Roles Marketing & Communications
Insight Manager
Accountable for an insight or research team and for the research programme it delivers.
6tasks in this job
96skills it uses
8tools named
The skills this job leans on most
- 1Interpreting customer feedback and qualitative evidenceNeeded in 3 of 6 tasks
- 2Mapping customer journeys and evidence gapsCore to this role
ToolsQualtricsIBM SPSS StatisticsMedalliaMicrosoft PowerPointNielsenIQ (incl. GfK)Microsoft ExcelSalesforceMicrosoft Power BI
All 6 tasks, where people get stuck and all 96 skills
Tasks
- Design a user research investigation
- Investigate customer-experience evidence
- Produce an accountable operating budget
- Design, administer and quality-check a professional survey
- Conduct or review a data protection impact assessment for a data, analytics or AI processing
- Set and maintain working standards for a team's analytical, reporting and modelling work products
Where people get stuck
- Designing and quality-checking survey instruments
- Designing and appraising samples
- Designing decision-oriented research with customers, users or employees
All skills
- Interpreting customer feedback and qualitative evidence
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- 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
- Assigning and operating data ownership and stewardship
- 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
- 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
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- 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 appraising samples
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and quality-checking survey instruments
- Designing auditable decision models
- Designing decision rights and dispute routes for data and AI work
- 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
- 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 quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating whether people understood, adopted and sustained a change
- Explaining control types, dependencies and compensating controls
- 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
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- 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 and how to use AI for a task
- Judging whether uncertainty changes the decision
- Mapping stakeholders' decisions, constraints and needs
- 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 cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- 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 investment business cases under uncertainty
- Training reproducible machine learning models
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing evidence-led findings and auditable recommendations with their limits
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
Paths to start
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
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonProduct and Digital Behaviour Analytics
8tasks in this job
116skills it uses
10tools named
The skills this job leans on most
- 1Designing and quality-checking survey instrumentsNeeded in 3 of 8 tasks
- 2Designing and appraising samplesNeeded in 3 of 8 tasks
- 3Designing decision-oriented research with customers, users or employeesNeeded in 3 of 8 tasks
- 4Interpreting customer feedback and qualitative evidenceNeeded in 3 of 8 tasks
- 5Mapping customer journeys and evidence gapsCore to this role
ToolsQualtricsIBM SPSS StatisticsMedalliaMicrosoft PowerPointNielsenIQ (incl. GfK)Amplitude+ 4 more
All 8 tasks, where people get stuck and all 116 skills
Tasks
- Design a user research investigation
- Investigate customer-experience evidence
- Review supplier performance against contract obligations and recommend renewal or remedy
- Produce an accountable operating budget
- Design, administer and quality-check a professional survey
- Conduct or review a data protection impact assessment for a data, analytics or AI processing
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Set and maintain working standards for a team's analytical, reporting and modelling work products
Where people get stuck
All skills
- Designing and appraising samples
- Designing and quality-checking survey instruments
- Designing decision-oriented research with customers, users or employees
- Interpreting customer feedback and qualitative evidence
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- 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 data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- 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
- 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
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- 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 comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing auditable decision models
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing experiments
- Designing implementable change plans
- Designing testable data controls
- 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 quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Evaluating whether people understood, adopted and sustained a change
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- 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
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- 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 and how to use AI for a task
- Judging whether uncertainty changes the decision
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- 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 cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Resolving consent and suppression status across systems
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Specifying AI transparency and explanation duties to affected people
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Training reproducible machine learning models
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- Qualtrics
- IBM SPSS Statistics
- Medallia
- Microsoft PowerPoint
- NielsenIQ (incl. GfK)
- Amplitude
- Coupa
- Microsoft Excel
- Salesforce
- Microsoft Power BI
Paths to start
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
Skill Path
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonProduct and Digital Behaviour Analytics
8tasks in this job
116skills it uses
10tools named
The skills this job leans on most
- 1Designing and quality-checking survey instrumentsNeeded in 3 of 8 tasks
- 2Designing and appraising samplesNeeded in 3 of 8 tasks
- 3Designing decision-oriented research with customers, users or employeesNeeded in 3 of 8 tasks
- 4Interpreting customer feedback and qualitative evidenceNeeded in 3 of 8 tasks
- 5Mapping customer journeys and evidence gapsCore to this role
ToolsQualtricsIBM SPSS StatisticsMedalliaMicrosoft PowerPointNielsenIQ (incl. GfK)Amplitude+ 4 more
All 8 tasks, where people get stuck and all 116 skills
Tasks
- Design a user research investigation
- Investigate customer-experience evidence
- Review supplier performance against contract obligations and recommend renewal or remedy
- Produce an accountable operating budget
- Design, administer and quality-check a professional survey
- Conduct or review a data protection impact assessment for a data, analytics or AI processing
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Set and maintain working standards for a team's analytical, reporting and modelling work products
Where people get stuck
- Designing decision-oriented research with customers, users or employees
- Applying research ethics and participant consent
All skills
- Designing and appraising samples
- Designing and quality-checking survey instruments
- Designing decision-oriented research with customers, users or employees
- Interpreting customer feedback and qualitative evidence
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- 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 data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- 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
- 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
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- 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 comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing auditable decision models
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing experiments
- Designing implementable change plans
- Designing testable data controls
- 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 quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Evaluating whether people understood, adopted and sustained a change
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- 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
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- 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 and how to use AI for a task
- Judging whether uncertainty changes the decision
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- 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 cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Resolving consent and suppression status across systems
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Specifying AI transparency and explanation duties to affected people
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Training reproducible machine learning models
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- Qualtrics
- IBM SPSS Statistics
- Medallia
- Microsoft PowerPoint
- NielsenIQ (incl. GfK)
- Amplitude
- Coupa
- Microsoft Excel
- Salesforce
- Microsoft Power BI
Paths to start
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
Skill Path
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonProduct and Digital Behaviour Analytics
About the role
Accountable for an insight or research team and for the research programme it delivers. Decides research priorities, the team's capacity and the quality bar findings must meet before release. Data and AI judgement sits in sample and method quality, research ethics and governing AI-assisted synthesis.
Also called Customer Insight Manager, Research Manager (customer or market research), Insight Lead (when the role manages a team)