Roles Marketing & Communications
Marketing Manager
Accountable for a marketing team and its budget across several channels or campaigns.
5tasks in this job
107skills it uses
8tools named
The skills this job leans on most
- 1Measuring campaign outcomes with valid denominatorsNeeded in 4 of 5 tasks
- 2Critically evaluating marketing attribution claimsNeeded in 2 of 5 tasks
- 3Allocating spend across channels by marginal returnNeeded in 2 of 5 tasks
ToolsGoogle Analytics 4Microsoft ExcelMicrosoft Power BIHubSpotSalesforce Marketing CloudMicrosoft Dynamics 365 Sales and Customer InsightsGoogle AdsMeta Ads Manager
All 5 tasks, where people get stuck and all 107 skills
Tasks
- Audit and set campaign attribution and measurement rules
- Produce an accountable operating budget
- Plan and evaluate a consent-aware CRM lifecycle intervention
- Evaluate statistical and causal evidence for an intervention's impact
- Plan and reallocate marketing spend across channels on measured return
Where people get stuck
All skills
- Allocating spend across channels by marginal return
- Critically evaluating marketing attribution claims
- Measuring campaign outcomes with valid denominators
- 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 research ethics and participant consent
- 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
- 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
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Constructing a controlled cash-flow plan
- Constructing and challenging customer segments
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- 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 auditable decision models
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- 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
- 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
- Evaluating and improving chart accessibility
- Evaluating experimental results and integrity
- Evaluating learning transfer and performance outcomes
- Evaluating whether people understood, adopted and sustained a change
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Forecasting business trends and turning points for planning
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether uncertainty changes the decision
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping personal data processing and flows
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- 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 misleading numerical claims
- Recognising personal data and privacy duties
- Reconstructing a record's state at a point in time from history and audit trails
- Resolving consent and suppression status across systems
- Selecting charts for evidence and decisions
- Setting and reviewing action thresholds for scores and model predictions
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Training reproducible machine learning models
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
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
Skill Path
Experimentation and Causal Impact Evaluation
Answer "did it work" with randomised experiments where possible and quasi-experimental methods where not.
- Modules
- 5 open
- Practice data
- Raket Parts
7tasks in this job
120skills it uses
8tools named
The skills this job leans on most
- 1Measuring campaign outcomes with valid denominatorsNeeded in 4 of 7 tasks
- 2Critically evaluating marketing attribution claimsNeeded in 2 of 7 tasks
- 3Allocating spend across channels by marginal returnNeeded in 2 of 7 tasks
ToolsMicrosoft ExcelMicrosoft Power BIGoogle Analytics 4Google AdsHubSpotSalesforce Marketing Cloud+ 2 more
All 7 tasks, where people get stuck and all 120 skills
Tasks
- Investigate variance of actuals against budget or forecast
- Audit and set campaign attribution and measurement rules
- Produce an accountable operating budget
- Design an ethical decision-relevant experiment
- Plan and evaluate a consent-aware CRM lifecycle intervention
- Evaluate statistical and causal evidence for an intervention's impact
- Plan and reallocate marketing spend across channels on measured return
Where people get stuck
All skills
- Allocating spend across channels by marginal return
- Critically evaluating marketing attribution claims
- Measuring campaign outcomes with valid denominators
- 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 research ethics and participant consent
- 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
- 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
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Constructing a controlled cash-flow plan
- Constructing and challenging customer segments
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- 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 appraising samples
- Designing and comparing decision scenarios
- 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 auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- 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
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating learning transfer and performance outcomes
- Evaluating product outcomes and metric integrity
- Evaluating whether people understood, adopted and sustained a change
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Forecasting business trends and turning points for planning
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether uncertainty changes the decision
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- 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 misleading numerical claims
- Recognising personal data and privacy duties
- Reconstructing a record's state at a point in time from history and audit trails
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Setting and reviewing action thresholds for scores and model predictions
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Microsoft Excel
- Microsoft Power BI
- Google Analytics 4
- Google Ads
- HubSpot
- Salesforce Marketing Cloud
- Microsoft Dynamics 365 Sales and Customer Insights
- Meta Ads Manager
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
7tasks in this job
121skills it uses
8tools named
The skills this job leans on most
- 1Measuring campaign outcomes with valid denominatorsNeeded in 4 of 7 tasks
- 2Critically evaluating marketing attribution claimsNeeded in 2 of 7 tasks
- 3Allocating spend across channels by marginal returnNeeded in 2 of 7 tasks
ToolsMicrosoft ExcelMicrosoft Power BIGoogle Analytics 4Google AdsHubSpotSalesforce Marketing Cloud+ 2 more
All 7 tasks, where people get stuck and all 121 skills
Tasks
- Investigate variance of actuals against budget or forecast
- Audit and set campaign attribution and measurement rules
- Produce an accountable operating budget
- Design an ethical decision-relevant experiment
- Plan and evaluate a consent-aware CRM lifecycle intervention
- Evaluate statistical and causal evidence for an intervention's impact
- Plan and reallocate marketing spend across channels on measured return
Where people get stuck
All skills
- Allocating spend across channels by marginal return
- Critically evaluating marketing attribution claims
- Measuring campaign outcomes with valid denominators
- 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 research ethics and participant consent
- 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
- 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
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Constructing a controlled cash-flow plan
- Constructing and challenging customer segments
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- 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 appraising samples
- Designing and comparing decision scenarios
- 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 auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- 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
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating learning transfer and performance outcomes
- Evaluating product outcomes and metric integrity
- Evaluating whether people understood, adopted and sustained a change
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Forecasting business trends and turning points for planning
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether uncertainty changes the decision
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- 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 misleading numerical claims
- Recognising personal data and privacy duties
- Reconstructing a record's state at a point in time from history and audit trails
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- 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
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Microsoft Excel
- Microsoft Power BI
- Google Analytics 4
- Google Ads
- HubSpot
- Salesforce Marketing Cloud
- Microsoft Dynamics 365 Sales and Customer Insights
- Meta Ads Manager
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 a marketing team and its budget across several channels or campaigns. Allocates others' work and decides spend shifts and campaign priorities within the team budget. Data judgement sits in reading attribution and test evidence before moving money between channels.
Also called Digital Marketing Manager, Performance Marketing Manager, Campaign Manager