"A third of the list was people who tried the product in 2024 and left."
A B2B project-management SaaS was mailing every address that had ever started a trial. Trial-to-paid conversion sat at 3.1% and the complaint rate at 0.24%, more than double Google's 0.1% guidance. Ten weeks later: 4.6% trial-to-paid, complaints at 0.05%, and a list 31% smaller. Priya Raghavan ran the engagement; Cole Bergeron rebuilt the flows.
| Metric | Before | After (10 weeks) |
|---|---|---|
| Trial-to-paid conversion | 3.1% | 4.6% |
| Onboarding email CTR | 2.4% | 4.9% (flow avg 5.58%) |
| List size | baseline | −31% (expired trials suppressed) |
| Spam-complaint rate | 0.24% | 0.05% |
What made a SaaS list different from an ecommerce one?
Priya: Ecommerce lists decay gradually; SaaS trial lists decay in a cliff. Someone who abandoned a trial twelve months ago is not a lapsed customer, they are a stranger who once clicked a button, and mailing them product updates is what pushed complaints to 0.24%. The first decision was suppressing every trial profile inactive for twelve months or more. That was 31% of the list, gone from routine sends in week two.
Did anyone push back on deleting a third of the list?
Priya: The CEO did, reasonably. The list was treated as an asset on par with pipeline. We reframed it with one number: those segments had produced zero conversions in six months while generating most of the complaints that were throttling delivery to active trials. Suppression is also not deletion; the profiles stay for a re-permission campaign later. That argument took one call.
What changed in the onboarding flow itself?
Cole: The old flow was six emails on a fixed daily drip, identical for every trial. We rebuilt it around product behavior: a user who created a project got different mail than one who never finished signup, and the upgrade ask moved to the moment usage crossed a threshold instead of day four. Click-through went from 2.4% to 4.9%, which is what pulled trial-to-paid from 3.1% to 4.6%.
What did not go to plan?
Cole: The behaviour-triggered flow needed product events the ESP did not have, and wiring those took the client's engineers two sprints we had not budgeted. Weeks four and five shipped nothing visible. The lesson we carried into the process: audit event availability in week one, before promising flow logic that depends on it.
What transfers to other SaaS teams?
Priya: Suppress expired trials before optimizing anything; reputation gains make every later change cheaper. Then trigger onboarding on what users do, not on elapsed days. Both are covered in thelifecycle-automation service, and the suppression logic comes fromaudience analysis.
Client identity anonymized. Figures are representative engagement data; Klaviyo's published flow benchmarks (5.58% average flow click rate) are the reference frame.
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