Use Google Gemini/OpenAI/Claude to analyze operator performance data and get actionable insights.
Click the AI Settings button below to configure your API key(s) and model(s).
Quick Analysis Prompts
Stats
Debug Info
Department Data
Department Performance
Size = Sales Amount | Color = Return Rate
Top 10 Departments by Sales Amount
Top 10 Departments by Return Amount
Bottom 10 Departments by Sales Amount
Operator Data
Explore KPIs by operator. Sort columns or download the data.
Timing Weights (affect Composite)
Operator Leaderboard (Composite Score)
ranks operators based on weighted KPIs for a single view of best/worst performersSpeed vs Accuracy — Avg Sales per Txn vs Void Rate (size = Txn Count)
shows trade-offs between ring speed and void rate; bubble size indicates transaction volumeKPI Heatmap (Z-Scores)
Compares all KPIs across operators, highlighting strengths (green) and weaknesses (red). Green = above average; Red = below average.Operator Time Breakdown
Top 10 Customers by Sales $
Visits (Top 10 by Txns)
Visits ≈ # of transactions with an attached customer ID.Pareto of Sales $
Sorted by Sales $. Bars show per-customer contribution; the line shows cumulative share (often a small group contributes most revenue).Home Store Leaderboard — Top 10 by Sales $
Aggregated across customers by their home store from the 11:FF record. Txns count includes only transactions where a customer was identified.Customers — Top 100
| Customer # | Home Store | Txns | ItmSalesCnt | ItmRtrnCnt | ItmVoidCnt | Sales $ | Return $ | Void $ |
|---|
Top 10 Items by Sales $
Void % (Top 10 by Sales Count)
Void % = void $ / (sales $ + void $)Pareto of Sales $ (Bars + Cumulative %)
The Pareto chart sorts items by Sales $. Bars show each item's contribution; the line shows cumulative contribution. Typically, a small set of items accounts for a large share of sales (e.g., 20% of items drive 80% of sales).Items — Top 50
| Item | Occurrences | Sales Cnt | Return Cnt | Void Cnt | Sales $ | Return $ | Void $ |
|---|
Sales Heatmap
Visualize sales data over the course of the day with a heatmap based on transaction times.
TLOGic: Actionable Intelligence from Tlog Data
TLOGic is a powerful analysis tool for IBM Transaction Log (Tlog) files. It transforms raw transaction data into critical business insights, enabling retailers to take decisive action and build advanced AI models.
Powered by the REDList Tlog Parser
At the heart of TLOGic is a next-generation parsing engine developed by REDList Solutions. Built entirely in Rust, it delivers a foundation of elite performance and security.
- Secure & Resilient: Leveraging Rust's memory safety features to protect your critical data.
- High-Performance: Engineered for speed to process massive Tlog volumes effortlessly.
- Scalable Architecture: Designed for modern infrastructure, ready to run anywhere—from the Edge to the Cloud.
Features
- Transaction parsing and analysis
- Advanced filtering capabilities
- Statistical analysis
- Multiple data export formats
- Real-time data visualization
Usage Tips
- Use the filter panel to narrow down transactions
- Click on any transaction to view detailed information
- Export data in multiple formats for further analysis
- Use the statistics panel to understand data patterns
Supported Formats
.tlg- IBM Transaction Log files.dat- IBM Transaction Log files.db0- IBM Tlog Archive files
Technical Details
Parser Engine: WASM-based
Frontend: Bootstrap 5 + Vanilla JS
Data Processing: Client-side