marketing-analytics

name	marketing-analytics
description	Analyze marketing performance, attribution, channel metrics, forecasts, A/B tests, and optimization opportunities.

Channel Metric Reference

Email

Metric	How It Is Calculated	Typical Range	Insight Provided
Delivery rate	Delivered / Sent	95-99%	Sender reputation and list hygiene
Open rate	Unique opens / Delivered	15-30%	Subject line and sender name effectiveness
Click-through rate (CTR)	Unique clicks / Delivered	2-5%	Relevance of content and CTA
Click-to-open rate (CTOR)	Unique clicks / Unique opens	10-20%	In-email content quality among openers
Unsubscribe rate	Unsubscribes / Delivered	Below 0.5%	Audience-content fit and send frequency tolerance
Bounce rate	Bounces / Sent	Below 2%	List data quality
Conversion rate	Conversions / Delivered	1-5%	Full-funnel email performance
Revenue per send	Total revenue / Emails sent	Varies	Direct monetary contribution
List growth rate	(New subs - Unsubs) / Total list	2-5% per month	Audience acquisition health

Social Platforms

Metric	How It Is Calculated	Insight Provided
Impressions	Times content appeared in feeds	Distribution breadth
Reach	Unique users who saw content	Audience coverage
Engagement rate	(Reactions + Comments + Shares) / Reach	Content resonance
Click-through rate	Link clicks / Impressions	Ability to drive traffic
Follower growth rate	Net new followers / Total followers per period	Audience expansion pace
Share/Repost rate	Shares / Reach	Virality and advocacy signal
Video view rate	Views / Impressions	Hook effectiveness for video
Video completion rate	Completed views / Total views	Content quality and length fit
Share of voice	Your mentions / Category total mentions	Competitive visibility

Paid Advertising (Search and Social)

Metric	How It Is Calculated	Insight Provided
Impressions	Times the ad appeared	Budget utilization and audience sizing
Click-through rate (CTR)	Clicks / Impressions	Creative and targeting relevance
Cost per click (CPC)	Spend / Clicks	Traffic generation efficiency
Cost per thousand impressions (CPM)	Spend per 1,000 impressions	Awareness cost efficiency
Conversion rate	Conversions / Clicks	Landing page and offer effectiveness
Cost per acquisition (CPA)	Spend / Conversions	Full-funnel cost efficiency
Return on ad spend (ROAS)	Revenue / Ad spend	Revenue generation return
Quality Score (search)	Platform relevance rating (1-10)	Alignment of ad, keyword, and destination
Frequency	Average exposures per user	Ad fatigue risk indicator
View-through conversions	Conversions from users who saw but did not click	Influence of display and awareness placements

Organic Search / SEO

Metric	How It Is Calculated	Insight Provided
Organic sessions	Visits originating from search engines	Overall SEO health
Keyword positions	Rank for target search terms	Search result visibility
Organic CTR	Clicks / Search impressions	Title and meta description appeal
Indexed pages	Pages present in the search index	Crawlability and site architecture
Domain authority	Third-party composite score	Aggregate site strength
Backlink count	External domains linking inward	Off-page authority and content value
Page speed	Time to interactive	UX quality and ranking signal
Organic conversion rate	Conversions / Organic sessions	Intent alignment and content quality
Top organic entry pages	Most-visited pages from search	Highest-performing SEO content

Content Performance

Metric	How It Is Calculated	Insight Provided
Pageviews	Total views across content pages	Content reach
Unique visitors	Distinct users consuming content	True audience size
Average time on page	Duration spent on content pages	Depth of engagement
Bounce rate	Single-page sessions / All sessions	Content-audience alignment and UX
Scroll depth	Percentage of page scrolled	Engagement persistence
Social shares	Times content was distributed socially	Audience advocacy
Backlinks generated	External links earned by content	SEO value and authority
Leads attributed	Leads traced to content interaction	Conversion power
Content ROI	Attributed revenue / Production cost	Investment return

