The CRO Process: A Step-by-Step Framework

Random changes based on gut feelings don’t work. The businesses that consistently improve their conversion rates follow a structured, repeatable process.

This framework has been refined through thousands of optimization projects. Whether you’re a solo founder or part of a dedicated CRO team, these six steps will guide your efforts.

Overview: The 6-Step CRO Framework

Research & Discovery — Understand the current state
Analysis & Insights — Identify problems and opportunities
Hypothesis Creation — Form testable predictions
Prioritization — Decide what to tackle first
Testing & Implementation — Execute and measure
Learning & Iteration — Document and repeat

Let’s dive into each step.

Step 1: Research & Discovery

Before optimizing anything, you need to understand what’s happening now. This phase combines quantitative data (numbers) with qualitative data (human insights).

Quantitative Research

Analytics audit: Review your Google Analytics 4 or similar platform.

Where does traffic come from?
What’s the conversion rate by source, device, and page?
Where do users drop off in your funnel?

Technical performance:

Page load times (especially on mobile)
Core Web Vitals scores
Broken links or error pages
Cross-browser compatibility

Funnel analysis:

Cart abandonment rate
Form completion rate
Step-by-step drop-off in checkout

Qualitative Research

Session recordings: Watch real users navigate your site. Look for:

Hesitation and confusion
Rage clicks (frustrated clicking)
Dead clicks (clicking non-clickable elements)
Unexpected navigation patterns

Heatmaps: Visualize aggregate behavior.

Click maps: What gets attention?
Scroll maps: How far do users get?
Move maps: Where does the cursor travel?

User surveys: Ask visitors directly.

On-site polls: “What almost stopped you from completing your purchase?”
Post-purchase surveys: “What nearly made you leave?”
Exit-intent questions: “What information were you looking for?”

Customer interviews: Deeper conversations with actual customers.

What problem were you trying to solve?
What other options did you consider?
What almost stopped you from buying?

Research Output

Document your findings in a research summary:

Key metrics and benchmarks
Major friction points identified
User quotes and behavioral observations
Technical issues discovered

Step 2: Analysis & Insights

Raw data isn’t useful until you extract meaning from it. Analysis transforms observations into actionable insights.

Look for Patterns

Behavioral patterns:

Do users consistently miss the CTA button?
Is there a specific step where most people abandon?
Do mobile users struggle more than desktop?

Correlation, not causation:

Users who view product videos are 40% more likely to purchase—but does the video cause conversion, or do high-intent users seek out videos?
Be careful about assumptions.

Segment Your Analysis

Averages lie. Segment your data:

By traffic source (paid vs. organic behavior differs)
By device (mobile vs. desktop experience)
By visitor type (new vs. returning)
By geography (international users may have different needs)

Ask “Why” Repeatedly

When you spot a problem, dig deeper:

Observation: “Cart abandonment is 70%” Why? Shipping costs are shown late in checkout Why does that matter? Users feel surprised and distrustful Why don’t we show shipping earlier? We never thought to Insight: Display shipping costs on product pages to set expectations

Analysis Output

A prioritized list of problems and opportunities:

[Problem] + [Evidence] + [Potential Impact]
[Problem] + [Evidence] + [Potential Impact]
…

Step 3: Hypothesis Creation

A hypothesis is a testable prediction about how a specific change will affect a specific metric.

The Hypothesis Format

“We believe that [making this change] will result in [this outcome] because [this reasoning based on research].”

Good hypothesis: “We believe that adding customer review ratings to product listing pages will increase product page click-through rate by 10-15% because our session recordings show users searching for social proof, and competitor sites prominently feature ratings.”

Bad hypothesis: “We think the page should look better” — Not specific, not testable, not based on research.

Components of a Strong Hypothesis

The change: Exactly what you’ll do The metric: What you’ll measure The expected impact: Quantified prediction The rationale: Why you believe this will work

Multiple Hypotheses per Problem

One problem can have multiple solutions:

Problem: Users don’t scroll past the hero section Hypotheses:

Adding a “scroll for more” indicator will increase scroll depth by 20%
Showing a preview of below-fold content will increase scroll depth by 25%
Reducing hero section height will expose more content and increase engagement by 15%

You’ll test these individually or choose the most promising based on effort.

Hypothesis Output

A documented list of hypotheses, each with:

Clear change description
Target metric
Expected impact (even if estimated)
Supporting evidence/rationale

Step 4: Prioritization

You can’t test everything at once. Prioritization ensures you tackle high-impact, achievable opportunities first.

The ICE Framework

Rate each hypothesis 1-10 on:

Impact: How much will this move the needle? Confidence: How sure are we this will work? Ease: How simple is implementation?

