May 6, 2020
In today’s marketing landscape, social channels are no longer just tools for messaging. They offer a trove of insights into valuable metrics like user behavior, audience engagement, and purchase intent. To fully leverage these insights, however, marketers need solutions that can manage the vast amounts of social data while also illuminating key insights.
Unfortunately, many marketing teams still rely on excel spreadsheets or self-built API connections to extract and analyze social media data. These approaches can’t readily handle the volume of data needed to generate competitive insights. And even when social data is housed in a cloud data warehouse, the increased volume of social data doesn’t directly result in easy to visualize, timely insights.
That’s why Rivery and Looker have teamed up to provide marketers with a plug-and-play solution for social media data analysis.
The Social Analytics Block combines the Rivery ELT platform with Looker to enable marketers and social media managers to track and analyze critical metrics across key social media channels. With this Looker Block, users can harness dashboards and visualizations built to catalyze breakthroughs in social performance to uncover social insights hidden within their cloud data warehouses.
The Social Analytics Block is powered by Rivery’s pre-built data ingestion pipelines for Facebook, Instagram, and Twitter. The initial data feeds include:
Rather than pre-hashed reports and aggregated totals, Rivery captures the granular data marketers need to craft precision-guided campaigns, careful marketing strategies, and data-driven messaging.
In order to prepare the extracted data for social media analysis and visualization in Looker, Rivery first orchestrates and transforms the data into its required format. The extracted social data passes through an orchestration pipeline that applies business logic and transformations to the data stream. This process combines all the social data from Facebook, Instagram, and Twitter into a single database table.
Here’s how the data workflow is executed:
Pre-built data connectors allow teams to avoid making API connectors from scratch.
Rivery’s Logic Rivers automate this entire process.
The data is now ready for use in Looker.
measure: page_impressions_current_30 {
type: sum_distinct
view_label: "Time Comparison Fields"
description: "Number of impressions per page for the last 30 days. Facebook and Instagram only."
sql_distinct_key: ${account_id}|| ${date_date} ;;
sql: ${page_impressions};;
filters: {
field: date_date
value: "30 days"
}
}
measure: page_impressions_previous_30 {
type: sum_distinct
view_label: "Time Comparison Fields"
description: "Number of impressions per page for the previous 30 days. Facebook and Instagram only."
sql_distinct_key: ${account_id}|| ${date_date} ;;
sql: ${page_impressions};;
filters: {
field: date_date
value: "60 days ago for 30 days"
}
}
measure: page_impressions_percent_change {
type: number
view_label: "Time Comparison Fields"
description: "Percent change between impression count for the last 30 days compared to the previous 30 days. Facebook and Instagram only."
value_format_name: percent_2
sql: (${page_impressions_current_30}-${page_impressions_previous_30})/ NULLIF(${page_impressions_previous_30},0) ;;
}
After transforming the data into the correct format, users can leverage pre-built dashboards in Looker to visualize their data, compare social metrics side-by-side, and track page and post engagement across social media platforms. A few of these analyses include:
Analyze key engagement metric trends and growth across all social channels.
Evaluate post-level engagement metrics across all social channels.
Track tweet performance across dynamic interval buckets.
Learn more about how to maximize your social media engagement and performance during our upcoming webinar and try out the Social Analytics by Rivery Block to take your social media analysis to the next level.