Step 4. Turn decisions into analytics opportunities
You now have a list of questions from each stage of the customer journey. The next step is to turn those questions into defined analytics use cases — something specific enough to build, prioritise, and assign.
Two things make a question into a proper use case.
A stakeholder. Every use case needs a named owner in the business who will act on the output. A question without a stakeholder is a question that will produce analytics nobody uses. Go back through your list and attach a name or a role to every question before moving forward.
A pattern. An analytics pattern is the type of solution the question calls for. Knowing the pattern tells you what kind of work is involved, what skills you need, and which questions can be grouped and built together.
The six patterns are:
- Descriptive / Monitoring — What is happening? (Dashboards, KPI tracking)
- Diagnostic / Root Cause — Why is it happening? (Segmentation, cohort analysis)
- Predictive / Forecasting — What will happen? (Models, scoring, forecasting)
- Prescriptive / Optimisation — What should we do? (Optimisation, recommendations)
- Detection / Alerting — What needs attention now? (Anomaly detection, alerts)
- Measurement / Attribution — Did it work? (A/B testing, causal inference)
For a full explanation of each pattern, see Analytics patterns.
Example — B2B SaaS
| Stage | Stakeholder | Question | Pattern | Example algorithm |
|---|---|---|---|---|
| Marketing | CMO | Which campaigns are driving signups, and what is the cost per trial? | Descriptive | Campaign performance dashboard; cost-per-trial by channel |
| Marketing | CMO | Which campaigns are most likely to bring in accounts that convert and stay? Where should we shift budget? | Predictive + Prescriptive | Logistic regression on trial-to-paid conversion by source; budget allocation model |
| Sales | Head of Sales | What is our win rate by deal size and segment? Where are we losing? | Descriptive + Diagnostic | Win rate dashboard; cohort analysis by deal size and segment |
| Sales | Head of Sales | Which prospects are most likely to close, and which should the team prioritise this week? | Predictive | Lead scoring model (gradient boosting on CRM activity and firmographic features) |
| Signup | Head of Product | Where are people dropping off in the signup flow? | Diagnostic | Funnel drop-off analysis by step and traffic source |
| Signup | Head of Growth | Which visitors are most likely to complete signup if we intervene? | Predictive | Propensity model on visitor behaviour signals |
| Onboarding | Head of Customer Success | What proportion of new accounts complete onboarding, and how long does it take? | Descriptive | Onboarding completion rate and time-to-completion dashboard |
| Onboarding | Head of Customer Success | Which new accounts are struggling and need intervention before they disengage? | Detection / Alerting | Early warning model on onboarding activity; threshold triggers on login and setup completion |
| Activation | Head of Product | What is our time to first value, and how does it vary by segment? | Descriptive + Diagnostic | Time-to-activation metrics; segment comparison |
| Activation | Head of Customer Success | Which users are at risk of never activating, and what nudge is most likely to help? | Predictive + Prescriptive | Classification model on first-week behaviour; recommendation model for intervention type |
| Ongoing usage | Head of Product | Which features are being used and which are being ignored? | Descriptive | Feature usage dashboard; engagement analysis by user and segment |
| Ongoing usage | CPO, Head of Customer Success | Which product investments would most improve retention? Which disengaged users are about to churn? | Diagnostic + Predictive | Feature importance analysis on retention cohorts; churn prediction model |
| Expansion | Head of Sales | Which accounts have grown usage without upgrading their plan? | Descriptive | Usage-vs-plan gap analysis |
| Expansion | Head of Sales | Which accounts are ready to expand, and what is the right offer and timing? | Predictive + Prescriptive | Expansion propensity model on usage signals; offer recommendation engine |
| Renewal | CFO, Head of Customer Success | What is our renewal rate by segment and cohort? | Descriptive | Renewal rate cohort dashboard |
| Renewal | Head of Customer Success | Which accounts are at risk of not renewing, and what should we do about each one? | Predictive + Prescriptive | Churn prediction model; next-best-action recommendation |
| Churn | CEO, Head of Customer Success | Why are customers leaving, and does the reason vary by segment? | Diagnostic | Exit reason analysis; churn driver segmentation |
| Churn | Head of Customer Success | Which active accounts are most likely to churn in the next 90 days? | Predictive | Time-to-churn model; survival analysis; binary classification |
