Head of Customer Success
Accountable for net revenue retention, churn and the customer success discipline that drives them.
2tasks in this job
32skills it uses
6tools named
The skills this job leans on most
- 1Building cohort and retention analyses with correct denominatorsNeeded in 1 of 2 tasks
- 2Designing and weighting rule-based scoring modelsNeeded in 1 of 2 tasks
- 3Analysing customer lifetime value and unit economicsCore to this role
ToolsGainsightPlanhatMicrosoft Power BISalesforceHubSpotMicrosoft PowerPointWorkdayMicrosoft Excel
All 2 tasks, where people get stuck and all 32 skills
Tasks
- Assess customer account health and churn risk, calibrate the health score and prioritise intervention
- Prepare a periodic performance or risk report for a management or governance committee
Where people get stuck
- Governing shared data definitions
- Calculating and verifying KPIs against their specification
- Specifying BI measure semantics: additivity, aggregation and time
All skills
- Building cohort and retention analyses with correct denominators
- Analysing staged funnels: conversion, time in stage and slippage
- Building decision-focused reports and dashboards
- Communicating sensitive or unwelcome analytical findings
- Constructing and challenging customer segments
- Constructing clear and honest charts
- Designing and validating digital analytics event measurement
- Diagnosing digital funnel conversion from event data
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Explaining technical work to accountable non-specialists
- 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
- Judging whether a change exceeds normal variation
- Making accountable decisions under uncertainty
- 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
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- 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
Coming soonProduct and Digital Behaviour Analytics
5tasks in this job
79skills it uses
8tools named
The skills this job leans on most
- 1Governing shared data definitionsNeeded in 1 of 5 tasks
- 2Calculating and verifying KPIs against their specificationNeeded in 1 of 5 tasks
- 3Specifying BI measure semantics: additivity, aggregation and timeNeeded in 1 of 5 tasks
- 4Building cohort and retention analyses with correct denominatorsNeeded in 1 of 5 tasks
- 5Analysing customer lifetime value and unit economicsCore to this role
ToolsGainsightPlanhatMicrosoft Power BISalesforceHubSpotMicrosoft PowerPoint+ 2 more
All 5 tasks, where people get stuck and all 79 skills
Tasks
- Assess customer account health and churn risk, calibrate the health score and prioritise intervention
- Define and govern a KPI metric contract
- Prepare a periodic performance or risk report for a management or governance committee
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Design sales territories, coverage and quotas from account potential and seller capacity
Where people get stuck
- Building cohort and retention analyses with correct denominators
- Designing and weighting rule-based scoring models
All skills
- Building cohort and retention analyses with correct denominators
- Calculating and verifying KPIs against their specification
- Governing shared data definitions
- Specifying BI measure semantics: additivity, aggregation and time
- Analysing error patterns and failure modes in models and AI systems
- Analysing queue behaviour, capacity and service constraints
- Analysing staged funnels: conversion, time in stage and slippage
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- 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 decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- 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
- 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 misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- 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 sustainability reporting standards and metric boundaries
- 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
- Making accountable decisions under uncertainty
- 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
- Monitoring model fitness and drift
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- 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
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying AI transparency and explanation duties to affected people
- 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
- 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
KPI, Variance and Performance Analysis
Explain performance against plan with variance decomposition and driver trees.
- 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 soonProduct and Digital Behaviour Analytics
6tasks in this job
101skills it uses
8tools named
The skills this job leans on most
- 1Governing shared data definitionsNeeded in 1 of 6 tasks
- 2Calculating and verifying KPIs against their specificationNeeded in 1 of 6 tasks
- 3Specifying BI measure semantics: additivity, aggregation and timeNeeded in 1 of 6 tasks
- 4Building cohort and retention analyses with correct denominatorsNeeded in 1 of 6 tasks
- 5Analysing customer lifetime value and unit economicsCore to this role
ToolsGainsightPlanhatMicrosoft Power BISalesforceHubSpotMicrosoft PowerPoint+ 2 more
All 6 tasks, where people get stuck and all 101 skills
Tasks
- Assess customer account health and churn risk, calibrate the health score and prioritise intervention
- Define and govern a KPI metric contract
- Investigate customer-experience evidence
- Prepare a periodic performance or risk report for a management or governance committee
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Design sales territories, coverage and quotas from account potential and seller capacity
Where people get stuck
- Building cohort and retention analyses with correct denominators
- Designing and weighting rule-based scoring models
All skills
- Building cohort and retention analyses with correct denominators
- Calculating and verifying KPIs against their specification
- Governing shared data definitions
- Specifying BI measure semantics: additivity, aggregation and time
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Analysing queue behaviour, capacity and service constraints
- Analysing staged funnels: conversion, time in stage and slippage
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- 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 decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Constructing and challenging customer segments
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Converting demand into capacity requirements
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing and appraising samples
- Designing and interpreting hypothesis tests
- Designing and quality-checking survey instruments
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- 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 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
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Facilitating group decisions using disputed evidence
- 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 customer feedback and qualitative evidence
- Interpreting sustainability reporting standards and metric boundaries
- 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
- Making accountable decisions under uncertainty
- 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
- Monitoring model fitness and drift
- 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 containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- 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
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying AI transparency and explanation duties to affected people
- 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
- 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
KPI, Variance and Performance Analysis
Explain performance against plan with variance decomposition and driver trees.
- 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 soonProduct and Digital Behaviour Analytics
About the role
Accountable for net revenue retention, churn and the customer success discipline that drives them. Decides health score design, coverage models and the measures customer success is run on and heads its managers and leads. Data judgement sits in whether retention measures and health scores reflect real risk, where a Head of Operations owns the wider operating model and fulfilment.
Also called VP Customer Success (when the role heads one discipline)