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DESIGN CASE STUDY

Customer Intake Lab

A CRM application-training prototype that turns a narrated walkthrough into guided practice, independent practice, and bulk-upload error correction.

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Customer Intake Lab fictional application-training prototype

The work

Thoughtful design. Practical results.

Each project reflects a different learner need, business challenge, and approach to effective learning.

Learning format

Application training / CRM simulation

Audience

Multiple business roles responsible for customer intake in a CRM

My role

Individual contributor: original Storyline design, learning redesign, Codex development and testing; team and operations leadership review

Tools

ChatGPT Codex, Articulate Storyline source files, HTML, CSS and JavaScript

From narrated walkthrough to realistic practice

Multiple roles needed to learn how to complete a customer intake in a business CRM. I originally built a click-to-navigate, voiceover-driven simulation in Articulate Storyline. For this rework, I used that deliverable as the starting point for a prototype that lets learners navigate and complete the process more like they would in the live application.

My role

I owned the original Storyline design, the revised learning approach, and the Codex development and testing as the individual contributor. Members of my team and operations leadership reviewed the outputs.

The learning design

Guided practice

Learners work through a customer intake with support while becoming familiar with the workflow. The experience shifts the emphasis from following a narrated sequence to navigating the application and making the required entries.

Independent practice

A separate customer scenario gives learners an opportunity to complete the process with greater independence, applying the workflow rather than repeating the original walkthrough.

Bulk upload and error correction

The bulk-upload scenario introduces the steps and issues learners may encounter when handling multiple accounts. Learners review the information, identify errors, make corrections, and retry the workflow.

AI-assisted development, human-led review

I imported the original deliverable files into ChatGPT Codex and used them to rework the experience into guided practice, independent practice, and a bulk-upload scenario. I directed the learning approach, reviewed the output, and tested the prototype, with additional review from my team and operations leadership. The goal was to establish a reusable template for similar application-training requests.

Development effort

The original Storyline simulation took approximately 8–10 hours to develop. This rework took approximately one hour, with minimal prompting, review, and testing.

These are my reported development-time estimates for the two versions. The rework started from existing files, so this is a comparison of the original build and a reuse-based rework—not a controlled comparison of equivalent builds from scratch. It does not measure learner performance or training effectiveness.

Prototype status and next steps

This version is a prototype and will be used to gather feedback in future courses. Learner and business outcomes have not yet been established. The next step is to use that feedback to refine both the practice experience and the reusable template.

The portfolio version presents the work without naming the company or CRM.

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