Sales Operations Analyst
Accountable for pipeline and conversion analysis and for the integrity of CRM lifecycle data used by the sales team.
5tasks in this job
61skills it uses
8tools named
The skills this job leans on most
- 1Analysing staged funnels: conversion, time in stage and slippageNeeded in 2 of 5 tasks
- 2Challenging probability-weighted sales forecastsNeeded in 2 of 5 tasks
- 3Qualifying sales opportunities with traceable evidenceNeeded in 1 of 5 tasks
ToolsSalesforceMicrosoft ExcelMicrosoft Power BIHubSpotLime CRMMicrosoft Dynamics 365 Sales and Customer InsightsTableauQlik Sense
All 5 tasks, where people get stuck and all 61 skills
Tasks
- Investigate pipeline conversion and challenge the sales forecast
- Diagnose CRM record and lifecycle integrity across marketing-to-sales handoffs, including consent
- Answer a commissioned business question with reproducible query-based analysis
- Deliver and sustain a trusted BI report or dashboard
- Calculate and verify sales incentive payouts against plan rules and booked revenue
Where people get stuck
- Establishing and declaring data grain
- Combining tables safely: cardinality, fan-out and conservation of totals
- Diagnosing duplicate records
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- Challenging probability-weighted sales forecasts
- Analysing customer lifetime value and unit economics
- Analysing event sequences, states and durations
- Applying data classification and handling rules
- Applying effective dating and retroactive change rules to records
- Assessing data source credibility and fitness for a question
- Building cohort and retention analyses with correct denominators
- Building driver-based financial forecasts
- Calculating and interpreting percentages, rates and ratios
- Calculating and verifying KPIs against their specification
- 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 and challenging customer segments
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing efficient and reproducible investigations
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing duplicate records
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing incorrect query and calculation results
- 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
- Explaining how business events flow into accounting records
- Exploring data to find patterns that survive validation
- Forecasting business trends and turning points for planning
- 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
- Mapping buying groups and account whitespace
- Mapping customer journeys and evidence gaps
- Measuring campaign outcomes with valid denominators
- Profiling data
- 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
- 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
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- 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
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
8tasks in this job
106skills it uses
13tools named
The skills this job leans on most
- 1Establishing and declaring data grainNeeded in 5 of 8 tasks
- 2Combining tables safely: cardinality, fan-out and conservation of totalsNeeded in 4 of 8 tasks
- 3Diagnosing duplicate recordsNeeded in 3 of 8 tasks
- 4Defining the analysis population: filters, nulls, dates and time zonesNeeded in 1 of 8 tasks
- 5Diagnosing incorrect query and calculation resultsNeeded in 1 of 8 tasks
ToolsSalesforceSalesforce Data LoaderMicrosoft ExcelMicrosoft Power BIHubSpotLime CRM+ 7 more
All 8 tasks, where people get stuck and all 106 skills
Tasks
- Investigate pipeline conversion and challenge the sales forecast
- Diagnose CRM record and lifecycle integrity across marketing-to-sales handoffs, including consent
- Investigate inconsistent BI reporting
- Answer a commissioned business question with reproducible query-based analysis
- Reconcile booked, fulfilled, billed and recognised revenue across CRM, ERP and finance records
- Deliver and sustain a trusted BI report or dashboard
- Monitor and correct data quality in a domain or system of record and drive remediation at source
- Calculate and verify sales incentive payouts against plan rules and booked revenue
Where people get stuck
- Verifying reports and dashboards before and after release
- Building decision-focused reports and dashboards
- Specifying BI measure semantics: additivity, aggregation and time
All skills
- Combining tables safely: cardinality, fan-out and conservation of totals
- Defining the analysis population: filters, nulls, dates and time zones
- Diagnosing duplicate records
- Diagnosing incorrect query and calculation results
- Establishing and declaring data grain
- Analysing customer lifetime value and unit economics
- Analysing event sequences, states and durations
- Analysing staged funnels: conversion, time in stage and slippage
- 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 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
- Communicating quantified uncertainty
- Comparing groups
- Comparing periods consistently
- Constructing and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- 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 operational handoffs and flow
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating ML generalisation and fitness
- Explaining how business events flow into accounting records
- 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
- Governing shared data definitions
- Identifying keys and testing referential integrity
- 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 analysis-ready datasets reproducibly
- Profiling data
- Qualifying sales opportunities with traceable evidence
- 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
- 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
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Salesforce
- Salesforce Data Loader
- Microsoft Excel
- Microsoft Power BI
- HubSpot
- Lime CRM
- Microsoft Dynamics 365 Sales and Customer Insights
- Tableau
- Qlik Sense
- SQL (query language)
- Oracle NetSuite
- SAP S/4HANA
- ZoomInfo
Paths to start
Tool Path
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
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
Exploratory Data Analysis and Pattern Discovery
Describe a distribution honestly and decide which unusual records need checking before anyone acts.
- Modules
- 5 open
- Practice data
- Raket Parts
11tasks in this job
117skills it uses
15tools named
The skills this job leans on most
- 1Verifying reports and dashboards before and after releaseNeeded in 11 of 11 tasks
- 2Diagnosing duplicate recordsNeeded in 4 of 11 tasks
- 3Specifying BI measure semantics: additivity, aggregation and timeNeeded in 4 of 11 tasks
- 4Building decision-focused reports and dashboardsNeeded in 2 of 11 tasks
- 5Analysing staged funnels: conversion, time in stage and slippageNeeded in 2 of 11 tasks
ToolsSalesforceSalesforce Data LoaderMicrosoft ExcelMicrosoft Power BIHubSpotPython+ 9 more
All 11 tasks, where people get stuck and all 117 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
- Investigate inconsistent BI reporting
- Answer a commissioned business question with reproducible query-based analysis
- 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
- 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
- Building decision-focused reports and dashboards
- Diagnosing duplicate records
- Specifying BI measure semantics: additivity, aggregation and time
- Verifying reports and dashboards before and after release
- Analysing customer lifetime value and unit economics
- Analysing event sequences, states and durations
- 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 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 and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- 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 interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- 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
- 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 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
- 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
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Profiling data
- Qualifying sales opportunities with traceable evidence
- Reading and tracing unfamiliar code
- 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
- 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 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
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing maintainable code for analytical and data work
All tools
- Salesforce
- Salesforce Data Loader
- Microsoft Excel
- Microsoft Power BI
- HubSpot
- Python
- Lime CRM
- Microsoft Dynamics 365 Sales and Customer Insights
- SQL (query language)
- Tableau
- Qlik Sense
- Oracle NetSuite
- SAP S/4HANA
- Power Automate
- ZoomInfo
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
Exploratory Data Analysis and Pattern Discovery
Describe a distribution honestly and decide which unusual records need checking before anyone acts.
- 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
About the role
Accountable for pipeline and conversion analysis and for the integrity of CRM lifecycle data used by the sales team. Investigates why deals stall or convert and reports what the data does and does not show. The Revenue Operations Lead owns the revenue process rules while the Sales Operations Analyst investigates and reports on them.
Also called Revenue Operations Analyst, Sales Analyst