How to Perform Email Marketing Cohort Analysis to Boost LTV

> TL;DR: Email marketing cohort analysis is the practice of grouping subscribers by shared characteristics—such as signup date or acquisition channel—and tracking their engagement and revenue behavior over time. Unlike aggregate open rates that mask list health, cohort tracking reveals true subscriber retention curves, uncovers high-LTV acquisition sources, and pinpoints exactly when subscribers drop off.

Measuring aggregate metrics like open rates or click-through rates across an entire email list often provides a misleading picture of campaign performance. An overall open rate of 25% might look healthy, but it could hide the fact that 80% of newly acquired subscribers stop opening emails after 30 days while a small core of historical subscribers carries the average.

Email marketing cohort analysis solves this blind spot by isolating groups of subscribers who joined your list during the same timeframe or through the same marketing channel. By tracking how each cohort performs across key milestones—such as 30-day retention, 90-day repeat purchase rate, and 12-month lifetime value (LTV)—and implementing effective email list cohort tracking, marketers can make precise adjustments to their acquisition budgets, onboarding automations, and list maintenance practices.

Last updated: March 2026

What Is Email Marketing Cohort Analysis and Why Does It Matter?

Email marketing cohort analysis is a data-driven framework that categorizes subscribers into distinct groups (cohorts) based on a shared event or attribute, and measures their behavior continuously across fixed time intervals. Rather than analyzing email list performance as a single static monolith, cohort analysis treats your list as a dynamic pipeline of evolving subscriber groups.

To understand why cohort tracking is essential for sustainable list growth, consider how aggregate analytics fail modern email programs:

• The Aggregate Fallacy: When you send a newsletter to 50,000 contacts and record a 22% open rate, that percentage blends brand-new leads, active buyers, unengaged subscribers, and dormant contacts. It tells you nothing about whether your onboarding sequence is retaining new signups or whether list decay is accelerating.
• The Acquisition Mask: A sudden spike in new subscriber acquisition can artificially inflate total engagement metrics in the short term, masking a severe churn problem among subscribers who joined three months prior.
• Channel Blindness: Aggregate reporting attributes revenue evenly across your list, making it impossible to determine whether paid search leads produce higher long-term revenue than organic blog subscribers.

By organizing your subscribers into structured cohorts, you can measure the true trajectory of engagement and revenue over time. You gain visibility into your subscriber retention curve—the visual representation of how engagement holds up over days, weeks, or months following list acquisition.

When integrated with advanced email campaign analytics and granular audience segmentation, cohort analysis turns raw email logs into predictable revenue forecasts and actionable list hygiene strategies.

2. Lead Nurturing (Content-Based Flows)

Not every subscriber is ready to buy immediately. Lead nurturing automations provide value and build trust over time, guiding prospects closer to a purchase decision with relevant, educational content.

Key Steps:

• Behavioral Triggers: Initiate nurturing based on website behavior (e.g., viewed product category, downloaded a guide, spent time on a blog post). This ensures content relevance.
• Educational Content Delivery: Send a series of emails (e.g., 3-7) that offer helpful tips, industry insights, product use cases, or comparisons. Focus on solving potential customer problems.
• Soft Product Introduction: Gradually introduce your products or services as solutions to the problems you’ve been addressing. Avoid hard selling; aim to inform and educate.
• Engagement Tracking: Monitor open rates, click-through rates, and time spent on content. Use this data to adjust future emails or segment users who show high intent.

Sendgrove Advantage: Use Sendgrove Automation to create sophisticated multi-path nurturing sequences. Segment users based on their engagement, viewed products, or downloaded resources, delivering hyper-personalized content at scale.

3. List Hygiene & Re-engagement (Sunset/Reactivation)

A clean and engaged email list is paramount for deliverability and ROI. Over time, subscribers become inactive, leading to lower open rates, higher bounce rates, and potential harm to your sender reputation. List hygiene and re-engagement automations proactively address these issues.

Key Steps:

• Inactivity Triggers: Identify subscribers who haven't opened or clicked an email in a defined period (e.g., 90-180 days). This signals a decline in engagement.
• Re-engagement Series: Send a short (2-3 email) series asking if they still want to hear from you. Offer incentives, highlight new products, or ask them to update preferences. Include a clear "Yes, keep me subscribed" CTA.
• Sunset Policy: If a subscriber remains unresponsive after the re-engagement series, implement a sunset policy. This involves moving them to a "suppressed" list or removing them entirely. It’s better to have a smaller, highly engaged list than a large, dormant one.
• Bounce Management: Regularly review and remove hard bounces immediately. Soft bounces should be monitored; if they persist, consider removing the address.

