‘Geopath Insights’ R/F Approach - Overview
Beginning with the launch of Geopath Insights in 2019, our 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.
This was the best data resource available to us at the time, as there was limited device-level data available for validation of the approach. In 2021, Geopath began using observed Frequency data, replacing the surveyed data that had been used for the last several years.
These limited data resources lead to outputs 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
Geopath’s new Reach & Frequency approach is centered around observed data. This approach utilizes predictive analytics 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%. Improvements to R&F at the package-level have been made to both Market Plan and Inventory Plan.
Geopath’s Reach & Frequency approach has been, and will continue to be, an iterative process; as new data resources become available, the approach is subject to further improvements. The shift from surveyed data to observed data has allowed for more precise measurement and advanced validation methods.
[What do I do about reports I previously ran?] – do we address?
→ What do I do when there is a fluctuation/big change/new model/new release, etc.?
→ Which release does this apply to?
Component | What Has Changed (examples) | Why It Matters |
|---|---|---|
Duplication – within a package of inventory, there are overlaps in audience delivery when reaching the target multiple times. | Previously, duplication of Reach was random at the package level. However, Reach duplication is not necessarily random - using 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. |
Geographic Dispersion/Clustering – the geographic spread of inventory within a package around a market (MOVE BELOW AUDIENCE) | By increasing resolution of where we knew there was coverage, we then understood where the reachable audience was not found. | The clustering or spread of an inventory package within a market has a significant impact on the audience that it is able to reach. If all of the inventory within a package is concentrated to one area, large portions of the target audience may never have the opportunity to see the campaign. |
Audience Coverage – how an audience is dispersed around a market | Coverage of target audience within target market. Now looking down to the neighborhood level (census tract, within the market). We knew in-market circulation, but we were not leveraging specific audience coverage within market (areas where people do/do not live within target market.) | Due to multiple factors, audiences are not always evenly spread across a target market. Understanding the areas in which your audience can be reached is key to media planning. |
Data Inputs (R/F) | ||
Reach Ceiling | Prior to the 2021 midyear release, |
Transit
Place-Based
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