Chief Revenue Officer
Accountable for enterprise revenue across the Functions that generate it.
3tasks in this job
32skills it uses
5tools named
The skills this job leans on most
- 1Designing and comparing decision scenariosNeeded in 2 of 3 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 2 of 3 tasks
- 3Building driver-based financial forecastsNeeded in 2 of 3 tasks
- 4Diagnosing forecast accuracy and bias and comparing methods fairlyNeeded in 2 of 3 tasks
- 5Analysing staged funnels: conversion, time in stage and slippageNeeded in 1 of 3 tasks
ToolsMicrosoft ExcelMicrosoft PowerPointClariMicrosoft Power BISalesforce
All 3 tasks, where people get stuck and all 32 skills
Tasks
- Build a scenario-based financial forecast
- Investigate pipeline conversion and challenge the sales forecast
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Judging whether uncertainty changes the decision
- Interpreting policy scope and delegated authority limits
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- Building driver-based financial forecasts
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Analysing customer lifetime value and unit economics
- Building cohort and retention analyses with correct denominators
- Challenging probability-weighted sales forecasts
- Communicating quantified uncertainty
- Constructing and challenging customer segments
- Designing and validating digital analytics event measurement
- Diagnosing digital funnel conversion from event data
- Estimating quantities and differences with intervals
- 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 correlation without inferring cause
- Interpreting financial statements and ratios for decisions
- Interpreting trends and seasonality
- Judging whether a change exceeds normal variation
- Making accountable decisions under uncertainty
- Mapping buying groups and account whitespace
- Mapping customer journeys and evidence gaps
- Measuring campaign outcomes with valid denominators
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reasoning about systems, feedback and second-order effects
- Recognising and resolving ethical conflicts in data and AI work
- Reconstructing a record's state at a point in time from history and audit trails
- Revising prior decisions when evidence changes
- Testing competing explanations with discriminating evidence
- Writing data quality rules
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
Forecasting and Scenario Planning
Build and challenge planning forecasts and scenarios.
- Modules
- 5 open
- Practice data
- Raket Parts
4tasks in this job
67skills it uses
6tools named
The skills this job leans on most
- 1Designing and comparing decision scenariosNeeded in 3 of 4 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 3 of 4 tasks
- 3Building driver-based financial forecastsNeeded in 2 of 4 tasks
- 4Diagnosing forecast accuracy and bias and comparing methods fairlyNeeded in 2 of 4 tasks
- 5Analysing staged funnels: conversion, time in stage and slippageNeeded in 1 of 4 tasks
ToolsMicrosoft ExcelMicrosoft PowerPointClariMicrosoft Power BISalesforceEsri ArcGIS (ArcGIS Pro, ArcGIS Online)
All 4 tasks, where people get stuck and all 67 skills
Tasks
- Build a scenario-based financial forecast
- Investigate pipeline conversion and challenge the sales forecast
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Judging whether uncertainty changes the decision
- Interpreting policy scope and delegated authority limits
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- Building driver-based financial forecasts
- Designing and comparing decision scenarios
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Presenting and defending evidence-based recommendations to decision-makers
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing customer lifetime value and unit economics
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Challenging probability-weighted sales forecasts
- 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 and challenging customer segments
- Constructing clear and honest charts
- Defining measurable outcomes and leading indicators
- Designing and validating digital analytics event measurement
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Diagnosing digital funnel conversion from event data
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- 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 customer feedback and qualitative evidence
- Interpreting financial statements and ratios for decisions
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping buying groups and account whitespace
- Mapping customer journeys and evidence gaps
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Reconstructing a record's state at a point in time from history and audit trails
- 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 data quality rules
- 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
Forecasting and Scenario Planning
Build and challenge planning forecasts and scenarios.
- Modules
- 5 open
- Practice data
- Raket Parts
5tasks in this job
87skills it uses
6tools named
The skills this job leans on most
- 1Designing and comparing decision scenariosNeeded in 3 of 5 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 3 of 5 tasks
- 3Building driver-based financial forecastsNeeded in 2 of 5 tasks
- 4Diagnosing forecast accuracy and bias and comparing methods fairlyNeeded in 2 of 5 tasks
- 5Analysing staged funnels: conversion, time in stage and slippageNeeded in 1 of 5 tasks
ToolsMicrosoft ExcelMicrosoft PowerPointClariMicrosoft Power BISalesforceEsri ArcGIS (ArcGIS Pro, ArcGIS Online)
All 5 tasks, where people get stuck and all 87 skills
Tasks
- Build a scenario-based financial forecast
- Investigate pipeline conversion and challenge the sales forecast
- Define and govern a KPI metric contract
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Judging whether uncertainty changes the decision
- Interpreting policy scope and delegated authority limits
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- Building driver-based financial forecasts
- Designing and comparing decision scenarios
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Presenting and defending evidence-based recommendations to decision-makers
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing customer lifetime value and unit economics
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Calculating and verifying KPIs against their specification
- Challenging probability-weighted sales forecasts
- 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 and challenging customer segments
- Constructing clear and honest charts
- Defining measurable outcomes and leading indicators
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- 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
- Governing shared data definitions
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting financial statements and ratios for decisions
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping buying groups and account whitespace
- Mapping customer journeys and evidence gaps
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconstructing a record's state at a point in time from history and audit trails
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying benefits realisation and attribution
- Writing data quality rules
- 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
Forecasting and Scenario Planning
Build and challenge planning forecasts and scenarios.
- Modules
- 5 open
- Practice data
- Raket Parts
About the role
Accountable for enterprise revenue across the Functions that generate it. Sits on the executive team and answers to the chief executive or board for revenue results. Decides the revenue plan the board relies on and how much forecast risk the enterprise carries.
Also called CRO (revenue)