Summary

This project aims to optimize revenue management processes, improve data accuracy, and provide actionable insights to support strategic decision-making. By streamlining subscription revenue recognition, identifying and correcting errors, and enhancing data analysis capabilities, we will deliver significant benefits to the organization.

Objectives

1. Optimize Revenue Recognition: Ensure accurate and timely recognition of subscription revenue by aligning invoicing with contracts and usage data.
2. Enhance Data Quality: Identify and rectify errors in MRR run-rate calculations to maintain data integrity and reliability.
3. Improve Data Analysis: Develop robust data models in Tableau and Power BI to facilitate in-depth analysis of business units, products, and customer performance.
4. Support Strategic Decision-Making: Provide actionable insights to inform revenue-related strategies, identify growth opportunities, and optimize resource allocation.

Scope

• Revenue Analysis and Reconciliation:
  • Review and analyze recurring revenue generated from subscriptions.
  • Identify and correct errors in MRR run-rate calculations.
  • Reconcile invoicing with contracts and usage data.
  • Prepare revenue accruals for upload to the accounting system.
• Data Modeling and Visualization:
  • Maintain and enhance Tableau and Power BI data models.
  • Develop interactive dashboards and reports for data visualization and analysis.
• Ad-hoc Analysis:
  • Conduct in-depth analysis of business units, products, and customers as needed.
  • Identify trends, patterns, and opportunities for improvement.

Deliverables

• Optimized Revenue Recognition Processes: Streamlined procedures for invoicing, contract management, and revenue accruals.
• Accurate MRR Run-Rate: Corrected and consistent MRR calculations.
• Robust Data Models: Well-structured and efficient data models in Tableau and Power BI.
• Informative Dashboards and Reports: Interactive visualizations to support decision-making.
• Actionable Insights: Recommendations based on data analysis to drive revenue growth and improve operational efficiency.