Building a scalable, always-on customer feedback engine
Background
To ensure a continuous understanding of customer experience, I built, organized, and implemented a survey intercept platform across all Safelite digital properties. This system captured post-booking feedback through a standardized survey measuring CSAT, CES, and open-ended comments, establishing baselines and tracking changes over time. Key components included:
Feedback surveys deployed across all sites, including texting bots and AI chat experiences
Integration with XM Discover to build automated dashboards for real-time insights
Use of 50+ embedded data attributes (e.g., appointment type, payment method) for filtering and analysis
Deployment of an automated sentiment and text analysis model to categorize open-text responses into consistent themes and sub-themes
My role:
Lead researcher
Impact:
+ Scaled feedback across all digital channels
+ Org-wide reporting that shaped CX strategy and decision-making
Tools:
Qualtrics, XM Discover
What barriers were preventing a clear view of the customer experience?
Challenges
Fragmented feedback channels made it difficult to gather consistent, actionable insights
Limited visibility into customer sentiment at scale due to unstructured open-end responses
Feedback was siloed and often not shared broadly across the organization
Teams lacked the tools to easily track performance trends over time or filter by meaningful operational data
The business needed a centralized, scalable solution to inform CX strategy and prioritization