Head of Sales
Accountable for the sales operating model, its investment and the reliability of its forecast.
3tasks in this job
49skills it uses
7tools named
The skills this job leans on most
- 1Analysing staged funnels: conversion, time in stage and slippageNeeded in 1 of 3 tasks
- 2Qualifying sales opportunities with traceable evidenceNeeded in 1 of 3 tasks
- 3Challenging probability-weighted sales forecastsNeeded in 1 of 3 tasks
- 4Balancing territories and quotas from account potentialNeeded in 1 of 3 tasks
- 5Running sensitivity and break-even analysis on model assumptionsCore to this role
ToolsClariMicrosoft Power BISalesforceMicrosoft Dynamics 365 Sales and Customer InsightsMicrosoft PowerPointMicrosoft ExcelWorkday
All 3 tasks, where people get stuck and all 49 skills
Tasks
- Investigate pipeline conversion and challenge the sales forecast
- Prepare a periodic performance or risk report for a management or governance committee
- Design sales territories, coverage and quotas from account potential and seller capacity
Where people get stuck
- Forecasting business trends and turning points for planning
- Designing and comparing decision scenarios
- Diagnosing forecast accuracy and bias and comparing methods fairly
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- Balancing territories and quotas from account potential
- Challenging probability-weighted sales forecasts
- Analysing customer lifetime value and unit economics
- Analysing queue behaviour, capacity and service constraints
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Constructing and challenging customer segments
- Constructing clear and honest charts
- Converting demand into capacity requirements
- Designing and validating digital analytics event measurement
- Diagnosing digital funnel conversion from event data
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Explaining technical work to accountable non-specialists
- Forecasting business trends and turning points for planning
- Giving specific developmental feedback on data and AI work
- 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 the materiality of defects and deficiencies for the intended use
- 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
- Reading charts and dashboards correctly, including their state
- 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
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- 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
Coming soonFinancial Modelling and Decision Support
7tasks in this job
100skills it uses
8tools named
The skills this job leans on most
- 1Forecasting business trends and turning points for planningNeeded in 2 of 7 tasks
- 2Designing and comparing decision scenariosNeeded in 2 of 7 tasks
- 3Diagnosing forecast accuracy and bias and comparing methods fairlyNeeded in 2 of 7 tasks
- 4Analysing staged funnels: conversion, time in stage and slippageNeeded in 2 of 7 tasks
- 5Qualifying sales opportunities with traceable evidenceNeeded in 2 of 7 tasks
ToolsClariMicrosoft Power BISalesforceMicrosoft Dynamics 365 Sales and Customer InsightsMicrosoft PowerPointWorkday+ 2 more
All 7 tasks, where people get stuck and all 100 skills
Tasks
- Investigate pipeline conversion and challenge the sales forecast
- Define and govern a KPI metric contract
- Prepare a periodic performance or risk report for a management or governance committee
- Design and govern the operating model for data, analytics and AI work in a function
- Assess forecast accuracy and bias and recommend which forecast methods to rely on
- Build an evidence-based plan for a strategic customer account
- Design sales territories, coverage and quotas from account potential and seller capacity
Where people get stuck
- Balancing territories and quotas from account potential
- Running sensitivity and break-even analysis on model assumptions
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- Designing and comparing decision scenarios
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Forecasting business trends and turning points for planning
- Analysing customer lifetime value and unit economics
- Analysing event sequences, states and durations
- Analysing queue behaviour, capacity and service constraints
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing customer creditworthiness and setting limits
- Assessing data source credibility and fitness for a question
- Assessing the control and reliability of end-user calculation files
- Assigning and operating data ownership and stewardship
- Balancing territories and quotas from account potential
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Calculating and interpreting percentages, rates and ratios
- Calculating and verifying KPIs against their specification
- Challenging probability-weighted sales forecasts
- Choosing and justifying missing-data treatments
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- Constructing and challenging customer segments
- Constructing clear and honest charts
- Converting demand into capacity requirements
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- 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 technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Forecasting operational demand at interval and site grain
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- 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 correlation without inferring cause
- Interpreting financial statements and ratios for decisions
- Interpreting sustainability reporting standards and metric boundaries
- Interpreting trends and seasonality
