Feedback Intelligence
AI Automation

Feedback Intelligence

AI pipeline that turns hundreds of learner responses into actionable insights

Role

AI Automation Specialist

Client

Training Company (Confidential)

Timeline

2 Weeks

Key Result

80% less manual review time

01

The Challenge

A training company collected hundreds of learner feedback responses per course via Google Forms — NPS scores, open-text feedback, and satisfaction ratings. Manual analysis took the L&D team days per course, and even then they were reading responses line-by-line rather than identifying patterns. Three courses had declining satisfaction scores for months, but nobody knew why.

02

The Approach

I built a fully automated feedback intelligence pipeline that collects, analyzes, and summarizes learner feedback — delivering weekly insight reports without any human intervention.

01

Data Architecture & Collection Setup

Standardized Google Forms across all 12 active courses with consistent fields. Make watches for new form submissions and routes data to a centralized Google Sheet with course tags and timestamps.

02

Sentiment Analysis Layer

A Make scenario (triggered via webhook) calls ChatGPT to perform sentiment classification (positive/neutral/negative) and urgency scoring on each open-text response.

03

Theme Extraction

Batch processing groups responses by course and runs theme extraction via ChatGPT — identifying recurring topics (navigation confusion, pacing issues, content gaps) across all responses.

04

Visual Summary Reports

Make generates a structured weekly report: sentiment breakdown, top 3 themes per course, trending issues, and NPS delta — formatted as a clean Google Doc sent via email.

05

Critical Issue Auto-Flagging

Responses with urgency score ≥ 8/10 or specific trigger phrases ("can't access", "broken", "error") are auto-flagged and sent immediately to the course creator via Slack and Gmail.

Tools Used

ChatGPTClaude AIGoogle SheetsMake (Integromat)GmailSlack

03

The Results

80%

less manual review time

3

recurring UX issues identified (previously missed)

+15%

course satisfaction after AI-suggested fixes

< 1 hr

weekly — down from 2 days of manual analysis

Next Project

Onboarding Accelerator