AI 提示词：数据洞察总结生成器

Management decision brief: Core event instrumentation and Login Onboarding A/B test

Important note

• No underlying metrics or experiment outputs were provided. The summary below defines the decision framework, prioritization, and validation plan required for management action. Replace placeholders with observed values (e.g., login completion +X pp, 95% CI [a, b], p=…).

Decision summary

• Proceed to rollout if:
  • Variant improves login completion rate by at least the pre-registered MDE (e.g., ≥+2–3 percentage points absolute) and the 95% CI excludes 0, with no degradation in guardrail metrics (crash/error rate, latency, support tickets).
  • Activation within 24h (first key action after login) is neutral or positive, and Day-1 retention is neutral or positive.
• Iterate and re-test if:
  • Lift is positive but the CI overlaps 0, or guardrails show material degradation.
  • Lift is concentrated in a narrow segment with neutral/negative effect elsewhere; proceed with targeted rollouts and segment-specific variants.
• Revert if:
  • Variant is neutral/negative on login completion or reduces activation/retention, or materially harms guardrails.

Core insights to extract and report (fill with results)

• Event instrumentation quality
  • Coverage: % sessions with required core events present; duplication rate; late or missing timestamps; client vs server timestamp skew.
  • Identity: user_id/device_id stability; cross-device merge rate; anonymous-to-logged-in mapping completeness.
  • Schema consistency: event/property null rates; cardinality explosions; version drift.
• Funnel performance (Control vs Variant)
  • Views of login → Start → Submit → Error → Success conversion by step, device, OS, browser, locale, acquisition channel.
  • Primary: Login completion rate (absolute pp and relative lift, CI).
  • Secondary: Time-to-login, error rate by reason code, OAuth vs email/password completion rate.
  • Downstream: Activation within 24h (first key action), Day-1/Day-7 retention, First-week engagement (events/user), monetization proxy if applicable.
  • Guardrails: App/web crashes, backend 5xx, p95 latency, support contacts per 1k logins, privacy/consent violations.

Iteration priorities (ordered, impact/effort balanced)

1. Fix instrumentation reliability (P0)
   • Enforce a minimal core taxonomy across client and server:
     • view_login, start_login, submit_login, login_error (error_code), login_success
     • onboarding_start, onboarding_step_{n}, onboarding_complete
     • first_key_action (name), activation_complete (definition-aligned), session_start
   • Ensure idempotency keys, deduplication, event versioning, client/server timestamps, and schema validation in the pipeline.
   • Implement unified identity: stable user_id; link anonymous_id → user_id on login; backfill historical join keys.
2. Resolve top funnel frictions found in data (P0)
   • Prioritize the step with highest drop-off or error spikes (e.g., password complexity, SMS OTP failure, email delivery).
   • Default to higher-converting auth providers (e.g., OAuth) where available; degrade gracefully to email.
   • Introduce real-time field validation, clearer error copy with actionable guidance, and progressive disclosure for optional fields.
3. Improve experiment design and analysis rigor (P0)
   • Use CUPED or pre-experiment covariates to reduce variance.
   • Stratify randomization by device, locale, and channel; pre-register primary/secondary metrics and guardrails.
   • Prevent contamination: one variant per user across devices; exclude employees, test traffic, and previously assigned users.
4. Onboarding content iteration (P1)
   • Test single-step vs multi-step, contextual hints, and progress indicators targeted to the first key action.
   • Personalize by acquisition channel or intended use-case if heterogeneity is strong.
5. Performance and reliability (P1)
   • Set SLOs: p95 page load <2s, auth API p95 <300ms, error rate <0.5%. Add canary alarms per variant.
6. Analytics & governance (P2)
   • Build a durable funnel dashboard with drill-down by step, segment, and error codes.
   • Establish event catalog, data contracts, and automated tests for schema drift.

Validation plan

• Primary and secondary metrics

  • Primary: Login completion rate (per-user, first session after exposure).
  • Secondary: Activation within 24h (first key action), Day-1 retention, time-to-first-value, error rate per step.
  • Guardrails: p95 latency, crash/5xx rates, support tickets per 1k users, privacy/compliance flags.

