FSI
Financial Services Industry
Use Case: Behavioral pattern analysis of suspicious activity in a digital retail bank account opening flow
Objective: Determine whether CX analytics and session behavioral data could be used to identify and quantify fraud attempts — a question that hadn't previously been asked in this context.
Results:
Detected 45 suspicious sessions in a single month using funnel analysis and session replay — identified by unusual username patterns, repeated authentication bypass attempts, and specific navigation sequences (selecting "None of the Above," clicking "Fund Later") that correlated with account takeover behavior
Built a behavioral signature for fraudulent accounts: cross-border money transfer selections, OTP bypass patterns, and a 60-day account access window exploited via the "Fund Later" action
Quantified fraud exposure and delivered a framework for real-time detection integrated with the existing security alert system
Opened a working collaboration between CX analytics and the cybersecurity team that hadn't existed before
Next Steps: Implement automated alerts for high-risk behavioral patterns, refine risk scoring models with behavioral data inputs, expand monitoring to cover additional suspicious scenarios identified by the security team
ROI: Up to $5M in annual fraud exposure identified and made actionable — plus a detection infrastructure that compounds in value over time
LoyaltyUse Case: Behavioral audit of a digital loyalty enrollment flow for a high-traffic consumer program
Objective: Understand why two rounds of A/B testing had produced almost no improvement in enrollment completion rates, and find the real barrier.
Results:
Discovered that aggregate conversion data was masking two distinct audience types — new users completing at 75% vs. existing/unsure members completing at only 45%
The existing/unsure segment (30% of traffic) was being sent into an enrollment flow that wasn't built for them — causing confusion, dead clicks, and abandonment
Introduced the BCE (Behavioral Conversion Efficiency) framework to isolate and measure friction by audience intent rather than overall volume
Redesigned experience separated the two paths upfront — new enrollment vs. account recovery — resulting in a significant lift in completed enrollments for the previously struggling segment
Next Steps: Apply BCE framework across other high-value journeys; expand audience segmentation approach to inform future A/B test design
ROI: Incremental annual revenue lift from addressing the root behavioral barrier — significantly larger outcome than the short-form test had produced
RetailUse Case: Post-launch behavioral audit of a redesigned checkout experience
Objective: Identify friction points in the new checkout journey, specifically around dead clicks, payment method failures, and inconsistencies between old and new checkout routing.
Results:
Found that 13% of users were clicking on non-interactive elements — white space and visual elements that looked clickable but weren't — indicating layout confusion in the new design
Identified a silent payment failure affecting users who selected PayPal or Apple Pay: the checkout button was disappearing or users were being thrown back to the top of the page with no explanation — no error log was catching it
Quantified that 5.2% of all checkout users were abandoning specifically because of this payment issue
Also caught a routing inconsistency sending 64 users to both old and new checkout experiences simultaneously — a QA gap that needed immediate resolution
Next Steps: Fix PayPal/Apple Pay integration, redesign non-clickable elements to reduce confusion, and implement routing logic to prevent dual-experience exposure
ROI: Prevented ongoing revenue loss from a payment failure that was live in production and undetected — impact scaled directly with checkout volume
TravelUse Case: Booking funnel friction audit across a multi-step vacation reservation journey
Objective: Identify why users were dropping off before completing bookings, and quantify the revenue impact of specific errors in the search and checkout flow.
Results:
Identified two high-volume error types on the search results page affecting roughly 19% of all sessions — users couldn't find available routes or were silently blocked by seasonal route gaps with no guidance
Found a 20-point drop in mobile checkout advancement compared to desktop, traced to a price transparency issue in the payment summary
Delivered a prioritized fix list with a revenue case behind each recommendation, giving the product team a clear roadmap instead of a list of complaints
Next Steps: Optimize origin/destination input validation, update the date picker to block unavailable routes, and restructure the mobile payment summary to surface total price upfront
ROI: Quantified missed monthly revenue opportunity across identified error scenarios — gave the team a defensible number to take to engineering and leadership
MortgageUse Case: Booking funnel friction audit across a multi-step vacation reservation journey
Objective: Identify why users were dropping off before completing bookings, and quantify the revenue impact of specific errors in the search and checkout flow.
Results:
Identified two high-volume error types on the search results page affecting roughly 19% of all sessions — users couldn't find available routes or were silently blocked by seasonal route gaps with no guidance
Found a 20-point drop in mobile checkout advancement compared to desktop, traced to a price transparency issue in the payment summary
Delivered a prioritized fix list with a revenue case behind each recommendation, giving the product team a clear roadmap instead of a list of complaints
Next Steps: Optimize origin/destination input validation, update the date picker to block unavailable routes, and restructure the mobile payment summary to surface total price upfront
ROI: Quantified missed monthly revenue opportunity across identified error scenarios — gave the team a defensible number to take to engineering and leadership