Tables/APIs

The following is the list of tables or APIs associated with the RetailNext connector:

>Query_Metrics

Query_Metrics (DataMine):
Source API: API - Public Documentation - Confluence

Source Schema:   Retail_Next_Schema

1. Daily Traffic Reporting Automate end-of-day footfall summaries for operations teams by pulling traffic_in and traffic_out grouped by date. Eliminates manual dashboard pulls and ensures district managers receive consistent store-level data every morning without needing platform access.

2. Conversion Rate & Sales Benchmarking Pull conversion_rate, net_sales, and sales_transactions_count daily grouped by location to compare each store against its rolling average. Identifies underperforming stores mid-week so regional managers can intervene before the trading period closes — turning reactive post-mortems into proactive decisions.

3. Workforce Scheduling Optimization Query traffic_in in 30-minute intervals grouped by day_of_week over a 90-day window to reveal peak arrival patterns per store. Feeds directly into scheduling models so staffing levels are driven by actual shopper behavior rather than fixed shift assumptions, reducing unnecessary labor costs during slow periods.

4. Occupancy Compliance & Capacity Management Poll total_occupancy and total_occupancy_rate at 15-minute intervals during store hours. When occupancy approaches the configured limit, trigger automated alerts for store managers to control entry. Particularly valuable during sales events, product launches, or in markets with legal capacity regulations.

5. Storefront Capture Rate & Marketing ROI Track passby_traffic_in and video_passby_capture_rate before, during, and after a campaign window. Comparing capture rates between stores that ran a campaign versus control stores provides an objective measure of whether window displays or local advertising are converting street traffic into store visits.

6. Dwell & Merchandising Analysis Pull dwell_count, avg_dwell_duration, engagement_rate, and sku_conversion_rate by sublocation zone (feature tables, entrances, displays). Zones with high dwell but low conversion signal a pricing, placement, or staffing issue — giving merchandising teams behavioral data to back layout decisions.

7. Basket Size & Revenue Quality Benchmarking Compare shopper_yield, avg_transaction_value, and avg_items_per_transaction across locations monthly. Two stores with similar traffic but different revenue totals may diverge on conversion, upsell rate, or basket size — each requiring a different operational response. This use case pinpoints exactly where the gap lies.

8. Enterprise Data Warehouse Integration Schedule a daily ETL pipeline that pulls a full operational metric set grouped by date and location, landing results into Snowflake, BigQuery, or Redshift. Joined with ERP and CRM data, this enables cross-system analytics — such as correlating loyalty program activity with in-store conversion. Use the validity field (complete, incomplete, imputed) and ok flag per metric to manage data quality in the pipeline.