Key Account Manager
Accountable for the largest or most complex customer accounts and the enterprise deals within them.
5tasks in this job
15skills it uses
9tools named
The skills this job leans on most
- 1Analysing customer lifetime value and unit economicsNeeded in 3 of 5 tasks
- 2Mapping buying groups and account whitespaceNeeded in 3 of 5 tasks
- 3Constructing and challenging customer segmentsNeeded in 2 of 5 tasks
ToolsSalesforceMicrosoft ExcelQlik SenseMicrosoft Power BILime CRMMicrosoft Dynamics 365 Sales and Customer InsightsLinkedIn Sales NavigatorMicrosoft PowerPointConga CPQ (Apttus)
All 5 tasks, where people get stuck and all 15 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
- Set discount guardrails and review price realisation and exceptions
- Qualify and prioritise own opportunities from account, credit and engagement evidence and record them for forecasting
Where people get stuck
- Analysing staged funnels: conversion, time in stage and slippage
- Qualifying sales opportunities with traceable evidence
- Challenging probability-weighted sales forecasts
All skills
- Analysing customer lifetime value and unit economics
- Mapping buying groups and account whitespace
- Analysing staged funnels: conversion, time in stage and slippage
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- Assessing customer creditworthiness and setting limits
- Challenging probability-weighted sales forecasts
- Escalating unresolved risks with evidence and clear ownership
- Interpreting correlation without inferring cause
- Interpreting financial statements and ratios for decisions
- Interpreting trends and seasonality
- Prioritising collection actions from aged debt
- Reading business metrics
- Reconstructing a record's state at a point in time from history and audit trails
- Testing competing explanations with discriminating evidence
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
5tasks in this job
71skills it uses
10tools named
The skills this job leans on most
- 1Analysing staged funnels: conversion, time in stage and slippageNeeded in 4 of 5 tasks
- 2Challenging probability-weighted sales forecastsNeeded in 4 of 5 tasks
- 3Analysing customer lifetime value and unit economicsNeeded in 3 of 5 tasks
- 4Qualifying sales opportunities with traceable evidenceNeeded in 3 of 5 tasks
- 5Constructing and challenging customer segmentsNeeded in 2 of 5 tasks
ToolsSalesforceMicrosoft ExcelQlik SenseMicrosoft Power BILime CRMMicrosoft Dynamics 365 Sales and Customer Insights+ 4 more
All 5 tasks, where people get stuck and all 71 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
- Set discount guardrails and review price realisation and exceptions
- Qualify and prioritise own opportunities from account, credit and engagement evidence and record them for forecasting
Where people get stuck
All skills
- Analysing customer lifetime value and unit economics
- Analysing staged funnels: conversion, time in stage and slippage
- Challenging probability-weighted sales forecasts
- Constructing and challenging customer segments
- Qualifying sales opportunities with traceable evidence
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- 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
- Communicating quantified uncertainty
- Controlling disclosure risk in shared data and outputs
- 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
- Disclosing AI contribution to professional work
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- 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
- 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
- Making accountable decisions under uncertainty
- 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
- Recognising and containing suspected personal data breaches
- Recognising personal data and privacy duties
- Reconstructing a record's state at a point in time from history and audit trails
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Setting and reviewing action thresholds for scores and model predictions
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
All tools
- Salesforce
- Microsoft Excel
- Qlik Sense
- Microsoft Power BI
- Lime CRM
- Microsoft Dynamics 365 Sales and Customer Insights
- LinkedIn Sales Navigator
- NielsenIQ (incl. GfK)
- Microsoft PowerPoint
- Conga CPQ (Apttus)
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
5tasks in this job
72skills it uses
10tools named
The skills this job leans on most
- 1Analysing staged funnels: conversion, time in stage and slippageNeeded in 4 of 5 tasks
- 2Challenging probability-weighted sales forecastsNeeded in 4 of 5 tasks
- 3Analysing customer lifetime value and unit economicsNeeded in 3 of 5 tasks
- 4Qualifying sales opportunities with traceable evidenceNeeded in 3 of 5 tasks
- 5Constructing and challenging customer segmentsNeeded in 2 of 5 tasks
ToolsSalesforceMicrosoft ExcelQlik SenseMicrosoft Power BILime CRMMicrosoft Dynamics 365 Sales and Customer Insights+ 4 more
All 5 tasks, where people get stuck and all 72 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
- Set discount guardrails and review price realisation and exceptions
- Qualify and prioritise own opportunities from account, credit and engagement evidence and record them for forecasting
Where people get stuck
All skills
- Analysing customer lifetime value and unit economics
- Analysing staged funnels: conversion, time in stage and slippage
- Challenging probability-weighted sales forecasts
- Constructing and challenging customer segments
- Qualifying sales opportunities with traceable evidence
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- 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
- Communicating quantified uncertainty
- Controlling disclosure risk in shared data and outputs
- 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
- Disclosing AI contribution to professional work
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- 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
- 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
- Making accountable decisions under uncertainty
- 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
- Recognising and containing suspected personal data breaches
- Recognising personal data and privacy duties
- Reconstructing a record's state at a point in time from history and audit trails
- Repairing data defects reversibly under owner 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
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
All tools
- Salesforce
- Microsoft Excel
- Qlik Sense
- Microsoft Power BI
- Lime CRM
- Microsoft Dynamics 365 Sales and Customer Insights
- LinkedIn Sales Navigator
- NielsenIQ (incl. GfK)
- Microsoft PowerPoint
- Conga CPQ (Apttus)
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
About the role
Accountable for the largest or most complex customer accounts and the enterprise deals within them. Shapes deal strategy across large buying groups and leads the account team across functions. Leads the work without managing a team and keeps account plans and forecast evidence that others can check.
Also called Enterprise Account Executive, Sales Manager (when the role has no team), National Account Manager