For AI agents: the docs index is at https://docs.lifesight.io/llms.txt. Append .md to any documentation page URL to get its markdown version.
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Centralizing & operationalizing your data
Lifesight
What is Lifesight?
Who is Lifesight For
How Lifesight Works
Quickstart Guides
Get Started with Cockpit
Connect your Data
How to add Integrations
Meta Ads
Google Ads
Microsoft Ads
Snapchat Ads
Reddit Ads
RTB House
StackAdapt
CSV Import
Google Sheets
Data Transformation
Map a field from a source
Set a fixed value for a source
Map a source to a channel
Data Taxonomy: Turn campaign names into a structure you can measure on
Assign tactics manually to cover your biggest spend first
Assign tactics automatically with rules
Model Schema: decide what your model gets to see
Build a schema for Marketing Mix Modelling
Build a schema for Geo Experiments
Model Overview (Marketing Mix Modeling)
Setting up your Marketing Mix Model
CSV Data Formatting Guidelines
Model Calibration: Make the model agree with your experiments
Causal Graph: See how your channels work together
Merging Models
Contribution: Understand what each channel contributes to revenue
Model Retraining: Keep the model fresh as your data changes
Interaction: See which channels lift each other and which cancel each other out
Model Refresh: Refresh your marketing model with the latest data
Profit & Cost Settings in Lifesight
Measure Incrementality of Creatives
Model Insights
Model Diagnostics
Model Data
Model Review
Create a Geo Experiment
Build a geo experiment that proves incremental impact
Test hypotheses for clear budget decisions
Geo test data schema
Geo Experiment Insights
Causal Attribution: See What Truly Drives Revenue
Getting Started With Causal Attribution
Overview: separate reported revenue from incremental revenue
Breakdown: find exactly where performance is won or lost
Attribution metrics guide
Target benchmark
Plan your budget with confidence
Build a media plan
Set your default scenario
Custom Budget Pacing
Intrepreting a plan
Artifacts (Manual Upload)
Key Concepts (METHODOLOGIES)
What is Unified Marketing Measurement
Marketing Mix Modelling
Understanding Causal DAGs: A Strategic Evolution in Marketing Mix Modeling
Adstock
Baseline
Goodness of fit
Estimation error
Normalized root mean squared error (NRMSE)
Model convergence
Halo Effect
Interaction Effect
Model selection
Calibration
Marginal ROAS (mROAS)
Bootstrapping
Causality in MMM
Advanced Modeling Scenarios
ML-based algorithms : Inference
Ridge regression
Predictive CLTV Modeling
Incrementality Testing
Why Experiments?
Split Testing
Split Testing : Statistical Significance
Split Testing : Treatment / Control Assignment
Split Testing : Understanding normalized Control
Geo-Testing
Geo Experiments: Comparison between methodologies
Designing Geo-Experiments: Identifying Geos
Geo Experiments : Power Analysis
Automated deployment of experiments
Geo Experiments - Insights & Validation
Causal Attribution
Touch Based Attribution
Single-Touch Attribution
Multi-Touch Attribution
View Through Attribution
Algorithmic Attribution
MCP Server
Lifesight MCP
Lifesight Connector for Claude
Setup Guide ChatGPT
MCP Prompt Library
HOW TO'S
Planning, forecasting & optimization
How to optimize budget using constraints
How to select planning base period
How to plan for seasonality & promotions
How to compare scenarios in plans
How to manage budget changes
How to optimize your budget
How to plan your optimal budget
Measuring marketing effectiveness
How to test for new channel launch
How to optimize a media mix for your goal
How to validate effectiveness and ROIs
How to validate your MMM accuracy
How to improve your MMM model
Analyzing marketing performance
How to track your goal progress
LEARN
General FAQ
Measurement Academy
Glossary
SECURITY & COMPLIANCE
Consent
Data localization
Data security compliance
GDPR
SOC 2
HIPAA (coming soon)
ISO 27001
PARTNERS
Lifesight Partner Program
Onboarding new clients/brands
Modify client workspace
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