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Case Study
Brief: An e-commerce company is creating and outsourcing brand new enforcement workflows for its platform-based messaging tool. Agents will play a major role in frontline enforcement by reviewing flagged messages for instances of spam, scams, off-platform transactions, review extortion, and interferences.
Solution: A multi-phased rollout consisting of self-guided micro-learnings, Guru cards (job aids), and train-the-trainer sessions
Results: Agents accurately flagged spam and scam content over a six month period without requiring any retraining. This led to a 99% precision rate at a threshold above .95 for new machine learning models.
Phase 1
Prior to launch, I chose to introduce agents to the workflow context at a very high-level.
In the first micro-learning, I described the intention of the platform’s messaging feature to ultimately support the distinction between in-scope and out-of-scope tasks. After all, you can’t identify “bad” if you don’t know what “good” looks like!
I also included a brief UI overview so agents could become familiar with all of the different ways they could access the platform’s messaging feature from their dashboards. Guru resources (job aids) were shared to provide real-time support as agents navigated through these steps.
Phase 2
Since this workflow relied heavily on the accuracy of human decision-making, I created another micro-learning about identifying spam and scams and differentiating between the two. In addition to these learning objectives, I wanted the agents to grasp why this work mattered and how they could directly impact the company.
Because the nature of this workflow was decision-based, the micro-learning provided plenty of practice examples and quiz questions to help agents prepare for live tasks.
An additional micro-learning was then leveraged to walk through the workflow at a more detailed level. This meant providing a UI walkthrough of the enforcement tool as well as a break down of the applicable action steps. Guru resources (job aids) were provided to guide real-time navigation.
Phase 3
Anyone who has worked in the trust and safety space can attest to the nuance that goes into enforcement decision-making. After building foundational knowledge about the feature, new tooling, and workflow, I focused the final micro-learning installment on somewhat hairier topics- off-platform transactions, review extortion, and interference. This module defined these violating behaviors and described how to identify them. This meant incorporating plenty of examples from SMEs and assessing the knowledge transfer via assessment.
Train-the-Trainer Sessions
I facilitated train-the-trainer sessions alongside the rollout of each module. These included:
Introductions to the new courses
Facilitator guides
Agent expectations
Workflow walkthroughs
SME sessions
Practice tasks
Q&A sessions