Customer Success Lab

A Branching Simulation for Risk Recognition & Decision-Making

This project demonstrates my ability to analyze a workplace performance problem and translate it into an authentic, scenario-based learning experience that builds judgment and decision-making through realistic practice and consequences.

Skills Demonstrated

  • Performance gap and needs analysis

  • Action mapping and performance-focused design

  • Branching scenario design

  • Consequence-based learning and feedback

  • Designing realistic, data-driven decision practice

  • UX/UI design for simulated workplace environments

  • High-fidelity prototyping

  • AI-assisted custom interaction development

  • Rapid prototyping with HTML/CSS/JavaScript

  • Application of adult learning principles

Project Overview

This prototype is a hypothetical training solution designed to help new Customer Success Associates recognize emerging account risk while balancing customer needs with productivity. To simulate a realistic design process, I used ChatGPT as a simulated subject matter expert (SME) to explore the performance problem, validate workplace scenarios, and refine realistic customer behaviors and account data.

I then translated that analysis into action mapping, branching scenarios, consequence-based feedback, and a high-fidelity simulated work environment. As part of the prototype, I also used Claude Code for AI-assisted development.

The Problem

The Customer Success team was experiencing preventable customer churn. Newer Customer Success Associates were often addressing customer concerns successfully, but account risk was sometimes being recognized too late to intervene effectively.

The company needed to help newer CSAs identify potential churn risk earlier, before customers reached the point of cancellation.

The Gap & Need

New Customer Success Associates were able to resolve immediate customer issues, but they did not always recognize when an interaction could signal a larger account risk.

They needed practice with:

  • Recognizing when deeper investigation is warranted

  • Identifying meaningful patterns across account data

  • Deciding when to close, follow up, or escalate

  • Balancing account health with productivity and time away from the customer queue

The challenge was not knowing the warning signs. It was determining when those signs mattered and what to do next.

The Solution

I designed a scenario-based simulation that gives new CSAs realistic practice identifying and responding to potential account risk.

Learners:

  • Respond to realistic customer interactions

  • Decide when deeper investigation is necessary

  • Explore account data in a simulated customer management system

  • Identify patterns across multiple sources of information

  • Choose whether to close, follow up, or escalate

  • Experience realistic consequences based on both account health and productivity

The goal was to build judgment through practice, rather than simply teach learners a list of churn warning signs.

Key Features

  • Four realistic customer scenarios with increasing complexity

  • Branching decisions based on learner choices

  • Simulated customer management system for account investigation

  • Data-driven decision making across usage, meetings, support, and account history

  • Realistic workplace consequences instead of traditional correct/incorrect feedback

  • Productivity trade-offs through simulated customer queue wait times

  • Evidence-based escalation to a senior CSA

  • Optional job aids available during decision making

  • Interactive custom prototype developed with AI-assisted coding

  • Replayable scenarios that allow learners to explore different outcomes

Explore the Prototype

This project is currently in the prototyping stage.

Explore selected components below to see how the full learning experience is designed to function.

Interactive CMS Prototype

High-Fideidy Canva Design

Navigate a simulated customer account and investigate information across multiple data sources.

View the scenario screens, decision points, workplace interface, and consequence system designed for the full branching experience.

This project followed a structured instructional design process, drawing on both the ADDIE framework and SAM.

Process Overview