Data Analysis Guide

Understanding Department Performance

The Departments tab provides visual and tabular analysis of sales performance across all product categories.

Key Metrics Available:

Interactive Treemap

The department treemap visualization uses:

• Rectangle Size: Proportional to sales amount (larger = more sales)
• Color Intensity: Indicates return rate (darker = higher returns)
• Hover Details: Shows exact values for department

Business Applications:

• Identify top revenue-generating departments
• Spot departments with concerning return rates
• Allocate floor space based on sales performance
• Set inventory priorities

Evaluating Cashier Performance

Operator analytics help identify training needs, recognize top performers, and maintain service standards.

Core Performance Metrics:

• Transaction Count: Volume of transactions processed
• Sales Amount: Total dollar value processed
• Average $/Transaction: Indicates upselling success or transaction complexity
• Void Rate: Frequency of voided transactions (lower is better)
• Refund Rate: Frequency of return processing
• Ring Time: Speed of item scanning/entry
• Tender Time: Speed of payment processing
• Inactive Time %: Logged-in time not processing transactions

Using Composite Scores:

The composite score combines multiple metrics with customizable weights. Default weights:

• Sales Amount: +0.35 (highest importance)
• Transaction Count: +0.25
• Avg $/Txn: +0.15
• Void Rate: -0.15 (penalty)
• Void $ Ratio: -0.10
• Refund Rate: -0.05
• Refund $ Ratio: -0.05

Tip: Adjust weights in the Operator tab to match your business priorities. For example, increase void rate penalties if accuracy is critical, or emphasize speed metrics for high-volume environments.

Analysis Workflows:

1. Performance Reviews: Sort by composite score to identify top/bottom performers
2. Training Needs: Filter operators with high void rates or slow timing metrics
3. Recognition Programs: Export top performers' data for awards
4. Scheduling: Identify high-performers for peak hours

Customer Insights

When customer identifiers are present in your TLOG data, analyze shopping patterns and loyalty program effectiveness.

• Visit Frequency: Identify repeat customers vs. one-time shoppers
• Basket Size: Average transaction values per customer
• Category Preferences: Which departments do customers frequent
• Loyalty Impact: Compare member vs. non-member behavior

Product Performance

Drill down to individual SKU performance for merchandising and inventory decisions.

Key Questions to Answer:

• Which products have the highest sales velocity?
• Are there items with unusually high return rates?
• Which price points perform best?
• Are promotional items moving as expected?

Using Item Data:

1. Sort by sales quantity to find top movers
2. Sort by return rate to identify problem products
3. Export data for inventory management systems
4. Cross-reference with department data

Sales Patterns Over Time

The Sales/HR tab visualizes transaction patterns by hour and day of week, essential for labor scheduling and operational planning.

Reading the Heatmap:

• Rows: Days of the week (Monday-Sunday)
• Columns: Hours of operation
• Color Intensity: Transaction volume or sales amount
• Hover: View exact transaction counts and amounts

Business Applications:

• Labor Scheduling: Schedule more staff during peak hours (darker colors)
• Break Planning: Schedule breaks during slower periods
• Promotions: Time marketing campaigns for high-traffic periods
• Training: Schedule training during consistently slow hours
• Efficiency: Identify underutilized time periods for operational improvements

Example Insights:

Getting Data Out of TLOGic

Export data for deeper analysis in Excel, databases, or business intelligence tools.

Export Options:

• CSV Format: Universal format for most software (Excel, databases, BI tools)
• Excel Format: Formatted spreadsheets with proper data types
• Raw Binary: Original TLOG records for archival or specialized analysis

Export Workflows:

1. Use search/filters to select specific data subsets
2. Select transactions or use "Select All" for bulk export
3. Click export button and choose format
4. Large exports (10,000+ records) use optimized bulk processing

Tip: For regular reporting, create a consistent workflow: load TLOG → apply standard filters → export → import into your reporting template.

Multi-Dimensional Analysis

Get the most insights by combining multiple analysis views:

Example: Investigating High Returns

1. Department View: Identify which department has high return rate
2. Item View: Find specific products driving returns
3. Operator View: Check if returns concentrate around specific operators
4. Time View: See if returns happen at specific times
5. Raw Data: Examine individual return transactions for patterns

Example: Optimizing Labor Costs

1. Heatmap: Identify peak and slow periods
2. Operator Data: See transaction counts per operator per shift
3. Raw Data Filtering: Analyze specific time windows
4. Export: Create labor planning reports