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