Email Marketing Analyses: Essential Métriques, Tools & Reporting Guide [2025]

Email marketing delivers an average ROI of $36-42 for every dollar spent, but only if you know how to measure and optimize it. Without proper analyses, you’re flying blind—sending campagnes invers le void with no idea what’s working.

Ce guide complet couvre tout ce que vous devez savoir about email marketing analyses: the essential métriques to track, industry benchmarks to aim for, reporting meilleures pratiques, and how to use data to continuously improve your campagnes.

Why Email Marketing Analyses Matter

Before diving into specific métriques, let’s understand why analyses are fundamental to email marketing success.

The Data-Driven Advantage

Marketers who use data-driven stratégies see:

• 6x higher taux de conversion compared to non-data-driven approaches
• 23% higher revenus from email campagnes
• 50% reduction in customer acquisition costs through better targeting
• 40% improvement in customer engagement métriques

What Analyses Enable

Propar email analyses allow you to:

1. Identify what works - Discover which lignes d’objet, content, and offers resonate
2. Optimize send times - Find lorsque vousr audience is most engaged
3. Segment effectively - Use behavior data for better targeting
4. Prove ROI - Demonstrate email’s value to stakeholders
5. Predict outcomes - Use historical data to forecast campaign performance
6. Fix problems fast - Catch délivrabilité issues before they escalate

Core Email Marketing Métriques

Let’s break down the essential métriques every email marketer needs to track, organized by category.

Délivrabilité Métriques

Before measuring engagement, vous devez ensure emails actually reach inboxes.

Delivery Rate

What it measures: The percentage of emails that were accepted by receiving mail servers.

Formula: (Emails Delivered / Emails Sent) × 100

Benchmark: 95%+ is good; below 90% indicates problems

What affects it:

• Sender reputation
• Email list quality
• Authentication (SPF, DKIM, DMARC)
• Content filtering triggers

Taux de rebond

What it measures: The percentage of emails that ne pourrait pas be delivered.

Benchmark: Below 2% total; hard bounces should be under 0.5%

Red flags:

• Hard taux de rebond above 2% suggests list quality issues
• Sudden spike indicates possible list problems or domain issues

Spam Complaint Rate

What it measures: The percentage of recipients who marked your email as spam.

Formula: (Spam Complaints / Emails Delivered) × 100

Benchmark: Below 0.1% (ideally under 0.05%)

Why it matters: High complaint rates directly damage réputation d’expéditeur and can lead to blacklisting.

Engagement Métriques

These métriques show how recipients interact with your emails.

Taux d’ouverture

What it measures: The percentage of delivered emails that were opened.

Formula: (Unique Opens / Emails Delivered) × 100

Important caveat: Apple’s Mail Privacy Protection (MPP) pre-fetches images, artificially inflating taux d’ouverture for Apple Mail users (40-50% of many lists). Consider:

• Segmenting Apple Mail users separately
• Relying more on click-based métriques
• Tracking “machine opens” vs. “human opens” if your platform supports it

Benchmarks by Industry (2025):

What affects taux d’ouverture:

• Subject line quality
• Sender name and reputation
• Send time
• List engagement level
• Preheader text

Taux de clic (CTR)

What it measures: The percentage of delivered emails that received au moins one click.

Formula: (Unique Clicks / Emails Delivered) × 100

Benchmarks by Industry:

What affects CTR:

• Content relevance and personnalisation
• CTA clarity and placement
• Email design and mobile optimization
• Offer attractiveness
• Link positioning

Click-to-Taux d’ouverture (CTOR)

What it measures: The percentage of opened emails that received clicks.

Formula: (Unique Clicks / Unique Opens) × 100

Why it matters: CTOR isolates content effectiveness from ligne d’objet effectiveness. If taux d’ouverture is high but CTOR is low, your ligne d’objet is working but content n’est pas delivering.

Benchmark: 10-15% is average; 15%+ is strong

Taux de désabonnement

What it measures: The percentage of recipients who unsubscribed after receiving an email.

Formula: (Unsubscribes / Emails Delivered) × 100

Benchmark: Below 0.5% per campaign; below 0.2% is excellent

Warning signs:

• Sudden spike suggests content mismatch or sending too frequently
• Consistent 0.5%+ indicates list fatigue or relevance issues
• Zero unsubscribes might indicate the link is hard to find (conformité risk)

Revenus Métriques

For e-commerce and revenus-focused email programs, these métriques connect email to business outcomes.

Taux de conversion

What it measures: The percentage of email recipients who completed a desired action.

Formula: (Conversions / Emails Delivered) × 100

What counts as conversion:

• Purchase completed
• Form submitted
• Sign-up completed
• Download initiated
• Other goal actions

Benchmark: Varies widely by action type. Purchase conversions typically range 1-5% for targeted campagnes.

