Product Analyst AI Prompts

Audit the conversion funnel for {{product_flow}} and identify the highest-impact drop-off points. Funnel stages provided: {{stages_and_counts}} 1. Compute conversion rates: - Step-by-step conversion rate: users_at_step_N / users_at_step_N-1 - Cumulative conversion rate: users_at_each_step / users_at_top_of_funnel - Overall funnel conversion: bottom_of_funnel / top_of_funnel 2. Identify the biggest drop-offs: - Rank steps by absolute user loss (not just % drop) - Rank steps by % conversion rate (lowest = most leaky) - Flag any step with conversion rate below {{threshold}}% 3. Benchmark against industry standards: - What is a typical conversion rate for each step in {{industry}}? - Which steps are performing below benchmark? 4. Segment the funnel: - Break conversion rates by: new vs returning users, device (mobile/desktop), traffic source, user cohort - Which segments have the lowest conversion at the biggest drop-off step? - Are any segments converting exceptionally well? (Best practice to replicate) 5. Qualitative context: - For the top 2 drop-off steps: list 3 possible reasons users are leaving - What data would confirm or rule out each reason? 6. Prioritized recommendations: - Top 3 interventions ranked by expected impact on overall funnel conversion - For each: hypothesis, test design, and expected lift Return: funnel table with conversion rates, drop-off ranking, segment breakdown, and prioritized recommendations.

Analyze how conversion rates differ across key user segments in this funnel. Funnel data: {{funnel_data}} Segmentation dimensions: {{dimensions}} (e.g. acquisition channel, device, plan type, geography, user tenure) 1. Per-segment funnel tables: For each dimension, produce a funnel table showing conversion at every step broken out by segment value. Highlight: which segment has the highest overall conversion? Which has the lowest? 2. Segment-step interaction: - Are drop-off patterns consistent across segments, or does one segment struggle at a specific step? - Example: mobile users may convert well at sign-up but drop at payment entry - Identify any step where segment A converts at more than 2x segment B 3. Volume-weighted impact: - A segment with 5% conversion but only 2% of volume has low total impact - Compute: (segment volume %) x (conversion gap vs best segment) = impact score - Rank segments by impact score to prioritize where improvement matters most 4. Cohort conversion analysis: - Do users acquired in recent months convert better or worse than older cohorts? - Is there a trend suggesting the product is getting easier or harder to convert? 5. Statistical significance: - For the largest conversion gap between segments: run a proportion z-test - Is the difference significant (p < 0.05) or within random variation? 6. Recommendations: - Which segment should be targeted for conversion improvement first and why? - What product or UX change would most help the lowest-converting high-volume segment? Return: per-segment funnel tables, segment-step interaction analysis, impact scores, significance test, and top recommendations.

Analyze which in-product touchpoints and features most contribute to conversion or activation. User journey data: {{journey_data}} (user_id, touchpoint_type, touchpoint_timestamp, converted: Y/N) Conversion event: {{conversion_event}} (e.g. first purchase, plan upgrade, feature activation) 1. Touchpoint inventory: - List all unique touchpoints users encounter before the conversion event - Count how often each appears in converting vs non-converting journeys - What % of converters touched each touchpoint? 2. Attribution models - compare all three: First touch: - 100% credit to the first touchpoint the user interacted with - Best for: understanding what initiates the conversion journey Last touch: - 100% credit to the touchpoint immediately before conversion - Best for: understanding what closes the conversion Linear: - Equal credit to all touchpoints in the path - Best for: understanding overall touchpoint contribution 3. Path analysis: - What are the top 10 most common touchpoint sequences for converters? - What sequences do non-converters follow? Where do they diverge? - Is there a specific touchpoint combination that strongly predicts conversion? 4. Time-to-conversion by path: - Do users with certain touchpoint paths convert faster? - Is there a touchpoint that accelerates conversion when added to the path? 5. Recommendations: - Which touchpoints should be promoted (high attribution, currently under-used)? - Which touchpoints appear to delay or interrupt conversion? - What is the optimal path to guide new users through? Return: touchpoint attribution table (all three models), top conversion paths, path divergence analysis, and recommendations.

