Driving $400K in Strategic Decisions with Behavioral Analytics in Store Operations
Cost Savings
$97,280
Annually
Compliance Gaps
3 key insights
For task completion uncovered
Strategic Adjustments
2 features
Previously considered key
Key Takeaways
Operational Scale: 4.2 million pallets verified in the first two periods.
Loss Prevention: Forecasted $8.4 million in shrink reduction.
Adoption: 98% storewide adoption of pallet scanning functionality.
Implemented a real-time scanning solution that improved pallet verification accuracy, reduced shrink, and increased operational efficiency across enterprise stores.
The UX Problem
Store associates lacked a real-time verification method, leading to errors and inefficiencies.
The lack of tracking visibility disrupted supply chain operations.
The Business Impact
Overstocking of incorrect inventory, increasing shrink risk.
Workarounds where store leaders sold misplaced inventory, causing financial losses.
Store associates previously relied on paper load sheets to verify pallet deliveries, leading to a manual and error-prone process that caused:
Problem Framing (Empathize & Define)
Early Explorations & Trade-offs
Manual Scanning for pallets with missing barcode label → Simple but still required associate effort.
Unload and stage time estimates to help associates better assess their productivity.Storage ID for better accountability of cold chain pallets
Design & Execution (Prototype & Decision-Making)
Challenge Overcome: Driving Adoption
Initial Barrier: Store associates were hesitant to change workflows, citing time constraints and unfamiliarity.
Solution: working with SMEs and store leaders to conduct in-store pilots, refining user training materials and the UX to reduce scanning friction and adding audio cues for real-time correction without slowing down work.
Outcome: The adjustments led to 98% adoption, surpassing expectations.
Design & Execution (Prototype & Decision-Making)
Final UX/UI Screens & Walkthrough
Design & Execution (Prototype & Decision-Making)
User Feedback & Testing Rounds
3 rounds of usability testing revealed the need to track and quantify missing pallets.
Post launch user feedback surfaced the need to use pallet data to inform nightly stocking strategies.
Behavioral analytics insights identified gaps in scanning compliance.Iterative Loop - Unmet Needs (Iteration & Impact)
Key Learnings & Strategic Influence
Leadership in Change Management:
Drove alignment between design, operations, and supply chain teams to ensure adoption.Scaling & Future Considerations from User Research: Explore ways to dynamically update BOH using verified delivery data and automate claims.
Leadership
Wrote business case for Behavioral Analytics: Made the case for why behavioral data was critical to improving associate tools and optimizing operating expenses
Strategic Alignment: Worked across Product, Engineering, and Operations to ensure implementation supported business goals like reducing shrink and increasing revenue
Empowered Designers & Teams: Ensured designers could access behavioral data for their own decision-making and optimizations.
Hands-On
Implementation Roadmap: Led workshops with engineers to ensure non-intrusive data capture that aligned with privacy and compliance guidelines.
Prototyped Dashboards & Reporting Insights: Iterated on segmentation views to analyze data by division, role, and department.
How we got there: Design Strategy
Implemented Auto-Capture Behavioral Analytics:
Provided real-time insights into how associates interact with both apps.
Reduced need for manual feedback collection, fast-tracking the discovery phase.
Enhanced Process Optimization & Task Completion Tracking:
Monitored bottlenecks in stocking, replenishment, and verification workflows.
Identified gaps in policy compliance and opportunities for task automation.
Results & Impact
Low Feature Utilization:
Identified a feature used by only a small group of stores, prompting a reassessment of its value.
One of the applications is only used by 1/3 of stores, questioning its necessity in the overall ecosystem.
Efficiency Gaps in Task Execution:
Store Walks are completed at an abnormally rapid pace, raising concerns about whether they are being rushed or bypassed.
Sanitation tasks were logged at an average of six per day, with each entry taking just eight seconds, suggesting potential compliance gaps.
Cost Savings & Strategic Adjustments:
Leadership initially planned to maintain three key features within the Store Walks app, assuming they were critical. However, analysis proved that two of those features did not justify the cloud and maintenance costs, leading to a shift in strategy.
Proved that at least one feature should not be upgraded immediately due to a dependency on an API upgrade.
Influencing Leadership Decisions on Third-Party Adoption:
Leadership sought to transition capabilities to a third-party vendor despite lacking strong data to justify the move.
Behavioral analytics provided concrete evidence on feature usage and inefficiencies, helping to counter poor decision-making based on assumptions.
Resource Optimization & Cost Savings:
More accurate tracking led to optimized training investments and higher ROI on tool enhancements.
Provided leadership with better visibility, reducing unnecessary feature development efforts.
Iterative Loop - Unmet Needs
Further Correlation Analysis:
In Phase 1, behavioral analytics provided insights into feature adoption and efficiency gaps, but did not correlate behavior with financial metrics at the store level.
Moving forward, we need to refine and enhance the definition of behavioral cohorts to better connect specific behaviors with financial outcomes.
This will ensure that strategic decisions regarding feature investment, training, and process automation are directly tied to measurable business impact.
Impact Highlights
Operational Scale
User verified in first 2 months
Loss Prevention
Across 8,000 mis-delivered pallets
Adoption
Adopted Pallet Scanning feature