Revenus Per Email (RPE)

What it measures: Average revenus generated par email sent.

Formula: Total Revenus Attributed / Emails Sent

Why it matters: RPE allows comparison across campagnes of different sizes and helps identify highest-value email types.

Comment use it:

• Compare promotional vs. automated emails
• Identify top-performing campaign types
• Calculate email channel ROI

Revenus Per Recipient (RPR)

What it measures: Revenus generated per person who received the email.

Formula: Total Revenus / Unique Recipients

Use case: Better for comparing subscriber value across segments.

Average Order Value (AOV) from Email

What it measures: Average purchase size from email-attributed orders.

Formula: Total Revenus / Number of Orders

Comparison: Track email AOV against site-wide AOV. Email often delivers 10-30% higher AOV en raison de targeting and personnalisation.

List Health Métriques

These métriques indicate the overall health and quality of your liste email.

List Growth Rate

What it measures: How quickly your list is growing (or shrinking).

Formula: ((New Abonnés - Unsubscribes - Hard Bounces) / Total Abonnés) × 100

Benchmark: Healthy lists grow 2-5% monthly

Active Subscriber Rate

What it measures: Percentage of abonnés who’ve engaged recently.

Definition of “active” varies:

• Opened or clicked in last 90 days (strict)
• Opened or clicked in last 180 days (moderate)
• Any engagement in last 365 days (lenient)

Benchmark: 30-50% active rate is typical; below 20% indicates list decay

Churn Rate

What it measures: Rate at which abonnés leave your list.

Formula: (Unsubscribes + Bounces + Complaints) / Total Abonnés

Benchmark: Monthly churn of 0.5-1% is normal; above 2% is concerning

Industry Benchmarks: What “Good” Looks Like

Understanding benchmarks helps contextualize your performance, but remember: your best benchmark is your own historical data.

Overall Email Marketing Benchmarks (2025)

Benchmarks by Email Type

Benchmarks by Company Size

Larger entreprises typically see lower engagement rates en raison de broader, less targeted lists:

Building Your Email Analyses Dashboard

A well-designed dashboard transforms raw data into actionable insights. Voici comment to build one that drives decisions.

Dashboard Design Principles

1. Focus on actionable métriques Include only métriques you’ll actually act on. Vanity métriques that ne faites pas drive decisions add noise.

2. Show trends au fil du temps Point-in-time numbers are less valuable than trend lines. Show week-over-week and month-over-month changes.

3. Segment where it matters Break down key métriques by campaign type, audience segment, and email type.

4. Include benchmarks Show your targets alongside actual performance for instant context.

Essential Dashboard Components

Executive Summary Section

Au top, display high-level KPIs:

• Total emails sent (period)
• Average taux d’ouverture (with trend arrow)
• Average click rate (with trend arrow)
• Total revenus attributed (pour le e-commerce)
• List size and growth rate

Campaign Performance Table

For each campaign dans le period:

Trend Charts

Visualize key métriques au fil du temps:

• Taux d’ouverture trend (30-60 days)
• Click rate trend
• List growth trend
• Revenus par email trend

Segment Performance

Compare performance across key segments:

Délivrabilité Health

Monitor réputation d’expéditeur indicators:

• Taux de rebond (hard vs. soft)
• Spam complaint rate
• Domain reputation status
• Blacklist monitoring

Setting Up Automated Reports

Configure these regular reports for your team:

Daily (automated):

• Délivrabilité alerts (bounce/complaint spikes)
• Revenus from previous day’s emails

Weekly:

• Campaign performance summary
• List growth and churn
• Top and bottom performing emails

Monthly:

• Comprehensive performance review
• Benchmark comparisons
• Segment analysis
• A/B test learnings

A/B Testing Analyses

Testing is essential for continuous improvement. Voici comment to approach email testing analytically.

What to Test

Prioritize tests by potential impact:

Testing Methodology

Sample Size Requirements

For statistically valid results, you need adequate sample sizes:

Rule of thumb: Send to au moins 1,000-2,000 per variation for meaningful results.

Statistical Significance

Don’t declare winners too early:

• 95% confidence is the standard threshold
• Wait for full results (ne faites pas peek and stop early)
• Use proper statistical tools (most ESP plateformes calculate this)

Analyzing Test Results

When reviewing A/B test outcomes, document:

1. Clear winner? - Was there statistical significance?
2. Magnitude - How big was the difference?
3. Consistency - Does this align with previous tests?
4. Context - Were there external factors?
5. Actionable insight - What does this tell us?

Example Test Analysis

Test: Subject line A vs. B for email promotionnel

Analysis:

• Variation B had 14% higher taux d’ouverture (statistically significant at 95%)
• Variation A had 17% higher CTR
• Revenus from A: $12,400 vs. B: $10,200

Insight: Personalized ligne d’objet drives opens, but urgency-focused subject with “Flash Sale” drove more valuable clicks. Test combining personnalisation with urgency.