Analyze the DAU/MAU ratio (stickiness) for this product and identify improvement opportunities. DAU and MAU data: {{engagement_data}} Product type: {{product_type}} Time period: {{period}} 1. Stickiness calculation: - DAU/MAU ratio: daily_active_users / monthly_active_users x 100% - Industry benchmarks by product type: - Social/messaging: 40-70% (high daily habit) - Productivity/SaaS: 20-40% - E-commerce: 5-15% (purchase frequency dependent) - Gaming: 20-40% - How does this product compare to benchmark? 2. Trend analysis: - Plot DAU/MAU over the last 12 months - Is stickiness improving, declining, or stable? - Is DAU growing faster or slower than MAU? (DAU growing faster = improving stickiness) - Identify any inflection points and what caused them 3. Stickiness by segment: - DAU/MAU for: new users (< 30 days), established users (30-90 days), power users (> 90 days) - DAU/MAU by acquisition channel, plan type, company size - Which segment has the highest stickiness? What drives it? 4. Usage pattern analysis: - What is the distribution of active days per user per month? - Are users clustered into daily users, weekly users, and monthly users? - What does the 'weekly user' segment use the product for? (May reveal a different use case) 5. Stickiness drivers: - Which features correlate most strongly with daily return visits? - Do users who complete {{onboarding_action}} have higher stickiness? - Is there a usage threshold that separates sticky from non-sticky users? Return: stickiness metrics, benchmark comparison, trend analysis, segment breakdown, and stickiness driver analysis.

Step 1: North Star definition - define or validate the North Star Metric for this product. Decompose it into Level 1 and Level 2 input metrics. Assign owners to each leaf metric. Step 2: Growth accounting - apply the growth accounting framework to the last 12 months. Compute the quick ratio trend. Diagnose whether this is a new user, retention, or resurrection problem. Step 3: Funnel audit - map the full acquisition-to-activation funnel. Identify the top 2 drop-off points. Segment the funnel by device, channel, and cohort. Step 4: Retention analysis - build the cohort retention matrix. Compute Day 1, Day 7, and Day 30 retention by cohort. Identify whether newer cohorts are improving or declining. Step 5: Feature adoption - for the top 3 features, compute adoption rates and time-to-first-use. Identify which feature has the strongest correlation with 30-day retention. Step 6: User segmentation - segment users into at least 4 behavioral groups (Champions, At-risk, Dormant, New). Size each segment and compute its contribution to revenue or activity. Step 7: Recommendations and roadmap - synthesize findings into a prioritized list of 5 product and analytics recommendations. For each: the problem it addresses, the expected impact, and the measurement plan.

Design a product health monitoring framework for {{product_name}}. Product type: {{product_type}} (SaaS, mobile app, marketplace, etc.) Business model: {{business_model}} Current data available: {{data_sources}} 1. AARRR metrics framework: Define the key metric for each stage: - Acquisition: how are users finding and signing up for the product? (CAC, sign-up rate, channel mix) - Activation: are new users experiencing the core value? (activation rate, time-to-value, onboarding completion) - Retention: are users coming back? (Day 1/7/30 retention, MAU/DAU ratio, churn rate) - Revenue: are users paying? (ARPU, MRR, conversion to paid, expansion revenue) - Referral: are users sharing? (NPS, referral rate, viral coefficient) 2. Leading vs lagging indicators: For each AARRR stage: identify one leading indicator (predicts future performance) and one lagging indicator (confirms past performance) 3. North Star Metric: - Define the single metric that best captures value delivered to users - It should be: measurable, predictive of revenue, influenceable by the team - Decompose it: what inputs drive the North Star? (Weekly Active Users x actions per user, for example) 4. Alert thresholds: - For each health metric: define the threshold that triggers an alert (e.g. Day 7 retention drops > 5% WoW) - Define monitoring frequency: real-time, daily, or weekly per metric 5. Dashboard layout: - Top section: North Star Metric + 4 AARRR headline numbers with WoW change - Middle section: retention cohort heatmap, funnel conversion rates - Bottom section: acquisition channel mix, revenue breakdown Return: AARRR metric definitions, North Star decomposition, alert thresholds, and dashboard spec.

Write a complete experiment readout for this concluded A/B test. Experiment: {{experiment_name}} Hypothesis: {{hypothesis}} Results data: {{results}} Audience: product team and leadership 1. TL;DR (3 sentences): - What was tested, what was the result, and what is the recommendation? 2. Background: - Problem being solved - Hypothesis and expected direction of change - Primary metric and guardrail metrics 3. Setup: - Variants: control and treatment description - Traffic allocation and targeting - Test duration and sample size achieved vs required 4. Results: - Primary metric: control value, treatment value, absolute difference, % difference, p-value, 95% CI - Secondary metrics: same format for each - Guardrail metrics: did any degrade significantly? - Segment breakdown: results by key segments (mobile/desktop, new/returning, plan type) 5. Interpretation: - Is the result statistically significant? Practically significant? - Are results consistent across segments or driven by one segment? - Any unexpected findings worth investigating? 6. Decision: - Ship / Do not ship / Iterate / Run follow-up test - If shipping: rollout plan (% of traffic, timeline) - If iterating: what specifically changes in the next version? 7. Learnings: - What did this test teach us about user behavior? - How does this inform future experiments or roadmap decisions? Return: complete experiment readout document suitable for sharing with the product team.

