--- id: sm-performance-analytics name: "performance-analytics" url: https://skills.yangsir.net/skill/sm-performance-analytics author: anthropics domain: data-analysis tags: ["performance-metrics", "data-visualization", "business-intelligence", "sql", "dashboarding"] install_count: 260 rating: 4.10 (48 reviews) github: https://github.com/anthropics/knowledge-work-plugins --- # performance-analytics > 提供衡量、报告和优化跨渠道营销绩效的分析框架和关键指标。，AI Agent Skill，提升工作效率和自动化能力 **Stats**: 260 installs · 4.1/5 (48 reviews) ## Before / After 对比 ### 营销活动效果分析 | Metric | Before | After | Change | |---|---|---|---| | - | - | - | - | | - | - | - | - | | - | - | - | - | ## Readme # performance-analytics # Performance Analytics Skill Frameworks for measuring, reporting, and optimizing marketing performance across channels and campaigns. ## Key Marketing Metrics by Channel ### Email Marketing Metric Definition Benchmark Range What It Tells You Delivery rate Emails delivered / emails sent 95-99% List health and sender reputation Open rate Unique opens / emails delivered 15-30% Subject line and sender effectiveness Click-through rate (CTR) Unique clicks / emails delivered 2-5% Content relevance and CTA effectiveness Click-to-open rate (CTOR) Unique clicks / unique opens 10-20% Email content quality (for those who opened) Unsubscribe rate Unsubscribes / emails delivered <0.5% Content-audience fit and frequency tolerance Bounce rate Bounces / emails sent <2% List quality and data hygiene Conversion rate Conversions / emails delivered 1-5% End-to-end email effectiveness Revenue per email Total revenue / emails sent Varies Direct revenue attribution List growth rate (New subscribers - unsubscribes) / total list 2-5% monthly Audience building health ### Social Media Metric Definition What It Tells You Impressions Number of times content was displayed Content distribution and reach Reach Number of unique users who saw content Audience breadth Engagement rate (Likes + comments + shares) / reach Content resonance Click-through rate Link clicks / impressions Traffic driving effectiveness Follower growth rate Net new followers / total followers per period Audience building Share/Repost rate Shares / reach Content virality and advocacy Video view rate Views / impressions Video content hook effectiveness Video completion rate Completed views / total views Video content quality and length fit Social share of voice Your mentions / total category mentions Brand visibility vs. competitors ### Paid Advertising (Search and Social) Metric Definition What It Tells You Impressions Times ad was shown Budget utilization and targeting breadth Click-through rate (CTR) Clicks / impressions Ad creative and targeting relevance Cost per click (CPC) Total spend / clicks Cost efficiency of traffic generation Cost per mille (CPM) Cost per 1,000 impressions Awareness cost efficiency Conversion rate Conversions / clicks Landing page and offer effectiveness Cost per acquisition (CPA) Total spend / conversions Full-funnel cost efficiency Return on ad spend (ROAS) Revenue / ad spend Revenue generation efficiency Quality Score (search) Google's relevance rating (1-10) Ad-keyword-landing page alignment Frequency Average times a user sees the ad Ad fatigue risk View-through conversions Conversions from users who saw but did not click Display/awareness campaign influence ### SEO / Organic Search Metric Definition What It Tells You Organic sessions Visits from organic search SEO effectiveness and content reach Keyword rankings Position for target keywords Search visibility Organic CTR Clicks / impressions in search results Title and meta description effectiveness Pages indexed Number of pages in search index Crawlability and site health Domain authority Third-party authority score Overall site strength Backlinks Number of external sites linking to you Content authority and off-page SEO Page load speed Time to interactive User experience and ranking factor Organic conversion rate Organic conversions / organic sessions Content quality and intent alignment Top entry pages Most-visited pages from organic search Content driving the most organic traffic ### Content Marketing Metric Definition What It Tells You Pageviews Total views of content pages Content reach and distribution Unique visitors Distinct users viewing content Audience size Average time on page Time spent on content pages Content engagement and depth Bounce rate Single-page sessions / total sessions Content-audience fit and UX Scroll depth How far users scroll on a page Content engagement through the piece Social shares Times content was shared on social Content resonance and virality Backlinks earned External links to content Content authority and SEO value Lead generation Leads attributed to content Content conversion effectiveness Content ROI Revenue attributed / content production cost Overall content investment return ### Overall