Multi-Variant Testing

Beyond A/B, consider testing multiple variables:

Multivariate testing: Test combinations of elements (subject + send time + CTA)

Holdout groups: Reserve 10% to receive no email, measuring true incrementality

Champion/Challenger: Always test new approaches against your proven best performer

Attribution and Revenus Tracking

Connecting email performance to revenus requires proper attribution setup.

Attribution Models for Email

Different models assign credit differently:

Setting Attribution Windows

Define how long after an email click you attribute conversions:

• Short window (24-48 hours): More conservative, high confidence
• Standard window (7 days): Common default, reasonable attribution
• Long window (30 days): Captures delayed purchases, may over-attribute

Recommendation: Start with 7-day click attribution, adjust based on your typical purchase cycle.

Email-Influenced vs. Email-Attributed

Important distinction:

• Email-attributed: Direct click-to-purchase (customer clicked email, then bought)
• Email-influenced: Customer received email, purchased later (without clicking)

Track both when possible. Email often influences purchases that occur through other channels.

Revenus Attribution in Practice

For accurate email revenus tracking:

1. UTM parameters: Tag all email links with campaign, medium, source
2. Intégration: Connect ESP to e-commerce platform
3. Consistent measurement: Use same attribution model across analysis
4. Cross-device tracking: Account for mobile open, desktop purchase

Example UTM structure:

utm_source=brevo
utm_medium=email
utm_campaign=flash-sale-march-2025
utm_content=hero-cta

Advanced Analyses Techniques

Beyond basic métriques, these advanced approaches unlock deeper insights.

Cohort Analysis

Group abonnés by signup date and track behavior au fil du temps:

Insight: If later cohorts retain better, your onboarding is improving. If they retain worse, investigate list source quality.

RFM Analysis

Score abonnés on Recency, Frequency, and Monetary value:

Predictive Analyses

Use historical data to predict future behavior:

• Purchase probability: Score likelihood of next purchase
• Churn prediction: Identify abonnés likely to disengage
• LTV prediction: Estimate customer lifetime value from email behavior
• Optimal send time: Predict best time for individual abonnés

Incrementality Testing

Measure true email impact with holdout groups:

1. Randomly select 10% of audience as holdout
2. Send campaign to 90% (test group)
3. Compare purchase rate: test vs. holdout
4. Difference = true incremental impact

Example:

• Test group conversion: 2.5%
• Holdout conversion: 1.8%
• Incremental lift: 0.7 percentage points (39% relative lift)

Reporting Meilleures pratiques

Effective reporting transforms data into decisions.

Reporting for Different Audiences

Executive Leadership:

• Focus on revenus, ROI, and growth
• Monthly or quarterly cadence
• High-level trends, not campaign details
• Compare to business goals

Marketing Team:

• Campaign-level performance
• Weekly or bi-weekly cadence
• Actionable insights and optimizations
• Test results and learnings

Technical/Operations:

• Délivrabilité health
• Daily monitoring
• System performance
• List hygiene métriques

Report Structure Template

1. Executive Summary (1 page)

• Key wins this period
• Primary métriques vs. targets
• Major learnings
• Top recommendations

2. Performance Overview

• All campagnes with key métriques
• Automated flow performance
• Segment performance comparison

3. Deep Dives

• Top performing campaign analysis
• Test results and learnings
• Problem areas and fixes

4. Délivrabilité Report

• Bounce and complaint rates
• Reputation monitoring
• List hygiene actions

5. Recommendations

• Immediate actions
• Tests to run
• Strategic priorities

Avoiding Common Reporting Mistakes

Don’t:

• Report métriques without context or benchmarks
• Focus only on vanity métriques (opens without clicks, clicks without conversion)
• Ignore negative trends hoping they’ll reverse
• Present data without recommendations

Do:

• Compare periods (this month vs. last, this year vs. last)
• Connect métriques to revenus impact
• Highlight both successes and failures
• End with clear action items

Using Data for Optimization

Analyses only matter if they drive improvement. Voici comment to act on your data.

