Customer Success Manager
Accountable for the health, retention and expansion signals of a book of customer accounts.
1task in this job
13skills it uses
5tools named
The skills this job leans on most
- 1Building cohort and retention analyses with correct denominatorsNeeded in 1 of 1 tasks
- 2Designing and weighting rule-based scoring modelsNeeded in 1 of 1 tasks
- 3Analysing customer lifetime value and unit economicsCore to this role
ToolsGainsightPlanhatTotangoMicrosoft ExcelSalesforceOracle NetSuiteZendesk
All 1 tasks, where people get stuck and all 13 skills
Tasks
Where people get stuck
- Building cohort and retention analyses with correct denominators
- Designing and weighting rule-based scoring models
All skills
- Building cohort and retention analyses with correct denominators
- Analysing staged funnels: conversion, time in stage and slippage
- Constructing and challenging customer segments
- Designing and validating digital analytics event measurement
- Diagnosing digital funnel conversion from event data
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Mapping customer journeys and evidence gaps
- Measuring campaign outcomes with valid denominators
- Reading business metrics
- Reconstructing a record's state at a point in time from history and audit trails
- Sampling and reviewing a team's work against standards
- Writing data quality rules
Paths to start
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
Customer, Revenue and Sales Analytics
Analyse pipeline, forecast quality, segments and lifetime value from CRM and billing data.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonProduct and Digital Behaviour Analytics
3tasks in this job
27skills it uses
7tools named
The skills this job leans on most
- 1Building cohort and retention analyses with correct denominatorsNeeded in 1 of 3 tasks
- 2Designing and weighting rule-based scoring modelsNeeded in 1 of 3 tasks
- 3Analysing customer lifetime value and unit economicsCore to this role
ToolsGainsightPlanhatTotangoMicrosoft ExcelSalesforceOracle NetSuite+ 1 more
All 3 tasks, where people get stuck and all 27 skills
Tasks
- Assess customer account health and churn risk, calibrate the health score and prioritise intervention
- Investigate customer-experience evidence
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
Where people get stuck
- Building cohort and retention analyses with correct denominators
- Designing and weighting rule-based scoring models
All skills
- Building cohort and retention analyses with correct denominators
- Designing and weighting rule-based scoring models
- Analysing staged funnels: conversion, time in stage and slippage
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Challenging probability-weighted sales forecasts
- Constructing and challenging customer segments
- Designing and validating digital analytics event measurement
- Designing experiments
- Diagnosing digital funnel conversion from event data
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Evaluating ML generalisation and fitness
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Mapping customer journeys and evidence gaps
- Measuring campaign outcomes with valid denominators
- Monitoring model fitness and drift
- Preparing leakage-resistant ML data
- Reading business metrics
- Recognising and resolving ethical conflicts in data and AI work
- Reconstructing a record's state at a point in time from history and audit trails
- Sampling and reviewing a team's work against standards
- Setting and reviewing action thresholds for scores and model predictions
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
Paths to start
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
Customer, Revenue and Sales Analytics
Analyse pipeline, forecast quality, segments and lifetime value from CRM and billing data.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonProduct and Digital Behaviour AnalyticsMachine Learning: from Data to Validated Models and Decisions
3tasks in this job
80skills it uses
7tools named
The skills this job leans on most
- 1Building cohort and retention analyses with correct denominatorsNeeded in 1 of 3 tasks
- 2Designing and weighting rule-based scoring modelsNeeded in 1 of 3 tasks
- 3Analysing customer lifetime value and unit economicsCore to this role
ToolsGainsightPlanhatTotangoMicrosoft ExcelSalesforceOracle NetSuite+ 1 more
All 3 tasks, where people get stuck and all 80 skills
Tasks
- Assess customer account health and churn risk, calibrate the health score and prioritise intervention
- Investigate customer-experience evidence
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
Where people get stuck
- Building cohort and retention analyses with correct denominators
- Designing and weighting rule-based scoring models
All skills
- Building cohort and retention analyses with correct denominators
- Designing and weighting rule-based scoring models
- Analysing event sequences, states and durations
- Analysing staged funnels: conversion, time in stage and slippage
- 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
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- 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
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Constructing and challenging customer segments
- 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 interpreting hypothesis tests
- Designing and quality-checking survey instruments
- Designing and validating digital analytics event measurement
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Diagnosing digital funnel conversion from event data
- 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 ML generalisation and fitness
- 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
- Independently checking analytical conclusions and claims
- 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 uncertainty changes the decision
- Mapping customer journeys and evidence gaps
- Measuring campaign outcomes with valid denominators
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Preparing leakage-resistant ML data
- 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
- Reconstructing a record's state at a point in time from history and audit trails
- 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
- 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
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
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
About the role
Accountable for the health, retention and expansion signals of a book of customer accounts. Decides which accounts need intervention, runs renewal and adoption plans and closes escalated issues with evidence. Data judgement sits in reading health scores and usage data critically and questioning thresholds that misclassify risk.