Sales Manager
Accountable for a sales team's pipeline and forecast and the development of its sellers.
5tasks in this job
64skills it uses
9tools named
The skills this job leans on most
- 1Analysing staged funnels: conversion, time in stage and slippageNeeded in 3 of 5 tasks
- 2Qualifying sales opportunities with traceable evidenceNeeded in 3 of 5 tasks
- 3Challenging probability-weighted sales forecastsNeeded in 3 of 5 tasks
ToolsClariSalesforceMicrosoft Power BIHubSpotLime CRMMicrosoft Dynamics 365 Sales and Customer InsightsLinkedIn Sales NavigatorMicrosoft ExcelWorkday
All 5 tasks, where people get stuck and all 64 skills
Tasks
- Investigate pipeline conversion and challenge the sales forecast
- Build an evidence-based plan for a strategic customer account
- Design sales territories, coverage and quotas from account potential and seller capacity
- Qualify and prioritise own opportunities from account, credit and engagement evidence and record them for forecasting
- Calculate and verify sales incentive payouts against plan rules and booked revenue
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
- Qualifying sales opportunities with traceable evidence
- Analysing customer lifetime value and unit economics
- Analysing working-capital drivers and cash conversion
- Applying effective dating and retroactive change rules to records
- Assessing customer creditworthiness and setting limits
- Assessing data source credibility and fitness for a question
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building driver-based financial forecasts
- Calculating and verifying KPIs against their specification
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing periods consistently
- Constructing and challenging customer segments
- Designing and validating digital analytics event measurement
- Designing experiments
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing duplicate records
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating accruals, provisions and expected credit loss under accounting policy
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating ML generalisation and fitness
- Explaining how business events flow into accounting records
- 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
- 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 policy scope and delegated authority limits
- Interpreting trends and seasonality
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Mapping buying groups and account whitespace
- Mapping customer journeys and evidence gaps
- Measuring campaign outcomes with valid denominators
- Prioritising collection actions from aged debt
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading data tables: rows, fields, types and units
- Recalculating pay and incentive outcomes from plan rules
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Reconstructing a record's state at a point in time from history and audit trails
- Resolving and escalating pay, incentive and payment exceptions within authority
- Setting and reviewing action thresholds for scores and model predictions
- 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
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
6tasks in this job
90skills it uses
10tools named
The skills this job leans on most
- 1Analysing staged funnels: conversion, time in stage and slippageNeeded in 4 of 6 tasks
- 2Challenging probability-weighted sales forecastsNeeded in 4 of 6 tasks
- 3Qualifying sales opportunities with traceable evidenceNeeded in 3 of 6 tasks
ToolsClariSalesforceMicrosoft Power BIHubSpotLime CRMMicrosoft Dynamics 365 Sales and Customer Insights+ 4 more
All 6 tasks, where people get stuck and all 90 skills
Tasks
- Investigate pipeline conversion and challenge the sales forecast
- Diagnose CRM record and lifecycle integrity across marketing-to-sales handoffs, including consent
- Build an evidence-based plan for a strategic customer account
- Design sales territories, coverage and quotas from account potential and seller capacity
- Qualify and prioritise own opportunities from account, credit and engagement evidence and record them for forecasting
- Calculate and verify sales incentive payouts against plan rules and booked revenue
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
- Challenging probability-weighted sales forecasts
- Qualifying sales opportunities with traceable evidence
- Analysing customer lifetime value and unit economics
- Analysing queue behaviour, capacity and service constraints
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- Applying effective dating and retroactive change rules to records
- Assessing customer creditworthiness and setting limits
- Assessing data source credibility and fitness for a question
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assigning and operating data ownership and stewardship
- Balancing territories and quotas from account potential
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building driver-based financial forecasts
- Calculating and verifying KPIs against their specification
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing periods consistently
- Constructing and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- Converting demand into capacity requirements
- Designing and comparing decision scenarios
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing experiments
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing duplicate records
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating accruals, provisions and expected credit loss under accounting policy
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating ML generalisation and fitness
- Evaluating service performance and quality trade-offs
- Explaining how business events flow into accounting records
- 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 quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting financial statements and ratios for decisions
