Context-Aware Customer Support
Designed and launched a contextual self-help platform for Zalando's 50M+ customers that proactively addresses needs before they contact support, focusing initially on returns & refunds which represent 33% of all customer inquiries.
Through behavioral analytics and ethnographic research, I identified that customers have highly predictable support needs based on order status and behavioral patterns.
Role
Lead Designer
Timeline
16 Months
Team
- Product Manager
- Engineers (3)
- Data Analyst
- Customer Care Experts (3)
Strategic Objectives
Shift Zalando's customer support from reactive problem-solving to anticipatory assistance, reducing contact volume while enhancing satisfaction and loyalty.
Reduce Support Burden
Achieve 25% reduction in returns-related contact volume while maintaining or improving customer satisfaction.
First-Contact Resolution
Increase customer satisfaction scores by 20% through an improved first-contact resolution to prevent repeat contacts.
Drive Customer Loyalty
Increase repeat purchase rate by 15% through improved experience that builds confidence and reduces friction.
Core Design Principles
Four strategic pillars that guide the design of proactive customer support experiences.
Anticipatory Design
−Predict customer needs based on order status, behavior, and historical patterns, surfacing information before customers realize they need it
Contextual Intelligence
+Trust Through Transparency
+Emotional Journey Design
+Solution Architecture
A comprehensive framework built on three core pillars designed to transform customer support from reactive to proactive.
Active Returns Dashboard
−Real-time status tracking with predictive delivery estimates, contextual action buttons, proactive problem alerts, and visual timeline
Intelligent Information Architecture
+Proactive Communication System
+Research Strategy
Implemented mixed-methods approach combining behavioral analytics, ethnographic research, and experimental validation:
Research Methods
- ✓Behavioral Data Analysis: 2.3M customer interaction analysis across all support channels
- ✓Journey mapping: High-contact customer segments and predictive modeling
- ✓Qualitative Research: 47 contextual inquiry sessions, 23 in-depth interviews, 12 diary studies
- ✓Usability Benchmarking: Competitive analysis of 12 e-commerce self-service experiences
Key Insights
- 💡78% of support inquiries could be anticipated based on order status + customer history
- 💡Customers contact support at 5 predictable moments in the return journey
- 💡Proactive communication reduces contact likelihood by 65%
- 💡84% preferred self-service when information was comprehensive and trustworthy
Business Impact
Direct improvements to customer experience and satisfaction
Reduction in returns inquiries (exceeded 25% target)
Customer satisfaction improvement (self-service rating from 3.1 to 4.2 out of 5)
Faster issue resolution through contextual information
Reduction in repeat contacts through comprehensive information
Business Results
Organizational gains and operational efficiency achieved
Annual cost savings through operational cost reduction
Agent productivity increase with focus shifted to complex interactions
Increase in automation rate as intelligent routing handles routine inquiries
Protected Case Study
Case study is only available on request.