Analytics patterns

When you have a long list of business questions, it helps to recognise which kind of analytics each one needs. Patterns are reusable approaches that map to types of questions. Using them lets you estimate effort more accurately, reuse technical approaches across use cases, and batch similar work in delivery.

This part defines the six patterns, when to use each, what you need to build them, and then shows how they map to real use cases in two worked examples (B2B SaaS and non-bank lending) — the same structure used in TrueState 360 to turn decisions into a prioritised roadmap.

The six patterns

1. Descriptive / Monitoring — "What is happening?"

The pattern: Define a metric, aggregate it, visualise it.

Dashboards, KPI tracking, and reporting fall into this category. You're not explaining causes or predicting the future; you're giving a clear, timely view of the current state.

When to use	What you need	Example approach
Primary need is visibility and alignment on the numbers.	Defined metrics, a data source, a refresh schedule, and a visualisation tool.	Funnel by step; cohort table; time series of a KPI; breakdown by segment or region.

Examples: Funnel conversion dashboard, portfolio composition report, arrears ageing, monthly revenue by segment.

2. Diagnostic / Root Cause — "Why is it happening?"

The pattern: Segment the data, compare groups, isolate drivers.

Drill-down analysis, segmentation, and cohort comparison help explain why a metric moved or why one segment behaves differently from another.

When to use	What you need	Example approach
You need to explain a trend or a gap between segments so that people can act on the cause.	Same as descriptive, plus a clear hypothesis or dimension to segment by (e.g. channel, product, cohort).	Compare segments side by side; cohort retention curves; contribution to change (which segment drove the move?).

Examples: Why do broker-originated loans default at higher rates? Which document types cause the longest delays? Which channel has the highest CAC and why?

3. Predictive / Forecasting — "What will happen?"

The pattern: Define features, train a model, score or forecast.

Statistical models, machine learning, and scenario analysis produce estimates of future outcomes or likelihoods.

When to use	What you need	Example approach
Decisions today depend on what happens next, and you have enough history to train or calibrate.	Historical data with the outcome you want to predict; a definition of the target variable and the horizon (e.g. churn in next 90 days).	Regression or classification (e.g. gradient boosting, logistic regression); time series forecast; survival analysis.

Examples: Probability-of-default model, demand forecasting, customer churn prediction, lead scoring, hardship propensity.

4. Prescriptive / Optimisation — "What should we do?"

The pattern: Define an objective, set constraints, optimise.

Decision models, optimisation, and recommendation engines tell you what action to take, not just what will happen.

When to use	What you need	Example approach
You have a clear objective (e.g. maximise margin, minimise loss) and levers you can control.	A quantified objective; constraints (capacity, rules, risk limits); and data linking actions to outcomes.	Linear or nonlinear optimisation; recommendation engine (e.g. next-best-action); allocation or scheduling solver.

Examples: Pricing optimisation, collections contact strategy, marketing spend allocation, inventory replenishment, retention offer selection.

5. Detection / Alerting — "What needs attention now?"

The pattern: Establish a baseline, monitor for deviations, alert.

Anomaly detection, threshold monitoring, and automated triggers surface exceptions so that people can act before a small issue becomes a big one.

When to use	What you need	Example approach
The cost of missing something is high and you can define "normal" vs "not normal."	A stream or regular snapshot of the metric(s); a definition of "normal" (e.g. historical range, rules) and escalation path.	Threshold rules; statistical control limits; anomaly detection (e.g. deviation from expected pattern); early-warning score.

Examples: Fraud detection, early warning on borrower stress, covenant breach alerts, sudden drop in conversion.

6. Measurement / Attribution — "Did it work?"

The pattern: Define an intervention, establish a control, measure the difference.

A/B testing, causal inference, and attribution modelling answer whether a change (policy, campaign, process) actually caused the observed outcome.

When to use	What you need	Example approach
You need to prove or disprove that a specific change drove results.	Clear definition of the intervention and the outcome; a way to compare treated vs control (randomised or quasi-experimental).	A/B test; difference-in-differences; attribution model (e.g. last-touch, multi-touch, or model-based).

Examples: Credit policy effectiveness, marketing attribution, broker incentive ROI, impact of a new onboarding flow.

Using patterns in practice

Map each of your business questions to one or more of these patterns. That tells you what kind of analytics capability you need and lets you group similar work (e.g. all "descriptive" dashboards, or all "predictive" models) for delivery. In TrueState 360, we do this in Step 4: every use case gets a stakeholder and a pattern. The two worked examples below show the same idea applied end to end — journey stage, stakeholder, question, pattern, and example algorithm — so you can see how patterns look in context.

Example — B2B SaaS

Strategy context: grow to $500M ARR by moving upmarket and expanding into the US. The tables below show how questions at each stage of the customer journey map to patterns and example algorithms.

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

Strategy context: grow the loan book by lending to a broader range of borrowers, winning more customers directly, and lowering the cost of funding. The tables below show how questions at each stage map to patterns and example algorithms.

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

Next steps

Once you've mapped your questions to patterns (and, if you use it, to stakeholders and journey stages as in the examples above), you can score them for business impact and feasibility, then build a roadmap. For that full process — from business context to prioritised use cases and a phased plan — see TrueState 360. The technical essentials behind the patterns (regression, classification, data cleaning, feature engineering) are covered in the earlier parts of this guide.