Evidence Over Assumptions
Build a System for Learning What Works
Most growth decisions rely on opinions, anecdotes, or isolated wins. Testing happens sporadically, without rigor, and learnings don’t compound.
Experimentation-Led Growth installs a continuous experimentation system grounded in clear hypotheses, disciplined prioritization, and right-sized test cycles. Each experiment is designed to answer real business questions quickly and reliably, so learning compounds instead of resetting
The Missing Piece
We Fill The Gap
Many organizations track what happened but can’t explain why it happened or what to do next. Without proper instrumentation, teams chase false positives, debate dashboards, and struggle to diagnose bottlenecks.
Most teams run tests, but few operate a true experimentation system.
Ideas are generated ad-hoc. Tests are prioritized subjectively. Results are declared without sufficient power. Learnings live in slide decks—or disappear entirely. Over time, teams lose confidence in experimentation, and decisions drift back to opinions.
Hypothesis-driven testing.
Every experiment begins with a clearly articulated hypothesis tied to a specific business outcome conversion, retention, revenue, or efficiency. No vague tests. No “let’s see what happens.”
Disciplined prioritization.
We apply structured frameworks to rank experiments based on potential impact, confidence, and effort so teams focus on the levers that matter most.
Right-sized test cycles.
Experiments are calibrated for both speed and statistical validity. We size tests based on traffic, risk, and decision criticality not arbitrary timelines.
Statistical rigor by default.
Results are evaluated using proper baselines, confidence intervals, and significance thresholds eliminating false positives and misleading wins.
Documented learning loops.
Every experiment win or loss is captured, contextualized, and rolled forward. Learnings compound instead of resetting with each new test.
Measurement architecture & analytics design
We design analytics systems aligned to your funnel, product, revenue model, and operating goals so data supports decisions, not debates.
Event tracking & lifecycle instrumentation
We implement clean, consistent event tracking across acquisition, activation, conversion, retention, and expansion.
Attribution for prioritization
We build attribution models that reflect your business reality, so teams know what actually drives outcomes and where to focus.
Reporting tied to action
Dashboards and reports are built around decisions, not vanity metrics—giving leaders clarity on what to do next.
Foundation for downstream growth
Everything is designed to support experimentation, automation, and unit-economics optimization that follows.
Built for scale
Why Choose Mxdify for Experimentation-Led Growth
Rigor Over Guesswork
We design experiments to answer real business questions with statistical confidence. That means proper baselines, power considerations, confidence intervals, and documented outcomes not directional tests or vanity wins.
System-First Execution
Experimentation is treated as an operating system, not a marketing tactic. We build the backlog, prioritization logic, testing cadence, and review process so experimentation becomes a repeatable discipline.
Built to Compound
Every experiment feeds the next. Learnings—wins and losses—are captured, contextualized, and rolled forward so insight accumulates instead of resetting each quarter.
Decision-Focused Testing
Experiments are designed to inform decisions, not just generate lift. Each test clarifies what to do next across pricing, funnel design, messaging, and lifecycle strategy.
Integrated With Your Stack
We run experiments inside your existing tools and workflows—GA4, Segment, HubSpot, Optimizely, VWO, Webflow, WordPress, custom stacks, and more. No rebuilds or forced platforms.
Aligned With Downstream Scale
Experimentation outputs are built to feed automation, monetization, and operational improvements—ensuring results translate into real leverage, not isolated insights.
Frequently Asked Questions
We test across funnels, pricing, messaging, UX, onboarding, lifecycle flows, retention mechanics, and monetization models, always tied to a clear hypothesis and outcome.
We size tests appropriately, avoid peeking, and evaluate results using confidence intervals and significance thresholds suited to the decision being made.
Traffic volume influences test design and cadence, but we adapt methodologies for lower-traffic environments using prioritization, directional testing where appropriate, and longer cycles.
Yes. We collaborate closely with internal teams and leave behind processes, documentation, and capability not dependency.
No. We experiment across product flows, pricing models, lifecycle programs, and operational levers not just landing pages.