Pipeline and Revenue Metrics

Metric	How It Is Calculated	Insight Provided
Marketing qualified leads (MQLs)	Leads passing marketing qualification criteria	Top-of-funnel output
Sales qualified leads (SQLs)	MQLs accepted by the sales team	Lead quality
MQL-to-SQL conversion	SQLs / MQLs	Marketing-sales alignment
Pipeline created	Dollar value of new opportunities	Marketing revenue impact
Pipeline velocity	Speed of deal progression	Campaign urgency and quality signal
Customer acquisition cost (CAC)	Total marketing + sales spend / New customers	Acquisition efficiency
CAC payback period	Months to recoup CAC from revenue	Unit economics viability
Marketing-sourced revenue	Revenue from marketing-originated deals	Direct marketing contribution
Marketing-influenced revenue	Revenue from deals with any marketing touchpoint	Broader marketing footprint

Report Structures

Weekly Snapshot

Designed for rapid team consumption:

• Three headline metrics with week-over-week movement
• Wins: 1-2 data-backed highlights
• Watch items: 1-2 areas requiring attention with supporting numbers
• Upcoming actions: 3-5 priorities for the week ahead

Monthly Performance Review

Standard format for stakeholder reporting:

1. Executive summary (3-5 sentences)
2. Core metrics table with month-over-month and target comparisons
3. Channel-level performance breakdown
4. Campaign results and highlights
5. What succeeded and what underperformed, with working hypotheses
6. Recommendations and priorities for the coming month
7. Budget spent vs. planned

Quarterly Strategic Review

For leadership-level analysis:

1. Quarter results against stated goals
2. Year-to-date progress and trajectory
3. Channel-by-channel ROI assessment
4. Campaign portfolio performance summary
5. Competitive and market landscape observations
6. Strategic recommendations for the next quarter
7. Budget proposal and reallocation plan
8. Experiment outcomes and key learnings

Dashboard Construction Principles

• Feature the metrics that tie directly to business goals, not vanity numbers
• Display trends over multiple periods rather than isolated data points
• Provide comparison anchors: prior period, target, industry benchmark
• Apply uniform color signaling: green for on-track, yellow for at-risk, red for off-track
• Organize by funnel stage or the business question being answered
• Confine the dashboard to a single screen; relegate granular data to an appendix
• Match the refresh cadence to the decision cadence (real-time for paid media, weekly for content)

Trend Analysis and Projection

Spotting Patterns

When examining performance data, investigate:

1. Sustained direction: is the metric consistently rising, falling, or flat across 4+ consecutive periods?
2. Turning points: at what moment did the trajectory change, and what event coincided?
3. Cyclical patterns: are there recurring fluctuations by day of week, month, or quarter?
4. Outliers: isolated spikes or dips — what triggered them, and could the cause be replicated or avoided?
5. Predictive signals: which metrics shift first and foreshadow downstream outcomes?

Analytical Process

1. Plot the metric across time with at least 8-12 data points for statistical relevance
2. Characterize the overall trajectory (rising, declining, stable, or oscillating)
3. Quantify the rate of change — is the trend accelerating or flattening?
4. Layer in external events (campaign launches, product updates, market shifts)
5. Benchmark against targets or industry norms
6. Look for correlations with related metrics
7. Formulate causal hypotheses and design experiments to test them

Projection Techniques

• Trend extension: project the existing trajectory forward (works best for stable metrics)
• Rolling average: average the most recent 3-6 periods to dampen noise
• Year-over-year overlay: use the prior year's seasonal pattern, adjusted for a growth coefficient
• Funnel arithmetic: forecast outputs from inputs (X leads at Y% conversion rate yields Z customers)
• Scenario planning: model optimistic, expected, and pessimistic cases

Projection Guardrails

• Near-term forecasts (1-3 months) carry far more reliability than long-range ones
• Projections built on fewer than 12 data points should be labeled low-confidence
• External disruptions (market shifts, competitive moves, economic changes) can invalidate trend-based models
• Always express forecasts as ranges rather than single numbers

Attribution Fundamentals

Why Attribution Matters

Buyers rarely convert after a single interaction. Attribution assigns credit across the multiple touchpoints that precede a conversion, informing channel investment decisions.

