{"page":"main","data":[{"title":"Onboarding","metrics":[{"title":"Onboarding & Activation","value":"76.79999%","insight":"Activation measures how efficiently the product converts a new signup into someone who has experienced the core value proposition. For Series, the aha moment is receiving a reply to your first share.\n\n⚠️ Onboarding Completion trending down — L30: 17% (Danger). Investigate funnel step drop-off for recent cohorts.\n\nDefinitions:\n• Onboarding Completion = (Completed all steps) ÷ (Began onboarding) × 100\n• Activation Rate = (Users with ≥1 share within 24h) ÷ (Total signups) × 100\n• Time to First Share = MEDIAN(first share − signup)\n• First Reply Speed = MEDIAN(first reply received − first share)","description":"Onboarding Completion (All Time) — Great tier","benchmarkRef":"Onboarding Completion\nDanger <25% · Early 25–45% · Good 45–65% · Great 65–82% · Elite >82%\nSource: Appsflyer/Adjust\n\nActivation Rate (first share)\nDanger <10% · Early 10–25% · Good 25–45% · Great 45–65% · Elite >65%\nSource: Series internal — modeled on Instagram “post within 24hr”\n\nTime to First Share\nDanger >48hr · Early 6–48hr · Good 1–6hr · Great 10–60min · Elite <10min\nSource: Reforge activation framework\n\nFirst Reply Speed (median)\nDanger >72hr · Early 24–72hr · Good 4–24hr · Great 30min–4hr · Elite <30min\nSource: Series internal — reward delay discounting","expandedData":[{"label":"Onboarding Completion","value":"76.7%","description":"Great (65–82%) · L90: 35% · L30: 17%"},{"label":"Activation Rate (first share)","value":"6.11%","description":"Danger (<10%) · L90: 5.22% · L30: 3.43%"},{"label":"Time to First Share","value":"19.3min","description":"Great (10–60min) · L90: 2.6hr · L30: 39.5min"},{"label":"First Reply Speed (median)","value":"5.2d","description":"Danger (>72hr) · L90: 5.8d · L30: 3.2d"}]}]},{"title":"Core Metrics","metrics":[{"title":"Active Users","value":"4,050","insight":"Active user = someone who messages Series or sends a reaction. DAU = distinct users with ≥1 active event on a given calendar day. WAU = rolling 7-day window. MAU = rolling 30-day window.","description":"Avg MAU (All Time) — People who messaged or reacted","expandedData":[{"label":"Avg DAUs","value":"221.66","description":"L90: 227.99 · L30: 265.90 · L7: 143.38"},{"label":"Avg WAUs","value":"1,173.64","description":"L90: 1,218.42 · L30: 1,389 · L7: 869"},{"label":"Avg MAUs","value":"4,050.33","description":"L90: 4,483"},{"label":"Jan 2026 MAU","value":"3,185","description":""},{"label":"Feb 2026 MAU","value":"2,476","description":""},{"label":"Mar 2026 MAU","value":"6,490","description":""},{"label":"Apr 2026 MAU","value":"1,823","description":"Partial month"}]},{"title":"Passive Users","value":"1,042","insight":"Passive user = someone who has read receipts ON and reads a message from Series. 22.71% of users on Series have read receipts on.","description":"Avg MAU (All Time) — Read receipts on (22.71% of users)","expandedData":[{"label":"Avg DAUs","value":"70.77","description":"L90: 73.44 · L30: 87.39 · L7: 46.13"},{"label":"Avg WAUs","value":"336.33","description":"L90: 346.46 · L30: 350.20 · L7: 135.5"},{"label":"Avg MAUs","value":"1,041.75","description":"L90: 1,089"},{"label":"Jan 2026 MAU","value":"900","description":""},{"label":"Feb 2026 MAU","value":"745","description":""},{"label":"Mar 2026 MAU","value":"1,981","description":""},{"label":"Apr 2026 MAU","value":"541","description":"Partial month"}]},{"title":"Latent Users","value":"13,422","insight":"Latent user = someone who