以下为对周报数据的结构化总结与可执行建议。

一、核心结论

• 规模与效率
  • 付费渠道合计：花费32万，获客7600，付费渠道加权CAC≈42.1元/客
  • 自然获客：2100，占总获客的≈21.6%
  • 渠道A：CAC=50元，LTV30=72元，30日回收倍数≈1.44，单位毛利≈22元
  • 渠道B：CAC=33元，LTV30=58元，30日回收倍数≈1.76，单位毛利≈25元
  • 结论：B在回收倍数与单位毛利上优于A；A的用户质量（LTV30）更高但获取成本偏高
• 漏斗表现
  • 点击→注册：A 7%，B 9%（A在首触转化偏弱）
  • 注册→付费：A 10%，B 8%（A在变现转化较强）
  • 点击→付费整体：A≈0.70%，B≈0.72%（相近）
  • 由现有转化率反推估算（以本周量级为基准）
    • A：约57.1万点击、4.0万注册、CPC≈0.35元
    • B：约50.0万点击、4.5万注册、CPC≈0.24元
• 时段与日趋势
  • 周三、周六获客高；20:00–22:00 CTR高
  • 结论：存在明确的日与时段窗口，可集中预算获取更优的流量质量与成本

二、趋势与问题定位

• 渠道差异
  • A：流量端转化（点→注）偏弱，后端变现（注→付）与LTV更好；推断为上游触达/落地页效率不足
  • B：流量端效率高、成本低，但后端变现与LTV偏弱；推断为注册后培育/付费激励和产品首日体验有优化空间
• 整体回收
  • 两渠道30日均为正回收，B的边际回报更高；在无明显投放衰减曲线信息前，优先加大B的预算利用率

三、预算建议（总上限40万，库存充足）

• 分配策略
  • 以ROI最大化为目标：优先投放渠道B，同时保留A以分散风险并维持高LTV用户占比
  • 建议起配：B占80%–90%（32–36万），A占10%–20%（4–8万）
• 示例测算（80%投B，20%投A，按当前周平均效率）
  • A：8万预算，预计付费≈1600，LTV收入≈11.52万，毛利≈3.52万
  • B：32万预算，预计付费≈9697，LTV收入≈56.24万，毛利≈24.24万
  • 合计：预计付费≈1.13万，LTV收入≈67.76万，毛利≈27.76万，30日ROI≈1.69
  • 若无递增成本效应，投入越向B集中，理论毛利越大；实际执行需监控边际CAC变化
• 投放时段与排期
  • 将当日40%–50%的预算集中在20:00–22:00；周三、周六放大日预算系数（如+20%–30%）
  • 非高效时段与非高效日适当降出价与预算上限，避免稀释ROI

四、渠道优化重点

• 渠道A（提升点击→注册）
  • 落地页与首屏加载优化、减少表单字段、提升素材与落地页一致性，AB测试提升7%→≥8.5%
  • 目标：在保持CAC≤50的前提下，提高上游转化从而摊薄CPC
• 渠道B（提升注册→付费与LTV30）
  • 加强注册后首日引导与激励（新手礼包、定向优惠、短信/Push引导支付）
  • 优化首购路径与支付动线；以把8%提升到≥9%为阶段目标
  • LTV分层运营（新客生命周期任务、个性化推荐）以提升LTV30至60+
• 通用策略与门槛
  • 以30日回收为准的CAC红线：
    • A：CAC≤72元为不亏损红线；建议运营目标CAC≤48元（对应回收≥1.5）
    • B：CAC≤58元为不亏损红线；建议运营目标CAC≤38.7元（对应回收≥1.5）
  • 建立分时段出价系数与频控，避免高频低效曝光

五、监控与下一步数据需求

• 逐日监控边际CAC随预算提升的变化，设立自动调价/停投阈值（如CAC滚动3天均值超目标10%即降档）
• 回传分时段与素材级别转化数据，完善出价时段系数
• 建立渠道分层LTV曲线（7/14/30/60天）与队列分析，验证预算扩张对LTV的影响

总结

• 本周B的单位经济性优于A，建议在40万上限内以B为主的投放架构（80%–90%），并在周三、周六与20:00–22:00集中预算以放大高效窗口。A保留10%–20%预算并重点优化上游转化。全程以30日回收与目标CAC门槛做动态调整，确保扩量同时维持ROI。

Weekly summary of key findings

• Core KPIs aligned: DAU, ARPU, and Pay Rate are set as the unified headline metrics for weekly reporting.
• Product change: App version 3.2 introduced push notification throttling. Impact on engagement and monetization needs validation.
• Anomaly detected: iOS page views (PV) on Tuesday were 8% below the baseline mean. Requires root-cause analysis and baseline standardization.
• Cohort performance:
  • New users: 0–7 day retention at 18% (assumed Day-7 retention; see terminology).
  • Existing customers: repurchase rate at 26% for the period.
• Channel mix: “Content Alliance” contributed 28% of traffic/acquisition (denominator to be standardized; see terminology).

