Welcome to Data Pills!

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, GA4, Google Ads, GMP and Third Party 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.

Available Pills:

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
[Google Ads] Customer Market Intelligence (CMI)	Find out which Google Audiences are more aligned with your Customer Match audiences	Google Ads Audience Insights report	- Python (pandas)
[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
[Third Party] Appsflyer Install Report Analysis	Analyse CTIT behavior of multiple media sources using export file from Appsflyer's Install Report	Appsflyers Install Report CSV Export	- Python (numpy, pandas, seaborn, matplotlib)

How to use a Data Pill

1. Download .ipynb file to your local machine

 Option #1 - From you browser:

1. Click on Code and then Download ZIP.
2. After download finished, simply unzip the folder.

 Option #2 - git clone:

1. in your terminal, run git clone https://github.com/google/data-pills.git

2. Upload your .ipynb to a Google Drive folder

As any other file...

3. Open .ipynb in Google Colaboratory

1. 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

5. Follows notebook instructions

You are all set, just follow the notebook instructions to run the use-case.

Feedback?

Email: data-pills-faq@google.com