Over the summer I had the chance to intern at OmniSynkAi as a Product Management Intern, where I worked to optimize product listings with AI-Powered Monitoring and A/B Testing. I worked cross-functionally with UX Researchers, Designers, and Marketers to complete a sprint for possible optimization for product listings.

Product Requirements Document:

Title: Optimizing Product Listings with AI-Powered Monitoring and A/B Testing

Project Lead: Sarah Lee

Collaborators:

• UX Designer

• Precious John

Enhancing Product Listing Optimization through UX Design

• Data Analyst

• Wongpiwat Sang-niam

• AI/ML Engineer Lead - Damien Dhingra

Timeline: July 11 - August 23

Problem Statement

E-commerce sellers often create product listings and then neglect them, missing out on opportunities to improve performance. Manually monitoring and optimizing listings is time-consuming and requires expertise.

Objectives

• Continuous Optimization: Automatically monitor and analyze the performance of product listings across all platforms.

• A/B Testing: Enable sellers to experiment with different variations of titles, descriptions, images, and prices to find the most effective combination.

• Data-Driven Insights: Provide clear, actionable insights to help sellers understand what works and what doesn't.

• Increased Sales: Improve listing performance, drive more clicks, and ultimately increase sales.

Phase 1: Feature Refinement & Research

1. Performance Metrics:

• Define key metrics for evaluating listing performance (clicks, views, conversions, etc.).

• Identify benchmarks and thresholds for triggering optimization suggestions.

1. A/B Testing Options:

• Determine which elements of a listing can be tested (title, description, images, price).

• Decide on the types of tests to offer (e.g., simple A/B tests, multivariate tests).

• Consider how to present results to users (clear statistics, visual comparisons).

1. AI Integration (Optional):

• Explore how AI could be used to:

• Generate alternative titles and descriptions.

• Analyze competitor listings and suggest improvements.

• Predict the impact of different changes on performance.

Phase 2: UI/UX Design - Enhancing Product Listing Optimization through UX Design

1. Listing Performance Dashboard:

• Design a dashboard to display key performance metrics for each listing.

• Include clear visual representations of trends and comparisons across platforms.

• Highlight listings that are underperforming or could benefit from optimization.

1. A/B Testing Interface:

• Design a simple interface for sellers to set up and manage A/B tests.

• Allow for easy comparison of test variations.

• Clearly display test results and their statistical significance.

1. Optimization Suggestions:

• Develop a system for presenting AI-powered or rule-based suggestions for improving listings.

• Allow users to accept or reject suggestions with a single click.

Phase 3: Development & Testing

1. Implement Monitoring & A/B Testing:

• Develop backend logic to track listing performance and run A/B tests.

• Integrate with e-commerce platforms to collect data and apply changes.

1. AI Integration (Optional):

• If applicable, integrate the chosen AI model into the system.

1. Thorough Testing:

• Test the functionality and accuracy of monitoring, A/B testing, and AI-powered suggestions.

Deliverables

• User Flow Diagram

• Wireframes/Mockups:

• Listing performance dashboard

• A/B testing interface

• Optimization suggestions UI

• Technical Specification Document

• Testing Plan

• User Feedback Report (if testing is conducted)

Success Metrics

• Increased Listing Performance: Measure the average improvement in key metrics (clicks, conversions, etc.) for listings that have been optimized.

• User Engagement: Track the percentage of sellers actively using the A/B testing and optimization tools.

• Customer Satisfaction: Gather feedback from users on the effectiveness and ease of use of the features.