FreeFuse is an AI-powered platform for building interactive, multi-path digital experiences. As the company expands into personalized content journeys and Agentic AI assistance, there is growing interest in understanding which types of interactive pathways lead to higher engagement and long-term user retention.

This project will focus on analyzing and forecasting content journey effectiveness using structural data and behavioral metrics from FreeFuse pathways. In addition to traditional engagement data (e.g., completion rates, drop-offs), students will explore time-to-decision—how long a user takes between choice points—as a signal of content clarity, complexity, and user confidence.

Learners will apply data science, predictive modeling, and visualization techniques to identify high-performing pathways, segment engagement styles, and forecast content success based on journey composition and user behavior.

Journey Performance Analysis Report

Analyzes factors contributing to journey success, including decision depth, interaction frequency, and average time-to-decision.

Predictive Model

Forecasts drop-off likelihood or success rate of a journey using input features such as branching complexity, content length, and time-to-decision per node.

Segmented Engagement Dashboard

Interactive visualization comparing pathway formats and decision behavior metrics, including time-based journey flow analysis (via Power BI, Tableau, or Python-based tools).

Strategic Recommendations Deck

A 10-minute presentation summarizing predictive insights and optimization strategies, including how to design better-performing content paths based on user pacing and cognitive flow.