• Experiment design

  • Unit: User-level randomization; cookie → device_id → user_id reconciliation.
  • Sample size/power: Pre-register alpha=0.05, power=0.8; compute N per arm for absolute MDE (e.g., +2 pp). Apply CUPED to reduce N if baseline covariates available.
  • Duration: Run through full weekly cycle (≥14 days) to capture seasonality; avoid mid-experiment UX changes.
  • Sequential monitoring: Use alpha-spending or group-sequential boundaries to control type I error if peeking.
  • Analysis: Intention-to-treat; difference-in-proportions with robust SEs; logistic regression with covariates for sensitivity; CUPED-adjusted estimates; Benjamini–Hochberg for multiple variants/metrics.
  • Heterogeneity: Report effects by device, locale, channel; interaction tests; control family-wise error where applicable.

• Pre-launch QA

  • A/A test to validate randomization and metric stability (p-value uniformity, variant share ≈50/50 within strata).
  • Event parity test: Each core event within 1–2% relative between arms prior to treatment exposure; missingness <1%.
  • Synthetic flows to validate error_code coverage and timestamps ordering.

• Ramp and rollout

  • Ramp: 1% → 10% → 50% → 100% with real-time guardrails; auto-halt thresholds (e.g., error rate +50% over baseline or p95 latency +25%).
  • Post-rollout: 7–14 days of monitoring; regression alerting; re-verify activation and retention stability.

Data and reporting package required for sign-off

• Baseline and variant metrics:
  • Login completion: control vs variant (point estimate, absolute pp, relative lift, 95% CI, p-value).
  • Activation within 24h, Day-1 retention: same stats.
  • Stepwise funnel drops and error_code distribution deltas.
  • Guardrails: crash/5xx, p95 latency, support tickets, privacy events.
• Data quality:
  • Event coverage %, null/dup rates, clock skew, identity linkage rate, schema drift report.
• Segmentation:
  • Breakouts by device, OS, browser, locale, acquisition channel; interaction p-values.

Next actions

• Provide the experiment output and event quality diagnostics to populate the decision gates above.
• If Variant meets rollout criteria, schedule a progressive ramp with guardrails and begin P0 fixes to instrumentation and top friction step concurrently.
• If results are inconclusive, prioritize P0 instrumentation, re-run with CUPED/stratification, and test the most plausible friction fix as Variant C.

If you share the A/B results (counts per arm, baseline rates, stepwise funnel counts, and guardrail metrics), I will compute effect sizes, confidence intervals, power, and provide a concrete go/no-go recommendation with a tailored iteration plan.

以下为基于“多渠道投放数据 + 注册转化率 + 7日留存”的整合分析洞察与可执行建议。由于未提供具体数据，输出为可直接套用的决策规则与实验清单，避免臆测。

一、指标与口径统一（用于跨渠道可比）

• 基础口径
  • 注册转化率 CVR_reg = 注册数 / 点击数（或会话数，需统一口径）
  • 7日留存 D7 = 7日内活跃用户 / 注册当日用户（留存按获客日 cohort 计算）
• 统一效率指标
  • 每个7日留存注册的成本（CPRU）= 花费 / (注册数 × D7)
  • 若可估算早期LTV，则用 LTV7 或 PredLTV 替代 D7，效率分数 = LTV7 / CAC
• 归因与去重
  • 统一UTM/事件映射；按cohort去重注册；iOS使用SKAN或聚合回传，必要时用D3留存作为D7代理进行短期决策

二、核心洞察（规则化）

• 渠道与投放包的效率以CPRU为主、CVR_reg和D7为辅：高CVR但低D7通常为低质承诺或受众错配；低CVR但高D7为高意向但漏斗前端弱
• 创意与落地页一致性直接影响D7：点击预期与产品首屏不一致时，D7显著下滑
• 创意疲劳可用“周环比CTR↓且频次↑且CVR_reg↓”三信号判定，需及时轮转
• 规模与效率的边际：单日放量增长过快（>30%）常伴随CPRU恶化，应渐进式扩量

