Extract your data from Impact Radius and automatically load it to a Databricks data warehouse. Reduce manual reporting time by 90%Explore
Improvado is an ETL platform that extracts data from Impact Radius, transforms it, and seamlessly loads the results to Databricks. Speed up your decision-making process and quickly implement optimizations without wasting time crunching the data.
Every platform uses different words to describe its metrics. What one platform calls “impressions” another calls “imps” and another calls “views.” This is a major hassle for marketers who want to combine and analyze the data they’ve gathered from multiple platforms. Instead of diving into analytics, they waste 90% of their time just preparing the data for analysis, resulting in hours of productive time lost.
With Improvado, you get analysis-ready data with unified naming across all platforms. You always have access to the raw data and unified reports should you need them. This flexibility allows you to get answers faster and uncover more valuable insights.
Use Improvado’s data transformation tool to perform a variety of tasks, from simple to complex data transformations, based on your business’ needs.
Simply connect Impact Radius to Improvado and start building data pipelines faster—no coding required. Anyone familiar with Excel has the skills to build scalable data pipelines in Databricks.
Control what creatives are being run or monitor them on your dashboard. Streaming active creatives directly to your database presents many opportunities to increase your marketing efficacy.
Take advantage of our Solution Engineering team to help you to automate your entire marketing pipeline.
You are not only getting access to the most advanced and innovative marketer-focused reporting software but also unparalleled customer experience, including:
Databricks is a cloud platform that combines the best features of data warehouses and data lakes into a lakehouse architecture. When companies use it, they can store and manage all the data for the analytics workloads.