has not opted-out of Series.","description":"Avg MAU (All Time) — Not opted-out","expandedData":[{"label":"Avg DAUs","value":"7,058.9","description":"L90: 7,306.24 · L30: 8,180.39 · L7: 9,632"},{"label":"Avg WAUs","value":"10,393","description":"L90: 10,306 · L30: 9,921.6 · L7: 6,795.5"},{"label":"Avg MAUs","value":"13,421.75","description":"L90: 13,931 · L30: 15,025"},{"label":"Jan 2026 MAU","value":"11,894","description":""},{"label":"Feb 2026 MAU","value":"11,147","description":""},{"label":"Mar 2026 MAU","value":"15,621","description":""},{"label":"Apr 2026 MAU","value":"15,025","description":"Partial month"}]},{"title":"Summary Counts","value":"13,988","insight":"","description":"Total current users (non-deleted, non-opted-out)","expandedData":[{"label":"Total users w/ read receipts ON","value":"3,177","description":""},{"label":"Total current users","value":"13,988","description":"Non-deleted, non-opted-out"},{"label":"Total users since v3","value":"19,888","description":""}]}]},{"title":"Retention","metrics":[{"title":"Active Retention (Fixed-Day Cohort)","value":"98.9%","insight":"The habit metric. Every major consumer social company that reached escape velocity did so by converting latent interest into fixed-day return behavior.\n\nDx Active Retention = (Users active on exactly day X after signup) ÷ (Total cohort) × 100. Active = ≥1 sent message.\n\n⚠️ D1 is Elite (98.9%), but drops sharply to 7.8% by D3. This gap is a primary product roadmap diagnostic. Latent retention remains strong — the gap signals a habit loop that hasn't formed yet, not user disinterest.","description":"D1 Active Retention — Elite","benchmarkRef":"D1 Active Retention\nDanger <15% · Early 15–30% · Good 30–45% · Great 45–60% · Elite >60%\nSource: a16z (Bryan Kim) — OK=50%\n\nD3 Active Retention\nDanger <10% · Early 10–22% · Good 22–35% · Great 35–50% · Elite >50%\nSource: Amplitude consumer social cohort norms\n\nD7 Active Retention\nDanger <8% · Early 8–18% · Good 18–30% · Great 30–45% · Elite >45%\nSource: a16z (Bryan Kim) — OK=35%, >20% strong early-stage\n\nD14 Active Retention\nDanger <5% · Early 5–12% · Good 12–22% · Great 22–35% · Elite >35%\nSource: Amplitude/Mixpanel plateau test\n\nD30 Active Retention\nDanger <3% · Early 3–10% · Good 10–18% · Great 18–28% · Elite >28%\nSource: a16z (Bryan Kim) — OK=20%, >10% fundable seed, >20% Series A\n\nD60 Active Retention\nDanger <2% · Early 2–6% · Good 6–12% · Great 12–20% · Elite >20%\nSource: a16z framework; D60/D30 >0.6 ratio = healthy (Sequoia)","expandedData":[{"label":"D1 Active Retention","value":"98.9%","description":"Elite (>60%) · a16z (Bryan Kim)"},{"label":"D3 Active Retention","value":"7.8%","description":"Danger (<10%) · Amplitude norms"},{"label":"D7 Active Retention","value":"3.9%","description":"Danger (<8%) · a16z: OK=35%"},{"label":"D14 Active Retention","value":"2.7%","description":"Danger (<5%)"},{"label":"D30 Active Retention","value":"2.4%","description":"Danger (<3%) · a16z: OK=20%"},{"label":"D60 Active Retention","value":"1.0%","description":"Danger (<2%) · Sequoia D60/D30 >0.6"}]},{"title":"Latent Retention (Rolling)","value":"90%","insight":"The gap between active and latent retention is the product roadmap, showing how much retained demand is being left on the table due to the absence of triggers, notifications, and retention-focused product improvements and delight.","description":"D1 Latent Retention — Elite","benchmarkRef":"D1 Latent Retention\nDanger <25% · Early 25–40% · Good 40–55% · Great 55–70% · Elite >70%\nSource: