Behavioral Analytics for Store Operations

Behavioral Analytics for Store Operations

Behavioral Analytics for Store Operations

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

Problem & Context

Store associates used two key applications—MyDay (Pallet Scanning & Deliveries) and Store Walks (Task Management)—but leadership lacked insights into how these tools were used.

Inefficiencies & Pain Points:

  • Unknown feature utilization: No clarity on which features were helping associates complete their tasks efficiently and which were causing friction.

  • Compliance Gaps: Uncertainty on whether associates were following best practices for safety, sanitation, and customer service tasks.

  • Training & Resource Allocation Issues: Leadership couldn’t pinpoint where associates struggled most, making targeted training and budgeting decisions difficult.

Business Impact: Potential overspending on unused features and inefficient resource deployment.

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.

Solutions

Data-Guided Feature Development:

  • Identified which features were most used, underutilized, or causing friction.

  • Enabled more accurate resource allocation and prioritization.

Improved Training & Resource Utilization:

  • Used data to uncover common errors and struggles, guiding targeted coaching strategies.

  • Provided insights into whether training budgets were being allocated effectively.

Solutions

Data-Guided Feature Development:

  • Identified which features were most used, underutilized, or causing friction.

  • Enabled more accurate resource allocation and prioritization.

Improved Training & Resource Utilization:

  • Used data to uncover common errors and struggles, guiding targeted coaching strategies.

  • Provided insights into whether training budgets were being allocated effectively.

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.

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.

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.

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.