Large-Scale A/B Testing at eduki:
Transforming Product Development

Overview

Led implementation of comprehensive A/B testing infrastructure at eduki, scaling from sporadic testing to systematic experimentation across multiple platforms.

Challenge

• No structured A/B testing framework

• Team resistance to data-driven decisions

• Need to transition between testing platforms

• Complex data management across platforms

• Limited testing processes

Solution

Testing Infrastructure

• Deployed tests across three platforms:

  • Google Optimize

  • Unleash

  • PostHog

• Built custom dashboards for centralized analysis

• Created standardized testing templates

Process Implementation

1. Established workflow for test management

2. Developed test prioritization system

3. Set up automated data collection

4. Created documentation standards

5. Built cross-team communication channels

Cultural Transformation

1. Moved from "ship and see" to "test and validate"

2. Trained teams on testing principles

3. Implemented regular results sharing

4. Developed test hypothesis framework

5. Created standardized success metrics

Results

Metrics Impact

• Increased conversion rates across platforms

• Reduced decision-making time

• Improved feature adoption rates

• Enhanced user engagement metrics

Process Improvements

• Reduced test setup time by 60%

• Increased number of concurrent tests

• Improved test documentation quality

• Enhanced cross-team collaboration

• Streamlined decision-making process

Testing Scale

• Ran hundreds of tests across platforms

• Tested multiple user segments

• Validated features pre-release

• Optimized pricing strategies

• Refined content presentation

Key Learnings

1. Testing Platform Flexibility

   • Multiple platforms needed for different use cases

   • Custom solutions fill gaps in standard tools

2. Process Structure

   • Kanban system essential for test management

   • Documentation standards drive consistency

   • Clear success metrics guide decisions

3. Team Adoption

   • Gradual implementation builds buy-in

   • Regular wins demonstrate value

   • Clear processes reduce resistance

Technical Details

Testing Stack

• Google Optimize: Initial A/B testing

• Unleash: Feature flagging and testing

• PostHog: User behavior analysis

• Custom dashboards: Data visualization

Implementation Steps

1. Set up tracking infrastructure

2. Define key metrics

3. Create test templates

4. Build analysis frameworks

5. Establish monitoring systems

Best Practices Developed

1. One variable per test

2. Clear success metrics

3. Statistical significance requirements

4. Documentation standards

5. Review processes

Future Applications

1. AI integration for test design

2. Automated analysis systems

3. Predictive modeling implementation

4. Enhanced visualization tools

5. Machine learning for test optimization