The Optimization Loop

1. Measure: Collect accurate data
2. Analyze: Identify patterns and opportunities
3. Hypothesize: Form theories about what will improve
4. Test: Run controlled experiments
5. Implement: Roll out winning variations
6. Repeat: Continue the cycle

Data-Driven Optimization Examples

Low Taux d’ouvertures

Symptom: Taux d’ouvertures below benchmark (under 15%)

Analysis checklist:

• Subject line length and content
• Send time and day
• From name recognition
• List quality and engagement
• Délivrabilité issues

Actions:

• Test new ligne d’objet formulas
• Segment by engagement level
• Clean inactive abonnés
• Verify authentication (SPF, DKIM)

Low Click Rates

Symptom: CTR below 2% for emails promotionnels

Analysis checklist:

• CTA clarity and placement
• Content relevance
• Mobile optimization
• Link placement and density

Actions:

• Test single vs. multiple CTAs
• Improve personnalisation
• Optimize for mobile (larger buttons, shorter content)
• A/B test offers

Declining Engagement

Symptom: Engagement métriques trending down over 3+ months

Analysis checklist:

• Send frequency changes
• Content quality shifts
• List source quality
• Competitive pressure

Actions:

• Survey abonnés on preferences
• Implement preference center
• Test reduced frequency
• Refresh content approach

Implementing Analyses with Tajo

Tajo’s intégration between Shopify and Brevo provides comprehensive analyses capabilities that unify your customer data and email performance.

Unified Customer View

Tajo syncs your complete customer data to Brevo, enabling:

• Purchase history intégration: See email engagement alongside buying behavior
• Product-level analyses: Track which products drive email engagement
• Customer lifecycle métriques: Measure performance by customer stage
• Fidélité program data: Connect points and tier status to email behavior

Advanced Reporting Fonctionnalités

With Tajo, you get:

• Automated revenus attribution: Accurate tracking of email-driven sales
• Real-time sync: Up-to-date data for timely decisions
• Segment performance: Compare email métriques across customer segments
• Multi-channel view: See email alongside SMS and WhatsApp performance

Analyses-Driven Automatisation

Use analyses insights to power smarter automatisations:

• Trigger flows based on engagement patterns
• Personalize content using purchase data
• Adjust frequency based on engagement level
• Route high-value clients to priority treatment

FAQ: Email Marketing Analyses

Qu’est-ce que the le plus important email marketing metric?

There’s no single “le plus important” metric—cela dépend de your goals. For awareness campagnes, taux d’ouverture matters most. For conversion-focused emails, click rate and taux de conversion are key. For e-commerce, revenus par email is often the north star metric. Track a balanced set of métriques aligned with your business objectives.

How often should I review email analyses?

Review délivrabilité métriques daily (set up alerts for spikes). Analyze campaign performance after each send. Conduct weekly reviews of overall email program performance. Do deep-dive analysis and strategic planning monthly or quarterly.

Why are my taux d’ouverture suddenly lower?

Several factors can cause sudden taux d’ouverture drops: délivrabilité issues (check taux de rebond and spam complaints), landing in spam folders (test with seed lists), ligne d’objet problems, list fatigue, or Apple Mail Privacy Protection masking actual opens. Investigate systematically—check délivrabilité first, then engagement factors.

How do I track email revenus accurately?

Accurate revenus tracking requires: proper UTM tagging on all links, intégration between your ESP and e-commerce platform, consistent attribution windows, and cross-device tracking where possible. Tajo’s Shopify-Brevo intégration handles this automatically, syncing purchase data for accurate attribution.

What’s a good benchmark for email ROI?

The DMA reports average email marketing ROI of $36-42 per dollar spent. Cependant, ROI varies significantly by industry, business model, and email program maturity. Your best benchmark is your own historical performance and improvement au fil du temps.

Should I worry about Apple Mail Privacy Protection affecting my métriques?

Yes, MPP inflates taux d’ouverture for Apple Mail users (40-50% of many lists). Adapt by: focusing more on click-based métriques, segmenting Apple Mail users separately in analysis, using click-to-taux d’ouverture (CTOR) au lieu de taux d’ouverture, and tracking “human opens” vs. “machine opens” if your ESP supports it.

How long should my attribution window be?

Standard practice is 7-day click attribution. Shorter windows (24-48 hours) are more conservative but may undercount email’s impact. Longer windows (30 days) capture delayed purchases but may over-attribute. Consider your typical purchase cycle—longer consideration products warrant longer windows.

How do I measure the impact of my welcome series?

Track welcome series-specific métriques: taux de conversion (signups who purchase during series), time to first purchase, average order value of first purchase, and long-term rétention of clients who completed the series vs. those who n’a pas. Compare welcome series revenus against promotional campagnes.

Conclusion

Email marketing analyses transform guesswork into stratégie. By tracking the right métriques, establishing proper benchmarks, building actionable dashboards, and committing to data-driven optimization, vous pouvez continuously improve your email performance.

Remember these key principles:

1. Track what matters: Focus on métriques tied to business outcomes
2. Benchmark appropriately: Compare to your industry and your own history
3. Test systematically: Use proper methodology for reliable insights
4. Act on data: Analyses without action is just overhead
5. Iterate continuously: Small improvements compound au fil du temps

The best email marketers ne sont pas those with le plus sophisticated tools—ils sont those who consistently turn data into better decisions.

Ready to unify your email analyses with complete customer data? Essayez Tajo gratuitement and connect your Shopify store to Brevo with comprehensive analyses built in.