Brand awareness analysis is one of the most important quantitative tools marketing teams have, but to do it well, analysts need to aggregate relevant fields across social media platforms.
Our new data model for social media reporting makes that incredibly easy. In a few minutes, you can have all your social media data — already cleaned, tested and normalized by our marketing connectors — aggregated into one table. You’ll have the industry-standard fields and metrics you need to accurately calculate brand awareness.
Key metrics from key platforms
The data model rolls up social engagement metrics — clicks, impressions, shares, likes and comments — for the following major platforms:
- Facebook Pages
- Instagram Business
- Organic Twitter
- LinkedIn Company Pages
Legacy marketing reporting shows social media performance in silos, but this model allows marketing teams to easily compare metrics like comments and likes against all their social media profiles to gauge what's working and what's not. The model allows analysts to seamlessly combine social data from multiple data sources into one schema for use in a final report.
Analysts can also combine data from multiple accounts on the same data connector. For example, if you want to analyze data from three different Twitter accounts, you can configure the model to point at those accounts and the data will be rolled up into one table for easy reporting.
Easily compare advertising and social media data
Marketing analytics teams want to look at data across media channels and our data models enable that holistic approach. Just leverage our social media data model in concert with our data model for ad reporting, which rolls up ad spend, clicks and impressions for major ad services, including Facebook, Google, Pinterest, Bing, Twitter and LinkedIn. Side-by-side comparison of the two models will allow analysts to look at engagement and reach by day and see whether ad engagement increased alongside popular social media posts.
Keep in mind that all the data models we offer and maintain use the same core transformation logic, which is continually iterated on by the analytics engineers at Fivetran and community contributors.