{ "schema_version": "core-reviewed-term-ai-handoff-v1", "version": "core-reviewed-term-ai-handoff-v1", "contract_status": "preview", "distribution": "per_term_read_only_ai_handoff", "no_write_operations": true, "canonical": { "url": "https://core.yogoq.com/en-US/core/product-usage-activation", "slug": "product-usage-activation", "locale": "en-US" }, "summary": { "schema_version": "core-reviewed-term-summary-v1", "term_id": "01de243d-a130-5ca5-9351-1eab04f5da9c", "canonical_slug": "product-usage-activation", "canonical_url": "https://core.yogoq.com/en-US/core/product-usage-activation", "locale": "en-US", "display_name": "Product Usage Activation", "english_name": null, "abbreviation": null, "short_definition": "Product usage activation measures whether new users reach the key behavior that delivers initial product value.", "quality": "reviewed", "publication_status": "published_reviewed", "version": "core-reviewed-term-summary-v1", "last_reviewed_at": "2026-05-14T00:00:00.000Z", "sources_count": 2, "limitations_key": "core-trust-policy-v1-2026-06-22" }, "content": { "definition": { "key": "definition", "title": "一言でいうと", "text": "Product usage activation measures whether new users reach the key behavior that delivers initial product value.", "items": [] }, "formula": { "key": "formula", "title": "計算の考え方", "text": "Activation rate = users completing the activation behavior / eligible new users. Formula | Activation rate = users completing the activation behavior / eligible new users. | Use it as the primary operating calculation Bridge | New users x setup completion x key-action completion x early retention = activated users | Use it to explain changes between reviews Segment | Split by customer, product, channel, and period | Use it to find deterioration hidden by averages", "items": [ "Formula | Activation rate = users completing the activation behavior / eligible new users. | Use it as the primary operating calculation", "Bridge | New users x setup completion x key-action completion x early retention = activated users | Use it to explain changes between reviews", "Segment | Split by customer, product, channel, and period | Use it to find deterioration hidden by averages" ] }, "boundary": { "key": "boundary", "title": "含めるもの / 含めないもの", "text": "This metric is comparable only when inclusion and exclusion rules stay stable. Include | Eligible users, value behavior, deadline, event definition, exclusions | These define value reached Exclude | Signup alone, login alone, clicks unrelated to value | They are not meaningful activation Define explicitly | Time window, event, free/paid users, team usage | Activation differs by product", "items": [ "Include | Eligible users, value behavior, deadline, event definition, exclusions | These define value reached", "Exclude | Signup alone, login alone, clicks unrelated to value | They are not meaningful activation", "Define explicitly | Time window, event, free/paid users, team usage | Activation differs by product" ] }, "usage": [ { "key": "meaning", "title": "意味", "text": "Product usage activation goes beyond signup or installation. It measures whether users experienced the first meaningful value moment, making it a leading indicator for onboarding quality, PMF, retention, and churn risk. Product Usage Activation should be read through the decision it informs, the assumptions behind it, and the action that changes it. This page treats the term as an operating decision metric: it fixes the formula, boundaries, drivers, companion metrics, and comparison points so teams can interpret the number consistently. Use it with an explicit period, segment, owner, and data source rather than as a dictionary label.", "items": [] }, { "key": "usage", "title": "役立つ場面", "text": "Use Product Usage Activation to decide prioritizing onboarding and feature prompts because it highlights time to value and the feature richness versus simplicity tradeoff. It changes prioritization by forcing teams to state the horizon, boundary conditions, and controllable drivers. It informs adjustments when activation milestones or friction points shift, so decisions stay grounded in current conditions.", "items": [ "Use Product Usage Activation to decide prioritizing onboarding and feature prompts because it highlights time to value and the feature richness versus simplicity tradeoff.", "It changes prioritization by forcing teams to state the horizon, boundary conditions, and controllable drivers.", "It informs adjustments when activation milestones or friction points shift, so decisions stay grounded in current conditions." ] }, { "key": "usage", "title": "使い方のポイント", "text": null, "items": [ "Define the unit and horizon before comparing time to value across options.", "Keep the primary driver separate from secondary noise and one-off shocks.", "Document data sources, estimation steps, and confidence ranges for review.", "Translate the tradeoff into thresholds that can be monitored over time.", "Revisit assumptions when the market boundary or policy setting changes." ] }, { "key": "drivers", "title": "何が数字を動かすか", "text": "Breaking the metric into drivers clarifies what action should follow the review. Onboarding friction | More steps reduce activation Value communication | Users need to understand the job Segment fit | Poor-fit users activate less", "items": [ "Onboarding friction | More steps reduce activation", "Value communication | Users