Product Analyst
Strategic product analytics expertise for data-driven product decisions — from metrics framework selection to experimentation design and impact measurement.
Philosophy
Great product analytics isn't about tracking everything. It's about measuring what matters to drive better product decisions.
The best product analytics:
- Start with decisions, not data — What will you do differently based on this metric?
- Instrument once, measure forever — Invest in solid event tracking upfront
- Balance leading and lagging — Predict outcomes, don't just report them
- Make data accessible — Self-serve dashboards beat SQL queues
- Experiment before you ship — Validate hypotheses with real users
How This Skill Works
When invoked, apply the guidelines in rules/ organized by:
metrics-* — Frameworks (AARRR, HEART), KPI selection, metric hierarchiesfunnel-* — Conversion analysis, drop-off diagnosis, optimizationcohort-* — Retention analysis, segmentation, lifecycle trackingfeature-* — Adoption tracking, usage patterns, feature successexperiment-* — A/B testing, hypothesis design, statistical rigorinstrumentation-* — Event tracking, data modeling, collection best practicesdashboard-* — Visualization, stakeholder reporting, self-serve analytics
Core Frameworks
AARRR (Pirate Metrics)
| Stage |
Question |
Key Metrics |
| Acquisition |
Where do users come from? |
Traffic sources, CAC, signup rate |
| Activation |
Do they have a great first experience? |
Time-to-value, setup completion, aha moment |
| Retention |
Do they come back? |
DAU/MAU, D1/D7/D30 retention, churn |
| Revenue |
Do they pay? |
Conversion rate, ARPU, LTV |
| Referral |
Do they tell others? |
NPS, referral rate, viral coefficient |
HEART Framework (Google)
| Dimension |
Definition |
Signal Types |
| Happiness |
User attitudes, satisfaction |
NPS, CSAT, surveys |
| Engagement |
Depth of involvement |
Sessions, time-in-app, actions/session |
| Adoption |
New users/features uptake |
New users, feature adoption % |
| Retention |
Continued usage over time |
Retention curves, churn rate |
| Task Success |
Efficiency and completion |
Task completion, error rate, time-on-task |
The Metrics Hierarchy
┌─────────────────┐
│ North Star │ ← Single metric that matters most
│ Metric │
├─────────────────┤
│ Primary │ ← 3-5 key performance indicators
│ KPIs │
├─────────────────┤
│ Supporting │ ← Diagnostic and health metrics
│ Metrics │
├─────────────────┤
│ Operational │ ← Day-to-day tracking
│ Metrics │
└─────────────────┘
Retention Analysis Types
┌───────────────────────────────────────────────────────────┐
│ RETENTION VIEWS │
├───────────────────────────────────────────────────────────┤
│ N-Day Retention │ % who return on exactly day N │
│ Unbounded │ % who return on or after day N │
│ Bracket Retention │ % who return within a time window │
│ Rolling Retention │ % still active after N days │
└───────────────────────────────────────────────────────────┘
Experimentation Rigor Ladder
| Level |
Approach |
When to Use |
| 1. Gut |
Ship and hope |
Never for important features |
| 2. Qualitative |
User research, feedback |
Early exploration |
| 3. Observational |
Pre/post analysis |
Low-risk changes |
| 4. Quasi-experiment |
Cohort comparison |
When randomization hard |
| 5. A/B Test |
Randomized control |
Optimization, validation |
| 6. Multi-arm Bandit |
Adaptive allocation |
When speed > precision |
Metric Selection Criteria
| Criterion |
Question |
Good Sign |
| Actionable |
Can we influence this? |
Direct lever exists |
| Accessible |
Can we measure it reliably? |
<5% missing data |
| Auditable |
Can we debug anomalies? |
Clear calculation logic |
| Aligned |
Does it tie to business value? |
Executive cares |
| Attributable |
Can we trace changes to causes? |
A/B testable |
Anti-Patterns
- Vanity metrics — Tracking what looks good, not what drives decisions
- Metric overload — 50 dashboards, zero insights
- Lagging only — Measuring outcomes without predictive indicators
- Silent failures — No alerting on data quality issues
- HiPPO-driven — Highest-paid person's opinion beats data
- P-hacking — Running tests until you get significance
- Ship and forget — Launching features without success criteria
- Segment blindness — Looking only at averages, missing cohort differences
1---2name: product-analyst3description: Expert product analytics strategist for SaaS and digital products. Use when designing product metrics frameworks, funnel analysis, cohort retention, feature adoption tracking, A/B testing, experimentation design, data instrumentation, or product dashboards. Covers AARRR, HEART, behavioral analytics, and impact measurement.4---5
6# Product Analyst
7
8Strategic product analytics expertise for data-driven product decisions — from metrics framework selection to experimentation design and impact measurement.
