Roles Marketing & Communications
Customer Insight Analyst
Accountable for reliable customer evidence from well-designed surveys and research.
3tasks in this job
36skills it uses
9tools named
The skills this job leans on most
- 1Designing and quality-checking survey instrumentsNeeded in 3 of 3 tasks
- 2Designing and appraising samplesNeeded in 3 of 3 tasks
- 3Designing decision-oriented research with customers, users or employeesNeeded in 3 of 3 tasks
ToolsQualtricsMicrosoft ExcelMedalliaMicrosoft PowerPointNetigateQuestbackIBM SPSS StatisticsQ Research Software (Displayr)Salesforce
All 3 tasks, where people get stuck and all 36 skills
Tasks
- Design a user research investigation
- Investigate customer-experience evidence
- Design, administer and quality-check a professional survey
Where people get stuck
- Mapping customer journeys and evidence gaps
- Interpreting customer feedback and qualitative evidence
- 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
- 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 and justifying missing-data treatments
- 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
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing efficient and reproducible investigations
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Investigating anomalies
- Judging whether a change exceeds normal variation
- Profiling data
- Reading charts and dashboards correctly, including their state
- Recognising misleading numerical claims
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Writing evidence-led findings and auditable recommendations with their limits
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
Coming soonProduct and Digital Behaviour Analytics
4tasks in this job
72skills 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 PowerPointAmplitudeNetigate+ 5 more
All 4 tasks, where people get stuck and all 72 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
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 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
- 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
- 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 efficient and reproducible investigations
- Designing experiments
- 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
- 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
- Gating releases on evidence with rollback ready
- Governing shared data definitions
- Identifying keys and testing referential integrity
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Investigating anomalies
- Judging whether a change exceeds normal variation
- Judging whether uncertainty changes the decision
- Mapping stakeholders' decisions, constraints and needs
- 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 resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- 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
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Qualtrics
- Microsoft Excel
- Medallia
- Microsoft PowerPoint
- Amplitude
- Netigate
- Questback
- IBM SPSS Statistics
- Q Research Software (Displayr)
- Salesforce
- Microsoft Power BI
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
6tasks in this job
83skills it uses
13tools named
The skills this job leans on most
- 1Designing and quality-checking survey instrumentsNeeded in 3 of 6 tasks
- 2Designing and appraising samplesNeeded in 3 of 6 tasks
- 3Designing decision-oriented research with customers, users or employeesNeeded in 3 of 6 tasks
- 4Interpreting customer feedback and qualitative evidenceNeeded in 3 of 6 tasks
- 5Mapping customer journeys and evidence gapsCore to this role
ToolsQualtricsMicrosoft ExcelMedalliaMicrosoft PowerPointAmplitudeR (with RStudio / Posit)+ 7 more
All 6 tasks, where people get stuck and all 83 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
- 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 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 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
- 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 efficient and reproducible investigations
- Designing experiments
- 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
- 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
- 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
- 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 uncertainty changes the decision
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- 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 misleading numerical claims
- Reconciling conflicting reports
- Reviewing human- and AI-generated code changes for correctness and risk
- 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
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Qualtrics
- Microsoft Excel
- Medallia
- Microsoft PowerPoint
- Amplitude
- R (with RStudio / Posit)
- Netigate
- Questback
- IBM SPSS Statistics
- Q Research Software (Displayr)
- Salesforce
- Microsoft Power BI
- 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 reliable customer evidence from well-designed surveys and research. Decides research design, sampling and synthesis methods within a commissioned question. Data and AI judgement sits in sample quality, research ethics and checking AI-assisted synthesis.
Also called Consumer Insight Analyst, CX Analyst, Market Research Analyst, Research Executive, Insight Analyst