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SERVICE 07
Service Growth Support
Service Growth Engineering
Working out “how do we grow this after launch?” together.
From building out measurement infrastructure to A/B testing, behavioral analysis, and improvement-cycle design—we stay alongside you on the engineering that growth requires.
Process
01
Measurement infrastructure & metric design
02
Hypothesis framing & experiment design
03
Implementation & A/B testing
04
Turning it into a learning cycle
Who this is for
Breaking out of the “launched it, but don’t know how to grow it” state
PAIN 01
Our measurement and metrics aren’t in place, so we can’t tell what to improve
We just dropped in GA. We aren’t capturing product-specific behavioral metrics, and hypothesis testing has become gut-driven.
PAIN 02
We have no A/B testing setup in-house, so every improvement idea becomes a release-day gamble
PAIN 03
Our improvement cycle lives in one person’s head, and the know-how walks out when they leave
You can come to us even with no measurement infrastructure and only fuzzy metrics.
Tell us what you’re facing →
The SYSTEMI approach
Turning improvement into a learning cycle
Rather than leaving each initiative as a one-off, we implement a structure where hypotheses and validation run every week and every month.
01
Measurement infrastructure & metric design
We design a KPI tree specific to your product and build out the measurement infrastructure with GA4, BigQuery, Amplitude, and the like.
Output
KPI tree / measurement infrastructure / dashboards / metric-definition doc
Output
Hypothesis list / experiment-design doc / sample size / stop-criteria
02
Hypothesis framing & experiment design
We form hypotheses from data and user interviews and break them down into testable forms, defining priorities and sample sizes up front.
03
Implementation & A/B testing
We build an A/B testing foundation with feature flags and validate improvement ideas safely in production.
Output
A/B testing foundation / implementation / test results / release decision
Output / What happens next
Review operations / knowledge base / growth backlog
04
Turning it into a learning cycle
We design weekly and monthly review sessions, building operations where what you learn stays as organizational knowledge.
What sets us apart
Can they run measure → hypothesize → build → learn end to end?
Not a consultancy, not an in-house coach—we come in with a team that runs it all, measurement infrastructure and implementation included.
| Growth consultancies | Analytics firms | Outsourced dev shops | SYSTEMI | |
|---|---|---|---|---|
| KPI design | ○ | ○ | △ | ○ Aligned with product and operations |
| Measurement infrastructure build | ✕ Recommendations only | △ Mostly tool deployment | ○ After the spec | ○ Operations × implementation |
| A/B testing implementation | ✕ Recommendations only | ✕ Out of scope | △ Depends on the engagement | ○ Including a feature-flag foundation |
| Learning operations | △ Stops at recommending | ✕ Out of scope | ✕ Out of scope | ○ Weekly review design |
AI × growth
Using AI to accelerate the hypothesis cycle
Structuring user behavior
We classify logs, inquiries, and social reactions across the board with an LLM, rapidly generating the raw material for hypotheses.
Interpreting A/B test results
We combine experiment results with on-the-ground knowledge to help make sense of them, surfacing hypotheses for “why the numbers moved.”
Generating copy & UI improvement ideas
Claude generates improvement-copy ideas in volume, people select, then we validate—a cycle that raises initiative throughput.
Related cases
Where we make the difference
A mix of cases we can disclose and illustrative model cases.
A long-term engagement with Ambish, continuing through to improving usage after each feature release
Challenge
They needed a team that wouldn’t just build features but would own adoption rates too.
Result
Measurement infrastructure and continuous improvement steadily lifted adoption of new features.
MODEL CASE
FDE in action
Introducing a measure → hypothesize → A/B test mechanism to a service that had stalled after launch
Why they reached out
MAU had plateaued. Improvement ideas kept coming, but the basis for decisions was weak.
What we worked out
Redesigned the KPI tree, then built the measurement infrastructure, and stayed on to make it a weekly review.
See all cases →
DELIVERABLES
A look at what we produce on the front line
Sample structures of the documents we actually hand over—organized so they serve directly as decision input for the next phase.
DOCUMENT 01 — KPI tree & measurement-design sheet
Growth_KPI_Tree_v1.0.xlsx
North Star MetricThe single metric at the core of the business and its definition
Lever metricsIntermediate metrics that move the NSM (acquisition, activation, retention, revenue)
Behavioral KPIsMeasurement units at the user-behavior level (events, properties)
Measurement implementationEvent names, send timing, property specs
Dashboard layoutThe metrics and granularity reviewed in the weekly review
DOCUMENT 02 — Proposed architecture diagram
Example growth-platform architecture — measure → hypothesize → experiment
UI
Next.js / MobileProduction app🚦
Feature FlagsLaunchDarkly / StatsigEvents
📊
GA4 / AmplitudeBehavior tracking
EventBridge / KinesisStreamStorage
🧮
BigQuery / RedshiftDWHAnalysis
📈
Looker / MetabaseDashboards
ClaudeHypothesis generation & root-cause analysisOperations
📝
Weekly reviewTurned into a learning cycle* We design a closed measure → hypothesize → experiment → learn loop, so initiatives never end as one-offs.
* Feature flags let you validate improvement ideas safely in production.
* Feature flags let you validate improvement ideas safely in production.
FAQ
Common questions about service growth support
We have almost no measurement infrastructure. Where should we start?
We start with designing your product’s KPI tree and a minimal measurement implementation (GA4 plus key events). Once data is flowing, we move into the hypothesize-then-validate cycle.
We have no A/B testing setup in-house.
We support you from selecting a feature-flag service (LaunchDarkly, Statsig, or a custom build) through to implementation. Designing decision criteria and sample sizes comes as part of the package.
We don’t have a growth team yet.
We work alongside you on the premise of developing the three roles in-house—PM, data analyst, and engineer. The goal is for you to run the cycle yourselves in the end.
How long until we see results?
As a rough guide: one to two months to build out measurement infrastructure, three months for the hypothesis-validation cycle to start turning, and six months for meaningful improvements to accumulate into visible impact.
Related services
FDE · Forward Deployed Engineering →
New Digital Service Development →
Data & Analytics Platform Builds →
Let’s work out “how to grow it after launch” together.
Tell us about your current metrics and where it hurts. We’ll stay alongside you from the very first step of your growth foundation.
Talk to us about growth support (free)