Building a Revenue Engine Analytics System, Part II
Revenue Operations Expert Series
I’ve just finished surveying the Revenue Operations and Revenue Intelligence landscape. Recently in New York City I spoke at the Revenue Operations Summit regarding the art and science of revenue engine analytics.
During the conference it dawned on me that the journey makes us. The most common path into RevOps appears to be contributors and managers who moved over to RevOps because they were process-driven, loved technology and/or wanted to contribute at a broader scale in their org.
There aren’t many ex-investment bankers or software engineers in RevOps.
That’s a shame because my i-banking days helped with big picture synthesis, project management and communication skills and because SQL and Python form a power combination when utilized together. This combination not only streamlines the process of extracting, processing, and analyzing data but also unlocks new opportunities for automation, predictive analytics, and strategic decision-making. By harnessing the strengths of both SQL and Python, RevOps teams can drive efficiency, uncover actionable insights, and contribute to sustainable revenue growth.
You can take this even further and develop integrated revenue engine analytics systems featuring a database like BiqQuery to hold revenue engine telemetry, aggregations and change logs. Then you bolt-on Looker or another BI tool onto this database and visualize reporting using well-built SQL queries.
Last time out we demonstrated the power of the Star Schema as the underlying structure of your revenue engine analytics system.
Building a Revenue Engine Analytics System
An effective analytics framework in Revenue Operations needs a combination of the right tools and the right methods to use those tools.
This section focuses on using the data warehouse you've set up, emphasizing analysis, reporting, and actionable insights. It also touches on some advanced concepts and future-proofing.
From Data to Decisions – Analyzing Your Revenue Engine
You've built your star schema data warehouse, diligently integrating data from Salesforce, HubSpot, and QuickBooks. Congratulations! You now have a single source of truth for your revenue operations. But having the data is only half the battle. The real power comes from using that data to understand your revenue engine, identify opportunities for improvement, and make informed decisions. This section focuses on how to turn your data warehouse into an insight-generating machine.