QUANTI:
QUANTI:
  • DATA WAREHOUSES
    • Manage rights on GCP
  • CONNECTORS
    • Marketing connectors
      • Affilae
      • Awin
      • CJ
      • Criteo
      • Effinity
      • Email
      • Google Ads
      • Google Search Console
      • Google Sheets
      • Linkedin Ads
      • Meta Ads
      • Mailchimp
      • Microsoft Advertising
      • Rakuten Advertising
      • RTB House
      • Stylight
      • Tiktok
      • TimeOne
      • Wonderpush
    • Sales analytics connectors
      • Hubspot
    • Analytics connectors
      • Google Analytics 4
      • Piano Analytics
      • Piwik Pro
    • Reverse connectors
      • Adobe Analytics
      • Google Ads
  • TAG TRACKER
    • Tag setup
    • Tag data model
      • raw_hits
      • raw_sessions
      • advanced_attribution
    • Rules for calculated attribution
  • TRANSFORMATIONS
    • The principle of reconciliation
    • Pre-built tables
      • ads_import
      • ads_import_conv
      • quanti_ids
    • Tracking templates
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  • Why pre-prebuilt transformations ?
  • How
  • Pre-built tables

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  1. TRANSFORMATIONS

Pre-built tables

Quanti: syncs pre-built tables to simplify data analysis. Let's jump into transformations.

Last updated 1 year ago

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Why pre-prebuilt transformations ?

Having data in your cloud is one thing. Having structured data ready for analysis is another. Once your data is fed into the tables, it remains scattered across different data sets. Consolidating these data can be lengthy and tedious.

QUANTI: manage some transformations, always useful for 100% of our customers.

How

This chapter deals with the topic of transformation and the aggregation work also called RECONCILIATION. This is made possible thanks to:

  • Perfect knowledge of the specificities of advertising partners' APIs allows us to extract data correctly and centralize their storage in client-dedicated Data Warehouses.

  • A method to standardize and harmonize data originally coming from different advertising platforms.

  • A method to align navigation data (Site-centric - e.g., Google Analytics 4) with campaign data (Ad-centric - e.g., Meta) to automate the calculation of key performance indicators (e.g., Return On Ads Spend).

The general idea of this transformation step is to make the data more digestible and more usable for visualization, analysis, and decision-making purposes.

Pre-built tables

Reconciliation = Automatic calculated fields
ads_import
ads_import_conv
quanti_ids