifies the reporting.
The origin of the normalized variables is indicated by two icons:
Advertising Sources ( etc.) are normalized in the Ad Centric, icon
Analytics Sources ( etc.) are normalized in the Site Centric, icon
And to complete the explanation, Adloop specific Variables are illustrated by the icon
C. Matching or how to make sure that the Ad and Analytics or Adloop Source understand each other
Adloop's goal is to offer the most appropriate KPIs for all levels of campaign granularity. In order to match a campaign name coming from an Advertising Source with indicators coming from Analytics or Adloop, it is necessary to make sure that the two worlds recognize each other, this is the purpose of Matching .
As an example:
Let’s say I want to see the number of Sessions in my Data Source Facebook Ads
In Google Analytics, the Facebook channel is identified when the Source dimension is 'facebook' and the Medium dimension is 'paid social'.
Matching allows you to create this connection:
D. Attribution ( function) for optimal quality indicators
Adloop offers a Data-Driven Attribution feature that allows you to reconstruct the conversion and engagement paths of users and calculate attributed metrics and KPIs.
The Adloop Tracking & Attribution feature will generate many new metrics in Adloop, all of which will be identified by an icon. For example:
Conversions attr. (for Attributed Conversions)
Income attr. (for Attributed income)
etc.
Adloop's attribution functionality takes the form of a Data Source that needs to be activated and configured. A subscription to Adloop is required to activate it.
E. Data is available for use
All data presented here is available for use in Reports , Exports and Notifications .
More information in the following sections:
[[ Dimensions |Dimensions-in-Adloop]]
[[ Metrics |Metrics-in-Adloop]]
[[ Matching |Matching]]
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