Each Data Pill consists in a codelab styled, jupyter notebook built for Google Colabs (hosted by Google Drive), which performs advanced analysis on top of GA360, Google Ads and GMP data and provides a actionable next steps.
All Data Pills were built with simplicity in mind, in a way that it should only take a couple of minutes to set the environment variables to run an analysis against your media and analytics data.
The complexity of each pill varies, requiring from basic python and SQL knowledge to more advanced Machine Learning proficiency
Disclaimer: This is not an officially supported Google product.
Data Pill | Objective | Data Source | Technologies used |
---|---|---|---|
[CM] Frequency Analysis | Calculate the ideal frequency for campaigns ran through Campaign Manager or DV360 | Data Transfer or ADH | - Python (pandas) -SQL |
[CM] Campaign Overlap | Understand the impact of each step of the funnel on client's conversion rate | Data Transfer or ADH | - Python (pandas) -SQL |
[CM] Offline Conversions Uploader | Upload gclid or device_id based floodlight conversions to Campaing manager | Google Sheets | - Python |
[Google Ads] Frequency and Audience analysis | Calculate the ideal frequency and top performing audiences for display and video ads in Google Ads | ADH | - Python (pandas) -SQL |
[GA360] Conversion Blockers | Automatically Identify UX issues on the website which are blocking conversions (i.e. lack of browser or language support, bad performing landing pages, …) | GA360 export to BigQuery | - Python (pandas) -SQL |
[GA360] Feature Importance | Understand what behaviors (features) are most correlated with purchase probability | GA360 export to BigQuery | - Python (pandas) - Sklearn (ML) -SQL |
[GA360] Predictive Lifetime Value | Calculate the predicted lifetime value (amount of money the client will spend) in the future | GA360 export to BigQuery | - Python (pandas) -SQL |
[GA] Measurement Protocol sender | Import offline conversions to Google Analytics Ecommerce | GA (free or 360) | - Python - ClientId collection - Offline Sales data |
[GA] Customer Market Intelligence (CMI) | Find out which Google Audiences are more aligned with your products or content | GA(free or 360) | - Python (pandas) -JavaScript |
Option #1 - From you browser:
- Click on
Code
and thenDownload ZIP
. - After download finished, simply unzip the folder.
Option #2 - git clone:
- in your terminal, run
git clone https://github.com/google/data-pills.git
As any other file...
- In your drive folder, right click on the
.ipynb
file and select open with >> Google Colaboratory
If that option is not available, go to drive.google.com, Select New >> More >> Google Colaboratory, close the new tab and try again
You are all set, just follow the notebook instructions to run the use-case.
Email: [email protected]