Retail Store Analytics

Part of the worlds-biggest-software-project initiative.

An open, AI-native operational intelligence layer that unifies foot traffic, basket analysis, and staff scheduling for physical retail stores.

Retail Store Analytics is a candidate platform that integrates people-counting sensors, POS transaction data, and staff-scheduling systems into a single operational view for store managers and regional leaders. It is intended for independent and mid-market retailers who need the same operational insights enterprise chains buy from RetailNext or Sensormatic, but at an accessible price point and without proprietary hardware lock-in. The core problem it solves: most retailers schedule staff from historical sales averages rather than actual visitor patterns, and basket-level insights remain buried in POS exports that few store managers can query.

Why Retail Store Analytics?

Incumbent platforms (RetailNext, Sensormatic ShopperTrak) are enterprise-priced managed-service offerings with opaque contracts and proprietary hardware lock-in, excluding SMB retailers.
V-Count and Trakwell.ai expose strong sensor accuracy but offer limited or undocumented public APIs, constraining integration with existing BI and workforce stacks.
Dor Technologies lowered the hardware barrier but provides no zone heatmaps, demographic analytics, or staff scheduling integration.
No surveyed vendor combines foot traffic, basket analysis, and AI-generated staff schedules in a single affordable package; the SMB-accessible full stack is an explicit market gap.
All leading solutions are proprietary commercial SaaS — no open-source-licensed retail analytics platform was identified in the surveyed market.

Key Features

Foot Traffic & Conversion

Real-time and historical foot traffic counts per store, per hour, per day
Conversion rate calculation (visitors to transactions) with staff exclusion for accuracy
Multi-store benchmarking on a standardised set of operational metrics
Period-over-period trend views across day, week, month, and year

POS & Basket Analysis

POS transaction ingestion via Square, Lightspeed, and Shopify APIs
Market basket association rule mining (Apriori, FP-Growth) for product co-purchase patterns
Basket size, basket value, and items-per-transaction reporting
Spatial implications of co-purchase patterns surfaced as layout and end-cap recommendations

Staff Scheduling Optimisation

AI-generated roster recommendations aligned to predicted hourly traffic
Time-series forecasting (Prophet, Statsmodels) for foot-traffic prediction
Service-level alerting when staff-to-visitor ratios fall below thresholds

Dashboards & Alerting

Store-manager-facing dashboards designed for non-technical users
Zone-level heatmap integration where supported by sensor hardware
Weather and promotional calendar overlay on traffic trends
Anomaly alerts for unexpected traffic spikes and queue-length breaches via Slack and Twilio

Privacy-by-Design Pipeline

No identifiable personal data stored or transmitted
GDPR and EU AI Act compliance treated as architectural constraints, not buyer responsibilities
Consent and anonymisation workflow patterns aligned with state-level privacy regulations

AI-Native Advantage

AI-driven scheduling closes a loop the incumbent dashboards leave open: rather than presenting traffic data and asking the manager to translate it into a roster, the platform generates the recommended schedule directly. Anomaly detection surfaces unusual patterns — weather impact, nearby events, conversion drops — proactively rather than waiting for the manager to notice. A natural-language query interface lets a store manager ask "Why was last Saturday's conversion rate 3% below average?" without writing SQL, and association-rule mining over basket data is presented as plain-language layout recommendations rather than raw co-purchase tables.

Tech Stack & Deployment

The reference stack uses Python with mlxtend for basket analysis, Prophet and Statsmodels for traffic forecasting, Apache Airflow for orchestration, and dbt for multi-store metric standardisation. Storage is PostgreSQL or Snowflake; dashboards are built with Metabase or Tableau, with D3.js and Leaflet for heatmap and geographic visualisations. Sensor ingestion supports V-Count, Sensormatic, and RetailNext device APIs alongside CSV import for hardware-agnostic deployments. Alerting uses Slack and Twilio webhooks. Self-hosted and cloud deployments are both intended targets.

Market Context

Retail foot traffic grew 2.8% year-over-year through late 2025, and 2026 industry commentary describes retail analytics as moving from optional to essential for competitive operations. Retailers report full ROI on people-counting and analytics investments within three to six months, driven primarily by optimised staff scheduling and improved conversion rates. Primary buyers are independent and mid-market retailers and regional retail chains for whom the enterprise managed-service model (RetailNext, Sensormatic) is structurally inaccessible.

Project Status

This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.