| Win-back | Head of Sales | What is our win-back rate, and which types of former customers return? | Descriptive + Diagnostic | Win-back rate analysis; segment comparison of returning vs non-returning accounts |
| Win-back | Head of Sales | Which churned accounts are worth pursuing, and what offer has the best chance of working? | Predictive + Prescriptive | Win-back propensity model; offer optimisation |
Example — Non-bank lender
| Stage | Stakeholder | Question | Pattern | Example algorithm |
|---|---|---|---|---|
| Marketing | CMO | Which campaigns are generating applications that go on to settle? What is the cost per settled loan? | Descriptive | Campaign-to-settlement attribution dashboard; cost per settled loan by channel |
| Marketing | CMO | Where should we shift budget to get more of the right applications? | Prescriptive | Channel ROI model; budget allocation optimisation |
| Referral | Head of Distribution | Which brokers are sending the most volume and the best quality applications? | Descriptive + Diagnostic | Broker performance dashboard; quality scoring by settlement rate and default rate |
| Referral | Head of Distribution | Which brokers should we invest more in, and which are costing more than they are worth? | Diagnostic + Prescriptive | Broker value model; commission cost-benefit analysis |
| Application | COO | What is application volume by channel and how is the mix trending? | Descriptive | Application mix dashboard; channel trend analysis |
| Application | CEO, COO | Are there sources of applications we are underinvesting in? | Diagnostic + Prescriptive | Channel opportunity analysis; segment gap analysis |
| Pre-approval | CCO | What proportion proceed to full approval, and where do they fall over? | Descriptive + Diagnostic | Pre-approval conversion funnel; decline reason analysis |
| Pre-approval | CCO | Which applications are likely to fail at approval so we can flag them early? | Predictive | Early approval failure prediction model; classification on application features |
| Document collection | COO | How long is this taking, and where are the delays? | Descriptive + Diagnostic | Time-in-stage analysis; delay driver analysis by document type and borrower segment |
| Document collection | COO | Which applications are going to miss settlement targets unless we intervene? | Predictive + Detection | Time-to-completion prediction; escalation trigger model |
| Approval | CCO | What is our approval rate, and how does credit performance compare to what we expected? | Descriptive + Diagnostic | Approval rate dashboard; predicted vs actual default performance monitoring |
| Approval | CCO, COO | Are there applications in the queue we should prioritise or escalate? | Prescriptive | Application prioritisation scoring; queue optimisation model |
| Settlement and funding | COO | What proportion of settlements are on time, and where do delays come from? | Descriptive + Diagnostic | Settlement time dashboard; delay reason analysis |
| Settlement and funding | COO | Which loans in the pipeline are at risk of not settling on time? | Predictive | Settlement delay prediction model; risk scoring |
| Servicing | CFO | How is the book performing by cohort, product, and channel? | Descriptive | Portfolio performance dashboard; cohort analysis |
| Servicing | CCO, CFO | Which borrowers are showing early signs of stress before they miss a payment? | Detection / Alerting | Early warning model on repayment behaviour; anomaly detection on payment patterns |
| Hardship | CCO | What proportion of hardship arrangements result in the borrower returning to normal repayments? | Descriptive + Diagnostic | Hardship outcome tracking; cohort comparison by arrangement type |
| Hardship | CCO | Which borrowers are at risk of entering hardship in the next three months? | Predictive | Hardship propensity model; early intervention scoring using repayment and behavioural features |
| Arrears | Head of Collections | Which contact approaches are most effective at resolving arrears? | Measurement / Attribution | A/B analysis of contact strategies; intervention effectiveness measurement |
| Arrears | Head of Collections | Which borrowers in arrears are most likely to resolve without escalation, and which need immediate action? | Predictive + Prescriptive | Arrears resolution prediction model; next-best-action for collections team |
| Discharge or refinance | CFO | How many customers are leaving at refinance and what is the typical timing? | Descriptive + Diagnostic | Discharge reason analysis; prepayment speed by cohort and product |
| Discharge or refinance | CFO, Head of Retention | Which borrowers are most likely to refinance away in the next six months, and is there an offer that would retain them? | Predictive + Prescriptive | Prepayment prediction model; retention offer optimisation |
In Step 5, you will score each of these use cases for business impact — using the strategic context from Step 3 to separate what matters from what is merely interesting.