Standard Attribution Models

Model	Mechanism	Strength	Weakness
Last interaction	All credit to the final touchpoint	Identifies closing channels	Overlooks awareness and nurture
First interaction	All credit to the initial touchpoint	Highlights discovery channels	Ignores conversion drivers
Even distribution	Equal credit across all touchpoints	Acknowledges every channel	Fails to reflect relative influence
Recency-weighted	Increasing credit as touchpoints approach conversion	Balances awareness and closing	Can undervalue early awareness
Position-based (40/20/40)	Heavy credit to first and last, remainder split across the middle	Honors both discovery and conversion	Somewhat arbitrary weight assignment
Algorithmic	Machine-learned credit based on conversion path data	Most reflective of actual influence	Demands large conversion volumes

Practical Attribution Advice

• If you have no attribution system, begin with last-interaction — it is the simplest and most immediately actionable
• Contrast first-interaction and last-interaction views to learn which channels drive discovery vs. closure
• Position-based (40/20/40) is a pragmatic default for most B2B organizations
• Algorithmic models need high conversion volumes to produce statistically sound results
• Treat attribution as directional intelligence, never as absolute truth
• Any multi-touch model is more informative than a single-touch model, and any model outperforms none

Attribution Traps

• Optimizing a single channel based on single-touch data can starve the rest of the funnel
• Awareness-oriented channels (display, organic social, PR) will consistently underperform in last-touch reports
• Conversion-oriented channels (branded search, retargeting) will consistently underperform in first-touch reports
• Self-reported attribution ("How did you hear about us?") offers useful qualitative signal but is unreliable for quantitative allocation
• Cross-device and cross-channel tracking gaps guarantee that attribution data is always incomplete

Optimization Methodology

Systematic Improvement Process

1. Detect: which metrics fall short of targets or benchmarks?
2. Locate: where in the funnel does the breakdown occur? (impressions, clicks, conversions, retention)
3. Theorize: what is causing the shortfall? (targeting, messaging, creative, offer design, timing, technical issues)
4. Rank: which interventions promise the greatest impact relative to effort?
5. Experiment: run a controlled test to validate or disprove the hypothesis
6. Evaluate: did the metric improve meaningfully?
7. Act: scale successful changes broadly; iterate on inconclusive or negative results

Intervention Levers by Funnel Position

Funnel Position	Warning Sign	Available Levers
Awareness	Low impressions, limited reach	Budget levels, targeting parameters, channel mix, ad format
Interest	Low CTR, weak engagement	Creative execution, headline copy, content hooks, audience refinement
Consideration	High bounce rate, low dwell time	Page content, load speed, relevance alignment, user experience
Conversion	Low conversion rate	Offer structure, CTA wording, form complexity, trust elements, page layout
Retention	Elevated churn, declining re-engagement	Onboarding flow, email sequences, product experience, support quality

Impact-Effort Prioritization

Score every optimization idea on two axes:

Impact (potential metric movement):

• High: directly addresses the primary bottleneck
• Medium: improves a contributing factor
• Low: yields incremental gains

Effort (implementation difficulty):

• Low: copy tweak, targeting adjustment, quick A/B test
• Medium: new creative asset, page redesign, workflow modification
• High: new tooling, cross-team initiative, major content production

Execution order:

1. High impact, low effort — execute immediately
2. High impact, high effort — plan and staff
3. Low impact, low effort — pursue if bandwidth allows
4. Low impact, high effort — defer or deprioritize

Experimentation Discipline

• Isolate a single variable per test for interpretable results
• Lock in the success metric before the test begins
• Calculate the required sample size in advance and resist ending tests prematurely
• Run each test for at least one complete business cycle (usually a full week for B2B)
• Record all experiments and outcomes, including negative and null results
• Circulate learnings across the team — a test that confirms the current approach still builds confidence

Ongoing Optimization Rhythm

• Daily: check paid campaign pacing, flag anomalies, review ad approval status
• Weekly: assess channel-level performance, pause lagging efforts, amplify winners
• Biweekly: rotate ad creative and launch new test variants
• Monthly: conduct a comprehensive performance review, surface new optimization opportunities, refresh projections
• Quarterly: reassess channel strategy, budget distribution, and audience targeting at a strategic level