How Pyze Reduced User Friction Analysis Time by 10x using Agentic AI
Client
Pyze is a Silicon Valley-based, Venture Capital-backed market leader in digital transformation analytics. Founded in 2013, the company helps enterprises identify and eliminate user friction in standard and custom enterprise software by analyzing clickstream data at scale.
Project Context
The project focused on transforming billions of raw clickstream events into consistent, auditable, and decision-ready insights for Pyze’s enterprise clients. Pyze required an automated, scalable way to continuously detect, prioritize, and quantify user friction that was previously hidden within massive datasets.
Project Objectives
- Reduce the time required to identify and understand user friction.
- Remove analyst bias and ensure findings are 100% repeatable.
- Automatically prioritize and quantify the impact of novel issues and "hidden" friction.
- Establish a system for continuous detection of user friction.
Challenges
- Managing and analyzing billions of user interactions made quick identification of friction difficult.
- Identifying novel issues required repetitive manual effort to quantify their impact.
- Existing findings suffered from subjective inconsistency.
- Identifying specific issues like context-switching between apps and manual workarounds was difficult to automate.
Solution
- Organized billions of user events into a scalable data layer.
- Linked UI/system errors to retries and quantified failure severity.
- Pinpointed labeling errors and measured time loss versus ideal journeys.
- Exposed forced workarounds and context-switching to estimate time savings.
- Leveraged AI to uncover hidden patterns and prioritize high-impact fixes.