Marketing / Pipeline Metric Definition What It Tells You Marketing qualified leads (MQLs) Leads meeting marketing qualification criteria Top-of-funnel effectiveness Sales qualified leads (SQLs) MQLs accepted by sales Lead quality MQL to SQL conversion rate SQLs / MQLs Marketing-sales alignment and lead quality Pipeline generated Dollar value of opportunities created Marketing impact on revenue Pipeline velocity How fast deals move through pipeline Campaign urgency and quality Customer acquisition cost (CAC) Total marketing + sales cost / new customers Efficiency of customer acquisition CAC payback period Months to recover CAC from revenue Unit economics health Marketing-sourced revenue Revenue from marketing-originated deals Direct marketing contribution Marketing-influenced revenue Revenue from deals where marketing touched Broader marketing impact ## Reporting Templates and Dashboards ### Weekly Marketing Report Quick-scan format for team standups: - **Top 3 metrics** with week-over-week change - **What worked** this week (1-2 bullet points with data) - **What needs attention** (1-2 bullet points with data) - **This week's priorities** (3-5 action items) ### Monthly Marketing Report Standard stakeholder report: - Executive summary (3-5 sentences) - Key metrics dashboard (table with MoM and target comparison) - Channel-by-channel performance summary - Campaign highlights and results - What worked and what did not (with hypotheses) - Recommendations and next month priorities - Budget spend vs. plan ### Quarterly Business Review (QBR) Strategic review for leadership: - Quarter performance vs. goals - Year-to-date trajectory - Channel ROI analysis - Campaign performance summary - Competitive and market observations - Strategic recommendations for next quarter - Budget request and allocation plan - Key experiments and learnings ### Dashboard Design Principles - Lead with the metrics that map to business objectives (not vanity metrics) - Show trends over time, not just point-in-time snapshots - Include comparison context: prior period, target, benchmark - Use consistent color coding: green (on track), yellow (at risk), red (off track) - Group metrics by funnel stage or business question - Keep dashboards to one page/screen — detail goes in appendix - Update cadence should match decision cadence (real-time for paid, weekly for content) ## Trend Analysis and Forecasting ### Trend Identification When analyzing performance data, look for: - **Directional trends**: is the metric consistently going up, down, or flat over 4+ periods? - **Inflection points**: where did performance change direction and what happened then? - **Seasonality**: are there predictable patterns by day of week, month, or quarter? - **Anomalies**: one-time spikes or drops — what caused them and are they repeatable? - **Leading indicators**: which metrics change first and predict future outcomes? ### Trend Analysis Process - Chart the metric over time (at least 8-12 data points for meaningful trends) - Identify the overall direction (upward, downward, flat, cyclical) - Calculate the rate of change (is it accelerating or decelerating?) - Overlay key events (campaigns launched, product changes, market events) - Compare to benchmarks or targets - Identify correlations with other metrics - Form hypotheses about causation (and plan tests to validate) ### Simple Forecasting Approaches - **Linear projection**: extend the current trend line forward (useful for stable metrics) - **Moving average**: smooth out noise by averaging the last 3-6 periods - **Year-over-year comparison**: use last year's pattern as a baseline, adjusted for growth rate - **Funnel math**: forecast outputs from inputs (e.g., if we generate X leads at Y conversion rate, we will get Z customers) - **Scenario modeling**: create best case, expected case, and worst case projections ### Forecasting Caveats - Short-term forecasts (1-3 months) are more reliable than long-term - Forecasts based on fewer than 12 data points should be flagged as low confidence - External factors (market shifts, competitive moves, economic changes) can invalidate trend-based forecasts - Always present forecasts as ranges, not exact numbers ## Attribution Modeling Basics ### What Is Attribution? Attribution determines which marketing touchpoints get credit for a conversion. This matters because buyers typically interact with multiple channels before converting. ### Common Attribution Models Model How It Works Best For Limitation Last touch 100% credit to last interaction before conversion Understanding final conversion triggers Ignores awareness and nurture First touch 100% credit to first interaction Understanding top-of-funnel effectiveness Ignores nurture and conversion drivers Linear Equal credit to all touchpoints Fair representation of all channels Does not reflect relative impact Time decay More credit to touchpoints closer to