Founders and CEOs
You want to know which part of the funnel or product is blocking growth most and how to fix it within 90 days.
Growth & Execution
KPI and Growth Optimization
I diagnose where the company is losing conversion, retention and throughput — and build an experiment system that moves KPIs, not just closes tickets.
✓Identified and understood the biggest KPI drain
✓Experiment system with measurable ROI
✓Roadmap for growth, not just story point velocity
In short
KPI optimization is not about adding more tasks to the backlog. In most companies, the real problem is the missing link between roadmap, experiments, analytics and business decisions. ProLabs helps identify where the company loses conversion, retention, margin, throughput or team focus.
When KPIs do not improve despite a growing team
Common signals that a company has a growth system problem, not a talent problem:
- ⚠Sprint ends, features are closed, but business metrics did not move
- ⚠A/B experiments are launched without hypotheses and end without conclusions
- ⚠It is hard to say which initiative from last quarter had a real impact
- ⚠Retention or conversion is dropping but there is no clear diagnosis
- ⚠The roadmap is full of ideas but has no clear prioritization against KPIs
- ⚠Dashboard metrics do not correlate with business outcomes or decisions
Who this is for
CPOs and Product Managers
You need a KPI tree, an experiment system and a data-driven roadmap, not one based on opinions.
CTOs and Engineering Leads
You want to connect delivery velocity to real business metric impact.
Investors and portfolio companies
Post-round KPIs are stalled — you need a fast diagnosis and a remediation plan.
What it covers
- →KPI audit — funnel mapping and identification of the biggest leaks
- →KPI tree — metrics hierarchy from business to operational level
- →Experiment system — hypothesis, test and measurement framework
- →Backlog prioritization against KPI impact (ICE, RICE, impact vs effort)
- →Tracking plan — event definitions, data ownership, tooling
- →Decision rhythm — how to make weekly decisions based on data
- →Diagnosis of retention, conversion, activation and monetization
How it works
- 01
KPI and funnel audit
I map existing metrics, tracking and conversion funnel. I look for where the company loses users, revenue and throughput.
- 02
KPI tree and hypotheses
I build the metrics hierarchy from revenue/churn/ARR down to operational metrics. I define 3–5 high-impact hypotheses.
- 03
Experiment system
Design of first experiments: objective, hypothesis, measurement method, success criterion. First iteration in 2–4 weeks.
- 04
Decision rhythm
I set up a weekly rhythm: data → decision → priority. Not a monthly cycle that is too slow for experiments.
- 05
Results review and iteration
After each experiment: conclusion, decision and next iteration. The system learns and accelerates with each cycle.
What you receive
- ✓KPI audit — funnel map identifying the 3 biggest leaks
- ✓KPI tree — metrics hierarchy with owners and targets
- ✓Experiment backlog — 10–20 priority hypotheses with ICE/RICE scoring
- ✓Tracking plan — event definitions, tooling, data ownership
- ✓Decision rhythm — agenda and format for weekly growth review
- ✓30/60/90 plan — what we do at each stage and how we measure success
KPIs we diagnose and improve
Conversion rate — from lead/trial to paymentActivation rate — % of users reaching first valueRetention (D7/D30/D90) — how long users stayLTV:CAC ratio — customer acquisition profitabilityRevenue per user / ARPUDelivery velocity — how quickly experiments reach production
Frequently asked questions
Which KPIs can be improved?
I work across the full product growth spectrum: conversion, activation, retention, monetization, referral and delivery velocity. The starting point is always diagnosis — what is the biggest growth blocker for this specific company.
Is this more product, marketing or engineering?
All three. Most KPI problems exist at the intersection — the product does not deliver what marketing promises, or engineering builds features that do not affect retention. Diagnosis always covers all three layers.
How long does the diagnosis take?
Basic KPI audit and first hypotheses — 2 weeks. Full experiment system with decision rhythm — 4–6 weeks. Business results typically appear 6–10 weeks after the first experiment.
What is the difference between a growth system and a list of experiments?
A list of experiments is a backlog. A growth system is: data-backed hypotheses, a measurement method, a success criterion, a decision rhythm and a scalable framework that works independently after 3 months.
Do I need analytics before we start?
No — that is part of the initial diagnosis. If tracking is incomplete or unreliable, one of the first deliverables is a tracking plan. Even with limited data, the biggest leaks can be identified quickly.
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