Leveraging Trust Signals to Increase Funnel Conversion

How Mixed-Methods Research & Trust Signals Increased Annual Enrollments by 900+

Improved final-step conversion from 0.13% → 6.81%, projected +937 enrollments/year

Overview

To address persistent user abandonment at the final payment stage, I combined funnel analytics with qualitative interviews. This process uncovered a significant “Trust Gap.” I then designed and validated a solution focused on security perception, resulting in a projected increase of 937 annual enrollments.

Impact

• 📈 Conversion improved from 0.13% → 6.81%
• 🎯 Projected +937 additional enrollments annually
• 💳 Identified trust breakdown at final purchase stage
• 💡Validated trust signals as primary decision lever

This project transformed a low-performing funnel by reframing the problem from “price hesitation” to trust-driven decision friction.

Project Details

• Client: Aon
• Role: Lead UX Researcher & Product Designer
• Timeline: 28 days (June 28 – July 26, 2023)
• Tools: Axure RP, Google Analytics, UserTesting.com, and Optimizely
• Test Area: Acquisition Funnel
• Platform: Web Application
• Device: Desktop

My Role

I led the project end-to-end, from problem validation through experimentation and measurement.

Responsibilities

• Analyzed behavioral drop-offs using Google Analytics and Salesforce
• Identified friction points within enrollment process
• Designed interactive prototypes in Axure RP
• Planned and moderated qualitative usability tests via UserTesting.com
• Designed and evaluated quantitative experiments using Optimizely
• Synthesized insights into actionable UX principles used across the dashboard

The Problem: The “Final Hurdle” Drop-off

Our analytics revealed a paradox—users were highly engaged on the landing page and through the pricing tiers, but 53% abandoned the process at the insurance quote page.

The key question became: Why does a motivated user, who has already selected a plan, suddenly lose confidence at the finish line?

Analytics initially suggested pricing sensitivity. However, a deeper investigation revealed a more fundamental issue: users lacked confidence to complete the transaction.

The challenge was not optimization — it was transactional trust.

The Research Strategy

To bridge the gap between observed user behavior and underlying motivations, I adopted a mixed-methods approach that combined quantitative analysis with qualitative insights.

Measuring the Funnel Data

1. Analytics: Mapping the Leak

I audited the acquisition funnel to pinpoint the friction.

• The steepest drop-off was between 'Quote Page' and 'Purchase Confirmation,' indicating that the issue was not price but transactional anxiety.

2. Heatmap and session replay analysis

I tracked clicks, scrolls, and mouse movements using heatmaps and observed that most users scroll to the bottom of the page to click the CTA button, particularly on the step1 and price pages.

3. Competitive Benchmarking

I tracked clicks, scrolls, and mouse movements using heatmaps and observed that most users scroll to the bottom of the page to click the CTA button, particularly on the step1 and price pages, but users tended to click CTA immediately, without paying attention to the existing credibility section.

4. Quantitative: A/B Testing

I quickly created a high-fidelity prototype for A/B testing using Optimizely. (50/50 traffic splits)

The Hypothesis

Transforming the payment page from a simple form into a reassurance center by highlighting social proof and guarantees is expected to reduce transactional anxiety and increase conversion rates.

The Solution: Designing for Confidence

I designed a “Trust-Heavy” variant focused on psychological pillars:

Visual Proof of Credibility: Placed larger and clearer credibility logos directly beneath the “Purchase” button.

Testing & Results

To determine impact, we ran a 50/50 A/B test over 28 days to validate the design.

Key Learnings & Reflection

• This process reinforced a vital lesson: Data tells you where, but users tell you why. Without a quantitative study, we might have assumed the premium was too high and lowered it, hurting revenue. The real issue was psychological, not financial.
• Key Takeaway: Design shapes trust. Trust isn’t just about technical measures; visible cues strongly influence how safe users feel.
• Key Takeaway: Small changes can drive high ROI. Strategic tweaks, such as better placement of trust signals, can have significant effects without a full redesign.

Next Steps

• Mobile Optimization: Audit how these trust signals stack on smaller screens to ensure they don’t create “clutter” friction.
• Dynamic Social Proof: Explore testing “Live Activity” notifications (e.g., “5 people joined in the last hour”) to leverage FOMO alongside trust.

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