conversion Balanced view favoring recent interactions May undervalue awareness Position-based (U-shaped) 40% first, 40% last, 20% split among middle Valuing both discovery and conversion Somewhat arbitrary weighting Data-driven Algorithmic credit based on conversion patterns Most accurate representation Requires significant data volume ### Attribution Practical Guidance - Start with last-touch attribution if you have no model in place — it is the simplest and most actionable - Compare first-touch and last-touch to understand which channels drive awareness vs. conversion - Use position-based (U-shaped) as a reasonable middle ground for most B2B companies - Data-driven attribution requires high conversion volume to be statistically meaningful - No model is perfect — use attribution directionally, not as absolute truth - Multi-touch attribution is better than single-touch, but any model is better than none ### Attribution Pitfalls - Do not optimize one channel in isolation based on single-touch attribution - Awareness channels (display, social, PR) will always look bad in last-touch models - Conversion channels (search, retargeting) will always look bad in first-touch models - Self-reported attribution ("how did you hear about us?") provides useful qualitative color but is unreliable as quantitative data - Cross-device and cross-channel tracking gaps mean attribution data is always incomplete ## Optimization Recommendations Framework ### Optimization Process - **Identify**: which metrics are underperforming vs. target or benchmark? - **Diagnose**: where in the funnel is the problem? (impressions, clicks, conversions, retention) - **Hypothesize**: what is causing the underperformance? (audience, message, creative, offer, timing, technical) - **Prioritize**: which fixes will have the biggest impact with the least effort? - **Test**: design an experiment to validate the hypothesis - **Measure**: did the change improve the metric? - **Scale or iterate**: roll out wins broadly; iterate on inconclusive or failed tests ### Optimization Levers by Funnel Stage Funnel Stage Problem Signal Optimization Levers Awareness Low impressions, low reach Budget, targeting, channel mix, creative format Interest Low CTR, low engagement Ad creative, headlines, content hooks, audience targeting Consideration High bounce rate, low time on page Landing page content, page speed, content relevance, UX Conversion Low conversion rate Offer, CTA, form length, trust signals, page layout Retention High churn, low repeat engagement Onboarding, email nurture, product experience, support ### Prioritization Framework Rank optimization ideas on two dimensions: **Impact** (how much will this move the metric?): - High: directly addresses the primary bottleneck - Medium: addresses a contributing factor - Low: incremental improvement **Effort** (how hard is this to implement?): - Low: copy change, targeting adjustment, simple A/B test - Medium: new creative, landing page redesign, workflow change - High: new tool, cross-team project, major content production Priority order: - High impact, low effort (do immediately) - High impact, high effort (plan and resource) - Low impact, low effort (do if capacity allows) - Low impact, high effort (deprioritize) ### Testing Best Practices - Test one variable at a time for clean results - Define the success metric before launching the test - Calculate required sample size before starting (do not end tests early) - Run tests for a minimum of one full business cycle (typically one week for B2B) - Document all tests and results, regardless of outcome - Share learnings across the team — failed tests are valuable information - A test that confirms the status quo is not a failure — it builds confidence in your current approach ### Continuous Optimization Cadence - **Daily**: monitor paid campaigns for budget pacing, anomalies, and disapproved ads - **Weekly**: review channel performance, pause underperformers, scale winners - **Bi-weekly**: refresh ad creative and test new variants - **Monthly**: full performance review, identify new optimization opportunities, update forecasts - **Quarterly**: strategic review of channel mix, budget allocation, and targeting strategy Weekly Installs251Repository[anthropics/know…-plugins](https://github.com/anthropics/knowledge-work-plugins)GitHub Stars9.9KFirst SeenJan 31, 2026Security Audits[Gen Agent Trust HubPass](/anthropics/knowledge-work-plugins/performance-analytics/security/agent-trust-hub)[SocketPass](/anthropics/knowledge-work-plugins/performance-analytics/security/socket)[SnykPass](/anthropics/knowledge-work-plugins/performance-analytics/security/snyk)Installed onclaude-code200opencode192codex190gemini-cli181github-copilot173amp164 --- *Source: https://skills.yangsir.net/skill/sm-performance-analytics* *Markdown mirror: https://skills.yangsir.net/api/skill/sm-performance-analytics/markdown*