Sendgrove Advantage: Sendgrove's built-in Email Validation not only cleans your initial list but also offers ongoing tools to manage bounces and identify risky addresses before they impact your deliverability. Automate the suppression of unengaged contacts to protect your sender reputation and maximize campaign effectiveness. Remember, many ESPs warn when hard bounces climb toward ~1–2% and total bounce rates aim under 2%; proactive hygiene is key.

3 Core Types of Email Marketing Cohorts (And How to Segment Them)

To build a meaningful email cohort tracking system, you must first determine how to group your subscribers. Depending on your business model—whether ecommerce, B2B SaaS, publishing, or agency services—different cohort structures unlock different strategic insights.

1. Acquisition-Time Cohorts (Time-Based)

Time-based cohorts group subscribers according to the specific calendar period during which they opted into your list (for example, January 2026 Cohort, February 2026 Cohort, or Week 12 Cohort).

Tracking acquisition-time cohorts reveals how changes to your onboarding workflows, email send frequency, and content strategy impact long-term subscriber retention over time. If your March 2026 cohort demonstrates a 40% higher 90-day retention rate than your January 2026 cohort, you can directly correlate that improvement to the welcome sequence redesign implemented in late February.

2. Acquisition-Source Cohorts (Segment-Based)

Source-based cohorts group subscribers by the entry point or marketing channel through which they subscribed. Examples include paid Facebook ads, organic search landing pages, co-marketing webinars, interactive quizzes, or lead magnet downloads.

Because different acquisition channels attract users with varying intent and interest levels, source-based cohorts highlight which marketing investments generate high-LTV subscribers and which channels bring in low-quality leads prone to rapid list fatigue or hard bounces.

3. Behavioral & Transactional Cohorts (Event-Based)

Behavioral cohorts group subscribers based on specific actions or milestones completed within their subscriber lifecycle. Examples include:

• First-purchase cohorts (subscribers who placed their initial order in Month 1 vs. Month 3)
• Email validation cohorts (subscribers whose addresses were verified valid at capture vs. unverified single opt-in leads)
• Feature adoption cohorts (users who activated an integration within 7 days of onboarding)

Evaluating behavioral cohorts enables marketing teams to measure the downstream economic impact of early engagement triggers and product usage milestones.

| Cohort Type | Primary Tracking Variable | Key Strategic Insight | Recommended Optimization | | :--- | :--- | :--- | :--- | | Acquisition-Time | Signup Month / Week | Onboarding effectiveness & seasonal list decay | Refine welcome sequences; adjust automated drip timing | | Acquisition-Source | Lead Magnet / Channel UTM | Channel quality & long-term subscriber payback | Reallocate ad spend to high-LTV acquisition channels | | Behavioral & Event | First Purchase / Feature Trigger | Monetization velocity & conversion friction | Deploy targeted upsell & automated cross-sell sequences | | Verification & Quality | List Hygiene / Validation Status | Deliverability risk & bounce prevention | Automate real-time validation via API at form signup |

Structuring these cohort definitions requires clean metadata tagging at the moment of subscriber capture. Modern email marketing platforms allow you to append custom contact fields, source tags, and timestamps automatically during form submission or API contact ingestion.

Step-by-Step Playbook: How to Conduct an Email Cohort Analysis

Executing an effective email marketing cohort analysis requires a structured, repeatable process. Follow this 5-step playbook to transform raw list exports into actionable cohort reporting.

Step 1: Define Your Cohort Intervals and Tracking Metrics

Before pulling subscriber data, establish clear tracking parameters aligned with your sales cycle and campaign cadence:

1. Time Intervals: Select weekly intervals for high-frequency programs (such as news outlets or fast-fashion ecommerce) or monthly intervals for B2B SaaS and high-ticket products.
2. Primary Metric: Decide whether your cohort retention curve will be defined by engagement retention (percentage of cohort opening or clicking at least one email during the interval) or monetization retention (percentage of cohort making a repeat purchase or generating recurring revenue).
3. Observation Window: Standard cohort tracking spans 12 months, evaluated across key interval markers: Month 0 (acquisition month), Month 1, Month 3, Month 6, Month 9, and Month 12.

