Commercial Analytics Leader

The Role

The Commercial Analytics Leader serves as the primary strategic interface between commercial leadership and the organization's data, analytics, and data-science capabilities. This role connects trusted data, technical analytics, and business strategy to accelerate execution. The Leader makes accurate commercial intelligence continuously available, understandable, and actionable across core and expansion sales motions.

Focused on enhancing sales performance, revenue reporting, and commission-attainment metrics, the role will progressively expand into predictive analytics, commercial optimization, AI-powered intelligence, and governed self-service capabilities. This role will move the business beyond static reports and dashboards with an on-demand commercial intelligence engine that connects data and insights to recommendations for better decisions and measurable business outcomes.

The ideal candidate brings a builder's mindset to establish trust and reliability, standardization and scale, and continuously improve how the organization discovers and acts on commercial intelligence. Combining commercial leadership, analytical rigor, technical fluency, and the ability to influence decisions across organizational boundaries, this person translates sophisticated analytical outputs into clear business implications and practical recommendations.

1. Commercial Intelligence Strategy and Roadmap

• Define and own the commercial analytics strategy, roadmap, and operating standards across brands, products, regions, and commercial motions.

• Translate executive priorities and commercial objectives into a prioritized portfolio of analytical capabilities, business questions, and measurable outcomes.

• Establish the progression from reliable descriptive performance reporting to diagnostic, predictive, and prescriptive AI-enabled decision intelligence.

2. Commercial Performance Metrics and Reporting Architecture

• Own the commercial architecture and governance of RevOps performance reporting, including metric definitions, calculation rules, analytical views, reporting standards, and management cadences.

• Build a coherent reporting framework based in Salesforce, collaborating with Power BI, billing sources, finance systems, marketing systems, and other relevant commercial data tools.

• Reduce duplicated calculations, conflicting reports, spreadsheet dependencies, and legacy dashboards through standardized, reusable analytical assets.

3. Data Integrity, Reliability, and Analytical Trust

• Partner with RevTech, Data Engineering, Data Science, Finance, and source-system owners to validate analytical logic, reconcile discrepancies, identify data gaps, and resolve recurring quality issues.

• Establish appropriate standards for data freshness, reconciliation, completeness, lineage, calculation validation, change management, and issue escalation.

• Make analytical reliability, traceability, and business confidence measurable operating priorities.

4. Business Translation and Executive Decision Support

• Translate complex data and analytical outputs into concise narratives connecting performance, drivers, implications, recommendations, actions, and outcomes.

• Serve as a trusted analytical partner to Sales and GTM Leadership, RevOps, Customer Success, Marketing, Finance, Product, and Strategy.

• Support business reviews, forecasting, strategic planning, and other commercial decisions.

5. Revenue Performance and Commercial Optimization

• Analyze performance across new-logo acquisition, pipeline conversion, account expansion, cross-sell, renewals, channel partnerships, and other relevant revenue motions.

• Identify the drivers of attainment, forecast variance, sales productivity, deal velocity, customer growth, and revenue leakage.

• Evaluate trends to uncover growth opportunities and execution constraints.

6. AI-Powered Analytics and Decision Intelligence

• Partner with RevTech and Data Science to make governed commercial metrics and contextual business definitions accessible to approved AI tools, analytical agents, and natural-language interfaces.

• Ensure AI-generated conclusions are grounded in approved data and metric definitions, with safeguards against unsupported conclusions and unauthorized data access.

7. Self-Service Analytics and Commercial Adoption

• Develop governed self-service analytical capabilities that allow business users to answer common questions independently without creating competing versions of the truth.

• Design analytical experiences around the decisions users need to make, embedding insights into existing commercial workflows where practical.

• Establish standards for metric interpretation, analytical literacy, documentation, access, training, and responsible use.

8. Cross-Functional Governance and Continuous Improvement

• Identify upstream process, integration, data-model, and instrumentation improvements required to strengthen the analytical foundation.

• Coordinate delivery across commercial stakeholders, RevTech, Data Engineering, Data Science, Finance, and Technology without duplicating their functional responsibilities.

• Continuously improve the speed, consistency, reliability, accessibility, and business impact of commercial intelligence.