Lifecycle mapping
Defined the full journey from ad click to trial, activation, subscription, and upgrade.
SaaS · Lifecycle & Revenue Measurement
Paid growth, product usage, and revenue were moving—but not together.
Apimio was driving significant paid acquisition, but needed a deeper measurement model that connected trial growth, product behavior, subscription revenue, and long-term value.
Context
Apimio operates a modern SaaS platform built on a headless architecture, combining marketing, product, and commerce systems into a single user journey.
Their stack included Next.js and Sanity on the frontend, Shopify extensions, Stripe for subscription management, Heap and ContentSquare for product analytics, and BigQuery as the data warehouse.
The growth engine was aggressive—multi-million dollar spend across Meta, TikTok, and Google Ads—driving a steady flow of trial users into the platform.
But while acquisition scaled, the connection between marketing, product usage, and revenue remained unclear.
The problem
On the surface, the system was performing. Trials were increasing. Campaigns were scaling. Product engagement looked healthy.
But when the team tried to answer a deeper question— which channels are actually driving revenue, not just signups?—the picture fell apart.
Ad platforms optimized toward trial starts, not paid conversions. Product analytics showed behavior, but not value. Stripe held the real revenue— but lived outside the acquisition story.
Marketing, product, and revenue systems all had data. None of them were connected well enough to support confident decisions.
The issue wasn’t growth. It was understanding which growth actually mattered.
What was actually broken
This wasn’t a lack of tracking. It was a disconnected lifecycle.
- Trial signups weren’t tied to subscription revenue
- Product usage wasn’t linked to acquisition source
- Stripe lifecycle events weren’t feeding into marketing
- Heap and ContentSquare lived outside revenue analysis
- Ad platforms optimized on shallow funnel signals
Each stage of the journey existed—but they didn’t connect.
Acquisition, behavior, and revenue were being measured separately.
The approach
We approached Apimio as a full lifecycle measurement problem—connecting acquisition, product usage, and revenue into a single system tied to customer value.
Server-side signal layer
Implemented sGTM and Conversion APIs to send deeper funnel signals into Meta, Google, and TikTok.
Stripe integration
Connected subscription, upgrade, and revenue events back into the measurement layer.
Product analytics alignment
Mapped Heap and ContentSquare behavior to activation and revenue outcomes.
BigQuery data layer
Unified marketing, product, and revenue data into a single analysis layer.
Measurement blueprint
Defined lifecycle events, data flows, and governance so the system could scale with growth.
Outcome
The shift wasn’t just technical—it changed how growth decisions were made.
Campaigns could now be evaluated based on revenue and LTV—not just trial volume. Paid media platforms began optimizing toward higher value users, and product and marketing teams aligned around the same definition of success.
Customer acquisition cost could be understood in relation to actual revenue, not just conversion events.
Growth didn’t just scale—it became measurable in a way that could be trusted.