Step 2: Clean and Validate Your Subscriber Base Before Analysis

Inaccurate data destroys cohort validity. If 15% of an incoming signup cohort consists of invalid email addresses, spam traps, or syntax errors, your baseline cohort size will be inflated, artificially depressing your calculated engagement rates and subscriber retention metrics.

Before analyzing cohort retention, ensure your raw list data is validated. Using real-time email verification at signup removes invalid addresses before they enter your database. Cleaning historical contact records ensures that hard bounces do not skew your cohort benchmarks or damage sender reputation.

Step 3: Map Milestone Retention and Engagement Rates

For each cohort, calculate the email cohort retention rate at every interval using the following formula:

Cohort Retention Rate (%) = (Active Cohort Members in Interval N ÷ Initial Total Cohort Size at Month 0) × 100

For example, if the January 2026 cohort started with 1,000 validated subscribers, and 350 of those specific subscribers opened or clicked an email in Month 3 (April 2026), the Month 3 engagement retention rate for the January cohort is 35.0%.

Step 4: Calculate Cumulative Cohort LTV and Payback Period

To measure the financial yield of your email marketing list cohort tracking, combine engagement metrics with purchase data to calculate cumulative Cohort Lifetime Value (LTV):

Cumulative Cohort LTV ($) = Total Cumulative Revenue Generated by Cohort through Month N ÷ Initial Total Cohort Size at Month 0

Practical Calculation Example:

• Initial January Cohort Size: 1,000 subscribers
• Total Acquisition Cost (Ad spend + lead magnet production): t2,000 (t2.00 CAC per subscriber)
• Cumulative Revenue Generated:
• Month 0: t800 (t0.80 LTV per subscriber)
• Month 1: t600 (t1.40 cumulative LTV per subscriber)
• Month 2: t400 (t1.80 cumulative LTV per subscriber)
• Month 3: t500 (t2.30 cumulative LTV per subscriber)

In this scenario, the January cohort achieves full cost payback during Month 3 when cumulative LTV (t2.30) surpasses subscriber CAC (t2.00). Every dollar generated by this cohort from Month 4 onward represents net profit. Understanding subscriber lifetime value is central to optimizing campaign payback periods, as detailed in Sendgrove's guide on subscriber lifetime value optimization.

Step 5: Plot Subscriber Retention Curves

Chart your calculated retention percentages on a line graph where the x-axis represents time elapsed since acquisition (Month 0, 1, 2... N) and the y-axis represents the retention percentage. Plotting multiple monthly cohorts on the same axes visualizes whether your list health is improving or deteriorating over successive subscriber generations.

How to Interpret Subscriber Retention Curves and Fix Cohort Decay

Once you plot subscriber retention curves for your email cohorts, the shape of the curve reveals underlying strengths or systemic flaws in your email program. Healthy subscriber retention curves follow a predictable trajectory: a initial steep decline during early onboarding, followed by a flattening slope (the "retention plateau") where a stable baseline of engaged subscribers remains active indefinitely.

1. The Onboarding Drop-Off (Months 0–1)

It is normal for subscriber retention to drop between 20% and 40% during the first 30 days. New signups naturally filter out as single-use lead magnet seekers lose interest. However, if your retention curve drops by more than 60% within Month 1, your email program suffers from an Onboarding Disconnect.

Root Causes of Month 1 Churn:

• Misaligned Expectations: The lead magnet or signup incentive promised one value proposition, but subsequent email campaigns delivered unrelated sales pitches.
• Aggressive Send Frequency: Overwhelming new subscribers with 5 to 7 promotional broadcasts in their first week without proper educational pacing.
• Inbox Placement Failure: Welcome emails ending up in spam folders due to unverified sender domains or missing authentication protocols.

2. The Mid-Lifecycle Erosion (Months 2–6)

A continuous downward slope between Months 2 and 6 without a flattening plateau indicates List Fatigue. Subscribers are not necessarily unsubscribing, but they have grown indifferent to your broadcasts—leading to unopens, unclicks, and eventual spam complaints.

Diagnosing Mid-Lifecycle Fatigue:

• Generic newsletter broadcasts sent to the entire unsegmented database.
• Lack of automated re-engagement triggers or personalized content pathways.
• Stale messaging that fails to evolve alongside the subscriber's stage in the customer journey.

