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A Repeatable System for
Continuous Improvement

Design optimization isn't a one-time project — it's an ongoing discipline. Our 6-phase process transforms guesswork into a systematic machine that continuously finds and scales winning designs.

The 6-Phase Cycle

Every successful optimization program follows this cycle. Each phase builds on the last, creating a flywheel of compounding improvements.

DESIG LOOP 01 Research 02 Hypothesize 03 Build 04 Test 05 Analyze 06 Scale
1

Research & Discovery

Identify opportunities using quantitative and qualitative data. Understand where users drop off, what they're looking for, and where friction exists in your current design.

Key activities:

  • Analyze funnel drop-off rates in Google Analytics
  • Review heatmaps and session recordings
  • Conduct user interviews and surveys
  • Benchmark against industry conversion rates
2

Hypothesis Formation

Transform insights into testable hypotheses. Each hypothesis must have a clear prediction, measurable outcome, and estimated impact on your primary metric.

Hypothesis formula:

  • If we [design change] for [target audience]...
  • Then [primary metric] will [change direction]...
  • Because [insight/reasoning based on data]
  • We'll know when [success criteria is met]
3

Variation Design & Build

Create the challenger design using Desig's visual editor or integrate with your existing design tools. Ensure variations test the hypothesis clearly without introducing confounding variables.

Build checklist:

  • Single variable change (for A/B tests)
  • QA across all device types and browsers
  • Performance parity with control version
  • Analytics event tracking configured
4

Test Execution

Launch the test and let it run to statistical significance. Monitor for technical issues, traffic splits, and sample ratio mismatches. Resist the urge to stop early.

During the test:

  • Monitor for sample ratio mismatches (SRM)
  • Check for technical implementation issues
  • Avoid peeking at results before minimum sample
  • Document any external factors that may affect results
5

Analysis & Learning

Whether the test wins or loses, extract learning. Analyze segment-level results, secondary metrics, and interaction effects. Document insights for future hypotheses.

Analysis framework:

  • Primary metric statistical significance check
  • Secondary metric impact assessment
  • Segment breakdown (device, source, user type)
  • Document learnings in knowledge base
6

Scale & Iterate

Push winning designs to 100% of traffic. Use learnings to generate new hypotheses. Build a compounding advantage as each test adds to your institutional knowledge of what works.

After the test:

  • Implement winner across relevant pages
  • Generate follow-up hypotheses from insights
  • Add to Desig knowledge base for team reference
  • Update backlog with new test opportunities

How to Prioritize What to Test

Not all tests are created equal. Use the PIE framework to score and prioritize your test backlog.

P

Potential

How much improvement can this change make? Pages and elements with high drop-off rates, low engagement, or poor conversion scores have the most potential. Score 1–10 based on current performance gap.

I

Importance

How important is this page or element to your business? High-traffic pages with direct revenue impact should be weighted higher. The more visitors affected, the higher the importance score.

E

Ease

How easy is this test to implement? Consider technical complexity, design resources required, and stakeholder approval needed. Quick wins score higher — start with easy changes to build momentum.

PIE

PIE Score = (P + I + E) / 3

Calculate your PIE score for each test candidate and rank them from highest to lowest. Focus your testing resources on the top-scoring opportunities first. Revisit and update scores quarterly.

Speed Is a Competitive Advantage

The companies that run the most tests win. Not every test produces a winner, but more tests means more learnings and a faster compounding advantage over competitors who are guessing.

Desig customers who run 4+ tests per month see 3× the conversion improvement of those running 1 test per month. Build a testing culture, not just a testing tool.

4+
Tests/month for optimal velocity
60%
Avg. win rate on prioritized tests
Faster improvement vs. slow testers
12mo
To meaningful compounding effect

Before You Launch a Test

Technical Setup

Tracking snippet installed on all test pages
Conversion goals correctly configured
Test variations QA'd on desktop, tablet, mobile
Page load time checked — within 100ms of control
Traffic split confirmed (50/50 or custom)
Sample size calculator consulted for minimum duration

Strategic Alignment

Hypothesis documented with clear prediction
Primary success metric defined (single KPI)
Secondary metrics identified for context
No other tests running on same page
Marketing calendar checked for conflicts
Stakeholders informed and aligned on process