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Locating and acting on personal data for individual rights requests
- Managing master data identities and exceptions
- Mapping buying groups and account whitespace
- Mapping customer journeys and evidence gaps
- Mapping personal data processing and flows
- Measuring campaign outcomes with valid denominators
- Monitoring data quality and freshness in production
- Prioritising collection actions from aged debt
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading data tables: rows, fields, types and units
- Recalculating pay and incentive outcomes from plan rules
- Recognising and containing suspected personal data breaches
- Recognising personal data and privacy duties
- 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
- Resolving and escalating pay, incentive and payment exceptions within authority
- Resolving consent and suppression status across systems
- Sampling and reviewing a team's work against standards
- Setting and reviewing action thresholds for scores and model predictions
- 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
All tools
- Clari
- Salesforce
- Microsoft Power BI
- HubSpot
- Lime CRM
- Microsoft Dynamics 365 Sales and Customer Insights
- LinkedIn Sales Navigator
- Esri ArcGIS (ArcGIS Pro, ArcGIS Online)
- Microsoft Excel
- Workday
Paths to start
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
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
6tasks in this job
90skills it uses
10tools named
The skills this job leans on most
- 1Analysing staged funnels: conversion, time in stage and slippageNeeded in 4 of 6 tasks
- 2Challenging probability-weighted sales forecastsNeeded in 4 of 6 tasks
- 3Qualifying sales opportunities with traceable evidenceNeeded in 3 of 6 tasks
- 4Balancing territories and quotas from account potentialNeeded in 1 of 6 tasks
- 5Running sensitivity and break-even analysis on model assumptionsCore to this role
ToolsClariSalesforceMicrosoft Power BIHubSpotLime CRMMicrosoft Dynamics 365 Sales and Customer Insights+ 4 more
All 6 tasks, where people get stuck and all 90 skills
Tasks
- Investigate pipeline conversion and challenge the sales forecast
- Diagnose CRM record and lifecycle integrity across marketing-to-sales handoffs, including consent
- Build an evidence-based plan for a strategic customer account
- Design sales territories, coverage and quotas from account potential and seller capacity
- Qualify and prioritise own opportunities from account, credit and engagement evidence and record them for forecasting
- Calculate and verify sales incentive payouts against plan rules and booked revenue
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
- Qualifying sales opportunities with traceable evidence
- Analysing customer lifetime value and unit economics
- Analysing queue behaviour, capacity and service constraints
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- Applying effective dating and retroactive change rules to records
- Assessing customer creditworthiness and setting limits
- Assessing data source credibility and fitness for a question
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building driver-based financial forecasts
- Calculating and verifying KPIs against their specification
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing periods consistently
- Constructing and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- Converting demand into capacity requirements
- Designing and comparing decision scenarios
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing experiments
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing duplicate records
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating accruals, provisions and expected credit loss under accounting policy
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating ML generalisation and fitness
- Evaluating service performance and quality trade-offs
- Explaining how business events flow into accounting records
- 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 quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting financial statements and ratios for decisions
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Locating and acting on personal data for individual rights requests
- Managing master data identities and exceptions
- Mapping buying groups and account whitespace
- Mapping customer journeys and evidence gaps
- Mapping personal data processing and flows
- Measuring campaign outcomes with valid denominators
- Monitoring data quality and freshness in production
- Prioritising collection actions from aged debt
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading data tables: rows, fields, types and units
- Recalculating pay and incentive outcomes from plan rules
- Recognising and containing suspected personal data breaches
- Recognising personal data and privacy duties
- 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
- Resolving and escalating pay, incentive and payment exceptions within authority
- Resolving consent and suppression status across systems
- Sampling and reviewing a team's work against standards
- Setting and reviewing action thresholds for scores and model predictions
- 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
All tools
- Clari
- Salesforce
- Microsoft Power BI
- HubSpot
- Lime CRM
- Microsoft Dynamics 365 Sales and Customer Insights
- LinkedIn Sales Navigator
- Esri ArcGIS (ArcGIS Pro, ArcGIS Online)
- Microsoft Excel
- Workday
Paths to start
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
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 SupportService Operations Analytics and Capacity Planning
About the role
Accountable for a sales team's pipeline and forecast and the development of its sellers. Sets the qualification standards the team works to and checks deals against evidence in the CRM. Decides where the team spends effort using pipeline and conversion data.