Revenue Operations Manager
Accountable for a revenue operations team and for the CRM and go-to-market systems, forecasting process and revenue reporting it runs.
6tasks in this job
93skills it uses
13tools named
The skills this job leans on most
- 1Validating records by matching, recomputation and end-to-end tracingNeeded in 4 of 6 tasks
- 2Diagnosing duplicate recordsNeeded in 3 of 6 tasks
- 3Reconciling finance subledgers and close recordsNeeded in 3 of 6 tasks
- 4Analysing staged funnels: conversion, time in stage and slippageNeeded in 2 of 6 tasks
- 5Reconstructing a record's state at a point in time from history and audit trailsNeeded in 2 of 6 tasks
ToolsClariMicrosoft Power BISalesforceSalesforce Data LoaderHubSpotLime CRMMicrosoft Dynamics 365 Sales and Customer InsightsMicrosoft ExcelOracle NetSuiteSAP S/4HANAJiraServiceNowWorkday
All 6 tasks, where people get stuck and all 93 skills
Tasks
- Investigate pipeline conversion and challenge the sales forecast
- Diagnose CRM record and lifecycle integrity across marketing-to-sales handoffs, including consent
- Reconcile booked, fulfilled, billed and recognised revenue across CRM, ERP and finance records
- Monitor and correct data quality in a domain or system of record and drive remediation at source
- Configure, test and release a change to a business system of record under change control
- Calculate and verify sales incentive payouts against plan rules and booked revenue
Where people get stuck
- Challenging probability-weighted sales forecasts
- Resolving consent and suppression status across systems
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- Diagnosing duplicate records
- Reconciling finance subledgers and close records
- Reconstructing a record's state at a point in time from history and audit trails
- Validating records by matching, recomputation and end-to-end tracing
- Analysing customer lifetime value and unit economics
- Applying data classification and handling rules
- Applying effective dating and retroactive change rules to records
- 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
- Challenging probability-weighted sales forecasts
- 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
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing efficient and reproducible investigations
- Designing experiments
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing data quality exceptions
- 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
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating ML generalisation and fitness
- Explaining how business events flow into accounting records
- Facilitating evidence-led retrospectives and corrective actions
- Forecasting business trends and turning points for planning
- Forecasting operational demand at interval and site grain
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying keys and testing referential integrity
- 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 anomalies
- 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
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring data quality and freshness in production
- Preparing change records with impact assessment and rollback
- Profiling data
- Qualifying sales opportunities with traceable evidence
- 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
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Resolving and escalating pay, incentive and payment exceptions within authority
- Resolving consent and suppression status across systems
- Setting and reviewing action thresholds for scores and model predictions
- 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 changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing verifiable requirements and acceptance criteria
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- 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
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
12tasks in this job
134skills it uses
15tools named
The skills this job leans on most
- 1Validating records by matching, recomputation and end-to-end tracingNeeded in 5 of 12 tasks
- 2Diagnosing duplicate recordsNeeded in 4 of 12 tasks
- 3Reconciling finance subledgers and close recordsNeeded in 3 of 12 tasks
- 4Analysing staged funnels: conversion, time in stage and slippageNeeded in 2 of 12 tasks
- 5Reconstructing a record's state at a point in time from history and audit trailsNeeded in 2 of 12 tasks
ToolsClariMicrosoft Power BISalesforceSalesforce Data LoaderHubSpotLime CRM+ 9 more
All 12 tasks, where people get stuck and all 134 skills
Tasks
- Investigate pipeline conversion and challenge the sales forecast
- Diagnose CRM record and lifecycle integrity across marketing-to-sales handoffs, including consent
- Define and govern a KPI metric contract
- Reconcile booked, fulfilled, billed and recognised revenue across CRM, ERP and finance records
- Produce an accountable operating budget
- Deliver and sustain a trusted BI report or dashboard
- Automate a recurring reporting or data task with controls and a named owner
- Monitor and correct data quality in a domain or system of record and drive remediation at source
- Define master data match and survivorship rules and recommend golden-record changes for the data owner's approval
- Assess forecast accuracy and bias and recommend which forecast methods to rely on
- Configure, test and release a change to a business system of record under change control
- Calculate and verify sales incentive payouts against plan rules and booked revenue
Where people get stuck
- Governing shared data definitions
- Calculating and verifying KPIs against their specification
- Forecasting business trends and turning points for planning
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- Diagnosing duplicate records
- Reconciling finance subledgers and close records
- Reconstructing a record's state at a point in time from history and audit trails
- Validating records by matching, recomputation and end-to-end tracing
- 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 pay equity with raw and adjusted pay gaps
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- Applying effective dating and retroactive change rules to records
- Assessing change risk, collisions and go or no-go
- Assessing data source credibility and fitness for a question
- Assessing data-service continuity and recoverability
- 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 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 an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing groups
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Constructing and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- Coordinating accountable handoffs between teams
- Decomposing one's own tasks for AI assistance
- Defining the analysis population: filters, nulls, dates and time zones
- 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 automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing experiments
- Designing semantic models for governed analytics
- Designing testable data controls
- 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 and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating ML generalisation and fitness
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Facilitating evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Forecasting business trends and turning points for planning
