Anymail Finder
Cut Churn 18%, Increased LTV 33%, And Lifted Trial Conversion 28.5% In 7 Months
Fixing the hole in the bucket by optimizing bottom-of-funnel before top-of-funnel.
Client Snapshot
- Company
- anymailfinder.com
- Product
- B2B SaaS for finding and verifying professional email addresses
- Model
- Subscription with transactional usage (credits)
- Primary Metric
- Churn (retention in a transactional use case)
The Situation
By the time Anymail Finder reached out, they already had strong acquisition and a mature product. Their problem was the hole in the bucket.
Internally, they knew: “If our churn stays this high and any of our acquisition channels slow down, we're in real trouble.”
They weren't fighting objections. They were fighting uncertainty about where to focus.
Constraints & Misconceptions
Anymail Finder is in a naturally transactional space: people come in, get emails, leave. That creates two mental traps:
- “High churn is just the nature of the product.”
- “As long as we keep feeding the top of the funnel, we'll be fine.”
They were open to changing anything (pricing, onboarding, trial), but needed a thought partner to separate signal from noise and design a focused plan.
The Plan
We picked one money lever to obsess over: keep more of the customers they already win. Small churn improvements can dramatically increase profits and compounding growth.
- 1.Bottom-of-funnel first: Optimize trial → paid and week-1 onboarding before touching top-of-funnel
- 2.Clarify who's worth keeping: Identify and segment the best customers to optimize for them rather than the average user
- 3.Lightweight monetization tweaks: Make initial pricing and trial mechanics work with user behavior, not against it
- 4.Measure with hard numbers: Use Stripe and product data to establish a baseline and track progress monthly
The Results
Over 7 months (March → October), focused work on retention and bottom-of-funnel mechanics delivered significant improvements across all key metrics.
Top-Level Churn
-18%
Reduction in monthly churn rate
User Retention
+29%
Relative increase in user retention
LTV Per User
+33%
Increase in lifetime value per user
Trial Conversion
+28.5%
Lift in trial to paid conversion rate
M1 Revenue Retention
+61.3%
Increase in revenue retained after first month
M1 Subscriber Retention
+31.7%
Increase in subscribers retained after first month
Failed Payment Rate
-10.6%
Reduction in failed payment rate
We confirmed most of the improvement came from behavior and onboarding/trial changes, not just cleaning up failed payments.
Execution: What We Did
The goal was consumption and time-to-value. We focused on getting users to their first win fast, then aligned trial and pricing to convert and retain the right customers.
Rebuilt Early Onboarding Around "First Win"
Instead of a generic "buffet" experience, we focused onboarding on getting new users to their first meaningful result as fast as possible.
- -Re-mapped the first session to a clear "do this next" path
- -Reduced cognitive load so users could get to validated emails quickly
- -Installed simple product cues and comms around key activation moments
- -Goal: consumption and time-to-value, not just access
Smarter Trial Mechanics
Their trial was underperforming relative to the value of the product. We tightened structure and aligned limits with conversion readiness.
- -Tightened trial structure and messaging so users understood exactly what they'd get
- -Aligned trial limits and prompts with the point where serious users were most ready to convert
- -Reduced "tourists" and increased committed evaluators
- -Result: trial conversion rate increased 28.5%
Initial Pricing & Customer Quality
Even in a credit-based model, not all customers are equal. We identified best users and tuned positioning to attract higher-LTV segments.
- -Identified best users based on retention and usage, not just revenue
- -Tuned initial pricing recommendations and positioning for higher-LTV segments
- -Ensured changes didn't create implementation drag given dev capacity
- -Compounded churn work by lifting the value of each retained customer
Cancellation Flow & Persona-Based Saves
The cancel button isn't the end—it's a diagnostic moment. We rebuilt the cancellation flow to understand why users leave and present the right save offer based on their situation.
- -Added cancel reason survey to identify patterns and segment churning users
- -Built persona-based interventions: pause options for temporary needs, downgrades for budget concerns, usage reminders for forgotten value
- -Handled edge cases like annual-to-monthly switches and credit rollover requests
- -Turned cancellation data into a feedback loop for product and onboarding improvements
“Dan came in, looked at everything, and helped us make real improvements—we reduced churn by 18% and got 50 customers onto enterprise plans. It wasn't like we needed to change everything. Having someone with more experience confirm we were on the right track, while finding the things worth fixing, was valuable.”
Founder, Anymail Finder
Why It Worked
Bottom-of-Funnel First
Improved monetization and retention before asking for more traffic.
Customer Quality Over Volume
Oriented around best customers so every retained user was worth more.
Proof-Driven Decisions
Made small, targeted changes and watched the numbers move instead of redesigning everything at once.