Where Growth Is Measured, Not Assumed
Every engagement is measured with statistical significance—p-values, confidence intervals, and documented learnings. No vanity metrics.
Measured outcomes with statistical rigor
Outcomes grounded in data, validated through experimentation
Anonymized results from instrumentation, experimentation, and automation engagements.
Professional Services
Lead form submissions
4% → 15%
3x+ lead volume
- 12 months engagement
- p=0.01 (99% confidence)
- 10+ experiments via traffic data discovery
Professional Services
Lead-to-order conversion
8% → 12%
50% relative lift
- 12 months engagement
- p=0.01 (99% confidence)
- 30+ experiments including lead scoring & routing
SaaS PLG
Free-trial → paid
6% → 8%
33% relative lift
- 9 months engagement
- p=0.01 (99% confidence)
- Cohort + pricing experiments
Professional Services Ops
COGS & Contribution Margin
COGS ↓24%
CM ↑24%
24% relative lift
- 12 months engagement
- p=0.01 (99% confidence)
- Automation + middleware
How the system performs
Experimentation Operating Metrics
A continuous experimentation program built on hypotheses, prioritization, and right-sized test cycles.
Structured
experiment backlog
Right-sized
test cycles
~40%
avg. win rate
Statistical
rigor & documentation
Proven in practice
Case summaries
Lead-to-order conversion improved through instrumentation + experimentation
- Challenge
Lead conversion lacked visibility and consistency.
- Approach
Instrumented the funnel, implemented lead scoring/routing, and ran 100+ experiments over 12 months.
- Impact
50% relative lift in lead-to-order conversion (8% → 12%, p=0.01).
Trial-to-paid conversion improved through cohort analysis + pricing experiments
- Challenge
Free trial conversion was below benchmarks.
- Approach
Instrumented engagement signals, ran cohort-based plan recommendations and pricing tests.
- Impact
33% relative lift in trial-to-paid (6% → 8%, p=0.01) over 9 months.
COGS reduced and contribution margin expanded through automation
- Challenge
High COGs limiting margin expansion.
- Approach
Post-sale onboarding automation, agentic data collection, and middleware integrations.
- Impact
COGs reduced 50% → 38%, contribution margin expanded 35% → 47% over 12 months.
reduce COGs & expand margin
Automation Use Cases
AI-Powered Sales-to-Onboarding Handoff
Implemented AI automation for post-sale handoff to reduce friction and human intervention. Leveraged agentic systems to scrape relevant content for faster insights. Middleware connecting sales CRM with post-sale systems. Result: 24% COGS reduction, 24% CM expansion (12 months, 99% confidence).
Lead Response & Feedback Automation
Automated lead response via WhatsApp reducing time-to-first-contact from hours to seconds. Integrated feedback loops and NPS collection surfacing customer insights for product improvements.
Lifecycle Reminders & Follow-ups
Automated reminder sequences, improving show rates and reducing no-shows by 30–40%.
Hotel Revenue Optimization
Dynamic pricing and availability automation, improving RevPAR and occupancy optimization.
What Leaders Say
When The Guesswork Is Gone
Mxdify didn't just give us recommendations, they built the instrumentation, ran the experiments, and showed us exactly what was working with statistical proof.
VP of Growth | Series B SaaS
For the first time, our growth efforts are measurable. We can see what's working, why it's working, and scale it with confidence.
CEO | PE-backed Platform
The instrumentation alone was worth the engagement. We finally have visibility into the data that drives our numbers.
Head of Marketing | Enterprise Software