Analytical pm

Analytical PM Skill

Apply a structured framework to PM analytical, metrics, root-cause, and trade-off questions targeting AI product roles.

When to Use

• User asks "What metrics would you use for X"
• User asks "How would you measure success for X"
• User asks "Metric X dropped 20%, diagnose it"
• User asks about trade-offs between two product decisions
• User asks "Define a North Star metric for X"
• User says /analytical-pm followed by a question
• Any question about metrics, goals, root-cause analysis, A/B tests, or trade-offs

Context

• Tuned for: AI product roles at frontier AI companies
• What matters: Translating product intuition into measurable outcomes and debugging complex systems with data
• Common pitfall: Picking vanity metrics or being too qualitative. Be rigorous and quantitative.

Three Question Types

TYPE A: Metrics / Goal-Setting Questions

"Define success metrics for X" / "What would you measure for X" / "Set goals for X"

Framework: Analytical (6 Steps)

Step 1: Clarify the Product

• What is the product? Who uses it? What value does it deliver?
• What stage is it in? (launch, growth, mature, declining)
• What's the business model? (subscription, API usage, freemium, enterprise)

Step 2: Define the North Star Metric (NSM)

The NSM must capture the core value exchange between product and user.

• Formula: NSM = [engagement unit] per [user segment] per [time period]
• Example (ChatGPT): # of successful conversations per weekly active user
• Example (LLM API platform): # of API calls generating production value per monthly active developer
• Example (Claude): # of tasks completed per weekly active user

Decompose the NSM into a metric tree:

NSM = Factor A x Factor B x Factor C

Step 3: Supporting Metrics (3-5)

Leading indicators that the NSM will grow. Organized by AARRR:

• Acquisition: New users/developers, sign-up conversion
• Activation: First successful use, time-to-value
• Retention: D7/D30 retention, usage frequency
• Revenue: ARPU, conversion to paid, API spend
• Referral: Organic invites, word-of-mouth, virality coefficient

Step 4: Counter / Guardrail Metrics (2-3)

What we must NOT break while optimizing the NSM:

• Quality: Response accuracy, hallucination rate, harmful content rate
• Safety: Content policy violations, user reports, model refusals (false positive rate)
• Trust: User satisfaction (CSAT/NPS), enterprise churn, data privacy incidents
• System: Latency (TTFT, TPS), error rate, uptime

Step 5: Ecosystem Metrics

For platform companies, measure ecosystem health:

• Developer ecosystem: # of apps built, API integrations, plugin adoption
• Partner ecosystem: Revenue through partners, integration depth
• Content ecosystem: User-generated content, model fine-tunes, custom GPTs

Step 6: Trade-offs Between Metrics

Identify 2-3 key tensions:

• Growth vs. Safety (more users vs. more moderation needed)
• Speed vs. Quality (faster responses vs. more accurate responses)
• Revenue vs. Access (monetization vs. mission of broad access)

State how you'd resolve each (e.g., set guardrail thresholds, A/B test, phased rollout).

---

TYPE B: Root-Cause / Diagnostic Questions

"Metric X dropped 20% this week. Diagnose it."

Framework: MECE (Mutually Exclusive, Collectively Exhaustive)

Step 1: Clarify

• Which metric exactly? Over what timeframe? What's the baseline?
• Is this relative or absolute? Sudden or gradual?
• Any known events (launches, incidents, seasonality)?

Step 2: Segment to Isolate

Break the metric down systematically:

• By user segment: New vs. existing, free vs. paid, geography, platform (web/mobile/API)
• By product surface: Which feature/page/endpoint is affected?
• By time: When exactly did the drop start? Correlated with any deploy/event?
• By funnel stage: Where in the funnel is the drop?

Step 3: Hypothesize (MECE)

Generate hypotheses that are mutually exclusive and collectively exhaustive:

Internal factors:

• Product change (new deploy, A/B test, feature removal)
• Technical issue (latency increase, outage, bug, model regression)
• Data/instrumentation issue (logging break, tracking change, attribution error)

External factors:

• Seasonality (holiday, weekend, school schedule)
• Competitor action (new feature launch, pricing change)
• Market event (news cycle, regulatory change, viral moment)
• Platform change (app store policy, browser update, API deprecation)

Step 4: Validate

For each hypothesis, state:

• What data would confirm/deny it
• What team/tool you'd use to investigate
• Priority order for investigation

Step 5: Recommend Action

• Short-term: Immediate mitigation
• Medium-term: Root cause fix
• Long-term: Monitoring/alerting to catch this earlier

---

TYPE C: Trade-off Questions

"Feature A would increase engagement but decrease revenue. Ship or not?"

Framework: 3 Trade-off Types

Type 1: Similar Product Cannibalization

Product A vs. Product B serving overlapping users.

• Quantify cannibalization risk (user overlap, usage substitution)
• Measure incremental value (does total pie grow?)
• Run holdout experiment

Type 2: Same Product, Different Variations

Version A vs. Version B of the same feature.

• Define ship/no-ship criteria upfront
• A/B test with clear primary metric and guardrails
• Set duration and statistical significance threshold
• Consider long-term effects (novelty bias, learning curves)

Type 3: Different Products, Same Surface

Feature X vs. Feature Y competing for the same real estate.

• Score each on: impact to NSM, strategic value, user demand, effort
• Consider: Can they coexist? Is this a false dichotomy?
• Propose: Experiment design, phased rollout, or user segmentation

For all trade-offs:

• State the decision framework explicitly
• Quantify where possible (even rough estimates)
• Identify the reversibility of each option
• Recommend with conviction, then acknowledge what you'd monitor

AI-Specific Analytical Considerations

• Model metrics: Perplexity, BLEU/ROUGE, human eval scores, Elo ratings
• Safety metrics: Harmful content rate, jailbreak success rate, refusal accuracy
• Cost metrics: Cost per query, GPU utilization, inference cost per token
• Latency metrics: Time to first token (TTFT), tokens per second (TPS), end-to-end response time
• Quality metrics: Hallucination rate, factual accuracy, instruction-following score

Output Format

Structure as a rigorous analytical walkthrough. Be quantitative where possible. For metrics questions, draw the metric tree. For root-cause, walk through the diagnostic systematically. Aim for ~2000 words.

Research-First Workflow

Before generating the answer:

1. Research — Use web search to find latest benchmarks, industry metrics, and analytical frameworks relevant to the question. Do 5-10 searches.
2. Cite sources — Include [linked source](url) inline for data points and benchmarks.
3. Display the complete structured answer.

What Good Looks Like

• Starts with clarifying the metric/situation (don't assume)
• NSM captures core user value (not vanity metrics)
• Metric tree is decomposable and actionable
• Counter metrics show product maturity (especially safety for AI)
• Root-cause analysis is structured and exhaustive (MECE)
• Trade-off analysis is quantitative, not just qualitative
• Shows awareness of AI-specific measurement challenges