Amazon CRO Data & Experimentation Analyst

We are looking for an

Amazon CRO Data & Experimentation Analyst to turn Amazon performance data into clear, evidence-based optimisation decisions. This role will take ownership of the

A/B testing and experimentation process , analyse listing and funnel performance, identify conversion opportunities, and translate data into actionable recommendations for CRO, Creative, SEO, Advertising, and leadership teams. The goal is simple:

replace assumptions with evidence and turn experimentation into measurable business growth. Works closely with: Category Performance Manager, SEO Specialists, Creative Designers, Video Editor, and Advertising Team Key Responsibilities

A/B Testing & Experimentation

• Own the end-to-end experimentation pipeline across main images, titles, bullet points, galleries, and A+ Content.
• Define a clear problem, baseline, hypothesis, success metric, and expected outcome for every test.
• Monitor active experiments and ensure sufficient data is collected before decisions are made.
• Evaluate results and recommend whether to

implement, reject, iterate, or retest a variation.

Test Result Analysis

• Measure experiment impact across

CTR, CVR, sessions, units ordered, revenue, and gross profit .

• Distinguish meaningful performance changes from normal fluctuations.
• Investigate why variations succeed or fail.
• Translate analytical findings into clear business recommendations for the CRO team and leadership.

Performance & Funnel Analysis Monitor and analyse the complete Amazon conversion funnel:

Impressions → Clicks → Sessions → Conversion → Revenue

• Identify performance gaps across priority ASINs and categories.
• Investigate changes in traffic, organic ranking, CTR, CVR, and sales.
• Connect listing performance with factors such as

pricing, inventory, advertising, reviews, and competitor activity .

• Perform root-cause analysis and recommend appropriate optimisation actions.

Dashboard & Reporting Management

• Build and maintain CRO dashboards for priority ASINs and active experiments.
• Track before-and-after performance for optimised listings.
• Produce clear weekly and monthly reporting across:
• Organic visibility
• Sessions
• CTR & CVR
• Revenue & gross profit
• ACoS & TACoS
• Active experiments
• Test win rate
• Ensure reporting is accurate, consistent, commercially relevant, and easy to understand.

Experiment Prioritisation

• Partner with the CRO Team Lead and Category Performance Manager to identify high-impact testing opportunities.
• Estimate potential experiment value based on traffic, conversion gaps, and commercial importance.
• Prioritise tests that can generate reusable insights across multiple ASINs or categories.
• Prevent poorly defined or low-value experiments from consuming unnecessary creative and technical resources.

Experiment Documentation & Learning

• Maintain a central record of hypotheses, baselines, variations, results, decisions, and learnings.
• Document both successful and unsuccessful experiments.
• Convert winning insights into reusable

CRO playbooks and best practices .

• Identify patterns that can be scaled across categories, ASINs, and marketplaces.

AI & Agent-Based Analytical Workflows

• Use

Claude Skills, AI agents, and other AI-assisted analytical tools to support data summarisation, anomaly detection, experiment documentation, and reporting.

• Build repeatable AI-assisted workflows that reduce manual analytical and reporting effort.
• Use AI to compare performance periods and surface potential causes behind changes.
• Validate all AI-generated findings against source data before using them for business decisions.

Cross-Functional Collaboration

• Translate analytical findings into clear actions for SEO, Creative, Video, and Advertising teams.
• Help stakeholders understand what each experiment is designed to prove.
• Coordinate implementation dates, testing periods, and result reviews.
• Escalate data-quality issues, tracking gaps, and experimentation risks to the CRO Team Lead. Experimentation Framework The role will manage a structured CRO operating loop:

Insight → Hypothesis → Baseline → Variation → Launch → Measure → Decide → Document → Scale Depending on traffic volume, test stability, and Amazon experiment data, a typical experiment should collect approximately

1–2 weeks of data before a final decision is made. Required Experience

• Experience in

data analysis, digital analytics, CRO, or experimentation .

• Practical experience analysing A/B tests and conversion funnels.
• Strong understanding of

CTR, CVR, sessions, revenue, and profitability metrics .

• Ability to identify root causes behind performance changes.
• Experience building dashboards and working with spreadsheets and visual reporting tools.
• Hands-on experience with

Claude, AI agents, or similar AI-assisted analytical workflows .

• Strong ability to translate complex data into clear business decisions. Preferred Experience
• Experience with

Amazon Seller Central and Amazon Brand Analytics .

• Familiarity with

Search Query Performance and

Manage Your Experiments .

• Experience analysing Amazon listing, advertising, and organic-performance data.
• Understanding of

ACoS, TACoS, organic ranking, and paid-to-organic relationships .

• Knowledge of statistical concepts including

sample size, confidence, test duration, and significance .

• Experience managing or analysing large ASIN portfolios is an advantage. Core Skills
• A/B Testing & Experiment Analysis
• CRO & Funnel Analysis
• Amazon Performance Analytics
• Dashboard Creation & Reporting
• Root-Cause Analysis
• Data Accuracy & Validation
• Commercial Interpretation
• Experiment Prioritisation
• Data Storytelling
• AI-Assisted Analysis
• Cross-Functional Communication Success Measures Success in this role will be measured through:
• Quality and accuracy of A/B-test analysis
• Percentage of experiments with clear hypotheses and baselines
• Experiment win rate
• Speed from test completion to business decision
• Revenue and conversion impact from implemented winners
• Accuracy and reliability of CRO dashboards
• Number of actionable experiment learnings documented
• Percentage of winning insights scaled across related ASINs
• Reduction in decisions based solely on opinions or assumptions
• Clarity and usefulness of weekly and monthly performance reporting What Success Looks Like The ideal candidate does more than report numbers. They can explain

what changed, why it changed, whether the change is meaningful, and what the business should do next . If you enjoy combining

Amazon analytics, CRO experimentation, commercial thinking, and AI-assisted workflows to drive measurable growth, we’d love to hear from you.

Mail: info@beeyoondgaming.com / vignesh.hrbeeyoond@gmail.com Location: Chennai, Tamil Nadu

Contact: +91 9952560396