Explore Tasks
Assess customer account health and churn risk, calibrate the health score and prioritise intervention
Customer success leadership receives an account risk ranking from usage, service, payment and commercial signals, with intervention and renewal priorities and the accounts where the health score looks wrong.
2jobs do it
1job helps with it
42skills it needs
Does itHelps with it
- Reading and tracing unfamiliar code
- Reviewing human- and AI-generated code changes for correctness and risk
- Sampling and reviewing a team's work against standards
- Diagnosing misleading data visualisations
- Verifying reports and dashboards before and after release
- Verifying AI-assisted outputs against sources
- Disclosing AI contribution to professional work
- Testing competing explanations with discriminating evidence
- Independently checking analytical conclusions and claims
- Escalating unresolved risks with evidence and clear ownership
- Giving specific developmental feedback on data and AI work
- Judging the materiality of defects and deficiencies for the intended use
- Designing and validating digital analytics event measurement
- Setting and reviewing action thresholds for scores and model predictions
- Interpreting averages, weighted measures and mix effects
- Reading business metrics
- Writing data quality rules
- Interpreting correlation without inferring cause
- Designing experiments
- Estimating and challenging causal effects
- Evaluating ML generalisation and fitness
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Analysing staged funnels: conversion, time in stage and slippage
- Mapping customer journeys and evidence gaps
- Constructing and challenging customer segments
- Diagnosing digital funnel conversion from event data
- Reconstructing a record's state at a point in time from history and audit trails
- Challenging probability-weighted sales forecasts
- Measuring campaign outcomes with valid denominators
- Building cohort and retention analyses with correct denominators
- Identifying and quantifying decision downside and exposure
- Making accountable decisions under uncertainty
- Revising prior decisions when evidence changes
- Reasoning about systems, feedback and second-order effects
- Facilitating group decisions using disputed evidence
- Rating risks against appetite and choosing proportionate treatment
- Recognising and resolving ethical conflicts in data and AI work
- Statistical power and sample size
- Preparing leakage-resistant ML data
- Monitoring model fitness and drift
- Working through stakeholder conflict over evidence and responsibility
- Designing and weighting rule-based scoring models
Paths that teach the skills it needs
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
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
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