三、预算调整建议（可执行流程）

• 排序与过滤
  1. 用近14–28天滚动cohort按“渠道/系列/广告组/创意”计算CPRU
  2. 剔除样本不足单元（注册数<阈值，建议≥100；或点击数<1,000）
• 相对效率与迁移额度
  • 相对效率 RE_i = 中位CPRU_all / CPRU_i
  • 增配规则：RE_i>1 且规模稳定的单元，增配10–20%；RE_i>1.2 可逐步增至30%，每3天评估一次
  • 减配规则：RE_i<0.8 且有显著性差异（两比例或比值检验通过，FDR校正），减配10–30%
  • 护栏：单单元日内预算变动≤30%；维持平台学习期最低花费；为头部单元设上限防止迅速边际恶化
• 质量优先
  • 在同等CAC下，优先D7更高的渠道/受众/创意；当月目标偏增长时，允许在CPRU可接受范围内换取规模
• 混合策略
  • 搜索/品牌词等高D7单元作为“效率底座”；社交流量用于扩量但需严管频控与创意轮转
  • iOS短期用D3或首周事件价值作为优化目标，Android可直控tCPA/tROAS

四、创意方向（基于“CVR_reg × D7”四象限）

• 高CVR × 高D7：主力素材，做轻迭代扩量（多切尺寸、开头3秒变体、CTA颜色/文案微调）
• 高CVR × 低D7：对齐承诺与首屏体验；减少“强激励/夸张收益”措辞；收紧受众或优化落地页相关性
• 低CVR × 高D7：保留核心价值点，改首屏钩子、前3秒节奏、信息层级与可读性，提高点击与理解
• 低CVR × 低D7：淘汰
• 通用创意要点
  • 一致性：广告承诺=落地页首屏=首程功能路径
  • 结构化演示：问题-方案-证据-行动；加入社会证明（评分/案例）
  • 素材轮转：设定疲劳阈值（如CTR周降>20%且频次>4）自动下线

五、短期实验清单（2–4周内）

• 落地页/注册流程
  • 表单简化：一步式 vs 多步引导；社交登录开关
  • 首屏承诺一致性：广告价值点与落地页首屏文案/视觉一致 vs 常规模块
  • 目标与判停：CVR_reg提升≥相对10%，D7不下降；两比例检验，功效≥80%
• 竞价与优化目标
  • tCPA vs 价值优化（以“注册后关键事件”或D3/D7 proxy）；分广告组AB
  • 判停：CPRU改善≥15%且规模不降
• 频控与分时
  • 频次上限2/3/5对比；分时投放（高效时段白名单）
  • 判停：CPRU与CVR_reg综合改善，且展示损失可控
• 受众策略
  • 相似人群1% vs 3–5%；兴趣包拆分与去重；品牌/竞品关键词精确 vs 广泛+否词
  • 判停：D7与CPRU双指标优化
• 创意变体
  • 钩子（价格/痛点/场景/功能演示）× 开头3秒节奏 × 字幕/画幅
  • 判停：创意级CPRU与CTR双优
• 激励与教育
  • 首次优惠/引导教程A/B，评估D7净效应（避免“空增长”）
• 反作弊/低质流量筛选
  • 异常点击率、极短停留、设备指纹规则阈值AB，观察CPRU与D1/D7改善
• 归因与数据质量
  • UTM与事件字典AB（精细化来源标注 vs 现状），提升可测性与归因匹配率

六、测量与统计保障

• 样本量与显著性
  • 两比例检验估算样本：n≈16·p·(1−p)/Δ²（p为基线转化率，Δ为期望提升幅度）；用Wilson区间；多实验用FDR控制
• 时间窗与延迟
  • 用获客日cohort对齐D7；iOS使用延迟补数；评估周期至少T+7
• 看板与告警
  • 渠道/创意维度的CPRU、CVR_reg、D7、频次、CTR；疲劳与异常波动自动告警（如周内CPRU z-score>2）

落地步骤建议

• 第1周：统一口径、搭建CPRU看板、筛选可放量与需降配单元
• 第2–3周：按上述预算规则进行10–20%幅度的结构性迁移，同时开展落地页与竞价目标AB
• 第4周：复盘D7与规模，放大有效实验，淘汰低效创意与受众；准备下一轮创意迭代

如提供具体渠道、系列、创意与cohort数据，可据此输出精确的预算迁移比例、创意优先级与单实验样本量。