Roles Marketing & Communications
Marketing Lead
Accountable for planning, budgeting and evaluating campaigns and channels against measured outcomes.
1task in this job
0skills it uses
4tools named
The skills this job leans on most
- 1Measuring campaign outcomes with valid denominatorsNeeded in 1 of 1 tasks
- 2Critically evaluating marketing attribution claimsNeeded in 1 of 1 tasks
- 3Allocating spend across channels by marginal returnNeeded in 1 of 1 tasks
ToolsMicrosoft ExcelGoogle AdsMeta Ads ManagerLinkedIn Campaign Manager
All 1 tasks, where people get stuck and all 0 skills
Tasks
Where people get stuck
All skills
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
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
104skills it uses
6tools 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 ExcelGoogle Analytics 4Meta Ads ManagerGoogle AdsHubSpotLinkedIn Campaign Manager
All 7 tasks, where people get stuck and all 104 skills
Tasks
- Investigate variance of actuals against budget or forecast
- Audit and set campaign attribution and measurement rules
- 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
- Build and verify a permitted campaign audience
Where people get stuck
- Evaluating experimental results and integrity
- Estimating and challenging causal effects
- Weighing conflicting evidence by design strength
All skills
- Allocating spend across channels by marginal return
- Critically evaluating marketing attribution claims
- Measuring campaign outcomes with valid denominators
- 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 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
- 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
- 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 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 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 experiments
- Designing implementable change plans
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing duplicate records
- 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 employee experience evidence responsibly
- 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
- Formulating answerable data questions
- Governing shared data definitions
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- 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
- Managing master data identities and exceptions
- Mapping customer journeys and evidence gaps
- Mapping personal data processing and flows
- Monitoring data quality and freshness in production
- 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
- Repairing data defects reversibly under owner authority
- 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
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Training reproducible machine learning models
- Verifying benefits realisation and attribution
- 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
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
110skills it uses
6tools named
The skills this job leans on most
- 1Estimating and challenging causal effectsNeeded in 4 of 7 tasks
- 2Measuring campaign outcomes with valid denominatorsNeeded in 4 of 7 tasks
- 3Evaluating experimental results and integrityNeeded in 3 of 7 tasks
- 4Critically evaluating marketing attribution claimsNeeded in 2 of 7 tasks
- 5Allocating spend across channels by marginal returnNeeded in 2 of 7 tasks
ToolsMicrosoft ExcelGoogle Analytics 4Meta Ads ManagerGoogle AdsHubSpotLinkedIn Campaign Manager
All 7 tasks, where people get stuck and all 110 skills
Tasks
- Investigate variance of actuals against budget or forecast
- Audit and set campaign attribution and measurement rules
- 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
- Build and verify a permitted campaign audience
Where people get stuck
- Evaluating experimental results and integrity
- Critically evaluating marketing attribution claims
- Weighing conflicting evidence by design strength
All skills
- Allocating spend across channels by marginal return
- Critically evaluating marketing attribution claims
- Estimating and challenging causal effects
- Evaluating experimental results and integrity
- Measuring campaign outcomes with valid denominators
- 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 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
- 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
- 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 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 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 experiments
- Designing implementable change plans
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing duplicate records
- 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 quantities and differences with intervals
- Evaluating ML generalisation and fitness
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- 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
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- 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
- Judging whether uncertainty changes the decision
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- Mapping customer journeys and evidence gaps
- Mapping personal data processing and flows
- Monitoring data quality and freshness in production
- 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
- Repairing data defects reversibly under owner 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
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Training reproducible machine learning models
- 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
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 planning, budgeting and evaluating campaigns and channels against measured outcomes. Decides channel mix, spend and targeting within an agreed budget without line management. Data judgement sits in reading attribution and test results honestly when allocating spend.
Also called Campaign Lead, Digital Marketing Lead, Performance Marketing Lead, Brand Manager, Product Marketing Manager