Roles Marketing & Communications
Communications Lead
Accountable for planning communications and measuring whether they changed awareness and behaviour, including change communications.
2tasks in this job
16skills it uses
8tools named
The skills this job leans on most
- 1Communicating sensitive or unwelcome analytical findingsNeeded in 1 of 2 tasks
- 2Judging whether external evidence applies to the decision contextCore to this role
- 3Mapping stakeholders' decisions, constraints and needsCore to this role
ToolsMeltwaterGoogle Analytics 4Sprout SocialMicrosoft ExcelMicrosoft SharePointRetrieverQualtricsZendesk
All 2 tasks, where people get stuck and all 16 skills
Tasks
- Evaluate a communications or change campaign against its objectives
- Monitor coverage, assess reputational risk and decide the communications response during an emerging issue
Where people get stuck
- Evaluating whether people understood, adopted and sustained a change
- Measuring campaign outcomes with valid denominators
- Critically evaluating marketing attribution claims
All skills
- Applying research ethics and participant consent
- Choosing an analytical method to fit the question and data
- Controlling disclosure risk in shared data and outputs
- Designing and appraising samples
- Designing and quality-checking survey instruments
- Designing decision-oriented research with customers, users or employees
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- Evaluating employee experience evidence responsibly
- Evaluating learning transfer and performance outcomes
- Interpreting customer feedback and qualitative evidence
- Interpreting trends and seasonality
- Measuring campaign outcomes with valid denominators
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- 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
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
5tasks in this job
70skills it uses
10tools named
The skills this job leans on most
- 1Evaluating whether people understood, adopted and sustained a changeNeeded in 3 of 5 tasks
- 2Communicating sensitive or unwelcome analytical findingsNeeded in 2 of 5 tasks
- 3Measuring campaign outcomes with valid denominatorsNeeded in 2 of 5 tasks
- 4Judging whether external evidence applies to the decision contextCore to this role
- 5Mapping stakeholders' decisions, constraints and needsCore to this role
ToolsAsanaMicrosoft SharePointMeltwaterGoogle Analytics 4Sprout SocialMicrosoft Excel+ 4 more
All 5 tasks, where people get stuck and all 70 skills
Tasks
- Plan a data, AI or system-enabled change with measurable adoption outcomes
- Evaluate a communications or change campaign against its objectives
- Design, administer and quality-check a professional survey
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Monitor coverage, assess reputational risk and decide the communications response during an emerging issue
Where people get stuck
- Judging whether external evidence applies to the decision context
- Communicating sensitive or unwelcome analytical findings
- Critically evaluating marketing attribution claims
All skills
- Communicating sensitive or unwelcome analytical findings
- Evaluating whether people understood, adopted and sustained a change
- Measuring campaign outcomes with valid denominators
- Analysing event sequences, states and durations
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- 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
- 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 decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- 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 and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating learning transfer and performance outcomes
- 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
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting trends and seasonality
- 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
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Selecting charts for evidence and decisions
- 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 evidence-led findings and auditable recommendations with their limits
All tools
- Asana
- Microsoft SharePoint
- Meltwater
- Google Analytics 4
- Sprout Social
- Microsoft Excel
- Retriever
- Qualtrics
- Questback
- Zendesk
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- 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
5tasks in this job
76skills it uses
10tools named
The skills this job leans on most
- 1Evaluating whether people understood, adopted and sustained a changeNeeded in 3 of 5 tasks
- 2Communicating sensitive or unwelcome analytical findingsNeeded in 2 of 5 tasks
- 3Measuring campaign outcomes with valid denominatorsNeeded in 2 of 5 tasks
- 4Judging whether external evidence applies to the decision contextCore to this role
- 5Mapping stakeholders' decisions, constraints and needsCore to this role
ToolsAsanaMicrosoft SharePointMeltwaterGoogle Analytics 4Sprout SocialMicrosoft Excel+ 4 more
All 5 tasks, where people get stuck and all 76 skills
Tasks
- Plan a data, AI or system-enabled change with measurable adoption outcomes
- Evaluate a communications or change campaign against its objectives
- Design, administer and quality-check a professional survey
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Monitor coverage, assess reputational risk and decide the communications response during an emerging issue
Where people get stuck
- Judging whether external evidence applies to the decision context
- Communicating sensitive or unwelcome analytical findings
- Critically evaluating marketing attribution claims
All skills
- Communicating sensitive or unwelcome analytical findings
- Evaluating whether people understood, adopted and sustained a change
- Measuring campaign outcomes with valid denominators
- Analysing event sequences, states and durations
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing groups
- 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 decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- 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 and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating learning transfer and performance outcomes
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- 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
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting trends and seasonality
- 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
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- 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 evidence-led findings and auditable recommendations with their limits
All tools
- Asana
- Microsoft SharePoint
- Meltwater
- Google Analytics 4
- Sprout Social
- Microsoft Excel
- Retriever
- Qualtrics
- Questback
- Zendesk
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- 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 communications and measuring whether they changed awareness and behaviour, including change communications. Decides messages, channels and evaluation measures for campaigns without line management. Data and AI judgement sits in honest measurement and in reviewing AI-drafted content.