Shipping and package delivery operations are fundamental to both customer satisfaction and business performance. As consumer expectations rise for faster, more affordable delivery, businesses must go beyond just data collection — they need ongoing analytics to continuously measure and improve performance across the shipping ecosystem.

To maintain a competitive edge, organizations must implement performance measurement frameworks supported by reliable data and analytics. These insights help optimize service delivery, minimize costs, ensure carrier compliance, and drive strategic decision-making.

Below, we explore the essential types of data and the ongoing analytics necessary to optimize shipping and delivery operations across key performance areas.

1. Rate Shopping Analytics

Rate shopping is essential to compare shipping rates across multiple carriers and service levels in real time to select the most cost-effective and reliable option. As shipping rates and service availability can vary based on destination, package characteristics, and timing, rate shopping tools combined with analytics are crucial for daily cost optimization and service quality.

Key Data and Analytics Requirements:

• Real-time rate comparison: Integrate APIs from multiple carriers to dynamically compare rates for each shipment across available service levels.
• Service-level analytics: Evaluate trade-offs between price and delivery time based on customer preferences carrier agreements.
• Historical pricing trends: Use analytics to spot patterns in carrier pricing and predict when to shift volumes or negotiate contracts.
• Carrier surcharges and accessorial fees: Track and compare non-base charges (e.g., fuel surcharges, residential delivery fees) that can significantly impact total cost.
• Performance-adjusted rate evaluation: Combine rate shopping with carrier performance analytics (e.g., on-time performance, damage claims) to choose the best value option.

Performance Metrics:

• Average savings per shipment from rate shopping
• Service level chosen vs. optimal service level recommended
• Percentage of shipments using lowest-cost compliant carrier
• Frequency of rate exceptions or anomalies
• Carrier mix balance (to avoid overdependence and maximize leverage)

2. Order Data and Analytics

Order data forms the basis of all shipping activities. Key metrics and analytics include:

• Order volume trends: Use analytics to identify seasonal spikes and long-term trends for capacity and labor planning.
• Weight and dimensional accuracy: Analyze variances to detect packaging inefficiencies that could increase costs.
• Geographic distribution: Map delivery heatmaps to align warehouse locations and shipping methods with demand clusters.
• Order cadence analytics: Identify high-frequency customers and peak times to improve forecasting and staffing models.

Performance Metrics:

• Order fulfillment rate
• Forecast accuracy
• Adherence for processing times

3. Carrier Performance and Compliance Analytics

Carrier performance directly affects customer experience and delivery timelines. Ongoing monitoring and benchmarking should cover:

• On-time delivery analytics: Track promised vs. actual delivery times per carrier, service level, and route.
• Exception reporting: Analyze delays, lost packages, and damage claims to evaluate carrier service reliability.
• Carrier scorecards: Develop dashboards to track KPIs like delivery accuracy, incident rates, and responsiveness.
• Rate variance analysis: Compare invoice data to contracted rates to detect overcharges and ensure contract compliance.

Performance Metrics:

• On-time delivery percentage
• Claims ratio
• Cost per package per service and carrier
• Carrier agreement compliance rates

4. Shipping Cost Analytics

Analyzing cost drivers ensures shipping remains profitable and efficient:

• Cost-per-shipment analysis: Break down total shipping spend by carrier, service level, region, and customer type.
• Packaging optimization: Monitor dimensional weight charges vs. actual weights to find cost-saving opportunities.
• Shipping method mix: Analyze the performance and profitability of free vs. paid shipping offers.
• Freight audit analytics: Regularly compare carrier invoices to expected charges to catch errors or inconsistencies.

Performance Metrics:

• Average shipping cost per order
• Dimensional weight impact
• Carrier rate adherence
• Return on shipping promotions

5. Warehouse and Fulfillment Data Insights

Warehouse operations are central to fast and cost-effective delivery. Ongoing analytics should cover:

• Order cycle time: Track time from order placement to dispatch across fulfillment centers.
• Pick-pack efficiency: Monitor employee productivity, error rates, and order accuracy.
• Location benchmarking: Compare performance across fulfillment centers to identify best practices.
• Returns handling efficiency: Analyze turnaround time and restocking rates to improve reuse and resale.