Industry standard\n\nD3 Latent Retention\nDanger <20% · Early 20–35% · Good 35–50% · Great 50–65% · Elite >65%\nSource: Industry standard\n\nD7 Latent Retention\nDanger <15% · Early 15–30% · Good 30–45% · Great 45–60% · Elite >60%\nSource: Amplitude product analytics framework\n\nD14 Latent Retention\nDanger <10% · Early 10–22% · Good 22–35% · Great 35–50% · Elite >50%\nSource: Industry standard\n\nD30 Latent Retention\nDanger <10% · Early 10–18% · Good 18–28% · Great 28–40% · Elite >40%\nSource: Andrew Chen rolling/fixed gap analysis\n\nD60 Latent Retention\nDanger <6% · Early 6–14% · Good 14–24% · Great 24–36% · Elite >36%\nSource: Industry standard — D60/D30 rolling ratio >0.7 = durable pull","expandedData":[{"label":"D1 Latent Retention","value":"90%","description":"Elite (>70%)"},{"label":"D3 Latent Retention","value":"72.9%","description":"Elite (>65%)"},{"label":"D7 Latent Retention","value":"60.1%","description":"Elite (>60%)"},{"label":"D14 Latent Retention","value":"51.7%","description":"Elite (>50%)"},{"label":"D30 Latent Retention","value":"52.9%","description":"Elite (>40%)"},{"label":"D60 Latent Retention","value":"49.12%","description":"Elite (>36%) · D60/D30 rolling >0.7 = durable pull"}]},{"title":"Active / Latent Gap","value":"0.064","insight":"D7 Active/Latent Ratio = D7 Active Retention ÷ D7 Latent Retention. A ratio of 0.10 means 90% of the users who want to return are failing to do so on a predictable cadence. Every point of improvement here compounds directly into growth, retention, and LTV.","description":"D7 Active/Latent Ratio — Danger","benchmarkRef":"D7 Active/Latent Ratio\nDanger <0.10 · Early 0.10–0.25 · Good 0.25–0.45 · Great 0.45–0.65 · Elite >0.65\nSource: Series internal\n\nD30 Active/Latent Ratio\nDanger <0.05 · Early 0.05–0.18 · Good 0.18–0.35 · Great 0.35–0.55 · Elite >0.55\nSource: Series internal\n\nGap Trend (cohort-over-cohort)\nDanger: Widening · Early: Flat · Good: Narrowing 5–10pp · Great: Narrowing 10–20pp · Elite: Narrowing >20pp\nSource: Series internal","expandedData":[{"label":"D7 Active/Latent Ratio","value":"0.064","description":"Danger (<0.10)"},{"label":"D30 Active/Latent Ratio","value":"0.045","description":"Danger (<0.05)"},{"label":"Gap Trend (cohort-over-cohort)","value":"—","description":"Widening = Danger · Narrowing >20pp = Elite"}]},{"title":"DAU/WAU by Week (Read Receipt Users)","value":"34.0%","insight":"","description":"Apr 6–13 DAU/WAU — most recent","expandedData":[{"label":"March Week 1","value":"19.4%","description":"DAU: 35.29 · WAU: 182"},{"label":"March Week 2","value":"19.3%","description":"DAU: 181.57 · WAU: 939"},{"label":"March Week 3","value":"22.9%","description":"DAU: 71.86 · WAU: 314"},{"label":"March Week 4","value":"13.7%","description":"DAU: 128.2 · WAU: 936"},{"label":"Apr 6–13","value":"34.0%","description":"DAU: 46.13 · WAU: 135.5"}]}]},{"title":"Demographics","metrics":[{"title":"Demographics","value":"—","insight":"Demographics data collection in progress. User survey instrumentation shipping Q2 2026. College campus breakdown (UConn, UT Austin camp cohorts) available upon request.","description":"Data collection in progress"}]},{"title":"Trends","metrics":[{"title":"MAU Trends (Active Users)","value":"6,490","insight":"Monthly active users who messaged or reacted.","description":"March 2026 MAU (peak month)","expandedData":[{"label":"January 2026","value":"3,185","description":""},{"label":"February 2026","value":"2,476","description":""},{"label":"March 2026","value":"6,490","description":""},{"label":"April 