Chief Commercial Officer
Accountable for commercial performance across the enterprise and the commercial risk the enterprise accepts.
2tasks in this job
20skills it uses
3tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 2 of 2 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 1 of 2 tasks
- 3Judging whether uncertainty changes the decisionNeeded in 1 of 2 tasks
- 4Maintaining a risk register with owners, triggers and responsesCore to this role
- 5Interpreting policy scope and delegated authority limitsCore to this role
ToolsMicrosoft SharePointMicrosoft Power BIMicrosoft PowerPoint
All 2 tasks, where people get stuck and all 20 skills
Tasks
- Maintain a data and AI risk register and record risk treatment and acceptance
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Analysing price realisation from list to pocket price
- Comparing pricing scenarios with demand and margin sensitivity
All skills
- Rating risks against appetite and choosing proportionate treatment
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Classifying AI uses against regulatory obligations
- Escalating unresolved risks with evidence and clear ownership
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Judging whether and how to use AI for a task
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- Recognising and containing suspected personal data breaches
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Evidence-based Briefing and Influence
Brief and influence accountable decision-makers with evidence, its limits and unwelcome findings, and check the message landed.
- Modules
- 5 open
- Practice data
- Raket Parts
4tasks in this job
67skills it uses
4tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 4 of 4 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 2 of 4 tasks
- 3Analysing price realisation from list to pocket priceNeeded in 1 of 4 tasks
- 4Comparing pricing scenarios with demand and margin sensitivityNeeded in 1 of 4 tasks
- 5Maintaining a risk register with owners, triggers and responsesCore to this role
ToolsMicrosoft ExcelMicrosoft SharePointMicrosoft Power BIMicrosoft PowerPoint
All 4 tasks, where people get stuck and all 67 skills
Tasks
- Build a defensible business case
- Maintain a data and AI risk register and record risk treatment and acceptance
- Set discount guardrails and review price realisation and exceptions
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Rating risks against appetite and choosing proportionate treatment
- Judging whether uncertainty changes the decision
- Interpreting policy scope and delegated authority limits
All skills
- Comparing pricing scenarios with demand and margin sensitivity
- Presenting and defending evidence-based recommendations to decision-makers
- Rating risks against appetite and choosing proportionate treatment
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing auditable decision models
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Forecasting business trends and turning points for planning
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Making accountable decisions under uncertainty
- Monitoring and testing data control effectiveness
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Variance analysis
- Verifying benefits realisation and attribution
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Customer, Revenue and Sales Analytics
Analyse pipeline, forecast quality, segments and lifetime value from CRM and billing data.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- Modules
- 5 open
- Practice data
- Raket Parts
4tasks in this job
67skills it uses
4tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 4 of 4 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 2 of 4 tasks
- 3Analysing price realisation from list to pocket priceNeeded in 1 of 4 tasks
- 4Comparing pricing scenarios with demand and margin sensitivityNeeded in 1 of 4 tasks
- 5Maintaining a risk register with owners, triggers and responsesCore to this role
ToolsMicrosoft ExcelMicrosoft SharePointMicrosoft Power BIMicrosoft PowerPoint
All 4 tasks, where people get stuck and all 67 skills
Tasks
- Build a defensible business case
- Maintain a data and AI risk register and record risk treatment and acceptance
- Set discount guardrails and review price realisation and exceptions
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Rating risks against appetite and choosing proportionate treatment
- Judging whether uncertainty changes the decision
- Interpreting policy scope and delegated authority limits
All skills
- Comparing pricing scenarios with demand and margin sensitivity
- Presenting and defending evidence-based recommendations to decision-makers
- Rating risks against appetite and choosing proportionate treatment
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing auditable decision models
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Forecasting business trends and turning points for planning
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Making accountable decisions under uncertainty
- Monitoring and testing data control effectiveness
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Variance analysis
- Verifying benefits realisation and attribution
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Customer, Revenue and Sales Analytics
Analyse pipeline, forecast quality, segments and lifetime value from CRM and billing data.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- Modules
- 5 open
- Practice data
- Raket Parts
About the role
Accountable for commercial performance across the enterprise and the commercial risk the enterprise accepts. Sits on the executive team and answers to the chief executive or board for margin and commercial results. Decides the pricing principles and risk appetite that every business unit works within.
Also called CCO (commercial)