Root Cause Analysis Version 3 (RCA v3)

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Client

Problem

Mode of Delivery

Project Details

Project Title: Root Cause Analysis Version 3

Company: TikTok

Target Audience: New Moderators & Quality Specialist

Year : 2022

Tools used: Adobe illustrator, Fieshu Doc, Figma, and Articulate Storyline

Accomplishment: Recognized for designing an online simulation training that increased Root Cause Analysis (RCA) appeal accuracy by 10% for 800 Global Quality and Operations frontline team members.


This project is under a NDA.

Tools Used

About Project

The Subject Matter Expert (SME) identified the need to update the existing onboarding training, Root Cause Analysis (RCA v3). This decision was driven by insights from the internal Quality Audit Platform, which revealed that users—specifically Quality (QA) personnel and Moderators—were facing challenges due to knowledge gaps. These gaps led to issues such as QA personnel overusing specific filters like "people error" and Moderators allowing expired appeals to accumulate by failing to prioritize those with the shortest time remaining.

Analysis & Development

Target Audience

The Content

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During our one-on-one meetings, the SME shared various instances of knowledge gaps among employees and pointed out several shortcomings in the existing RCA v3 training. For instance, the current training lacked learner engagement as it followed a passive "click and learn" approach in the eLearning format. During one-on-one meetings, the SME shared examples of knowledge gaps among employees and highlighted shortcomings in the existing RCA v3 training. For example, the current eLearning followed a passive "click and learn" approach, which lacked learner engagement. Based on these discussions and a thorough review of the Audit Platform Tool, I designed a training solution that directly addressed the SME’s concerns. I developed an immersive, scenario-based eLearning module simulating the daily workflows of both Moderators and Quality Specialists (QA) while minimizing potential risks. Learners were placed in realistic situations where they made decisions as they would in their day-to-day tasks, allowing them to see the consequences of their actions. For instance, failing to prioritize appeals with the shortest expiration time led to the filter with the least time remaining expiring.

What I learned

Goal

Solution

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