- Forecasting operational demand at interval and site grain
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- 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 policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- 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
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Preparing change records with impact assessment and rollback
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Qualifying sales opportunities with traceable evidence
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- 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
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Resolving and escalating pay, incentive and payment exceptions within authority
- Resolving consent and suppression status across systems
- Running incident command: severity, timeline and status updates
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Setting and reviewing action thresholds for scores and model predictions
- 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 changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- Clari
- Microsoft Power BI
- Salesforce
- Salesforce Data Loader
- HubSpot
- Lime CRM
- Microsoft Dynamics 365 Sales and Customer Insights
- Microsoft Excel
- Oracle NetSuite
- SAP S/4HANA
- Tableau
- Looker
- Jira
- ServiceNow
- Workday
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
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
12tasks in this job
138skills it uses
15tools named
The skills this job leans on most
- 1Governing shared data definitionsNeeded in 6 of 12 tasks
- 2Validating records by matching, recomputation and end-to-end tracingNeeded in 5 of 12 tasks
- 3Calculating and verifying KPIs against their specificationNeeded in 5 of 12 tasks
- 4Diagnosing duplicate recordsNeeded in 4 of 12 tasks
- 5Forecasting business trends and turning points for planningNeeded in 2 of 12 tasks
ToolsClariMicrosoft Power BISalesforceSalesforce Data LoaderHubSpotLime CRM+ 9 more
All 12 tasks, where people get stuck and all 138 skills
Tasks
- Investigate pipeline conversion and challenge the sales forecast
- Diagnose CRM record and lifecycle integrity across marketing-to-sales handoffs, including consent
- Define and govern a KPI metric contract
- Reconcile booked, fulfilled, billed and recognised revenue across CRM, ERP and finance records
- Deliver and sustain a trusted BI report or dashboard
- Automate a recurring reporting or data task with controls and a named owner
- Monitor and correct data quality in a domain or system of record and drive remediation at source
- Define master data match and survivorship rules and recommend golden-record changes for the data owner's approval
- Assess forecast accuracy and bias and recommend which forecast methods to rely on
- Configure, test and release a change to a business system of record under change control
- Analyse a current business process from event and operational data and define the future-state process
- Calculate and verify sales incentive payouts against plan rules and booked revenue
Where people get stuck
- Challenging probability-weighted sales forecasts
- Resolving consent and suppression status across systems
All skills
- Calculating and verifying KPIs against their specification
- Diagnosing duplicate records
- Forecasting business trends and turning points for planning
- Governing shared data definitions
- Validating records by matching, recomputation and end-to-end tracing
- 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 pay equity with raw and adjusted pay gaps
- Analysing staged funnels: conversion, time in stage and slippage
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- Applying effective dating and retroactive change rules to records
- Assessing change risk, collisions and go or no-go
- Assessing data source credibility and fitness for a question
- Assessing data-service continuity and recoverability
- 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 decision-focused reports and dashboards
- Building driver-based financial forecasts
- Calculating and interpreting percentages, rates and ratios
- Challenging probability-weighted sales forecasts
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing groups
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Constructing and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- Coordinating accountable handoffs between teams
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Defining the analysis population: filters, nulls, dates and time zones
- 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 automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing experiments
- Designing semantic models for governed analytics
- Designing testable data controls
- 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
- Discovering process variants from event logs
- Eliciting needs through interviews, workshops and observation
- 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 and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating ML generalisation and fitness
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Facilitating evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Forecasting operational demand at interval and site grain
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- 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 policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- 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
- Maintaining requirements traceability and change control
- Managing master data identities and exceptions
- Mapping buying groups and account whitespace
- Mapping customer journeys and evidence gaps
- Mapping personal data processing and flows
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Preparing change records with impact assessment and rollback
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Qualifying sales opportunities with traceable evidence
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- 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
- 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
- Resolving and escalating pay, incentive and payment exceptions within authority
- Resolving consent and suppression status across systems
- Running incident command: severity, timeline and status updates
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Setting and reviewing action thresholds for scores and model predictions
- 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 changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- Clari
- Microsoft Power BI
- Salesforce
- Salesforce Data Loader
- HubSpot
- Lime CRM
- Microsoft Dynamics 365 Sales and Customer Insights
- Microsoft Excel
- Oracle NetSuite
- SAP S/4HANA
- Tableau
- Looker
- Jira
- ServiceNow
- Workday
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
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
About the role
Accountable for a revenue operations team and for the CRM and go-to-market systems, forecasting process and revenue reporting it runs. Allocates the team's work and tooling and settles disputes on pipeline and bookings data with Sales and Finance. Manages the people who run the process, where a Revenue Operations Lead sets its rules without line management.
Also called Sales Operations Manager