Optimizing the Shopping Experience with Native Biometrics
Sole product designer @ZÉ EXPRESS Smart Coolers - 2024
Context
As a Product Designer at Zé Express, I worked on a cutting-edge solution to enhance the shopping experience in our smart, self-service stores. These stores, powered by artificial intelligence and machine learning, provide a quick and cost-effective way to purchase beverages in locations like condominiums, gyms, and offices.
Our goal was twofold:
1. Improve the shopping journey to increase user satisfaction and retention while maintaining compliance with age-restricted purchases.
2.Prepare for business expansion into new markets such as universities, subways, and hotels.
This project tackled complex challenges in balancing legal, business, and user needs while delivering a scalable solution for future growth.
Problem
The existing shopping experience faced obstacles that impacted both users and business performance:
1. High rejection rate (9%) for facial validation: External factors like poor lighting, blurry photos, and prank attempts created frustration and inefficiency.
2. Lengthy shopping time (66 seconds): The process was slower than expected, leading to user dissatisfaction.
3. Inefficient user flow: Bureaucratic steps disrupted the seamless shopping experience, creating frustration.
4. Scalability challenges: Errors and the cumbersome process limited our ability to expand into new locations.
User Insights
To uncover the root causes of these issues, I employed qualitative and quantitative research methods:
→ Purchase reviews: To gather behavioral insights from real interactions.
→ Data analysis: To investigate patterns in rejected validations.
→ User interviews: To understand their pain points and perceptions.
Key Findings:
“The process feels bureaucratic.” Users were frustrated by repetitive validation steps for each purchase.
“Why do I need to do this every time?” The need for frequent validation negatively impacted trust and satisfaction.
Disrupted flow: The act of taking a photo and waiting for analysis interrupted the natural rhythm of shopping.
External factors: Issues like poor lighting, shaky hands, and prank photos were major contributors to validation failures.
Approach
1. Research and discovery:
→ Conducted qualitative and quantitative research to identify root causes of friction.
→ Collaborated with the engineering and compliance teams to understand technical constraints and legal requirements.
2. Strategic design alignment:
→ Advocated for leveraging native biometric authentication (fingerprint/face ID) to simplify the process, reduce errors, and ensure compliance.
→ Gained buy-in from stakeholders by presenting a clear roadmap showing how the solution aligned with both user goals and business expansion objectives.
3. Iterative prototyping and testing:
→ Designed prototypes for iOS and Android platforms, emphasizing native biometrics integration.
→ Conducted usability tests with users in controlled environments to refine the solution based on feedback.
Solution
The final solution introduced native biometrics as the primary validation method, while retaining facial validation as an alternative. Key features included:
Simplified shopping flow:
Biometric activation to the main screen, reducing unnecessary steps and friction.
Instant feedback:
Real-time responses for errors allowed users to quickly resolve issues without disrupting the experience.
Platform-specific designs:
Tailored interfaces and interactions for iOS and Android to ensure a seamless, native experience.
Flexibility for users:
Offered users the choice to switch between biometric and facial validation, improving accessibility and adoption.
Results
The implementation delivered measurable success, positively impacting both users and the business:
Reduced average shopping time: From 66 seconds to 51 seconds (-15 seconds).
Increased user adoption: 39% of users opted for biometrics within the first month.
Fewer journey errors: Error rates dropped by 6%.
Lower rejection rates: Facial validation rejections decreased by 10%, improving user trust.
Key Learnings
This project highlighted the importance of designing for both users and scalability:
Addressing user pain points: Focusing on users’ frustrations not only improved satisfaction but also reduced errors that hindered business growth.
Balancing user needs and business goals: Legal compliance and scalability were critical, but they couldn’t come at the expense of the user experience.
Iterative design: Testing and refining prototypes allowed us to confidently launch a solution that worked seamlessly across platforms.