2026","value":"1,823","description":"Partial month"}]},{"title":"MAU Trends (Latent Users)","value":"15,621","insight":"Latent users = not opted-out.","description":"March 2026 MAU (peak month)","expandedData":[{"label":"January 2026","value":"11,894","description":""},{"label":"February 2026","value":"11,147","description":""},{"label":"March 2026","value":"15,621","description":""},{"label":"April 2026","value":"15,025","description":"Partial month"}]}]},{"title":"Series Algorithm Service (v.6.0)","metrics":[{"title":"Algorithm Service","value":"—","insight":"Algorithm documentation available under NDA. Contact team for access.","description":"Documentation available under NDA"}]},{"title":"North Star Metrics","metrics":[{"title":"North Star Metrics","value":"6.71%","insight":"Taken together, these stats tell you whether Series has a functioning social loop. Formed Contact Rate measures whether new users are building real relationships. DAU/MAU measures whether the product earns daily attention. Reply Rate measures whether content posted into the network generates a response. These are the only subjective benchmarks in this framework, since they are highly platform-specific.\n\nFormed Contact Rate = (Users with ≥1 formed contact by D4) ÷ (Total users at D4) × 100. A formed contact is a back-and-forth exchange (A→B→A→B).","description":"Formed Contact Rate (D4) — Early","benchmarkRef":"Formed Contact Rate (D4)\nDanger <5% · Early 5–15% · Good 15–30% · Great 30–50% · Elite >50%\nSource: Series internal — modeled on Facebook “7 friends in 10 days”\n\nDAU/MAU Stickiness (meaningful)\nDanger <8% · Early 8–18% · Good 18–30% · Great 30–45% · Elite >45%\nSource: a16z (Bryan Kim) — OK=25%, Good=40%, Great=50%+\n\nReply Rate per Share\nDanger <0.05 · Early 0.05–0.15 · Good 0.15–0.35 · Great 0.35–0.65 · Elite >0.65\nSource: Series internal — calibrated against Twitter avg engagement (~0.05)","expandedData":[{"label":"Formed Contact Rate (D4)","value":"6.71%","description":"Early (5–15%) · L90: 6.83% · L30: 7.05%"},{"label":"Formed Contact Rate (All Time)","value":"13.53%","description":"All-time scope · L90: 13.08% · L30: 8.41%"},{"label":"DAU/MAU Stickiness (meaningful)","value":"7.7%","description":"Danger (<8%) · L90: 8.3% · L30: 12.4%"},{"label":"Reply Rate per Share","value":"5.77","description":"Elite (>0.65) · L90: 6.32 · L30: 3.54"}]},{"title":"Share-to-Reply Validation","value":"42.4%","insight":"The demand-side stress test. Standardizes one question across every time horizon: \"If a user posts, does the network respond?\" D7 window is the primary comparable across cohorts.\n\nDaily Share-Reply Rate = (Shares with ≥1 reply within window) ÷ (Total shares) × 100.","description":"Daily Share-Reply Rate (D7) — Good","benchmarkRef":"Daily Share-Reply Rate (D4)\nDanger <15% · Early 15–30% · Good 30–45% · Great 45–60% · Elite >60%\nSource: Series internal — habit-formation window\n\nDaily Share-Reply Rate (D7)\nDanger <25% · Early 25–40% · Good 40–55% · Great 55–70% · Elite >70%\nSource: Series internal — standard weekly window\n\nDaily Share-Reply Rate (D14)\nDanger <30% · Early 30–45% · Good 45–60% · Great 60–75% · Elite >75%\nSource: Series internal\n\nBest Cohort Share-Reply (D7/D13/D20)\nD7: D <20% · E 20–35% · G 35–50% · Gr 50–65% · El >65%\nD13: D <25% · E 25–40% · G 40–55% · Gr 55–70% · El >70%\nD20: D <30% · E 30–45% · G 45–60% · Gr 60–75% · El >75%\nSource: Series internal — cohort comparison metrics","expandedData":[{"label":"Daily Share-Reply Rate (D4)","value":"38.16%","description":"Good (30–45%) · L90: 40.79% · L30: 41.01%"},{"label":"Daily Share-Reply Rate (D7)","value":"42.4%","description":"Good (40–55%) · L90: 45.26% · L30: 45.18%"},{"label":"Daily Share-Reply Rate (D14)","value":"46.15%","description":"Good (45–60%) · L90: 49.53% · L30: 47.81%"},{"label":"Best Cohort Share-Reply (D7)","value":"41.31%","description":"Good (35–50%) · L90: 44.61% · L30: 39.87%"},{"label":"Best Cohort Share-Reply (D13)","value":"45.41%","description":"Good (40–55%) · L90: 49.43% · L30: 42.48%"},{"label":"Best Cohort Share-Reply (D20)","value":"48.52%","description":"Good (45–60%) · L90: 53.07% · L30: 43.79%"}]}]},{"title":"Engagement","metrics":[{"title":"Engagement Metrics","value":"5.99x","insight":"The key diagnostic is the gap between any-activity DAU/MAU and meaningful DAU/MAU — a large gap means a lurker-heavy product where users consume but don’t contribute. For a messaging-native product like Series, reply-to-share ratio and thread depth are more important than session time because the core value is conversation, not consumption.\n\nMeaningful = user who has sent a message.","description":"Reply-to-Share Ratio (All Time)","benchmarkRef":"DAU/MAU (meaningful)\nDanger <8% · Early 8–18% · Good 18–30% · Great 30–45% · Elite >45%\nSource: a16z (Bryan Kim) — OK=25%, Good=40%, Great=50%+\n\nL5+ Weekly (meaningful)\nDanger <10% · Early 10–20% · Good 20–30% · Great 30–45% · Elite >45%\nSource: a16z (Bryan Kim) — Facebook L6/7 origin\n\nShares per DAU\nDanger <0.08 · Early 0.08–0.25 · Good 0.25–0.6 · Great 0.6–1.2 · Elite >1.2\nSource: Series internal\n\nReplies per DAU\nDanger <0.1 · Early 0.1–0.4 · Good 0.4–0.9 · Great 0.9–2.0 · Elite >2.0\nSource: Series internal\n\nReply-to-Share Ratio\nDanger <0.5x · Early 0.5–1.5x · Good 1.5–3x · Great 3–6x · Elite >6x\nSource: Industry comps — Twitter ~4–6x at scale\n\nThread Depth (avg replies/thread)\nDanger <0.8 · Early 0.8–2.0 · Good 2.0–4.5 · Great 4.5–8 · Elite >8\nSource: Series internal — Reddit ~3–5 avg\n\nReciprocity Rate\nDanger <8% · Early 8–20% · Good 20–38% · Great 38–58% · Elite >58%\nSource: Series internal","expandedData":[{"label":"DAU/MAU (meaningful)","value":"6.5%","description":"Danger (<8%) · L90: 7.1% · L30: 9.5%"},{"label":"L5+ Weekly (meaningful)","value":"1.8%","description":"Danger (<10%) · L90: 1.9% · L30: 3.3%"},{"label":"Shares per DAU","value":"0.15","description":"Early (0.08–0.25) · L90: 0.14 · L30: 0.14"},{"label":"Replies per DAU","value":"0.88","description":"Good (0.4–0.9) · L90: 0.93 · L30: 1.3"},{"label":"Reply-to-Share Ratio","value":"5.99x","description":"Great (3–6x) · L90: 6.5x · L30: 9.41x"},{"label":"Thread Depth (avg replies/thread)","value":"10.92","description":"Elite (>8) · L90: 11.08 · L30: 7.37"},{"label":"Reciprocity Rate","value":"32.4%","description":"Good (20–38%) · L90: 31.87% · L30: 26.34%"}]}]},{"title":"Activation","metrics":[{"title":"Activation Metrics","value":"76.7%","insight":"Activation measures how efficiently the product converts a new signup into someone who has experienced the core value proposition. For Series, the aha moment is receiving a reply to your first share — it’s the first time the network proves it works. TTFV.","description":"Onboarding Completion (All Time)","benchmarkRef":"Onboarding Completion\nDanger <25% · Early 25–45% · Good 45–65% · Great 65–82% · Elite >82%\nSource: Appsflyer/Adjust\n\nActivation Rate (first share)\nDanger <10% · Early 10–25% · Good 25–45% · Great 45–65% · Elite >65%\nSource: Series internal\n\nTime to