4 Strategic Interventions to Lift Cohort Retention

When your cohort analysis exposes premature subscriber churn, deploy these four tactical fixes to stabilize your retention curves and maximize LTV:

#### A. Optimize Automated Welcome & Onboarding Workflows First impressions set the trajectory for long-term cohort retention. Restructure your onboarding sequence to deliver maximum educational value before initiating hard commercial offers. Use progressive profiling during the first 14 days to collect zero-party data regarding subscriber preferences, enabling hyper-relevant content delivery.

#### B. Implement Engagement-Based List Segmentation Do not treat 6-month-old subscribers the same as 30-day-old subscribers. Establish automated activity segments based on recent opens, clicks, and web visits. Send high-frequency promotional campaigns exclusively to active cohorts while moving unengaged cohorts into low-frequency, value-focused re-engagement sequences. Learn how to implement this strategy effectively in Sendgrove's guide on email engagement segmentation.

#### C. Enforce Strict List Hygiene and Automated Validation Uncleaned email lists drag down overall sender reputation, hurting deliverability for your most engaged cohort members. Automatically validate new contact additions at the point of capture and execute routine bulk verification on inactive contacts prior to re-engagement campaigns.

#### D. Establish Automated Sunset Policies Carrying dormant subscribers indefinitely distorts cohort reporting and degrades mailbox provider trust. Implement an automated sunset policy that targets contacts who show zero engagement over a 90-day window. If a 3-part win-back sequence yields no response, automatically suppress or archive those records to preserve deliverability for active cohorts.

Industry Cohort Benchmarks & Advanced Analytics Frameworks

To accurately evaluate your email marketing cohort analysis, compare your performance against established industry standards. While exact metrics vary depending on price point, purchase frequency, and target audience, the following benchmarks provide realistic performance baselines across major digital sectors.

| Industry Sector | Typical Month 1 Engagement Retention | Typical Month 3 Engagement Retention | Average Month 12 Cohort LTV Payback Multiple | Primary Churn Risk Factor | | :--- | :--- | :--- | :--- | :--- | | Ecommerce (D2C) | 45% – 55% | 25% – 35% | 2.5x – 4.0x CAC | One-and-done discount seekers; poor post-purchase nurture | | B2B SaaS | 60% – 70% | 40% – 50% | 3.5x – 6.0x CAC | Incomplete product onboarding; feature underuse | | Digital Publishing / Newsletters | 50% – 65% | 35% – 45% | 1.8x – 3.0x CAC | Content fatigue; inconsistent publishing schedule | | Agencies & B2B Services | 40% – 50% | 30% – 40% | 4.0x – 8.0x CAC | Unaligned lead magnet intent; generic broadcast emails |

Mathematical Models for Predicting Cohort Payback and LTV

Advanced email marketing teams do not wait 12 months to determine whether an acquisition cohort will be profitable. By applying logarithmic decay models to early cohort performance data, you can project 12-month cohort LTV with remarkable accuracy using 30-day performance signals.

To project long-term retention tRR(t) at month ttt, marketers use power-law decay functions:

ttR(t) = R_0 × t^{-α}tt

Where tR_0t represents initial Month 1 retention and tαt represents the retention decay rate. When tαt is low (below 0.3), the retention curve flattens quickly, signaling a strong, sticky subscriber base. When tαt exceeds 0.6, the cohort experiences steep long-term drop-off, requiring immediate intervention in automated lifecycle workflows.

Integrating Cohort Data with Multi-Touch Marketing Attribution

Email cohort analysis achieves its highest strategic value when connected directly to multi-channel marketing attribution models. When subscriber acquisition tags pass UTM parameters—such as utm_source, utm_medium, and utm_campaign—into your email contact records, you can compare the 180-day LTV of paid Meta ad subscribers against organic Google search signups.

Frequently, acquisition channels with higher initial Customer Acquisition Costs (CAC) produce significantly higher 12-month cohort retention and LTV multiples. Conversely, low-cost lead sources—such as giveaway contests or sweepstakes—often exhibit steep decay curves with near-zero 90-day retention, resulting in a net negative return on investment once sender infrastructure costs and list hygiene expenses are accounted for.