ICE Score = (Impact + Confidence + Ease) ÷ 3

Hypothesis	Impact	Confidence	Ease	ICE
Add trust badges to checkout	7	8	9	8.0
Redesign product pages	9	6	3	6.0
Fix mobile CTA visibility	8	9	7	8.0
Add video to landing page	6	5	4	5.0

Higher scores get priority.

Alternative: PIE Framework

Potential: How much improvement is possible? Importance: How valuable is this traffic/page? Ease: How difficult is the test?

Consider Quick Wins

Some changes don’t need testing:

Fixing broken functionality
Correcting obvious errors
Improving page speed
Adding missing trust signals

Implement these immediately while you plan larger tests.

Prioritization Output

A ranked backlog of tests to run, with quick wins flagged for immediate implementation.

Step 5: Testing & Implementation

Now you execute. For significant changes, A/B testing provides statistical validation.

A/B Testing Basics

Split your traffic: 50% sees version A (control), 50% sees version B (variation). Measure which converts better.

Requirements for valid testing:

Sufficient traffic (typically 1,000+ conversions for reliable results)
Clear primary metric
Adequate test duration (usually 2-4 weeks minimum)
No overlapping tests on the same page

Calculating Sample Size

Before testing, determine how long you’ll need:

Your current conversion rate
Minimum detectable effect (how small an improvement matters?)
Statistical significance level (typically 95%)

Free calculators like Evan Miller’s A/B test calculator or Optimizely’s sample size calculator help with this math.

Running the Test

Week 1: Launch test, monitor for technical issues Week 2-3: Let data accumulate, resist peeking Week 4: Analyze results at predetermined endpoint

Common mistakes:

Stopping tests early when results look good (inflates false positives)
Running tests for arbitrary time instead of statistical requirements
Changing the test mid-flight

When You Can’t A/B Test

Low-traffic sites may not have enough volume for statistical significance. Alternatives:

Before/after analysis: Implement the change, compare metrics week-over-week or month-over-month. Less conclusive but still valuable.

User testing: Have 5-10 people complete tasks on your site while thinking aloud. Qualitative insights can guide changes.

Multi-armed bandit: Some tools automatically shift traffic toward winning variations. Useful when optimization speed matters more than certainty.

Implementation for Winners

When a test wins:

Document the results
Implement the winner as the new default
Update your testing backlog based on learnings
Consider if the winning concept can apply elsewhere

Testing Output

Test documentation including:

Hypothesis tested
Variations
Duration and sample size
Results (conversion rate, statistical significance)
Decision (winner, loser, or inconclusive)

Step 6: Learning & Iteration

CRO is a continuous cycle. Each test—win or lose—generates learning.

Document Everything

Maintain a test log:

Date and duration
Hypothesis
What you tested
Results and statistical confidence
Key learnings
Follow-up actions

Learn from Losses

“Failed” tests are valuable:

They prevent implementing changes that don’t work
They reveal what customers actually care about (or don’t)
They challenge assumptions that might have influenced other decisions

A test that shows no difference is information. Only a test that breaks something is truly failed.

Apply Learnings Broadly

Winning insights often apply beyond the original test:

If trust badges helped checkout, do they help landing pages too?
If shorter forms improved conversions, are other forms too long?
If urgency messaging worked for one product, does it work site-wide?

Update Your Research

New test results inform future research:

Adjust your understanding of customer psychology
Update benchmarks for your site
Refine your prioritization criteria

Iteration Output

Updated test backlog with new hypotheses inspired by learnings
Documented insights library for future reference
Refined prioritization based on what you’ve learned

Putting It All Together

The full cycle typically takes 4-8 weeks per iteration:

Week	Activity
1	Research & discovery
2	Analysis & hypothesis creation
3	Prioritization & test setup
4-7	Test runs
8	Analysis, documentation, next cycle planning

High-velocity teams run multiple tests simultaneously. Smaller teams might run one test at a time but move through the cycle continuously.

Common Pitfalls

Over-researching: Analysis paralysis is real. At some point, you need to test.

Under-researching: Testing random ideas wastes time. Research first.

Ignoring qualitative data: Numbers tell you what; user feedback tells you why.

Chasing small wins: A 0.5% improvement might not be worth the effort. Focus on meaningful impact.

Stopping after one test: CRO compounds. Keep iterating.

Getting Started

You don’t need fancy tools to begin:

Free: Google Analytics 4, Clarity (Microsoft’s free heatmap tool)
Low-cost: Hotjar, Crazy Egg for recordings and heatmaps
For testing: Google Optimize (sunsetting, but alternatives like VWO, Optimizely, or Convert exist)

Start with the research phase. Understand your current state before trying to change it.

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