First Share\nDanger >48hr · Early 6–48hr · Good 1–6hr · Great 10–60min · Elite <10min\nSource: Reforge activation framework\n\nFirst Reply Speed (median)\nDanger >72hr · Early 24–72hr · Good 4–24hr · Great 30min–4hr · Elite <30min\nSource: Series internal","expandedData":[{"label":"Onboarding Completion","value":"76.7%","description":"Great (65–82%) · L90: 35% · L30: 17%"},{"label":"Activation Rate (first share)","value":"6.11%","description":"Danger (<10%) · L90: 5.22% · L30: 3.43%"},{"label":"Time to First Share","value":"19.3min","description":"Great (10–60min) · L90: 2.6hr · L30: 39.5min"},{"label":"First Reply Speed (median)","value":"5.2d","description":"Danger (>72hr) · L90: 5.8d · L30: 3.2d"}]},{"title":"Activation Funnel","value":"76.7% → 6.11%","insight":"End-to-end activation funnel from install to core value realization.\n\nFull Funnel: Install → D30 = (Users active on D30) ÷ (Total installs) × 100.","description":"Download → Onboarding → First Share → Reply → Contact Formed","expandedData":[{"label":"Download","value":"100%","description":""},{"label":"Onboarding Completion","value":"76.7%","description":"Great (65–82%)"},{"label":"First Share","value":"6.11%","description":"Danger (<10%)"},{"label":"First Reply Received","value":"—","description":"Instrumentation pending"},{"label":"Contact Formed (D4)","value":"6.71%","description":"Early (5–15%)"}]}]},{"title":"Virality & Growth","metrics":[{"title":"Virality & Growth","value":"—","insight":"K-factor and viral cycle time together determine the organic growth engine: K tells you what fraction of a new user each existing user generates, and cycle time tells you how fast that generation happens. Even a K of 0.5 with a 3-day cycle time compounds dramatically. Quick Ratio separates real growth from churn-masked growth. CAC and referred-user retention determine whether paid acquisition is viable long-term.\n\nK-Factor = (Avg invites per user) × (Invite accept rate). K > 1 = organic viral growth.\n\nCohort data from select 50-user pool experiment planned Week 1–2 of May.","description":"K-Factor instrumentation shipping mid-May 2026","benchmarkRef":"K-Factor (Viral Coefficient)\nDanger <0.15 · Early 0.15–0.4 · Good 0.4–0.7 · Great 0.7–1.0 · Elite >1.0\nSource: David Skok / For Entrepreneurs — K>1 = organic viral growth\n\nViral Cycle Time\nDanger >21d · Early 10–21d · Good 5–10d · Great 2–5d · Elite <2d\nSource: David Skok\n\nInvite Accept Rate\nDanger <5% · Early 5–15% · Good 15–30% · Great 30–50% · Elite >50%\nSource: Branch.io data\n\nReferred User D30 Retention\nDanger <8% · Early 8–15% · Good 15–25% · Great 25–40% · Elite >40%\nSource: Dropbox/PayPal/Airbnb case studies\n\nMoM User Growth\nDanger <5% · Early 5–15% · Good 15–30% · Great 30–60% · Elite >60%\nSource: a16z (Bryan Kim)\n\nWoW New User Growth\nDanger <1% · Early 1–5% · Good 5–12% · Great 12–25% · Elite >25%\nSource: Y Combinator — Paul Graham “7% WoW” standard\n\nQuick Ratio (MAU)\nDanger <1.0 · Early 1.0–2.0 · Good 2.0–4.0 · Great 4.0–8.0 · Elite >8.0\nSource: IVP (Mamoon Hamid) — QR>4 = fast growth + healthy retention\n\nBlended CAC\nDanger >$25 · Early $10–25 · Good $3–10 · Great $0.50–3 · Elite <$0.50\nSource: Industry standard — campus activation target <$5/user","expandedData":[{"label":"K-Factor (Viral Coefficient)","value":"—","description":"Benchmark: >1.0 = viral growth"},{"label":"Viral Cycle Time","value":"—","description":"Benchmark: <2d = Elite"},{"label":"Invite Accept Rate","value":"—","description":"Benchmark: 15–30% = Good"},{"label":"Referred