Roles Marketing & Communications
Marketing Executive
Accountable for delivering campaigns, emails and content to an agreed plan and for the accuracy of their set-up in marketing tools.
3tasks in this job
19skills it uses
9tools named
The skills this job leans on most
- 1Controlling disclosure risk in shared data and outputsNeeded in 2 of 3 tasks
- 2Resolving consent and suppression status across systemsNeeded in 2 of 3 tasks
- 3Constructing and challenging customer segmentsNeeded in 1 of 3 tasks
ToolsMicrosoft ExcelHubSpotSalesforce Marketing CloudApsisMicrosoft Dynamics 365 Sales and Customer InsightsAdobe Marketo EngageVoyadoMailchimpMeta Ads Manager
All 3 tasks, where people get stuck and all 19 skills
Tasks
- Assess data quality and fitness for a stated use
- Plan and evaluate a consent-aware CRM lifecycle intervention
- Build and verify a permitted campaign audience
Where people get stuck
All skills
- Applying data classification and handling rules
- Assessing data source credibility and fitness for a question
- Assigning and operating data ownership and stewardship
- Designing and validating digital analytics event measurement
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Interpreting correlation without inferring cause
- Investigating procurement spend leakage and duplicate commitments
- Locating and acting on personal data for individual rights requests
- Mapping personal data processing and flows
- Profiling data
- Reading business metrics
- Recognising and containing suspected personal data breaches
- Repairing data defects reversibly under owner authority
- Standardising and cleaning data under agreed rules
- Writing data quality rules
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
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- Modules
- 5 open
- Practice data
- Raket Parts
5tasks in this job
100skills it uses
11tools named
The skills this job leans on most
- 1Controlling disclosure risk in shared data and outputsNeeded in 3 of 5 tasks
- 2Building cohort and retention analyses with correct denominatorsNeeded in 2 of 5 tasks
- 3Resolving consent and suppression status across systemsNeeded in 2 of 5 tasks
- 4Constructing and challenging customer segmentsNeeded in 1 of 5 tasks
ToolsMicrosoft ExcelHubSpotSalesforce Marketing CloudMailchimpGoogle AdsMeta Ads Manager+ 5 more
All 5 tasks, where people get stuck and all 100 skills
Tasks
- Assess data quality and fitness for a stated use
- Design an ethical decision-relevant experiment
- Deliver and sustain a trusted BI report or dashboard
- Plan and evaluate a consent-aware CRM lifecycle intervention
- Build and verify a permitted campaign audience
Where people get stuck
- Building cohort and retention analyses with correct denominators
- Resolving consent and suppression status across systems
All skills
- Building cohort and retention analyses with correct denominators
- Constructing and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- Resolving consent and suppression status across systems
- 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 data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- 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
- Comparing groups
- 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 automated pipelines and processes that fail safely and recover
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Designing semantic models for governed analytics
- 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
- 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 employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating learning transfer and performance outcomes
- Evaluating whether people understood, adopted and sustained a change
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- 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 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
- Locating and acting on personal data for individual rights requests
- Managing master data identities and exceptions
- 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
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising personal data and privacy duties
- Reconciling conflicting reports
- Reconstructing a record's state at a point in time from history and audit trails
- Repairing data defects reversibly under owner authority
- 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
- Statistical power and sample size
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- 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
All tools
- Microsoft Excel
- HubSpot
- Salesforce Marketing Cloud
- Mailchimp
- Google Ads
- Meta Ads Manager
- Looker Studio
- Apsis
- Microsoft Dynamics 365 Sales and Customer Insights
- Adobe Marketo Engage
- Voyado
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
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- Modules
- 5 open
- Practice data
- Raket Parts
6tasks in this job
113skills it uses
13tools named
The skills this job leans on most
- 1Controlling disclosure risk in shared data and outputsNeeded in 3 of 6 tasks
- 2Building cohort and retention analyses with correct denominatorsNeeded in 2 of 6 tasks
- 3Resolving consent and suppression status across systemsNeeded in 2 of 6 tasks
- 4Constructing and challenging customer segmentsNeeded in 1 of 6 tasks
ToolsGoogle Tag ManagerGoogle Analytics 4Microsoft ExcelHubSpotSalesforce Marketing CloudMailchimp+ 7 more
All 6 tasks, where people get stuck and all 113 skills
Tasks
- Define and repair a tracking plan and event taxonomy and restore reporting reliability
- Assess data quality and fitness for a stated use
- Design an ethical decision-relevant experiment
- Deliver and sustain a trusted BI report or dashboard
- Plan and evaluate a consent-aware CRM lifecycle intervention
- Build and verify a permitted campaign audience
Where people get stuck
- Building cohort and retention analyses with correct denominators
- Resolving consent and suppression status across systems
All skills
- Building cohort and retention analyses with correct denominators
- Constructing and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- Resolving consent and suppression status across systems
- 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 data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- 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
- Comparing groups
- Comparing periods consistently
- 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 automated pipelines and processes that fail safely and recover
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing experiments
- Designing implementable change plans
- Designing semantic models for governed analytics
- 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 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
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- 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 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
- Locating and acting on personal data for individual rights requests
- Managing master data identities and exceptions
- 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
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Recognising and containing suspected personal data breaches
- Recognising personal data and privacy duties
- Reconciling conflicting reports
- Reconstructing a record's state at a point in time from history and audit trails
- Repairing data defects reversibly under owner authority
- 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
- Statistical power and sample size
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- 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
- 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
- Google Tag Manager
- Google Analytics 4
- Microsoft Excel
- HubSpot
- Salesforce Marketing Cloud
- Mailchimp
- Google Ads
- Meta Ads Manager
- Looker Studio
- Apsis
- Microsoft Dynamics 365 Sales and Customer Insights
- Adobe Marketo Engage
- Voyado
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
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- Modules
- 5 open
- Practice data
- Raket Parts
About the role
Accountable for delivering campaigns, emails and content to an agreed plan and for the accuracy of their set-up in marketing tools. Builds audiences, schedules sends and reports campaign results without managing people. Data judgement sits in respecting consent, checking audience data before use and reading results honestly.
Also called Marketing Coordinator, Campaign Executive, CRM Executive, Digital Marketing Executive, Marketing Officer