performance-report

name performance-report description Build a marketing performance report with key metrics, trend analysis, wins and misses, and prioritized optimization recommendations. Use when wrapping a campaign, when preparing weekly, monthly, or quarterly channel summaries for stakeholders, or when you need data translated into an executive summary with next-period priorities. argument-hint <time period or campaign> Performance Report If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md . Generate a marketing performance report with key metrics, trend analysis, insights, and optimization recommendations. Trigger User runs /performance-report or asks for a marketing report, performance analysis, campaign results, or metrics summary. Inputs Report type — determine which type of report the user needs: Campaign report — performance of a specific campaign Channel report — performance across a specific channel (email, social, paid, SEO, etc.) Content performance — how content pieces are performing Overall marketing report — cross-channel summary (weekly, monthly, quarterly) Custom — user-defined scope Time period — the reporting window (last week, last month, last quarter, custom date range) Data source : If ~~marketing analytics is connected, discover what accounts and platforms are available, then pull performance data automatically If ~~product analytics is connected: pull performance data automatically If not connected: ask the user to provide metrics. Prompt with: "Please paste or share your performance data. I can work with spreadsheets, CSV data, dashboard screenshots described in text, or just the key numbers." Comparison period (optional) — prior period or year-over-year for trend context Stakeholder audience (optional) — who will read this report (executive summary style vs. detailed analyst view) Report Structure 1. Executive Summary 2-3 sentence overview of performance in the period Headline metric with trend direction (up/down/flat vs. prior period) One key win and one area of concern 2. Key Metrics Dashboard Present core metrics in a summary table: Metric This Period Prior Period Change Target Status Status indicators: On track (meeting or exceeding target) At risk (below target but within acceptable range) Off track (significantly below target) Metrics by Report Type Campaign Report: Impressions and reach Click-through rate (CTR) Conversion rate Cost per acquisition (CPA) Return on ad spend (ROAS) or ROI Total conversions/signups/leads Channel Report (Email): Emails sent, delivered, bounced Open rate Click-through rate Unsubscribe rate Conversion rate Channel Report (Social): Impressions and reach Engagement rate (likes, comments, shares) Follower growth Click-through rate Top-performing posts Channel Report (Paid): Spend Impressions and clicks CTR CPC and CPM Conversions and CPA ROAS Channel Report (SEO/Organic): Organic sessions Keyword rankings (movement) Pages indexed Backlinks acquired Top-performing pages Content Performance: Pageviews and unique visitors Time on page Bounce rate Social shares Conversions attributed to content Top and bottom performers Overall Marketing Report: Total leads generated Marketing qualified leads (MQLs) Pipeline contribution Customer acquisition cost (CAC) Channel-by-channel summary 3. Trend Analysis Performance trend over the period (week-over-week or month-over-month) Notable inflection points and what caused them Seasonal or cyclical patterns observed Comparison to benchmarks or targets 4. What Worked Top 3-5 wins with specific data Why these performed well (hypothesis) How to replicate or scale 5. What Needs Improvement Bottom 3-5 performers with specific data Hypotheses for underperformance Recommended fixes 6. Insights and Observations Patterns in the data that are not obvious from the metrics alone Audience behavior insights Content or creative themes that resonated External factors that may have influenced performance (seasonality, news, competitive moves) 7. Recommendations For each recommendation: What to do Why (linked to a specific insight from the data) Expected impact (high, medium, low) Effort to implement (high, medium, low) Priority (immediate, next sprint, next quarter) Prioritize recommendations in a 2x2 matrix format: Low Effort High Effort High Impact Do first Plan for next sprint Low Impact Do if time allows Deprioritize 8. Next Period Focus Top 3 priorities for the upcoming period Tests or experiments to run Targets for key metrics Metric Definitions and Benchmarks 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 by Cadence 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