User D30 Retention","value":"—","description":"Benchmark: 25–40% = Great"},{"label":"MoM User Growth","value":"—","description":"Benchmark: 15–30% = Good"},{"label":"WoW New User Growth","value":"—","description":"Benchmark: 5–12% = Good"},{"label":"Quick Ratio (MAU)","value":"—","description":"Benchmark: 2.0–4.0 = Good"},{"label":"Blended CAC","value":"—","description":"Benchmark: $3–10 = Good"}]}]},{"title":"Network Effects","metrics":[{"title":"Network Effects & Conversion","value":"68.26%","insight":"Network effects in consumer social — they’re what makes the product defensible and what makes user acquisition compound rather than reset. Creator participation measures supply-side health: are enough users posting to keep the feed fresh? Reply-back propagation measures whether conversations continue past the first exchange — the root cause of retention in any messaging product.\n\nCreator Participation Rate = (Users with ≥1 share in trailing 28d) ÷ (MAU 28d) × 100.\nReply-back Propagation = (Replies with creator follow-up) ÷ (Total replies) × 100.","description":"Reply-back Propagation (All Time) — Elite","benchmarkRef":"Creator Participation Rate\nDanger <5% · Early 5–15% · Good 15–30% · Great 30–50% · Elite >50%\nSource: 1% rule — Wikipedia/YouTube/Twitter participation data\n\nReply-back Propagation\nDanger <10% · Early 10–20% · Good 20–35% · Great 35–55% · Elite >55%\nSource: Series internal — conversation continuation probability","expandedData":[{"label":"Creator Participation Rate","value":"22.4%","description":"Good (15–30%) · L90: 21.5% · L30: 18.9%"},{"label":"Reply-back Propagation","value":"68.26%","description":"Elite (>55%) · L90: 71.15% · L30: 71.93%"}]}]},{"title":"Network Health","metrics":[{"title":"Network Density & Graph Health","value":"6.3","insight":"Contact (B&F): Bidirectional back-and-forth — A→B→A→B. Tracks genuine social bonds.\nConnection (B&R): One-direction reply received — A→B. Tracks reach.\nFormed Contact Rate: % of users who complete a B&F by D4. North Star proxy with ~70% D30 retention correlation.\n\nConnections per User = MEDIAN(unique B&R partners per user).","description":"Connections (B&Rs) per User (median) — Good","benchmarkRef":"Connections (B&Rs) per User (median)\nDanger <1 · Early 1–3 · Good 3–8 · Great 8–20 · Elite >20\nSource: Facebook growth team, a16z network effects framework\n\nIsolated Node Rate (D7)\nDanger >70% · Early 50–70% · Good 30–50% · Great 15–30% · Elite <15%\nSource: Network science — isolated nodes churn at ~100% rate\n\nNetwork Degree Distribution\nDanger: Top 5% >80% · Early: 60–80% · Good: 40–60% · Great: 25–40% · Elite: <25%\nSource: Barabási-Albert model — extreme power-law = fragile network","expandedData":[{"label":"Connections (B&Rs) per User (median)","value":"6.3","description":"Good (3–8) · L90: 6.6 · L30: 3.6"},{"label":"Isolated Node Rate (D7)","value":"—","description":"Benchmark: <15% = Elite · Users w/ 0 contacts at D7"},{"label":"Network Degree Distribution","value":"—","description":"Top 5% engagement share · Lower = healthier network"}]}]},{"title":"Power Users","metrics":[{"title":"Power User Signal","value":"44.6%","insight":"Power Score = # shares created + # replies sent to unique shares.