Prioritize this backlog of product experiments for the next quarter. Experiment ideas: {{experiment_list}} Current traffic: {{daily_active_users}} DAU Team capacity: {{capacity}} experiments per quarter 1. Score each experiment on ICE framework: - Impact (1-10): how much will this move the primary metric if it works? - Confidence (1-10): how sure are we the hypothesis is correct? (prior evidence, user research) - Ease (1-10): how quickly and cheaply can this be built and measured? - ICE score = (Impact + Confidence + Ease) / 3 2. Feasibility check: - For each experiment: calculate required sample size at 80% power, alpha=0.05, and the team's stated MDE - Calculate required duration: sample_size / (DAU x traffic_allocation_rate) - Flag experiments requiring > 8 weeks as impractical for the quarter 3. Dependency and conflict check: - Are any experiments testing overlapping UI elements or user flows? (Cannot run simultaneously) - Does any experiment depend on another being completed first? - Map experiment conflicts and dependencies 4. Learning value: - Even if a test is negative, what do we learn? - Prioritize experiments that resolve fundamental product questions over marginal optimizations 5. Recommended quarter plan: - Select experiments that fit within capacity, avoid conflicts, and maximize learning - Sequence them: which experiments must run first to unblock others? - Reserve 20% capacity for urgent or opportunistic tests Return: ICE scoring table, feasibility check, conflict map, and recommended quarter experiment plan with sequencing.

Analyze the adoption of this product feature and identify opportunities to increase usage. Feature: {{feature_name}} Adoption data: {{adoption_data}} (user_id, first_use_date, usage_frequency, user_segment) Launch date: {{launch_date}} 1. Adoption funnel: - Awareness: % of eligible users who have seen or been exposed to the feature - Activation: % who have used it at least once - Adoption: % who have used it more than {{n_times}} times - Habit: % who use it regularly (at least once per {{period}}) 2. Time to first use: - How long after account creation do users first try the feature? - Median and distribution of time-to-first-use - What % of eventual adopters used it within: 1 day, 7 days, 30 days of account creation? 3. Adoption by segment: - Which user segments have the highest adoption rates? (by plan, role, company size, acquisition channel) - Which segments are significantly below average? These are the opportunity segments. 4. Correlation with retention: - Do users who adopt this feature have higher 30-day and 90-day retention? - Compute retention rates for: adopters vs non-adopters (note: correlation, not causation) - If adopters retain significantly better: this feature is a potential activation lever 5. Usage depth: - Among adopters: how often do they use it? (sessions per week distribution) - Are there power users using it far more than average? What do they have in common? - At what usage frequency does the feature become 'sticky' (correlated with long-term retention)? 6. Barriers to adoption: - In the adoption funnel, which step has the biggest drop-off? - For low-adoption segments: what are 3 likely barriers (awareness, discoverability, complexity, value clarity)? Return: adoption funnel metrics, time-to-first-use analysis, segment breakdown, retention correlation, and barrier hypothesis.

Assess the business impact of this recently launched feature. Feature: {{feature_name}} Launch date: {{launch_date}} Primary success metric: {{primary_metric}} Data available: {{data}} 1. Pre/post comparison: - Define the pre-period (same length as post, ending at launch date) - Compare primary metric: pre-period average vs post-period average - Absolute change and % change - Account for trends: was the metric already trending up/down before launch? 2. Confound check: - What else changed during the post-period? (Seasonality, marketing campaigns, other feature launches) - How might these confounds explain the observed change? - Can any confounds be controlled for or isolated? 3. Adoption-outcome correlation: - Segment users by adoption level: non-adopters, light adopters, heavy adopters - Compare primary metric across adoption segments - Does heavier feature usage correlate with better outcomes? 4. Counterfactual estimation: - If possible: use a holdout group (users who did not have access to the feature) as a control - Difference-in-differences: compare the change in metric for treatment vs control groups - If no holdout: use synthetic control (similar product/market as proxy) 5. Secondary effects: - Did the feature have any unintended effects on other metrics? - Check: session length, support tickets, error rates, other feature usage 6. ROI estimate: - Translate the metric impact into business value: what is the estimated annual impact in revenue or cost? - How does this compare to the development cost of the feature? Return: pre/post comparison, confound analysis, adoption-outcome correlation, counterfactual estimate, and ROI.

