micsapp 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