Geo Experiment Using Google's Matched Markets Package | Open Source | Time Based Regression скачать в хорошем качестве

Geo Experiment Using Google's Matched Markets Package | Open Source | Time Based Regression

Geo-experimentation is one of the most common ways of carrying out incrementality tests. By now, unless you have been living under a rock, you know that incrementality tests are the gold standard for establishing causality of a marketing initiative. And among popular approaches for geo-experimentation is the open source package from Google called Matched Markets, which uses the principle of time-based regression (TBR). The best part about this is that you can run this even if you are not a data scientist - you just need to be a little familiar with Google Colab/Python environments and you can start running it by providing just a couple of inputs. 1. The Cost & Sales data for each geo per day 2. The Geo Eligibility criteria, if any Later, you also need to provide the post-test analysis data. At the end of the analysis, you will budget recommendations, split of geos into treatment and control groups and lift analysis. 00:00 What is Time Based Regression? 01:00 Why Matched Markets? 02:13 Why Geo-Experiments in the first place? 04:00 Overview of the Google Colab notebook 04:47 Input Data Sheet: Cost & Sales Data by Geo & Date 05:52 Input Data Sheet: Geo Inclusion Eligibility 08:05 Parameter Selection Process 10:26 Greedy Search for Research Design Selection 16:00 Design Diagnostics & tests 19:15 Post-Test analysis 22:14 Visualization of point-wise and cumulative lift 24:45 Summary Here is the link to the Google Colab: https://colab.research.google.com/dri... Here is the link to the official Matched_Markets Github repo: https://github.com/google/matched_mar... Here is the link to the Google Doc with notes: https://docs.google.com/document/d/1p... Here are the sheets for the inputs: https://docs.google.com/spreadsheets/... https://docs.google.com/spreadsheets/... Other related videos on incrementality & experimentation: Incrementality Test Using DiD    • Incrementality Test to Detect Lift Using D...   Google CausalImpact vs Meta GeoLift    • Google CausalImpact vs Meta GeoLift - Comp...   Statistical Significance vs Duration of Testing - When to stop a test    • Statistical Significance vs Duration of Te...   Different ways to measure incrementality    • Understand the Different Ways to Measure I...   Incrementality testing with Event Study Model    • Incrementality Testing with DiD/TWFE/Event...   BFCM Forecasting    • Black Friday/Cyber Monday - Forecasting Sa...   Incrementality Testing for Causal Impact of Google/Meta    • How to Use Incrementality Testing to Estab...   Geo-clustering for geoholdout test    • Geo Clustering for Geo Holdout Incremental...   Measuring incrementality of branded search    • Measuring Incrementality of Branded Search...   PMax incrementality testing template:    • Performance Max Incrementality Test | How ...   Multi-channel incrementality testing template:    • Incrementality Lift Template - Multi-Chann...   Understanding geo-experiments | Geo-Holdouts | Causal Inference    • Understanding Geo Experiments in Marketing...   #statisticalanalysis #incrementality #geoexperiment #marketinganalytics #marketingmeasurement #marketingeffectiveness #marketingscience #causalinference