Standardized terminology and metric definitions (to confirm)

• DAU: Count of distinct users with ≥1 qualified session in a calendar day (UTC or specified business timezone), de-duplicated across devices/platforms; exclude internal/test traffic and known bots.
• ARPU: Revenue per active user in a period = (Net revenue in period) / (Active users in same period). Net revenue should be defined to include/exclude taxes, refunds, discounts, and platform fees consistently.
• Pay Rate: Paying users / Active users in a period. Paying users are users with ≥1 successful payment in the period.
• PV (Page/Screen Views): Count of in-app screen views recorded by analytics SDK; define whether background prefetches and auto-refreshes are included; platform-specific differences documented.
• Retention (New user D7): Percentage of new users who were active on Day 7 after their first day (D0). “New user” defined as first-ever activation within the period; re-installs to be handled explicitly (count as returning or new).
• Repurchase rate (Existing customers): Among users with at least one purchase before the period, the share who made ≥1 additional purchase during the period. Specify period length (weekly) and whether cross-category purchases count equally.
• Channel contribution: Share of [denominator] attributed to “Content Alliance.” Recommended denominator for weekly reporting: share of new users (installs/first activations). Alternative: share of paying users or revenue by channel; choose one primary and apply consistently.
• Push throttling (v3.2): Policy limiting notification frequency/volume per user or segment. Define throttle rules (max per day/week, cool-off logic) and the scope (iOS/Android/both; which message types).
• Anomaly baseline definition: Use both (and report explicitly):
  • Same-weekday baseline: average of the last 4 Tuesdays.
  • Rolling baseline: 7-day moving average adjusted for seasonality. Flag anomalies when deviation exceeds pre-set thresholds (e.g., >3σ or >X% with p<0.05 after seasonality adjustment).

Implications to monitor

• Push throttling may reduce PV/sessions per user but can improve unsubscribe/opt-out rates and long-term retention. Net effect on ARPU and Pay Rate is unknown; requires causal analysis.
• The -8% iOS PV dip could stem from seasonality, data capture issues, rollout effects of v3.2, crashes/performance degradation, or acquisition fluctuations. Validate before attributing to product change.
• New user D7 retention at 18% and existing user repurchase rate at 26% set the current baselines for lifecycle metrics; track by source, platform, and version for quality assessment.
• “Content Alliance” at 28% indicates a material share of mix; evaluate quality (retention, Pay Rate, ARPU) vs. other channels to guide spend and optimization.

Follow-up analysis checklist

1. Data quality and instrumentation

• Verify iOS PV pipeline health on Tuesday: event volume by app version, SDK status, delayed batches, duplicate drops, and any analytics config changes.
• Check crash rate, app start failures, and latency (p95/p99) by hour on Tuesday vs. baseline.
• Confirm time zone alignment for daily cuts; ensure internal/test traffic is excluded.

2. Anomaly deep dive (iOS PV -8% on Tuesday)

• Break down by:
  • App version (3.2 vs. prior), device model, iOS version.
  • Geography, hour-of-day, traffic source (organic vs. paid).
  • Entry points (push deep links vs. app icon vs. other).
• Seasonality/context:
  • Compare to prior 4 Tuesdays and holiday/major-event calendars.
  • Compare Android PV as a control for macro factors.
• Causal leads:
  • Push volume/delivery/open rates on Tuesday vs. prior.
  • Any rollout/feature flags activated near the time.
  • Store outages or API partner incidents.
• Outcome: classify as data issue, product impact, traffic fluctuation, or external factor; quantify contribution of each.

3. v3.2 push throttling impact assessment

• Design:
  • Pre-post windows (e.g., 14 days before vs. after stable adoption); exclude ramp days.
  • Difference-in-differences using Android or non-throttled segments as control.
• Metrics:
  • Engagement: DAU, sessions/user, PV/user, session length, open rates, opt-outs/unsubscribes, uninstalls.
  • Monetization: Pay Rate, ARPPU, ARPU, conversion funnel (view → add-to-cart → purchase).
  • Reliability: crash rates, cold-start time, push delivery rate.
• Segmentation: user tenure (new vs. existing), high-frequency vs. low-frequency users, channel, region.
• Decision: determine net impact and whether to adjust throttle rules (by segment or content type).

4. Cohort and monetization diagnostics

• New users: retention curve D1/D3/D7/D14, new-to-activation funnel, first-session depth (PV, time on app), first-purchase conversion and time-to-first-purchase.
• Existing users: repurchase by RFM segments, time-since-last-purchase buckets, and by channel/content exposure.
• Unit economics: Pay Rate and ARPU by cohort/channel; ARPPU stability; early LTV estimates (e.g., 30/60-day) if available.

5. Channel mix and “Content Alliance” quality

• Define the denominator for “contribution” and lock it.
• Compare “Content Alliance” vs. other channels on:
  • New user D7 retention, Pay Rate, ARPU/ARPPU, refund rate.
  • Fraud/invalid traffic screens (if applicable).
  • CAC/ROAS (if cost data available); incremental lift via geo/temporal holdouts where possible.
• Actionables: budget allocation guidance based on quality-adjusted return, not just volume share.

6. Reporting and alerts

• Publish a weekly dashboard with:
  • Headline KPIs (DAU, ARPU, Pay Rate) + guardrails.
  • Platform split (iOS/Android), version split (3.2+), and channel split.
  • Cohort retention and repurchase panels.
• Set anomaly alerts using seasonality-aware baselines and severity scoring; document runbooks for triage.

7. Documentation and governance

• Finalize metric definitions and owners; record in a metrics catalog.
• Versioning: record v3.2 rollout timeline, throttle configurations, and any subsequent tweaks for auditability.

Assumptions and items needing confirmation

• “0–7 day retention 18%” interpreted as D7 retention; confirm if it denotes D1–D7 any-day retention instead.
• “Repurchase rate 26%” defined over existing customers within the weekly period; confirm numerator/denominator and window.
• “Content Alliance 28%” contribution: confirm denominator (new users, sessions, PV, paying users, or revenue).
• Specify the timezone and baseline definition used for “-8% vs. mean.”