Performance Metrics:

• Order processing time
• Picking accuracy
• Warehouse throughput
• Return processing time

6. Customer Behavior and Preference Analytics

Understanding customer preferences enables better shipping options and satisfaction strategies:

• Shipping method usage trends: Identify popular choices by customer segment.
• Delivery feedback loop: Analyze satisfaction surveys and reviews related to shipping.
• Urban vs. rural performance: Evaluate delivery success and timeliness in different markets
• Service level effectiveness: Test delivery windows to optimize cost vs. satisfaction.

Performance Metrics:

• Shipping satisfaction score
• Repeat purchase rate by delivery method
• Abandonment rate due to shipping options and cost
• Customer satisfaction segmented by delivery type

7. Route Optimization and Last-Mile Analytics

Delivery efficiency is highly dependent on real-time and historical logistics data:

• Route performance: Analyze route efficiency, drop density, and vehicle utilization across zones.
• Traffic and delay forecasting: Use predictive analytics for proactive route adjustments.
• Delivery time windows: Monitor compliance with promised delivery windows to enhance customer trust.
• Last-mile cost analysis: Break down final-mile delivery expenses and their correlation to delivery type and location.

Performance Metrics:

• Delivery success rate
• Cost per delivery
• On-time final mile delivery percentage
• Fuel and labor cost per route

8. Returns and Reverse Logistics Analytics

Reverse logistics is both a cost and a customer experience factor. Key analytics include:

• Return rate by SKU: Detect patterns in product defects or misalignment with customer expectations.
• Disposition tracking: Monitor outcomes of returns (resell, refurbish, scrap) for inventory management.
• Customer return behavior: Identify high-return customers or abuse patterns to refine policies.
• Return cycle time: Measure time from return initiation to restock or refund to minimize impact.

Performance Metrics:

• Return rate by category
• Cost per return
• Refund processing time
• Restock rate

9. Customs and International Shipping Data

Cross-border logistics require special attention to ensure smooth operations:

• Customs clearance performance: Track delays and common issues by country or region.
• International carrier benchmarking: Compare global service providers on cost, speed, and reliability.
• Regulatory analytics: Stay ahead of changes in trade agreements, tariffs, and compliance requirements.
• Landed cost calculators: Use data models to ensure accurate international pricing.

Performance Metrics:

• Customs delay rate
• International on-time delivery percentage
• Duty/tax calculation accuracy
• Compliance incident rate

10. Technology, Automation, and System Analytics

Modern shipping relies on technology. Tracking performance of these tools is critical:

• System uptime and integration health: Ensure platforms are reliably connected for real-time operations.
• Forecast accuracy with AI: Continuously monitor predictions against actual performance.
• Customer tracking engagement: Evaluate the effectiveness of tracking tools in reducing support inquiries.
• Automation ROI: Measure time and cost savings from robotics, WMS, and TMS platforms.

Performance Metrics:

• Forecast error rate
• Platform downtime
• Order tracking click-through rate
• Cost savings from automation

Going Beyond Data to Embrace Real-Time Analytics

To optimize shipping and package delivery operations, businesses must move beyond static data collection and embrace continuous, real-time analytics. These insights not only inform day-to-day operations but also empower strategic decision-making and long-term planning.

With the addition of rate shopping analytics, organizations can further optimize every shipment by balancing cost, speed, and reliability — ultimately boosting profitability while maintaining exceptional customer service.

By investing in robust data infrastructure and analytics capabilities, companies can ensure transparency, agility, and efficiency in an increasingly complex shipping landscape.

Thomas Andersen is Partner/Executive Vice President of Supply Chain Service for LJM Group. Since 1998, LJM is a premier partner for businesses aiming to control parcel shipping costs. As experts in parcel data analytics, contract optimization, and invoice auditing, LJM offers solutions that reduce expenses and improve efficiencies, uncovering savings without disrupting their operations. Please visit the website at myLJM.com or to contact Thomas, please call 631.844.9500 or email tandersen@myLJM.com.

This article originally appeared in the May/June, 2025 issue of PARCEL.