Apply a growth accounting framework to decompose MAU growth into its constituent components. User activity data: {{activity_data}} (user_id, active_month) Time period: {{period}} 1. User state classification: For each user in each month, classify their state: - New: first month of activity - Retained: active this month AND last month - Resurrected: active this month but NOT last month (but active at some prior point) - Churned: active last month but NOT this month (not visible in current month counts) 2. Growth accounting equation: MAU(t) = MAU(t-1) + New(t) + Resurrected(t) - Churned(t) - Verify this equation balances in the data 3. Monthly trend of each component: - Plot New, Retained, Resurrected, and Churned users over time - Quick ratio = (New + Resurrected) / Churned Quick ratio > 1: growing. < 1: shrinking. = 1: flat. - What is the trend in the quick ratio? 4. Component deep dive: - New users: growing or declining? What is driving acquisition? - Churn: is the churn count growing as MAU grows? (Structural churn problem if yes) - Resurrection: what brings users back? Is resurrection a meaningful growth driver? - Retention: what % of users are retained month over month? Is it improving? 5. Diagnosis: - Is this a new user problem (top of funnel), a retention problem, or both? - If the quick ratio < 1: which component needs improvement most? - If the quick ratio > 1 but slowing: is churn keeping pace with new user growth? Return: monthly growth accounting table, quick ratio trend, component analysis, and growth diagnosis.

Decompose the North Star Metric into its input metrics and build a measurement tree. North Star Metric: {{nsm}} (e.g. 'Weekly Active Engaged Users' or 'Messages Sent per Month') Product context: {{product_description}} 1. Level 1 decomposition: Break the NSM into 2-3 multiplicative or additive components. Example: Weekly Active Engaged Users = Weekly Active Users x Engagement Rate Example: Revenue = Users x Conversion Rate x Average Order Value 2. Level 2 decomposition: Break each Level 1 component further. Example: Weekly Active Users = New Users + Retained Users + Resurrected Users Example: Engagement Rate = % Users Completing Core Action 3. Level 3 decomposition (where meaningful): Continue decomposing into actionable leaf metrics that specific teams own. 4. For each leaf metric: - Current value - Owner: which team or squad controls this metric? - Lever: what specific action moves this metric? - Effort to improve by 10%: Low / Medium / High 5. Sensitivity analysis: - If each leaf metric improves by 10%, which has the largest impact on the NSM? - This identifies the highest-leverage improvement opportunity 6. Metric tree dashboard spec: - Top level: NSM with trend - Second level: Level 1 components with trend - Third level: Level 2 components with owner labeled - Color coding: green = above target, yellow = near target, red = below target Return: metric tree (all three levels), owner assignment, sensitivity analysis, and dashboard specification.

Identify the leading behavioral indicators that predict user churn before it happens. User behavior data: {{behavior_data}} Churn definition: {{churn_definition}} (e.g. no activity for 30 days, subscription cancelled) Observation window: {{observation_window}} (behavioral features measured in the N days before churn) 1. Feature engineering for churn prediction: Compute these behavioral features for each user in the observation window: - Login frequency: sessions per week - Days since last active - Core action completion rate: % of sessions where {{core_action}} was completed - Feature breadth: number of distinct features used - Engagement trend: comparing last 7 days vs prior 7 days - Support contacts: number of support tickets or error events - Billing events: failed payments, plan downgrades 2. Univariate analysis: For each feature, compare the distribution between: - Users who churned within {{horizon}} days - Users who did not churn Compute: mean, median, and statistical significance of the difference (Mann-Whitney U test) 3. Predictive ranking: - Which features show the largest and most statistically significant difference between churners and non-churners? - Rank features by predictive power (use AUC of a simple logistic regression per feature) 4. Early warning thresholds: - For the top 3 features: what threshold value separates high-churn-risk from low-churn-risk users? - Example: users with > 14 days since last login have a 3x higher churn rate than average 5. Churn risk segmentation: - Combine the top 3 indicators into a simple churn risk score (Low / Medium / High) - What % of users currently fall into each risk tier? - What intervention should each tier receive? Return: feature importance table, threshold analysis, risk tier definitions, and intervention recommendations.

