Activation Intelligence for Subscription Growth
Sendlyr finds the early behavior that predicts conversion, then guides every user toward it through the lifecycle tools you already use.
How Sendlyr works
One indicator, sharpened by real outcomes.
Customer data flows into Sendlyr. Sendlyr finds a leading indicator. The customer lifecycle stack applies treatment, then the measured outcome returns as evidence.
Your systems
Data
Existing product data enters from the stack you already own.
Sendlyr
Sendlyr
Finds the early behavior that predicts your chosen outcome.
Evidence
Leading indicator
A behavior, time window, and measurable relationship to conversion.
Your systems
Treatment
Your lifecycle stack guides users toward the behavior with testing.
Evidence
Outcome
Measured conversion returns as evidence for the next analysis.
Three-part deliverable
From raw behavior to an indicator your team can use.
We define the evidence, turn the indicator into lifecycle direction, and stay through the final readout.
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01
Signal analysis
Define the behavior, time window, relationship to the outcome, and coverage.
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02
Lifecycle guidance
Turn the indicator into a clear behavior for your existing lifecycle stack to reinforce.
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03
Outcome readout
Measure what happened, state the uncertainty, and feed the evidence back into the indicator.
What you leave with
The result, not just the setup.
A complete readout keeps the indicator, lifecycle direction, and business outcome together.
- 01Indicator definition
- 02Behavior window
- 03Relationship to outcome
- 04Coverage
- 05Lifecycle direction
- 06Measurement plan
- 07Learning update
A real result from eReflect’s Typesy
We moved the behavior. Conversion did not move.
The experiment changed the leading indicator. The business outcome stayed effectively flat.
Do not scale the intervention as evidence of conversion impact.
| Measure | Treatment | Control | Finding |
|---|---|---|---|
| Indicator | 69.4% 129/186 | 57.7% 56/97 | +11.6pp · +20.1% relative p ≈ 0.051 — borderline |
| Trial conversion | 76.8% 136/177 | 76.7% 66/86 | +0.1pp · effectively flat |
Leading explanation: conversion was already around 77%, leaving little headroom for the experiment to show a change.
Read the full case study →Founder evidence
The operator stays close to the evidence.
Stephen designed and operated a live randomised experiment across 485 users and 568 hand-executed sends. He detected a four-day pipeline outage himself. He corrected the published conclusion when the final data contradicted the hypothesis.
Signal Validation Pilot
Validate one activation theory against a real outcome.
The scope is fixed: one candidate behavior, one outcome, one lifecycle direction, and one complete readout.
- Bring
- A candidate behavior, an eligible cohort, and a business outcome.
- We run
- Signal analysis, lifecycle guidance, measurement planning, and the final analysis.
- Receive
- A clear indicator, its evidence, and the next action for your lifecycle stack.