From Button to Basket: Turning Store Ops Data into Actionable Intelligence
Customer Pain Point
A ISV in the Retail Store Operations space had a platform that collected tens of thousands of weekly data points from in-store call buttons, capturing how and when customers request assistance. But despite this data, most of their retail customers struggled to derive value beyond surface-level metrics like “Average Response Time.” They were sitting on a goldmine of operational insights, but had no clear path to uncover them.
Nexlytix Approach: From Raw Signals to Retail Strategy
Nexlytix partnered with the ISV to transform this raw, button-level data into a strategic asset.
Using our AI-native approach at analytics, we:
Integrated, cleaned, and standardized button event logs across store locations categorized by tenants.
Applied time-series and behavioral analytics to model patterns, exceptions, and risks.
Built a AI-Driven insights engine to drive store-level KPIs, staff benchmarks, and operations recommendations.
Key Insights Uncovered
Turning raw call button data into operational intelligence that tells the real story behind store performance.
Story Uncovering the Store Rankings with Context: Stores were ranked weekly, however what that meant for the Store Manager? Our Small Language Model helped the store manager uncover the story behind the rank. Think of a scenario where the store manager see something like this: "Your store ranked 1st because: You achieved a strong balance of low average wait time, high dispatch usage strong call button engagement, and high total customer serve time", instead of just seeing something like this: "Your store ranked 1st"
Departmental Risk Zones: Identified sections with consistent high wait times, due to low visibility and poor staffing ratios.
Operational Blind Spots: Spikes in call button usage during off-peak hours uncovered hidden staff fatigue and inefficient shift allocations.
Behavioral Patterns: The
High Claimers ≠ High Performers: some staff claimed most requests but lacked follow-through.
Fastest Responders sometimes ignored context, affecting resolution quality.
Call Pattern Intelligence:
Temporal patterns enabled scheduling optimizations.
Predictive signals flagged potential abandoned requests before they happened.
Want to dig deeper? Let’s schedule a call to explore more of the insights we uncovered.
Actionable Recommendations
From insight to impact, tactical prescriptions that drive efficiency, enhance service, and lift customer experience.
Monitor Hidden Wait Time Spikes: Caught those silent traffic jams? We flagged departments where wait times quietly ballooned beyond norms so no more flying blind.
Rethink on Metrics: Claim rates lie, at least in a few cases. Our AI-Recommendation engine recommended Impression % to measure who’s truly making an impact and not just who’s hitting the button fastest.
Clone Your Rockstars: High-efficiency staff behaviors were decoded to build “Playbooks” for coaching newbies and nudging the middle pack to greatness.
Staggered Shift Recommendations: Real-time heatmaps told the retailers when to stagger shifts, making sure someone’s always ready during rush-hour surges.
Fatigue & Burnout Alerts: We spotted staff on the edge handling high volume without breaks. Time for coaching, not chaos.
Post-Resolution Comments: Any request lasting over 2 minutes triggered a mandatory post-resolution feedback, helping identify sticky service spots which helped build on the playbook.
New KPIs Engineered
Along with the traditional KPIs, Nexlytix engineered a few new KPIs and started tracking them for much more optimized operational efficiency.
Customer Patience Index: Measures how long customers are willing to wait before abandoning.
Abandoned Request Probability: Predictive score indicating likelihood a request will go unserved.
Department Response Efficiency: Normalizes volume vs. response time for fair cross-store comparisons.
The Business Impact
CSAT Lift: Stores that adopted our AI-driven insights saw a 7–10% increase in customer satisfaction scores within 8 weeks.
Increased Basket Size: Fast, effective responses increased likelihood of purchase completion by up to 12% in target departments.
Reduced Wait Times: High-wait zones reduced average response times by 25% with smarter staff allocation.
Prevention of Operational Risk: Early detection of high-fatigue staff and underperforming zones helped reduce escalations.
Conclusion
This partnership showcases how Nexlytix redefines retail analytics from moving beyond reporting to decision intelligence. With our AI-powered AI-Native approach, we transformed raw call button data into strategic insights and actions that impacted real-time operations, frontline behavior, and ultimately the bottom line.
Ready to make your operational data work harder for you? Book a free demo with Nexlytix and discover how we can help transform your store data into growth.