\n\nTop 10% of users drive ~45% of all activity consistently across time windows — a healthy power-user concentration for early-stage consumer social. Next step: dog-feed product changes to this cohort and instrument qualitative feedback.","description":"Top 10% Share of Activity (Since 2026)","expandedData":[{"label":"Active Users","value":"1,508","description":"L90: 1,359 · L30: 591"},{"label":"Top 10% Count","value":"151","description":"L90: 136 · L30: 60"},{"label":"Top 10% Share of Activity","value":"44.6%","description":"L90: 44.8% · L30: 45.1%"},{"label":"Top 10% Median Score","value":"12","description":"L90: 12 · L30: 11"},{"label":"Top 10% Floor (min score)","value":"7","description":"L90: 8 · L30: 7"},{"label":"Overall Median Score","value":"2","description":"L90: 2 · L30: 2"}]}]},{"title":"Reactivation","metrics":[{"title":"Reactivation / Migrations","value":"1.69%","insight":"Recovering users who were lost due to infra failure, crashes, number failures, opt-outs etc. — and whether they stayed the second time.\n\nMigration 1: From 1.5M deployed agents (v1 accounts) → 50K users today.\nCalculation: 1,241,051 / 21,000 × 100 = 1.69%.","description":"Migration 1 (Twilio → SB) — Early","benchmarkRef":"Migration 1 (Twilio → SB) Conversion\nDanger <1% · Early 1–4% · Good 4–10% · Great 10–20% · Elite >20%\nSource: Braze/Iterable consumer social benchmarks\n\nMigration 2 (SB → Linq) Conversion\nDanger <1% · Early 1–4% · Good 4–10% · Great 10–20% · Elite >20%\nSource: Braze/Iterable consumer social benchmarks\n\nMigrated User D7 Retention\nDanger <5% · Early 5–12% · Good 12–25% · Great 25–40% · Elite >40%\nSource: Facebook growth team — resurrection quality metric","expandedData":[{"label":"Migration 1 (Twilio → SB)","value":"1.69%","description":"Early (1–4%) · Braze/Iterable benchmarks"},{"label":"Migration 2 (SB → Linq)","value":"—","description":"Benchmark: 4–10% = Good"},{"label":"Migrated User D7 Retention","value":"—","description":"Benchmark: 12–25% = Good"}]}]},{"title":"Trust & Safety","metrics":[{"title":"Trust, Safety & Platform Integrity","value":"In compliance","insight":"","description":"Underage User Safeguard Compliance","benchmarkRef":"User-Reported Safety Satisfaction\nDanger <30% · Early 30–50% · Good 50–70% · Great 70–85% · Elite >85%\nSource: ADL Online Hate & Harassment Survey methodology\n\nUnderage User Safeguard Compliance\nDanger: Non-compliant · Early: Partial · Good: Meets min · Great: Exceeds · Elite: Best-in-class\nSource: COPPA / KOSA / Age Appropriate Design Code","expandedData":[{"label":"User-Reported Safety Satisfaction","value":"—","description":"Benchmark: 50–70% = Good"},{"label":"Underage User Safeguard Compliance","value":"In compliance","description":"Good (Meets min) · COPPA / KOSA"}]}]},{"title":"Performance","metrics":[{"title":"Performance & Reliability","value":"—","insight":"","description":"Instrumentation in progress","benchmarkRef":"API Latency (P50)\nDanger >1s · Early 500ms–1s · Good 200–500ms · Great 50–200ms · Elite <50ms\nSource: AWS/GCP recommended targets\n\nUptime %\nDanger <95% · Early 95–98% · Good 98–99.5% · Great 99.5–99.9% · Elite >99.9%\nSource: Industry SLA tiers\n\nMessage Delivery Rate\nDanger <70% · Early 70–82% · Good 82–92% · Great 92–98% · Elite >98%\nSource: OneSignal / Firebase Cloud Messaging benchmarks\n\nMessage Delivery Latency\nDanger >10s · Early 3–10s · Good 1–3s · Great 200ms–1s · Elite <200ms\nSource: iMessage / WhatsApp standard","expandedData":[{"label":"API Latency (P50)","value":"—","description":"Benchmark: 200–500ms = Good"},{"label":"Uptime %","value":"—","description":"Benchmark: 98–99.5% = Good"},{"label":"Message Delivery Rate","value":"—","description":"Benchmark: 82–92% = Good"},{"label":"Message Delivery Latency","value":"—","description":"Benchmark: 1–3s = Good"}]}]}],"version":4}