Commercial Analyst — Key Responsibilities & Required Skills

🎯 Role Definition

The Commercial Analyst is responsible for turning commercial and sales data into actionable insights that drive revenue growth, margin optimization and smarter go-to-market decisions. This role partners with sales, finance, product and operations to build pricing models, produce accurate revenue forecasts, monitor KPIs, and design dashboards that inform senior leadership. The ideal candidate blends financial acumen, analytical rigor, and strong storytelling to influence commercial strategy and business outcomes.

📈 Career Progression

Typical Career Path

Entry Point From:

Junior Commercial Analyst or Sales Analyst
Financial Analyst with exposure to pricing and revenue
Business Analyst or Data Analyst supporting commercial teams

Advancement To:

Senior Commercial Analyst / Lead Commercial Analyst
Commercial Manager or Pricing Manager
Revenue Manager / Head of Commercial Analytics
Director of Commercial Strategy or VP, Commercial Operations

Lateral Moves:

Pricing Analyst
Business Intelligence Analyst
Product Operations Manager
Customer Insights / Market Research Analyst

Core Responsibilities

Primary Functions

Lead end-to-end revenue and margin analysis by consolidating sales data, cost inputs and promotional activity to quantify profitability drivers and recommend margin improvement actions.
Build, maintain and continuously improve rolling revenue forecasts and pipeline models using historic performance, seasonality and sales activity to support monthly and quarterly planning cycles.
Design and deliver executive-level dashboards and ad-hoc reports in Power BI, Tableau or Looker that track commercial KPIs (ARR, ACV, churn, conversion, average selling price) and enable fact-based decision making.
Develop and implement pricing strategies and price elasticity models, running scenario analysis and sensitivity testing to recommend list price changes, discounts and promotional mechanics.
Perform deal-level profitability and discount analyses, partnering with Sales to approve exceptions, document rationale and monitor the impact of concessions on margin and attainment.
Conduct territory and quota design analytics, calculating addressable market, rep capacity, attainment trends and quota fairness to optimize sales coverage and incentive effectiveness.
Translate complex datasets into concise presentations and business cases for senior leadership, including clear recommendations, risk assessment and expected financial impact.
Execute advanced SQL queries to extract, transform and validate commercial datasets from CRM, ERP and data warehouses, ensuring data completeness and integrity for reporting and modeling.
Create and maintain financial models and scenario planning tools in Excel (including VBA where appropriate) to support budgeting, forecasting and strategic initiative evaluation.
Lead cross-functional analysis for go-to-market initiatives (new product launches, channel expansions, bundling strategies), measuring adoption, revenue uplift and cannibalization.
Monitor competitor pricing, market movements and customer segmentation data to inform pricing strategy, promotions and product positioning.
Define, track and improve sales productivity metrics (lead conversion, sales cycle, average deal size) and advise on process changes that accelerate pipeline velocity and win rates.
Partner with Finance to reconcile commercial performance to financial statements, explain variances and support month-end and quarter-end close analysis.
Implement and govern data quality checks, KPIs and naming conventions across commercial data sources to ensure a single source of truth for analysis.
Run cohort and customer lifetime value (CLTV) analysis to identify high-value segments and support retention and upsell strategies.
Conduct win/loss and customer feedback analyses to surface product objections, pricing friction and competitor strengths to inform commercial tactics.
Support pricing governance and approval processes by documenting business cases, performing sensitivity tests and maintaining audit trails of pricing changes.
Automate recurring reporting and forecasting processes to reduce manual effort, improve accuracy and accelerate time-to-insight for commercial stakeholders.
Provide strategic support for contract negotiations by modeling financial outcomes, discount impacts and long-term revenue implications for key accounts.
Design experiments and A/B tests for pricing, packaging and promotional offers and analyze lift, ROI and statistical significance for informed rollouts.
Train sales, finance and operations partners on dashboard usage, commercial metrics and forecasting assumptions to increase cross-functional adoption and alignment.
Maintain awareness of macroeconomic drivers, industry trends and regulatory changes that could materially impact commercial performance and recommend mitigation plans.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

Advanced Excel modeling: pivot tables, advanced formulas, scenario analysis, and VBA automation.
SQL proficiency for extracting and transforming commercial and customer data from relational databases.
BI and data visualization: Power BI, Tableau, Looker or similar tools for dashboard creation and maintenance.
Financial modeling and forecasting: revenue scheduling, ARR/ACV modelling, and variance analysis.
Pricing analytics: price elasticity, discount optimization, and promotional effectiveness measurement.
Statistical analysis and experimentation: hypothesis testing, A/B test design, regression basics (R or Python preferred).
CRM and ERP familiarity: Salesforce (opportunity and pipeline analysis), NetSuite, SAP or similar systems.
Data warehousing and ETL concepts, including experience working with modern data stacks (Snowflake, Redshift, BigQuery).
Scripting and data manipulation: Python, R or similar for advanced analysis and automation.
SQL/BI performance tuning and data governance principles to ensure accuracy and scalability of reports.
Presentation and slide-building in PowerPoint to communicate insights and recommendations to executives.
Familiarity with revenue recognition principles and basic accounting concepts for alignment with Finance.

Soft Skills

Strong commercial mindset with the ability to prioritize analyses that move the business forward.
Excellent communication and storytelling—translate complex analysis into clear, actionable recommendations.
Cross-functional collaboration aptitude—work effectively with Sales, Finance, Product and Operations teams.
Problem-solving and critical thinking with attention to detail and data accuracy.
Stakeholder management and influence—drive adoption of insights and decisions without direct authority.
Time management and organizational skills to balance recurring reporting with high-impact projects.
Curiosity and continuous-learning orientation to keep pace with analytics best practices and commercial models.
Adaptability to operate in ambiguous environments and iterate on analyses as new data becomes available.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Business, Finance, Economics, Statistics, Mathematics, Data Science, or related field.

Preferred Education:

Master’s degree in Finance, Business Analytics, Economics or MBA preferred for senior roles.

Relevant Fields of Study:

Finance
Economics
Statistics / Mathematics
Data Science / Analytics
Business Administration
Computer Science (with analytics coursework)

Experience Requirements

Typical Experience Range:

2–5 years of experience in commercial analytics, sales operations, pricing, revenue analytics, or financial planning & analysis.

Preferred:

3+ years working with BI tools and SQL, experience with CRM systems (Salesforce) and a proven track record of delivering revenue-impacting insights. Experience in the industry (SaaS, retail, manufacturing, or distribution) is a plus.