Which product metric should I use to measure this feature?

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    "name": "Which product metric should I use to measure this feature?",
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    "description": "Determine which type of metric best measures a feature or product area you are responsible for. Guides product managers through primary goal, feature maturity, and actionability to select a metric type that drives meaningful decisions rather than vanity reporting. Use during OKR planning, launch prep, or quarterly business reviews.",
    "mode": "decision",
    "entry": "Q1",
    "tags": [
      "product management",
      "metrics",
      "analytics",
      "product strategy",
      "kpis"
    ],
    "image": "https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=1200&q=80"
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  "questions": [
    {
      "id": "Q1",
      "text": "What is the primary goal you are trying to achieve with this feature or product area?"
    },
    {
      "id": "Q2",
      "text": "Is this feature newly launched (less than 90 days live) or still in early rollout?"
    },
    {
      "id": "Q3",
      "text": "Is the metric you are considering something your team can directly influence through product changes in a single sprint?"
    },
    {
      "id": "Q4",
      "text": "Does the feature primarily affect how long users stay with the product over weeks or months rather than within a single session?"
    }
  ],
  "outcomes": [
    {
      "id": "ENGAGEMENT",
      "label": "Engagement Metric"
    },
    {
      "id": "RETENTION",
      "label": "Retention Metric"
    },
    {
      "id": "CONVERSION_REVENUE",
      "label": "Conversion / Revenue Metric"
    },
    {
      "id": "QUALITY_NPS",
      "label": "Quality / NPS Metric"
    },
    {
      "id": "LEADING",
      "label": "Leading Indicator"
    }
  ],
  "dsl": "dag: Which product metric should I use to measure this feature?\nversion: 1.0.0\nimage: https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=1200&q=80\ndescription: Determine which type of metric best measures a feature or product area you are responsible for. Guides product managers through primary goal, feature maturity, and actionability to select a metric type that drives meaningful decisions rather than vanity reporting. Use during OKR planning, launch prep, or quarterly business reviews.\ntags: product management, metrics, analytics, product strategy, kpis\nentry: Q1\n\nQ1: What is the primary goal you are trying to achieve with this feature or product area?\n  hint: Be specific about what success looks like in business terms. Growth goals (acquiring or converting new users) call for conversion and revenue metrics. Engagement goals (making existing users get more value more often) call for engagement metrics. Quality goals (ensuring users are satisfied and the product is reliable) call for NPS or quality metrics. Picking the wrong goal type leads to optimising for the wrong thing — for example, maximising daily active users for a feature that should drive revenue.\n  A: Grow — acquire new users, convert trials, or increase revenue -> Q2\n  B: Engage — increase depth or frequency of use among existing users -> Q3\n  C: Quality — improve reliability, satisfaction, or reduce complaints -> [QUALITY_NPS]\n\nQ2: Is this feature newly launched (less than 90 days live) or still in early rollout?\n  hint: New features often lack enough usage history to measure retention or long-term conversion reliably. For early-stage features, leading indicators — signals that predict future outcomes rather than measuring them directly — are more actionable because they give you enough data to iterate quickly. If a feature has been live for more than 90 days and has meaningful adoption, direct conversion or revenue attribution becomes more reliable. Always pair a lagging metric (the true outcome) with a leading indicator (the early signal).\n  yes -> [LEADING]\n  no  -> [CONVERSION_REVENUE]\n\nQ3: Is the metric you are considering something your team can directly influence through product changes in a single sprint?\n  hint: Actionability is the difference between a metric that drives decisions and one that just describes outcomes. A metric like \"monthly active users\" is often influenced by marketing, seasonality, and product simultaneously — making it hard for the product team to own. Metrics like \"feature activation rate\" or \"time to second core action\" are more tightly coupled to specific product decisions. If your team cannot draw a clear line from a product change to a movement in the metric within two to four weeks, you likely need a more granular leading indicator instead.\n  yes -> [ENGAGEMENT]\n  no  -> Q4\n\nQ4: Does the feature primarily affect how long users stay with the product over weeks or months rather than within a single session?