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In marketing, attribution refers to the evaluation of all marketing touchpoints a user encounters during their path to conversion. Assigning credits or weights to various touch points uncovers which channels or combinations of channels attributed the greatest impact/s on conversion. Attribution is used by advertisers to optimize marketing channels at a granular level and to support their understanding of their individual audiences and market as a whole.
Attribution lets you assign credit to each marketing channel and will allow you to observe how they contribute to conversions, for example online sales.
RBA is an approach to measuring digital media performance via econometric models built in open source code (SQL, Python, R). This methodology does not utilize user level data, thus making it privacy safe and durable through 3P cookie deprecation. RBA sits between Marketing Mix Models (MMM) and Multi-Touch Attribution models (MTA) - as it is more granular than MMM but does not rely on cookies.
RBA can answer questions including:
- What channel and sub-channel impression weights will generate the maximum return for a given line of business?
- Within each channel, what media partners and environments have the greatest impact on intended outcomes?
- Does buying methodology (Direct IO versus Programmatic) influence the impact of their respective channels?
RBA models are built using SQL or Python code.
- Data requirements for RBA include daily conversion volume and daily digital media investment broken down by desired tactics. Media data can be collected through Campaign Manager and/or Search Ads 360. Desired frequency of data is daily, however weekly or monthly data can be used as long as the industry standard of 10-20 observations per feature in the model is met. At least one year's worth of data is preferred for the model to gain a sufficient read of media signals.
Campaign Manager:
- Download daily data from the campaign manager UI for the past 12 months.
- Navigate to Reporting & Attribution > Report Builder > Offline Reporting > New Offline Report > Standard > Basic.
- Pull the following columns:
- Advertiser
- Placement
- Placement ID
- Date
- Impressions, clicks, spend
Search Ads 360:
- Download daily data from the Search Ads 360 UI
- Pull the following columns:
- From (start date)
- To (end date)
- Device segment
- Engine
- Account
- Campaign
- Ad group
- Impressions, clicks, spend
- Any other metrics you would like to utilize to segment your variables
Other digital media can be included such as social - as long as it meets the required frequency and timeframe for modeling.
CRM/Sales Data: - Pull the following columns: - Date - Sales (Conversions, Revenue, or other KPI) - Zip Code - Region (if media is planned at regional level, otherwise national)
After aggregating data in BigQuery or Google Sheets, execute the following notebooks for data cleaning and modeling:
-
Data cleaning and feature selection : pulls in data from BigQuery, performs exploratory data analysis, cleaning, and feature reduction
-
Modeling
-
Standard Regression (BQML) : runs SQL-based BigQuery Machine Learning linear regression model within Colab UI
-
Regularized Regression (AI Notebook) : runs standard ordinary least squares linear regression model
-
Bayesian Regression (AI Notebook) : conducts bayesian linear regression modeling including setting priors, sampling, and posterior analysis
-
Vector Autoregression (AI Notebook) : executes vector autoregression model with relevant pre-modeling tests, impulse response functions, and forecast error variance decomposition
-
- Teams looking for durable attribution solutions and/or teams with advanced custom attribution needs
-
Standard Regression (BQML)
- What is it? A modeling approach that utilizes BigQuery's pre built machine learning models
- Who is it good for? Teams with minimal programming or stats backgrounds
- What skills does it take? Teams with SQL knowledge and basic stats background
-
Regularized Regression (AI Notebook)
- What is it? A modeling approach that analyzes the trends of a set of statistically significant attributes/behaviors observed over regular intervals of time to predict a numerical outcome
- Who is it good for? Teams who want a "hands on" approach to modeling; Those who want to include variables that don't change over time (fixed effects) i.e. geography
- What skills does it take? Teams with SQL + Python/R capabilities and experience building statistical models
-
Bayesian Regression (AI Notebook)
- What is it? A modeling approach that can deal with insufficient or poorly distributed data. Includes priors on the coefficients and on the noise so that in the absence of data, the priors can take over
- Who is it good for? Teams who want a "hands on" approach to modeling; Those whose data sample size is limited and there is a concern about stable estimates (priors will help with this)
- What skills does it take? Teams with SQL + Python/R capabilities and experience building statistical models
-
Vector Autoregression (AI Notebook)
- What is it? A stochastic modeling approach that provides a view into how marketing mediums are attributed to sales as well as how they interact with each other
- Who is it good for? Teams who want a "hands on" approach to modeling; Teams who are interested in digital channel level (vs. tactic-level) insights and want to understand the effect of each tactic on conversions as well as how each of these media types interact with each other
- What skills does it take? Teams with SQL + Python/R capabilities and experience building statistical models
- An advantage of only looking at digital media is that the data ingestion from digital media technologies can be automated using APIs. So, RBA analysis can be run more frequently than other analyses. As you’re only looking at digital media that will have the same metrics (e.g clicks) across the board, you can run RBA analysis at a more granular level (micro level analysis), meaning it can be used at both the channel level, and tactic level. For example, the impact of a display awareness campaign compared to a display retargeting campaign.
git clone https://github.com/google/rba
mkdir my-new-thing
cd my-new-thing
git init
cp -r ../rba/* ../rba/.github .
git add *
git commit -a -m 'Applying RBA Code'