5 Common Pitfalls in Email Marketing Cohort Tracking

Avoid these critical analytical mistakes when designing and interpreting your cohort reports:

1. Blending Re-engaged Contacts with New Signups: Mixing reactivated historical subscribers into new acquisition cohorts inflates early retention rates and skews baseline CAC metrics. Always isolate brand-new email opt-ins into dedicated acquisition cohorts.
2. Ignoring Mailbox Provider Privacy Features: Privacy initiatives like Apple Mail Privacy Protection (MPP) pre-render email pixels, artificially inflating open rates for iOS users. When tracking engagement retention, rely on click-through rates, web session events, and purchase conversions alongside open data.
3. Failing to Account for List Hygiene Suppressions: When an automated sunset policy archives unengaged subscribers, your total cohort size at Month tNt drops. Ensure your cohort calculation methodology accounts for suppressed vs. unsubscribed vs. active contacts so retention formulas reflect true subscriber behavior.
4. Evaluating Cohorts Over Inadequate Time Horizons: Attempting to draw conclusions from a 7-day-old cohort often leads to premature campaign changes. Allow cohorts to mature through at least 30 to 60 days before making structural adjustments to acquisition channels or onboarding automations.
5. Treating All Unopens as Dead Leads: A subscriber who reads preview text or preheader copy without loading images may still receive brand value. Combine email tracking data with web analytics and store purchase logs to construct a complete multi-touch cohort retention profile.

Practical Case Study: How a D2C Brand Doubled Cohort LTV in 90 Days

To illustrate the transformational impact of email marketing cohort analysis, examine how a D2C wellness brand used cohort reporting to restructure its email marketing strategy and double 90-day subscriber LTV.

The Baseline Challenge

The brand was spending t45,000 monthly on paid acquisition campaigns, driving 15,000 new email signups each month with a 15% instant discount lead magnet. Despite generating high top-of-funnel signup volume, overall email revenue remained flat.

When the marketing team conducted their first comprehensive cohort analysis, they uncovered a severe retention drop:

• Month 0 (Acquisition): 100% active list baseline (15,000 subscribers)
• Month 1 Retention: 38% active engagement (62% of new signups went silent after using the initial discount)
• Month 3 Retention: 14% active engagement
• 90-Day Cumulative LTV: t1.65 per subscriber (against a t2.80 Customer Acquisition Cost)

The brand was losing money on every acquired subscriber because 86% of new contacts churned before making a second purchase or reaching payback break-even.

The Diagnostic Discoveries

By breaking down cohorts by acquisition channel and onboarding behavior, the team identified three root causes:

1. Unvalidated Inflow: 12% of paid ad signups contained disposable email addresses or typos, inflating subscriber CAC and causing high hard-bounce rates during early broadcasts.
2. Discount Dependency: Subscribers who received immediate promotional blasts without educational content experienced extreme list fatigue within 21 days.
3. Static Broadcast Scheduling: Non-buyers and repeat customers received identical weekly broadcast emails, leading to high unsubscribe rates among unengaged cohorts.

The 3-Part Strategic Overhaul

The brand executed three targeted interventions to fix its cohort retention curve:

#### 1. Integrated Real-Time Verification at Capture The team added API-based email verification to signup forms, blocking syntax typos, disposable domains, and toxic spam traps before contacts were added to the primary database.

#### 2. Redesigned Welcome Sequences Based on Cohort Intent Instead of sending immediate promotional blasts, the brand introduced a 5-part educational onboarding sequence tailored to the specific product category viewed during signup. Promotional offers were delayed until Day 10, after subscribers received brand storytelling and usage tips.

#### 3. Deployed Automated 60-Day Re-Engagement Triggers For subscribers showing no click activity by Day 45, Sendgrove's automated workflows triggered a dynamic "Preference Refresh" email, offering tailored content options or a reduced send frequency before placing dormant contacts into a structured 3-step win-back sequence.

The 90-Day Cohort Transformation Results

After running the optimized onboarding strategy for one quarter, the brand evaluated its new cohort metrics against historical benchmarks:

| Performance Metric | Historical Baseline Cohort | Optimized Post-Intervention Cohort | Strategic Improvement | | :--- | :--- | :--- | :--- | | Initial Invalid Lead Inflow | 12.4% | 0.2% | -98.4% Hard Bounce Prevention | | Month 1 Engagement Retention | 38.0% | 64.5% | +69.7% Early Engagement Lift | | Month 3 Engagement Retention | 14.0% | 36.2% | +158.5% Sustained Retention Lift | | 90-Day Cumulative Cohort LTV | t1.65 | t3.85 | +133.3% Revenue Yield per Subscriber | | CAC Payback Horizon | Never (Net Loss) | Day 42 (Net Profit) | Full Cost Recovery in 6 Weeks |

By using cohort analytics to diagnose lifecycle leaks rather than relying on aggregate open rates, the brand transformed its email list from a cost center into a predictable profit driver.