need to understand the job", "Segment fit | Poor-fit users activate less" ] } ], "misunderstandings": [ { "key": "misunderstandings", "title": "判断するときの注意点", "text": "Do not decide from the number alone; align assumptions, period, segments, and companion metrics. Do not rename login rate as activation. Short-term use does not prove long-term retention. Frequent definition changes break trend analysis.", "items": [ "Do not rename login rate as activation.", "Short-term use does not prove long-term retention.", "Frequent definition changes break trend analysis." ] }, { "key": "misunderstandings", "title": "よくある誤解 / 落とし穴", "text": null, "items": [ "Product Usage Activation is not a universal rule; results depend on boundary assumptions and data quality.", "A single metric like time to value is not sufficient without considering activation milestones and friction points.", "Short term movements can mislead when responses happen with lags." ] } ], "examples": [ { "key": "examples", "title": "最小例", "text": "An analytics product defines activation as connecting data and creating the first dashboard within seven days. Activation is 35%, so the team simplifies data setup and improves it to 50% the next month. After the review, the owner did not treat the metric in isolation. They compared it with companion metrics, checked segment differences, documented assumption changes, and verified data quality before changing the plan. Whether the number improved or deteriorated, the team identified the driver, assigned an owner, and fed the learning into the next budget, operating review, or experiment cycle.", "items": [] } ], "comparisons": [ { "key": "comparisons", "title": "似ている言葉との違い", "text": "Signup rate | Account creation | Activation measures value behavior DAU/MAU | Usage frequency | Activation measures initial value reached Conversion rate | Any conversion | Activation is tied to product value", "items": [ "Signup rate | Account creation | Activation measures value behavior", "DAU/MAU | Usage frequency | Activation measures initial value reached", "Conversion rate | Any conversion | Activation is tied to product value" ] }, { "key": "related_metrics", "title": "一緒に見る指標", "text": "Companion metrics turn a good-or-bad reading into a discussion of causes and actions. PMF | Product-market fit | Tests whether activation leads to retention Cohort Analysis | Retention curve | Shows survival after activation Churn Management | Retention intervention | Finds unactivated users at risk", "items": [ "PMF | Product-market fit | Tests whether activation leads to retention", "Cohort Analysis | Retention curve | Shows survival after activation", "Churn Management | Retention intervention | Finds unactivated users at risk" ] } ], "faq": [ { "question": "Is login activation?", "answer": "Usually no. Define the action that proves the user reached value." }, { "question": "How should the time window be set?", "answer": "Use the natural time in which a user should experience first value, such as 7 or 14 days." }, { "question": "Can all users share one definition?", "answer": "Use one primary definition when possible, but segment-specific supporting definitions may be needed." } ] }, "sources": { "source_refs": [ "concept:business-concept-0075:en-US", "concept:business-concept-0075:ja-JP", "manual_promotion_packet:synthetic-business-concepts-batch1-2026-04-28" ], "visible_sources": [ { "label": "OpenStax: Principles of Marketing", "url": "https://openstax.org/details/books/principles-marketing", "kind": "Tier-S open textbook" }, { "label": "Wikipedia: Marketing metrics", "url": "https://en.wikipedia.org/wiki/Marketing_metrics", "kind": "Supplemental reference" } ] }, "related_terms": [ { "topic_id": "653e2bfd-5cd0-52ea-b97b-ad9e0f1cdb41", "canonical_slug": "churn-management", "canonical_url": "https://core.yogoq.com/en-US/core/churn-management", "title": "Churn Management", "relation_type": "used_together" }, { "topic_id": "80649c29-00cf-5c94-b27d-154e1945503a", "canonical_slug": "product-market-fit", "canonical_url": "https://core.yogoq.com/en-US/core/product-market-fit", "title": "Product-Market Fit (PMF)", "relation_type": "related" }, { "topic_id": "32500bac-0643-51ab-94bf-f1bd724f6923", "canonical_slug": "retention-cohort-analysis", "canonical_url": "https://core.yogoq.com/en-US/core/retention-cohort-analysis", "title": "Retention Cohort Analysis", "relation_type": "related" } ], "quality": { "quality": "reviewed", "publication_status": "published_reviewed", "source_count": 2, "last_reviewed_at": "2026-05-14T00:00:00.000Z", "trust_policy_version": "core-trust-policy-v1-2026-06-22" }, "limitations": { "policy_version": "core-trust-policy-v1-2026-06-22", "professional_advice_boundary": "reference_only_not_professional_advice", "text": "This page is reference information for research and learning. For accounting, legal, finance, health, security, or other individual decisions, confirm against primary sources or qualified professionals.", "items": [ "Public pages support general understanding and practical context; they are not professional advice for individual cases.", "Fast-changing information such as regulations, accounting standards, prices, product specs, and legal requirements should be checked against primary sources before final decisions.", "Even when AI-assisted drafting or audit is used, publication relies on quality gates and human-readable evidence." ] } }