Case Study: e-mapping
Project Overview
Challenge: Retail merchandising teams and retailers lacked accurate, standardized data about store layouts and shelf configurations, leading to inefficient planogram execution, poor space allocation decisions, and missed merchandising opportunities across thousands of retail locations.
Solution: I led the development of e-mapping, a comprehensive store mapping platform that enables field teams to capture detailed shelf measurements, product category data, and spatial relationships through mobile devices, creating a centralized database of retail space intelligence.
My Role: Product Owner & Project Lead
The Problem I Solved
Merchandising teams and brand managers were operating without critical spatial data that impacted their ability to optimize retail execution:
Inconsistent shelf data: Teams relied on outdated or inaccurate store layouts, leading to failed planogram implementations
Manual measurement processes: Field teams spent hours with tape measures and paper forms, introducing human error
Lack of category insights: No systematic way to understand product category placement and adjacencies within stores
Space allocation disputes: Without accurate measurements, negotiations with retailers lacked data-driven foundation
Planogram execution failures: 35% of planogram implementations failed due to inaccurate space assumptions
Limited competitive intelligence: No visibility into competitor shelf positioning and space allocation strategies
Discovery & Requirements Gathering
Working closely with merchandising teams, category managers, and field representatives, I conducted extensive research across multiple retail environments. Key insights emerged:
Time-intensive data collection: Field teams spent 3-4 hours per store manually measuring and documenting shelf configurations
Inconsistent measurement standards: Different teams used varying methodologies, making data comparison impossible
Missing contextual information: Teams captured basic dimensions but missed critical details like shelf height variations, end-cap configurations, and category flow patterns
Data accessibility issues: Measurements were trapped in spreadsheets and paper forms, making analysis and sharing difficult
Planogram disconnect: Store reality often differed significantly from headquarters assumptions about available space
Technical Architecture
I collaborated with our development team to design a comprehensive mobile-first system that could:
Precision measurement capture: Integrate with laser measurement tools and manual input for accurate dimension recording
Category classification system: Standardized naming conventions for product categories with customizable hierarchies
Visual documentation: Photo capture with measurement overlays and annotation capabilities
Spatial relationship mapping: Record adjacencies, traffic patterns, and store flow characteristics
Cloud-based data management: Centralized database with real-time synchronization and analytics capabilities
User Experience Design
Recognizing that field teams worked in busy retail environments with time constraints, I prioritized efficiency and accuracy:
Intuitive measurement workflow: Step-by-step guided process reducing measurement time by 60%
Visual validation system: Photo confirmation of measurements with overlay graphics for accuracy verification
Standardized category picker: Consistent product classification across all users and locations
Offline-first functionality: Complete data capture capability without internet connectivity
Quick template system: Pre-configured store formats for chain retailers to accelerate data collection
Export and sharing tools: One-click reports for planogram teams and category managers
Implementation & Results
I led the development team through 15 two-week sprints, maintaining continuous collaboration with merchandising teams and retail partners. Regular field testing with pilot users ensured the platform met real-world operational requirements.
Key Metrics Achieved
Reduction in data collection time: Streamlined measurement process from 4 hours to 90 minutes per store
Measurement accuracy improvement: Eliminated manual errors through guided workflows and validation systems
Increase in planogram success rate: Accurate space data improved implementation success from 65% to 92%
Additional revenue through optimization: Better space utilization and category placement drove measurable sales increases
Stakeholder Impact
Field Merchandising Teams: Simplified data collection process with professional tools and clear workflows, enabling focus on strategic placement decisions
Category Managers: Comprehensive spatial intelligence for data-driven category optimization and competitive analysis
Planogram Teams: Accurate store dimensions eliminated redesign cycles and implementation failures
Retail Partners: Improved collaboration through shared spatial data and professional measurement standards
What Worked Well
Field-first design approach: Extensive testing in actual retail environments revealed critical usability requirements
Integration with existing tools: Compatibility with laser measurers and tablet devices accelerated adoption
Standardized data model: Consistent data structure enabled cross-store analysis and benchmarking capabilities
Visual validation system: Photo documentation with measurement overlays eliminated disputes about accuracy
Challenges Overcome
Measurement device integration: Developed universal adapter system supporting multiple laser measurement brands and manual input methods
Data standardization complexity: Created flexible system accommodating diverse retail formats while maintaining consistency
Store access coordination: Built scheduling and permission management system for coordinating with retail partners
Large dataset management: Implemented intelligent data compression and caching for handling thousands of store measurements on mobile devices
Large format printing: Rendering a print file that would be broken down into standard 8.5 x 11 sheets that could be joined together to show a clear store map.
Legible layout: System had to show category names in a number of directions as to not overlap names of adjacent categories
Key Takeaways
This project demonstrated the critical importance of accurate spatial intelligence in retail execution. By transforming manual measurement processes into a systematic, mobile-enabled platform, we created a foundation for data-driven merchandising decisions that directly impacted sales performance.
The success of e-mapping showed that seemingly operational tasks like measurements could become strategic assets when properly systematized and analyzed. The platform became essential infrastructure for category management teams, enabling them to optimize space allocation based on actual store conditions rather than assumptions.
Technologies Used: Mobile-native applications with laser measurement device integration, computer vision for photo analysis and measurement validation, cloud-based spatial database with advanced analytics, offline-first data synchronization, standardized retail categorization engine, and automated reporting and export capabilities.
Project Duration: 8 months (development) + 4 months (rollout across pilot stores)
Team Size: 3 developers, 1 UX designer, 1 QA engineer, 2 field test coordinators