Building Your Cohort Analysis Workflow with Sendgrove

Implementing a strong cohort tracking system does not require complex data warehousing or expensive enterprise business intelligence platforms. Sendgrove provides built-in tools designed to simplify cohort creation, contact segmentation, and campaign tracking.

1. Automated Contact Ingestion and Metadata Tagging

When contacts join your database via web forms, landing pages, or API integrations, Sendgrove automatically appends essential cohort metadata to each contact record:

• Exact creation timestamp (created_at)
• Acquisition source, form ID, and campaign UTM parameters
• Initial validation status (valid, risky, or catch-all)

This structured metadata allows you to generate time-based, source-based, or quality-based cohorts with a single click.

2. Dynamic Engagement Segmentation

Sendgrove's segment builder enables marketers to create real-time rules that update cohort membership automatically based on subscriber activity. You can define custom rules such as:

• "Subscribers created in January 2026 AND clicked at least 1 campaign in the last 30 days"
• "Subscribers created via Facebook Lead Ads AND verified valid AND zero opens in 60 days"

These dynamic segments allow you to target specific cohort slices with tailored messaging without manual CSV filtering or spreadsheet formulas.

3. API Webhooks and Real-Time Event Tracking

For brands that track store purchases, SaaS feature usage, or app logins outside their email platform, Sendgrove's developer API and webhooks stream conversion events directly into contact profiles. This real-time sync ensures your cohort LTV metrics reflect up-to-the-minute customer activity.

Summary Checklist: 6 Gates for Email Cohort Analysis Success

Before finalizing your email marketing cohort analysis framework, verify that your tracking infrastructure satisfies these six core requirements:

1. Clean Contact Capture: Implement real-time email verification on all signup forms to eliminate invalid addresses and preserve baseline cohort metrics.
2. Standardized Metadata Tagging: Ensure every subscriber record captures acquisition timestamp, source UTM parameters, and lead magnet category.
3. Defined Observation Intervals: Establish consistent tracking intervals (30, 60, 90, 180, 365 days) aligned with your business model and purchase cycle.
4. Multi-Metric Cohort Reporting: Track both engagement retention (clicks/opens) and monetization retention (LTV/payback) across every cohort generation.
5. Automated Lifecycle Interventions: Connect cohort drop-off points directly to automated welcome, re-engagement, and sunset workflows.
6. Routine List Hygiene Audits: Clean historical contact databases regularly to maintain sender reputation and protect inbox placement for active cohorts.

FAQ

What is email marketing cohort analysis?

Email marketing cohort analysis is a tracking methodology that groups subscribers by shared signup characteristics—such as acquisition date or marketing channel—and measures their engagement, retention, and revenue behavior over time. It provides clearer insights into list health than aggregate open rates.

How do you calculate email cohort retention rate?

Calculate cohort retention rate by dividing the number of active subscribers from a specific cohort who opened or clicked an email during a given interval by the total number of subscribers in that cohort at Month 0, then multiplying by 100.

What is the difference between time-based and source-based cohorts?

Time-based cohorts group subscribers by when they joined your email list (such as January signups), revealing lifecycle retention trends over time. Source-based cohorts group subscribers by where they joined (such as Facebook ads vs. organic search), highlighting acquisition channel quality.

How does cohort analysis improve subscriber lifetime value (LTV)?

Cohort analysis identifies the exact milestones where subscribers churn or lose interest. By revealing when retention drops occur, marketers can optimize onboarding automations, improve list hygiene, and deliver targeted re-engagement offers that extend subscriber active lifespans and increase overall LTV.

What key metrics belong in an email cohort report?

An effective email cohort report tracks initial cohort size, engagement retention percentage across 30/60/90/180-day intervals, cumulative cohort revenue, average order frequency, subscriber CAC payback period, and hard bounce/un-subscription rates per cohort.