Presenting Metrics
Last updated
Last updated
This aims to provide an overview of data maturity and analytics in revenue operations, guiding readers on how to effectively leverage data to drive strategic recommendations and improve overall revenue performance.
Data is not merely collected for the sake of curiosity or data exercises.
The primary purpose of data collection and tracking in revenue operations is to make recommendations that enhance the overall revenue operation.
Data maturity capabilities encompass various levels of data management and utilization.
The foundational level involves establishing a solid foundation of accurate data.
This includes having ready reports and dashboards to measure all relevant aspects of revenue operations.
However, stopping at this level is insufficient.
The next level of data maturity involves performing analytics on the collected data.
This includes measuring meaningful business metrics and deriving insights from the available data.
Based on these insights, strategic recommendations can be provided to improve revenue operations.
Consider a scenario where data reveals that the sales cycle time for a specific industry is longer compared to others.
The purpose of identifying this data is not merely to report the difference, but to explore potential solutions.
It may indicate a need for different training or education to accelerate the sales process and secure contracts.
As a result, a recommendation can be made to enhance the go-to-market lifecycle with new processes.
Avoid:
Dumping data without context: Avoid overwhelming your audience with raw data without providing context or insights.
Lacking context: Remember that the purpose of data is to make recommendations, so providing business context is critical.
Expecting others to derive insights: You are more familiar with the data and operations, so presenting your own insights will make a significant impact.
Do:
Tailor data to your audience: Customize the data presentation based on the audience, whether it's executives, individuals, or specific teams.
Align data to insights: Ensure that data is presented with insights and recommendations, even if they are not ultimately implemented.
Spark conversations: Use data to initiate discussions and collaborate with other teams to develop comprehensive recommendations that improve the revenue engine.