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:

1. Executive summary (3-5 sentences)
2. Key metrics dashboard (table with MoM and target comparison)
3. Channel-by-channel performance summary
4. Campaign highlights and results
5. What worked and what did not (with hypotheses)
6. Recommendations and next month priorities
7. Budget spend vs. plan

Quarterly Business Review (QBR)

Strategic review for leadership:

1. Quarter performance vs. goals
2. Year-to-date trajectory
3. Channel ROI analysis
4. Campaign performance summary
5. Competitive and market observations
6. Strategic recommendations for next quarter
7. Budget request and allocation plan
8. 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:

1. Directional trends: is the metric consistently going up, down, or flat over 4+ periods?
2. Inflection points: where did performance change direction and what happened then?
3. Seasonality: are there predictable patterns by day of week, month, or quarter?
4. Anomalies: one-time spikes or drops — what caused them and are they repeatable?
5. Leading indicators: which metrics change first and predict future outcomes?

Trend Analysis Process

1. Chart the metric over time (at least 8-12 data points for meaningful trends)
2. Identify the overall direction (upward, downward, flat, cyclical)
3. Calculate the rate of change (is it accelerating or decelerating?)
4. Overlay key events (campaigns launched, product changes, market events)
5. Compare to benchmarks or targets
6. Identify correlations with other metrics
7. 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

1. Identify: which metrics are underperforming vs. target or benchmark?
2. Diagnose: where in the funnel is the problem? (impressions, clicks, conversions, retention)
3. Hypothesize: what is causing the underperformance? (audience, message, creative, offer, timing, technical)
4. Prioritize: which fixes will have the biggest impact with the least effort?
5. Test: design an experiment to validate the hypothesis
6. Measure: did the change improve the metric?
7. 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:

1. High impact, low effort (do immediately)
2. High impact, high effort (plan and resource)
3. Low impact, low effort (do if capacity allows)
4. 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