\n  hint: Retention metrics measure whether users come back — they are the right choice for features designed to build habit, reduce churn, or deepen a user's long-term relationship with the product. Engagement metrics measure depth and frequency within shorter windows and are better suited to features used within a session. If your feature is part of the core loop that users return to repeatedly (e.g., a dashboard, a notification system, a weekly report), retention is likely the better frame. Ask yourself: would a user miss this feature if it disappeared for a month?\n  yes -> [RETENTION]\n  no  -> [LEADING]\n\n[ENGAGEMENT]: Engagement Metric\n  color: #2563eb\n  description: Engagement metrics measure how deeply and frequently users interact with a specific feature or product area within a defined time window. Suitable metrics include feature activation rate, average sessions per user per week, time-on-task, or actions-per-session for the relevant flow. Define your measurement window carefully — daily, weekly, or monthly active users mean very different things depending on your product's natural usage cadence. Set a baseline in the first two weeks after launch and establish a target uplift you expect to achieve within 60 days. Review the metric weekly during the active development cycle and investigate any drop with a combination of funnel analysis and qualitative session review.\n  code: PM_ENGAGEMENT\n\n[RETENTION]: Retention Metric\n  color: #16a34a\n  description: Retention metrics measure whether users return to the product or feature after their first interaction, making them the most direct indicator of long-term value delivery. Key retention metrics include Day-7, Day-30, and Day-90 retention rates, churn rate by cohort, and feature-specific re-engagement rate. Retention data takes time to accumulate, so establish a baseline cohort from your current user base before launch to give yourself a meaningful comparison point. Segment retention by acquisition channel, user persona, and plan tier to identify where churn is concentrated. Pair your retention metric with a qualitative exit survey or churn interview programme to understand the \"why\" behind the numbers.\n  code: PM_RETENTION\n\n[CONVERSION_REVENUE]: Conversion / Revenue Metric\n  color: #d97706\n  description: Conversion and revenue metrics are the right choice when a mature feature has a direct and attributable impact on trial-to-paid conversion, expansion revenue, or deal closure rates. Define the conversion event precisely — for example, \"user completes onboarding checklist and activates billing within 14 days\" — so attribution is clean and consistent. Work with your finance or revenue operations team to agree on how the metric will be calculated and reported to avoid discrepancies between product and commercial dashboards. Set a 90-day improvement target and a clear experiment plan (A/B test or staged rollout) to validate that product changes are actually driving the metric movement rather than external factors. Report this metric alongside a volume numerator so small absolute changes are not overstated as percentages.\n  code: PM_CONVERSION_REVENUE\n\n[QUALITY_NPS]: Quality / NPS Metric\n  color: #9333ea\n  description: Quality and satisfaction metrics are the right frame when your primary goal is to reduce friction, improve reliability, or raise customer perception of the product. Relevant metrics include Net Promoter Score (NPS), Customer Satisfaction (CSAT) for a specific flow, error rate, p95 latency, and support ticket volume per feature. NPS and CSAT are lagging indicators — collect them consistently (at least monthly) and track trend over time rather than reacting to single data points. Pair satisfaction scores with support ticket tagging so you can correlate score drops with specific product areas. Set a target score and a cadence for reviewing verbatim feedback with the product and engineering team to ensure quality issues get triaged and actioned promptly.\n  code: PM_QUALITY_NPS\n\n[LEADING]: Leading Indicator\n  color: #0891b2\n  description: Leading indicators are early signals that predict whether a feature is on track to deliver its intended outcome, making them essential for newly launched or rapidly iterating features where lagging metrics have not yet accumulated. Examples include account setup completion rate, time-to-first-value, number of users reaching a key activation milestone, or early feature adoption by the target cohort. Define your leading indicator before launch so you have a baseline and can detect problems quickly — ideally within the first two weeks of rollout. Establish a threshold (e.g., \"70% of new users reach activation within 7 days\") that you can use as a go/no-go signal for broader rollout or continued investment. Plan to migrate from leading indicators to outcome metrics (engagement, retention, or revenue) once you have 60 or more days of post-launch data.\n  code: PM_LEADING\n"
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