9
10## Philosophy
11
12Great product analytics isn't about tracking everything. It's about **measuring what matters** to drive better product decisions.
13
14The best product analytics:
151. **Start with decisions, not data** — What will you do differently based on this metric?
162. **Instrument once, measure forever** — Invest in solid event tracking upfront
173. **Balance leading and lagging** — Predict outcomes, don't just report them
184. **Make data accessible** — Self-serve dashboards beat SQL queues
195. **Experiment before you ship** — Validate hypotheses with real users
20
21## How This Skill Works
22
23When invoked, apply the guidelines in `rules/` organized by:
24
25- `metrics-*` — Frameworks (AARRR, HEART), KPI selection, metric hierarchies
26- `funnel-*` — Conversion analysis, drop-off diagnosis, optimization
27- `cohort-*` — Retention analysis, segmentation, lifecycle tracking
28- `feature-*` — Adoption tracking, usage patterns, feature success
29- `experiment-*` — A/B testing, hypothesis design, statistical rigor
30- `instrumentation-*` — Event tracking, data modeling, collection best practices
31- `dashboard-*` — Visualization, stakeholder reporting, self-serve analytics
32
33## Core Frameworks
34
35### AARRR (Pirate Metrics)
36
37| Stage | Question | Key Metrics |
38|-------|----------|-------------|
39| **Acquisition** | Where do users come from? | Traffic sources, CAC, signup rate |
40| **Activation** | Do they have a great first experience? | Time-to-value, setup completion, aha moment |
41| **Retention** | Do they come back? | DAU/MAU, D1/D7/D30 retention, churn |
42| **Revenue** | Do they pay? | Conversion rate, ARPU, LTV |
43| **Referral** | Do they tell others? | NPS, referral rate, viral coefficient |
44
45### HEART Framework (Google)
46
47| Dimension | Definition | Signal Types |
48|-----------|------------|--------------|
49| **Happiness** | User attitudes, satisfaction | NPS, CSAT, surveys |
50| **Engagement** | Depth of involvement | Sessions, time-in-app, actions/session |
51| **Adoption** | New users/features uptake | New users, feature adoption % |
52| **Retention** | Continued usage over time | Retention curves, churn rate |
53| **Task Success** | Efficiency and completion | Task completion, error rate, time-on-task |
54
55### The Metrics Hierarchy
56
57```
58 ┌─────────────────┐
59 │ North Star │ ← Single metric that matters most
60 │ Metric │
61 ├─────────────────┤
62 │ Primary │ ← 3-5 key performance indicators
63 │ KPIs │
64 ├─────────────────┤
65 │ Supporting │ ← Diagnostic and health metrics
66 │ Metrics │
67 ├─────────────────┤
68 │ Operational │ ← Day-to-day tracking
69 │ Metrics │
70 └─────────────────┘
71```
72
73### Retention Analysis Types
74
75```
76┌───────────────────────────────────────────────────────────┐
77│ RETENTION VIEWS │
78├───────────────────────────────────────────────────────────┤
79│ N-Day Retention │ % who return on exactly day N │
80│ Unbounded │ % who return on or after day N │
81│ Bracket Retention │ % who return within a time window │
82│ Rolling Retention │ % still active after N days │
83└───────────────────────────────────────────────────────────┘
84```
85
86### Experimentation Rigor Ladder
87
88| Level | Approach | When to Use |
89|-------|----------|-------------|
90| **1. Gut** | Ship and hope | Never for important features |
91| **2. Qualitative** | User research, feedback | Early exploration |
92| **3. Observational** | Pre/post analysis | Low-risk changes |
93| **4. Quasi-experiment** | Cohort comparison | When randomization hard |
94| **5. A/B Test** | Randomized control | Optimization, validation |
95| **6. Multi-arm Bandit** | Adaptive allocation | When speed > precision |
96
97## Metric Selection Criteria
98
99| Criterion | Question | Good Sign |
100|-----------|----------|-----------|
101| **Actionable** | Can we influence this? | Direct lever exists |
102| **Accessible** | Can we measure it reliably? | <5% missing data |
103| **Auditable** | Can we debug anomalies? | Clear calculation logic |
104| **Aligned** | Does it tie to business value? | Executive cares |
105| **Attributable** | Can we trace changes to causes? | A/B testable |
106
107## Anti-Patterns
108
109- **Vanity metrics** — Tracking what looks good, not what drives decisions
110- **Metric overload** — 50 dashboards, zero insights
111- **Lagging only** — Measuring outcomes without predictive indicators
112- **Silent failures** — No alerting on data quality issues
113- **HiPPO-driven** — Highest-paid person's opinion beats data
114- **P-hacking** — Running tests until you get significance
115- **Ship and forget** — Launching features without success criteria
116- **Segment blindness** — Looking only at averages, missing cohort differences
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