Roles Marketing & Communications
Lead Customer Insight Analyst
Accountable for a customer research programme and for the quality of the research others produce within it.
4tasks in this job
69skills it uses
11tools named
The skills this job leans on most
- 1Designing and quality-checking survey instrumentsNeeded in 3 of 4 tasks
- 2Designing and appraising samplesNeeded in 3 of 4 tasks
- 3Designing decision-oriented research with customers, users or employeesNeeded in 3 of 4 tasks
- 4Interpreting customer feedback and qualitative evidenceNeeded in 3 of 4 tasks
- 5Mapping customer journeys and evidence gapsCore to this role
ToolsQualtricsMicrosoft ExcelMedalliaMicrosoft PowerPointAmplitudeNielsenIQ (incl. GfK)QuestbackIBM SPSS StatisticsQ Research Software (Displayr)R (with RStudio / Posit)Microsoft Power BI
All 4 tasks, where people get stuck and all 69 skills
Tasks
- Design a user research investigation
- Investigate customer-experience evidence
- Design, administer and quality-check a professional survey
- 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 event sequences, states and durations
- 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 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
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- 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 interpreting hypothesis tests
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- 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 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
- Identifying and prioritising critical data elements
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- 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 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
- Training reproducible machine learning models
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing evidence-led findings and auditable recommendations with their limits
- 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
7tasks in this job
101skills it uses
15tools named
The skills this job leans on most
- 1Designing and quality-checking survey instrumentsNeeded in 3 of 7 tasks
- 2Designing and appraising samplesNeeded in 3 of 7 tasks
- 3Designing decision-oriented research with customers, users or employeesNeeded in 3 of 7 tasks
- 4Interpreting customer feedback and qualitative evidenceNeeded in 3 of 7 tasks
- 5Mapping customer journeys and evidence gapsCore to this role
ToolsQualtricsMicrosoft ExcelMedalliaMicrosoft PowerPointAmplitudeNielsenIQ (incl. GfK)+ 9 more
All 7 tasks, where people get stuck and all 101 skills
Tasks
- Design a user research investigation
- Investigate customer-experience evidence
- Design, administer and quality-check a professional survey
- Deliver and sustain a trusted BI report or dashboard
- 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
- Sampling and reviewing a team's work against standards
- Giving specific developmental feedback on data and AI work
- Writing testable work standards and review checklists
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 event sequences, states and durations
- 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
- 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
- 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 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 interpreting hypothesis tests
- Designing and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- 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 semantic models for governed analytics
- 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 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 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
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- 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
- 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
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Reading and tracing unfamiliar code
- Reading business metrics
- 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
- Reconciling conflicting reports
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- 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
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- 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 maintainable code for analytical and data work
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- Qualtrics
- Microsoft Excel
- Medallia
- Microsoft PowerPoint
- Amplitude
- NielsenIQ (incl. GfK)
- Questback
- IBM SPSS Statistics
- Q Research Software (Displayr)
- R (with RStudio / Posit)
- Tableau
- Microsoft Power BI
- Salesforce
- Snowflake
- ChatGPT
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
Coming soonProduct and Digital Behaviour Analytics
8tasks in this job
101skills it uses
15tools named
The skills this job leans on most
- 1Sampling and reviewing a team's work against standardsNeeded in 8 of 8 tasks
- 2Designing and quality-checking survey instrumentsNeeded in 3 of 8 tasks
- 3Designing and appraising samplesNeeded in 3 of 8 tasks
- 4Designing decision-oriented research with customers, users or employeesNeeded in 3 of 8 tasks
- 5Mapping customer journeys and evidence gapsCore to this role
ToolsQualtricsMicrosoft ExcelMedalliaMicrosoft PowerPointAmplitudeNielsenIQ (incl. GfK)+ 9 more
All 8 tasks, where people get stuck and all 101 skills
Tasks
- Design a user research investigation
- Investigate customer-experience evidence
- Design, administer and quality-check a professional survey
- Deliver and sustain a trusted BI report or dashboard
- 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
- Set disclosure control rules for sensitive data outputs and review exceptions
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
- Sampling and reviewing a team's work against standards
- Analysing event sequences, states and durations
- 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
- 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
- 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 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 interpreting hypothesis tests
- Designing and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- 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 semantic models for governed analytics
- 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 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 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
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- 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
- 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
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Reading and tracing unfamiliar code
- Reading business metrics
- 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
- Reconciling conflicting reports
- Selecting charts for evidence and decisions
- 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
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- 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 maintainable code for analytical and data work
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- Qualtrics
- Microsoft Excel
- Medallia
- Microsoft PowerPoint
- Amplitude
- NielsenIQ (incl. GfK)
- Questback
- IBM SPSS Statistics
- Q Research Software (Displayr)
- R (with RStudio / Posit)
- Tableau
- Microsoft Power BI
- Salesforce
- Snowflake
- ChatGPT
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
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
About the role
Accountable for a customer research programme and for the quality of the research others produce within it. Sets the programme's methods and decides which findings are fit to release without managing the analysts. Data and AI judgement sits in sample quality and in reviewing AI-assisted synthesis before release.
Also called Insight Lead, Insight Manager (when the role has no team)