Email List Decay & Prediction

Why Email Lists Decay Faster Today

There are six major reasons decay accelerated over the last 5 years:

1. More temporary/disposable emails

Users increasingly rely on temporary inboxes for one-time interactions.

2. Stricter mailbox provider algorithms

Gmail, Microsoft, Yahoo now aggressively filter senders with bad engagement.

3. More bots and automated form-fills

Bots create fake signups that decay essentially immediately.

4. Users maintain multiple inboxes

People have “real,” “shopping,” “spam,” and “throwaway” emails.

5. Customers churn faster

Subscription fatigue and competitive markets reduce engagement lifespan.

6. Email addresses age

Old but once-legitimate addresses can become recycled spam traps.

The 6 Primary Drivers of Email List Decay

1. Hard Bounces

Occurs when the address is syntactically or functionally invalid.
Examples:

• domain doesn’t exist
• mailbox disabled
• typo emails

2. Soft Bounces Turning Permanent

Temporary issues that become lasting delivery failures.

3. Inactivity (the #1 silent killer)

People stop opening, clicking, or engaging for 90+ days.

4. Unsubscribes

Users lose interest or feel overwhelmed by frequency.

5. Spam Traps Forming Over Time

Old, abandoned emails may be converted into trap addresses.

6. Temporary Emails Expiring

Disposable email lifespans can be as short as 10 minutes.

How to Measure Your Current Decay Rate

Use this standardized formula:

Decay Rate (%) = (Lost Contacts ÷ Starting Contacts) × 100

Lost contacts include:

• invalid addresses
• high-risk addresses
• hard bounces
• unsubscribes
• long-term inactive users

To measure accurately, calculate decay across:

• 30 days
• 90 days
• 12 months

You’ll see decay accelerates over time unless counterbalanced by list hygiene.

The Email List Decay Formula (Current Model)

Here’s a practical, easy-to-use calculation:

Annual Decay Rate = 1 – (Active List / Total Subscribers)

Example:
Total subscribers: 100,000
Active (engaged + safe) subscribers: 74,000

1 – (74,000 / 100,000) = 26% annual decay

This number is typical.

But raw decay rate doesn’t show the future. For that, we need prediction models.

How to Predict Future List Size (3 Practical Methods)

Below are three models, from simplest to most advanced.

Model 1: Straight-Line Decay (Simple but rough)

Assume the same annual decay rate continues.

Example:
26% yearly decay → you lose ~2.2% monthly.

Predictable but inaccurate for seasonal industries.

Model 2: Engagement-Based Cohort Model (Reliable)

Segment your list by:

• 0–30 day active
• 31–60 day active
• 61–90 day active
• 91–180 day inactive
• 180+ day inactive

Each cohort has a predictable decay trajectory.

This is how most ESPs model user activity.

Model 3: Impressionwise Decay Prediction Framework (Best Practice)

This combines:

• user engagement
• mailbox provider filtering patterns
• historical bounce risk
• trap proximity
• deliverability patterns
• list acquisition sources

By analyzing millions of address-level risk patterns, you can predict:

• how fast decay will occur
• which segments will decay first
• which sources produce decaying leads faster
• where traps are likely to appear

This model allows forecast accuracy up to 92%, depending on data volume.

How Decay Impacts Deliverability, Revenue & Reputation

Decay isn’t just about losing contacts — it’s about losing sender trust.

Deliverability Impact

• Places you into lower-tier filtering buckets
• Reduces inbox placement
• Lowers domain reputation
• Increases spam foldering

Revenue Impact

• Fewer valid inboxes to reach
• Lower engagement → lower conversion
• Higher sending cost for diminishing returns

Reputation Impact

• Higher bounce rates
• Higher trap hits
• Higher complaint rates
• ESP enforcement (throttling, blocks, or suspensions)

How to Reduce Email List Decay (Pro Strategies)

Here are the most effective approaches:

1. Verify emails at point-of-entry. Inline verification reduces fake signups 40%–80%.

2. Run continuous verification (not one-time scrubs). Continuous hygiene = dramatically lower decay.

3. Use an engagement-based sunset policy. Automatically cool inactive users with:

• reduced frequency
• targeted reactivation campaigns

4. Remove risky addresses proactively. Role accounts, seeds, toxic patterns → remove before sending.

5. Reduce form spam and bot traffic. Use:

• JS-based form fingerprinting
• hidden honeypot fields
• velocity detection
• Impressionwise intake protection

6. Improve email content relevance. Better engagement slows decay dramatically.

The Impressionwise Decay Prediction Framework

This framework gives a predictive model that competitors don’t provide. It uses 6 core scoring signals:

1. Address Stability Score. Measures domain age, role patterns, and consistency.

2. Historical Risk Score. Patterns that correlate with trap emergence.

3. Behavioral Decay Score. Engagement decline velocity across campaigns.

4. Source Integrity Score. Signup source analysis (landing page, funnel, traffic type).

5. Email Age Estimation. Time since first-seen in the ecosystem.

6. Deliverability Pressure Score. How mailbox providers are treating similar profiles recently.

Outcome:

Using these inputs, the model estimates:

• 30-day decay probability
• 90-day decay probability
• 180-day decay probability
• 12-month projected list size
• High-risk segments to remove
• Safe segments to send to more frequently

This creates a predictive hygiene cycle, not just a reactive clean-up.

Final Recommendations

✔ Verify emails at intake

✔ Run periodic hygiene — not occasional scrubs

✔ Use predictive analysis to anticipate user decay

✔ Maintain separate send lanes for risky segments

✔ Optimize engagement to slow natural losses

✔ Track monthly Active Rate and Annual Decay Rate

Using predictive models (like the Impressionwise framework) can reduce annual decay by 50% or more, improve deliverability, and stabilize long-term revenue.