Build and interpret a user retention cohort analysis. Event data: {{event_data}} (user_id, event_date, acquisition_date or cohort_date) Retention definition: {{retention_definition}} (e.g. any login, completed core action, purchase) Cohort granularity: {{granularity}} (weekly / monthly) 1. Build the retention matrix: - Rows: cohorts defined by {{cohort_period}} of first use or acquisition - Columns: periods since acquisition (Period 0, 1, 2, ... N) - Cell value: % of cohort still active in that period - Period 0 = 100% by definition (the acquisition period) 2. Key retention metrics: - Day 1 retention: % of users returning the day after first use - Day 7 retention: % returning in the first week - Day 30 retention: % returning within the first month - Long-term retention: at what period does the retention curve flatten? This is the product's natural retention floor. 3. Cohort comparison: - Are newer cohorts retaining better or worse than older ones? - Which cohort has the best Day 30 retention? What was happening during that acquisition period? - Plot cohort curves on the same chart: diverging curves indicate improving or worsening product health 4. Retention curve shape interpretation: - Sharp early drop then flat: high initial churn but strong core user base - Gradual continuous decline: no engaged user base, product is not habit-forming - Bump at specific period: seasonal return or notification-driven re-engagement 5. Retention by acquisition channel: - Which acquisition channels produce the highest Day 30 retention? - Are there channels bringing volume but low retention? (Wasted acquisition spend) 6. Recommendations: - At which period does the biggest retention drop occur? What is the likely cause? - What single change would most improve the retention curve shape? Return: retention matrix, key metrics table, cohort comparison chart description, curve interpretation, and top recommendations.

Segment users based on behavioral patterns in this product. Behavioral data: {{behavior_data}} (event logs: user_id, event_type, timestamp, session_id) Segmentation goal: {{goal}} (personalization, intervention targeting, resource allocation) 1. Feature engineering for segmentation: Create behavioral features per user over the last {{window}} days: - Recency: days since last active - Frequency: sessions per week - Depth: average actions per session - Breadth: number of distinct features used - Tenure: days since account creation - Core action rate: % of sessions with {{core_action}} 2. RFM-style segmentation (rule-based, interpretable): Apply percentile-based segmentation on Recency, Frequency, and Depth: - Champions: recent, frequent, deep engagement - At-risk: previously frequent but declining - Dormant: not active in > 30 days - New users: tenure < 14 days - Casual: low frequency, low depth 3. Cluster-based segmentation (data-driven): - Apply k-means clustering on the behavioral features - Test k = 3, 4, 5, 6 clusters; select using silhouette score - Profile each cluster: mean values for each behavioral feature - Name each cluster with a business-friendly label based on its profile 4. Segment stability: - How stable are segments over time? (Do users move between segments frequently?) - A good segment is both meaningful and stable 5. Segment sizing and value: - Count and % of users in each segment - Revenue, retention, or other outcome metric per segment - Which segment represents the highest business value? 6. Recommended actions per segment: - Champions: retain and leverage as advocates - At-risk: trigger a win-back flow - Dormant: re-engagement campaign or sunset - New users: accelerate activation Return: feature engineering code/SQL, RFM segment definitions, cluster profiles, segment sizing table, and recommended actions.

Identify and analyze the power users of this product to understand what drives exceptional engagement. Engagement data: {{engagement_data}} Power user definition: {{definition}} (top 10% by usage frequency, or specific behavior threshold) 1. Power user identification: - Define power users quantitatively: users who {{criterion}} in the last 30 days - What % of total users are power users? - What % of total activity or revenue do power users account for? (Often 80% of value from 20% of users) 2. Power user profile: - Demographics: tenure, acquisition channel, plan type, company size (if B2B) - Behavioral fingerprint: which features do they use most? What is their typical session pattern? - Onboarding: did they complete onboarding differently? How quickly did they activate? - First week behavior: what did power users do in their first 7 days that non-power users did not? 3. The aha moment: - Is there a specific action in the first week that strongly predicts becoming a power user? - Compute: % of power users who completed {{action}} in week 1 vs % of all users - This is the aha moment candidate - the action to optimize for in onboarding 4. Power user journey: - Map the typical sequence of feature adoption for power users - At what tenure do most users reach power user status? - Is there a specific feature or workflow that accelerates the journey? 5. Implications for product and growth: - How can onboarding be redesigned to guide more users toward the power user path? - Which acquisition channels produce the most power users? (Not just the most users) - What does retaining power users require? (Are they at risk of churning for any reason?) Return: power user definition and sizing, behavioral profile, aha moment analysis, journey map, and product/growth implications.