marketing-measurement-system

name	marketing-measurement-system
description	Use when a premium website needs a KPI tree, event plan, customer-insight loop, loyalty measures, and decision-ready reporting; use `observability` for telemetry implementation and `experimentation` for causal tests.
metadata	{"portable":true,"compatible_with":["claude-code","codex"]}

Marketing Measurement System

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, [phone].

Connect the website's business outcome to behavioural evidence, customer insight, channel contribution, retention, and operating decisions.

Use When

• A new website or retainer needs a measurement architecture and evidence cadence.
• Owners cannot explain whether the website contributes to business outcomes.
• Marketing, sales, CRM, email, paid, referral, or offline data must connect to website behaviour.
• Loyalty and retention need to be designed into the digital experience.

Do Not Use When

• The request is only to install analytics or alerts; use observability.
• A defined hypothesis needs a controlled test; use experimentation.
• The client will provide neither access nor approved measurement; renegotiate scope rather than invent a dashboard.

Required Inputs

Artefact	Source or provider	Required?	When missing
Business model, outcome, strategy, and decision cadence	Business owner	yes	Stop KPI design and return a decision-framing brief.
Experience map, sitemap, conversion paths, and offers	Website strategy	yes	Produce only a provisional outcome model.
Existing analytics, CRM, advertising, email, and offline sources	Data/tool owners	conditional	Mark source coverage gaps and avoid unsupported historical baselines.
Customer cohorts, retention data, targets, and definitions	Finance, sales, product, or marketing owners	conditional	Use hypotheses, not fabricated benchmarks.
Consent, privacy, retention, and access constraints	Compliance owner	yes before collection	Block collection designs that lack lawful governance.

Workflow

1. State one primary business outcome and the decisions measurement must support.
2. Stop if the outcome is not owned, measurable enough to guide action, or compatible with privacy constraints.
3. Build the KPI tree from business outcome through behavioural drivers to leading indicators using KPI definitions.
4. Define each metric: formula, grain, source, owner, target or decision band, comparison period, latency, and caveat.
5. Map channels to the website and define source/medium, handoff, attribution limits, and offline reconciliation.
6. Specify events against experience stages, including consent, identity, deduplication, and quality checks.
7. Define the customer-insight loop using customer insight guidance; do not impose a fixed quantitative/qualitative ratio without evidence.
8. Add loyalty and retention measures appropriate to the business model using the loyalty plan.
9. Design a one-page decision view and an evidence pack for monthly reporting.
10. Run the quarterly review; change priorities or budget only when evidence and authority support the decision.
11. If sources conflict, pause conclusions, reconcile definitions, and record the unresolved variance.

Recovery: repair metric definitions or source lineage, then rerun reconciliation before reporting.

Quality Standards

• Every KPI has a formula, source, owner, decision use, comparison period, and data-quality caveat.
• Vanity metrics appear only as diagnostic context, never as the primary outcome.
• Events map to a customer stage and a real decision.
• Attribution limits, consent, retention, and identity handling are explicit.
• Retention and loyalty measures match the business model rather than a generic dashboard.
• Reviews end with owned decisions, not unexplained charts.

Anti-Patterns

• Reporting page views as business success. Fix: connect diagnostic traffic to an owned outcome.
• Copying industry benchmarks as targets. Fix: establish the client's baseline and decision threshold.
• Treating the website as isolated from CRM or offline sales. Fix: define source handoffs and reconciliation.
• Optimising engagement while qualified demand falls. Fix: use outcome and guardrail metrics together.
• Measuring acquisition but ignoring retention in a recurring model. Fix: add cohort and renewal measures.
• Hiding bad or missing data. Fix: show the quality issue and recovery owner.
• Mixing conflicting metric definitions. Fix: create a governed definition and reconcile history explicitly.

Outputs

Artefact	Consumer	Observable acceptance condition
KPI tree and metric dictionary	Business, analytics, and reporting owners	Outcome, drivers, formulas, sources, owners, targets/bands, and caveats are complete.
Event and source plan	observability and implementation owners	Events, parameters, stages, consent, QA, and source handoffs are specified.
Insight and loyalty plan	Marketing and service owners	Cadence, cohorts, methods, owners, and decisions are explicit.
Monthly evidence and quarterly review templates	monthly-report and leadership	Each view supports named decisions and records data quality.

References

• KPI tree and definitions
• Customer insight loop
• Loyalty and retention plan
• Quarterly business review

Evidence Produced

Evidence	Format	Acceptance condition
Metric lineage	KPI-to-source map	Every reported metric is traceable to source and formula.
Source-quality register	Table	Access, freshness, completeness, reconciliation, and limitations are recorded.
Decision log	Monthly/quarterly record	Evidence, decision, owner, due date, and follow-up measure are explicit.

Capability Contract

Read access to business definitions and available data is required. Analysis defaults to read-only. Editing analytics plans or dashboards requires authority. Data collection, CRM changes, customer contact, production mutation, spending, and budget reallocation require explicit owner approval and privacy compliance.

Degraded Mode

When source access, reliable history, network, or analytics tooling is unavailable, return the narrowest qualified KPI dictionary and instrumentation plan with baselines and data quality marked not assessed. Never infer performance or set precise targets from absent data.

Decision Rules

Choice	Action	Failure or risk avoided
Metric changes a named decision	Keep as KPI	Dashboard clutter
Metric explains but does not decide	Keep as diagnostic	Vanity metric promotion
Sources disagree materially	Reconcile before interpretation	False driver narratives
Direct outcome is delayed	Use validated leading indicator with guardrail	Optimising a weak proxy
Collection lacks consent or purpose	Remove or redesign	Privacy and trust harm

Worked Example

A B2B site receives many form submissions but sales labels few as qualified. The system defines qualified lead rate with CRM ownership, maps form and CRM identifiers, reports the reconciliation gap, and avoids celebrating raw submission growth.

Read Next

• observability implements the telemetry contract.
• experimentation tests causal hypotheses.
• monthly-report communicates validated monthly evidence.
• ecommerce-analytics owns commerce-specific funnels and revenue measures.