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- 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
- Mapping buying groups and account whitespace
- Mapping customer journeys and evidence gaps
- Mapping entity results and eliminating intercompany balances in consolidation
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring and testing data control effectiveness
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising collection actions from aged debt
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- 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
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Clari
- Microsoft Power BI
- Salesforce
- Microsoft Dynamics 365 Sales and Customer Insights
- Microsoft PowerPoint
- Workday
- Esri ArcGIS (ArcGIS Pro, ArcGIS Online)
- Microsoft Excel
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
Coming soonFinancial Modelling and Decision Support
8tasks in this job
110skills it uses
8tools named
The skills this job leans on most
- 1Challenging probability-weighted sales forecastsNeeded in 4 of 8 tasks
- 2Analysing staged funnels: conversion, time in stage and slippageNeeded in 2 of 8 tasks
- 3Qualifying sales opportunities with traceable evidenceNeeded in 2 of 8 tasks
- 4Balancing territories and quotas from account potentialNeeded in 1 of 8 tasks
- 5Running sensitivity and break-even analysis on model assumptionsCore to this role
ToolsMicrosoft ExcelClariMicrosoft Power BISalesforceMicrosoft Dynamics 365 Sales and Customer InsightsMicrosoft PowerPoint+ 2 more
All 8 tasks, where people get stuck and all 110 skills
Tasks
- Build a scenario-based financial forecast
- Investigate pipeline conversion and challenge the sales forecast
- Define and govern a KPI metric contract
- Prepare a periodic performance or risk report for a management or governance committee
- Design and govern the operating model for data, analytics and AI work in a function
- Assess forecast accuracy and bias and recommend which forecast methods to rely on
- Build an evidence-based plan for a strategic customer account
- Design sales territories, coverage and quotas from account potential and seller capacity
Where people get stuck
- Balancing territories and quotas from account potential
- Running sensitivity and break-even analysis on model assumptions
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- Balancing territories and quotas from account potential
- Challenging probability-weighted sales forecasts
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing customer lifetime value and unit economics
- Analysing event sequences, states and durations
- Analysing queue behaviour, capacity and service constraints
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing customer creditworthiness and setting limits
- Assessing data source credibility and fitness for a question
- Assessing the control and reliability of end-user calculation files
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Calculating and interpreting percentages, rates and ratios
- Calculating and verifying KPIs against their specification
- Choosing and justifying missing-data treatments
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- Constructing a controlled cash-flow plan
- Constructing and challenging customer segments
- Constructing clear and honest charts
- Converting demand into capacity requirements
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing auditable decision models
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- 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
- Exploring data to find patterns that survive validation
- Facilitating group decisions using disputed evidence
- Forecasting business trends and turning points for planning
- Forecasting operational demand at interval and site grain
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- 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 financial statements and ratios for decisions
- Interpreting sustainability reporting standards and metric boundaries
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- 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
- Mapping buying groups and account whitespace
- Mapping customer journeys and evidence gaps
- Mapping entity results and eliminating intercompany balances in consolidation
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring and testing data control effectiveness
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising collection actions from aged debt
- Profiling data
- 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
- Reconciling finance subledgers and close records
- 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
- Standardising and cleaning data under agreed rules
- 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 AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Microsoft Excel
- Clari
- Microsoft Power BI
- Salesforce
- Microsoft Dynamics 365 Sales and Customer Insights
- Microsoft PowerPoint
- Workday
- Esri ArcGIS (ArcGIS Pro, ArcGIS Online)
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
Diagnostic Investigation and Root Cause Analysis
Resolve contradictory evidence and find, verify and prevent the causes of business and data anomalies.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision Support
About the role
Accountable for the sales operating model, its investment and the reliability of its forecast. Decides coverage and capacity and the reporting the business relies on for revenue. Manages sales managers while the Head of Commercial owns price, terms and margin.
Also called Head of Business Development