shelfwise.ai
Playbook uses internal store execution data and public NDC product launch dates to identify which stores historically miss new SKU placements and build pre-launch audit schedules targeting the worst performers.
autosigma.com
The playbook scrapes dealer inventory and pricing across all locations to audit multi-store offer consistency, and benchmarks promotional update velocity against aggregated peer dealerships to show lag versus the competitive market.
View Playbook →bevz.com
The playbook uses state liquor license databases and TTB COLA registry to identify newly licensed stores and cross-reference competitive craft beer SKU counts within 0.8 miles of the prospect's location.
View Playbook →car-fluent.com
The playbook uses US Census Bureau Hispanic population data and Google Maps review language detection to identify dealerships with Spanish-speaking customer presence but no bilingual digital capabilities, combined with competitor bilingual site analysis.
View Playbook →circana.com
Playbook uses internal POS velocity data, competitive launch tracking, retailer pricing feeds, and promotional calendar intelligence to alert CPG brands when competitors launch price-undercut SKUs in their shelf sets with quantified share capture risk.
View Playbook →complyauto.com
Playbook uses OSHA Establishment Search and FTC Safeguards Rule breach notification database to identify dealerships approaching willful violation reclassification and those with permanent public breach records creating compounding regulatory and reputational risk.
View Playbook →logicbroker.com
Playbook cross-references CPSC complaint databases with customer product catalogs to flag recall-pattern SKUs, and maps supplier compliance decay timelines using FDA warning letters and internal order history.
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