Sales Director
Directs the whole sales Function or several Heads and owns its budget and strategy.
2tasks in this job
49skills it uses
4tools named
The skills this job leans on most
- 1Analysing staged funnels: conversion, time in stage and slippageNeeded in 1 of 2 tasks
- 2Qualifying sales opportunities with traceable evidenceNeeded in 1 of 2 tasks
- 3Challenging probability-weighted sales forecastsNeeded in 1 of 2 tasks
ToolsClariMicrosoft Power BISalesforceMicrosoft ExcelCACI AcornEsri ArcGIS (ArcGIS Pro, ArcGIS Online)Microsoft PowerPoint
All 2 tasks, where people get stuck and all 49 skills
Tasks
- 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
- Testing investment business cases under uncertainty
- Appraising options on a common incremental basis against a do-nothing baseline
- Running sensitivity and break-even analysis on model assumptions
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- Challenging probability-weighted sales forecasts
- Analysing customer lifetime value and unit economics
- Appraising options on a common incremental basis against a do-nothing baseline
- 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
- Comparing options against explicit decision criteria
- Constructing and challenging customer segments
- Constructing clear and honest charts
- Designing and comparing decision scenarios
- 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
- 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 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 correlation without inferring cause
- Interpreting financial statements and ratios for decisions
- Interpreting trends and seasonality
- Judging whether a change exceeds normal variation
- 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
- Measuring campaign outcomes with valid denominators
- Presenting and defending evidence-based recommendations to decision-makers
- 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
- 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
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
4tasks in this job
67skills it uses
7tools named
The skills this job leans on most
- 1Testing investment business cases under uncertaintyNeeded in 2 of 4 tasks
- 2Appraising options on a common incremental basis against a do-nothing baselineNeeded in 2 of 4 tasks
- 3Analysing staged funnels: conversion, time in stage and slippageNeeded in 1 of 4 tasks
- 4Qualifying sales opportunities with traceable evidenceNeeded in 1 of 4 tasks
- 5Challenging probability-weighted sales forecastsNeeded in 1 of 4 tasks
ToolsClariMicrosoft Power BISalesforceMicrosoft ExcelCACI AcornEsri ArcGIS (ArcGIS Pro, ArcGIS Online)+ 1 more
All 4 tasks, where people get stuck and all 67 skills
Tasks
- Investigate pipeline conversion and challenge the sales forecast
- Build a defensible business case
- Design sales territories, coverage and quotas from account potential and seller capacity
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Appraising options on a common incremental basis against a do-nothing baseline
- Running sensitivity and break-even analysis on model assumptions
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- Appraising options on a common incremental basis against a do-nothing baseline
- Challenging probability-weighted sales forecasts
- Testing investment business cases under uncertainty
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing customer lifetime value and unit economics
- Analysing queue behaviour, capacity and service constraints
- 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
- 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
- Converting demand into capacity requirements
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing and validating digital analytics event measurement
- Designing auditable decision models
- 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
- 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 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
- Judging whether uncertainty changes the decision
- Making accountable decisions under uncertainty
- Mapping buying groups and account whitespace
- Mapping customer journeys and evidence gaps
- Measuring campaign outcomes with valid denominators
- Presenting and defending evidence-based recommendations to decision-makers
- 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
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
6tasks in this job
82skills it uses
7tools named
The skills this job leans on most
- 1Challenging probability-weighted sales forecastsNeeded in 3 of 6 tasks
- 2Analysing staged funnels: conversion, time in stage and slippageNeeded in 1 of 6 tasks
- 3Qualifying sales opportunities with traceable evidenceNeeded in 1 of 6 tasks
ToolsMicrosoft ExcelClariMicrosoft Power BISalesforceCACI AcornEsri ArcGIS (ArcGIS Pro, ArcGIS Online)+ 1 more
All 6 tasks, where people get stuck and all 82 skills
Tasks
- Build a scenario-based financial forecast
- Investigate pipeline conversion and challenge the sales forecast
- Build a defensible business case
- Assess forecast accuracy and bias and recommend which forecast methods to rely on
- Design sales territories, coverage and quotas from account potential and seller capacity
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Qualifying sales opportunities with traceable evidence
- Challenging probability-weighted sales forecasts
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- 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
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- 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
- Choosing and justifying missing-data treatments
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- 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
- Converting demand into capacity requirements
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing auditable decision models
- Designing efficient and reproducible investigations
- 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
- 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
- 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 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
- Judging whether uncertainty changes the decision
- Making accountable decisions under uncertainty
- Mapping buying groups and account whitespace
- Mapping customer journeys and evidence gaps
- Measuring campaign outcomes with valid denominators
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- 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
- 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
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Variance analysis
- Verifying benefits realisation and attribution
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Microsoft Excel
- Clari
- Microsoft Power BI
- Salesforce
- CACI Acorn
- Esri ArcGIS (ArcGIS Pro, ArcGIS Online)
- Microsoft PowerPoint
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
About the role
Directs the whole sales Function or several Heads and owns its budget and strategy. Sets sales strategy and investment across regions or segments and answers to the executive for the revenue forecast. Data judgement sits in deciding how far the forecast can be trusted and where coverage investment pays back.
Also called VP Sales, Business Development Director