Current R/F Approach - Overview
Geopath’s existing Reach & Frequency approach was built around highly aggregated, anonymous carrier data. This mobile data was used to determine average frequency and understand general home locations, which allowed us to look at the footprint of audiences in different markets.
However, there was no device-level data available to us at the time for validation of the approach. This lead to outputs from the existing methods that were misaligned for some packages of inventory, where higher package-level Reach across the market should have been reported.
New R/F Approach - Overview
The new Reach & Frequency approach is centered around observed data. This approach utilizes machine learning, observed Reach data, and several feature inputs to train on how Reach and Frequency function over time.
This then provides us with predictors for Reach, and a model that allows us to calculate package-level Reach and Frequency for any campaign length, aligned with the observed data ±5%.
Feature | What Has Changed | Why It Matters |
|---|---|---|
Duplication | Previously, duplication of Reach was random at the package level. However, Reach duplication is not necessarily random - using the mobile movement data, we now understand the overlap and duplication of audience within a package. | At the package level, there were instance where additional units were not adding incremental Reach to the plan. |
Media Weight (TRP) | Media weight, expressed as TRPs, is inter-related with Reach and Frequency. The higher the TRP value, the larger the portion of the reachable audience within a market | |
Coverage | ||
Data Inputs (observed) | ||
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