In complex, multicourier logistics environments, the ability to extract, segment, and interpret shipping data is a critical differentiator. Shipping analytics enables logistics managers to centralise performance metrics, monitor SLA compliance in real time, and simulate cost-saving scenarios using predictive models. This article outlines the strategic frameworks, tools, and KPIs that define effective shipping analytics in 2025.
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Shipping analytics refers to the systematic collection, interpretation, and use of logistics data to optimise delivery operations. Unlike generic shipping software, which focuses solely on label generation or courier integrations, analytics platforms offer deep visibility into operational performance and cost structures.
This includes everything from tracking delivery delays to identifying high-performing couriers and analysing shipping costs across regions.
Shipping analytics empowers logistics directors to take control of their data, identify inefficiencies, and make decisions based on performance trends rather than gut instinct.
Learn more about how AI enhances supply chain visibility.
Monitoring the right KPIs helps logistics leaders take meaningful action. Here are the most impactful metrics:
For more detailed KPI guidance, visit our logistics KPI guide.
Many logistics directors struggle with limited oversight into whether their couriers are truly meeting agreed-upon SLAs. Delivery delays, misrouted parcels, or failed first-attempt deliveries often go unnoticed until they become customer service issues. Without a clear performance audit, holding couriers accountable or renegotiating contracts becomes guesswork.
Shipping analytics tools provide real-time dashboards that monitor SLA compliance across all couriers and services. By consolidating this data in a central platform, logistics teams gain instant visibility into performance deviations—allowing for timely escalation, vendor comparisons, and data-backed renegotiations.
Shipping expenses are among the most volatile line items in a logistics budget—especially when working with multiple couriers, service levels, and regional surcharges. Without analytics, cost overruns are hard to detect and even harder to attribute to specific causes, such as inefficient routing or underperforming services.
Shipping analytics addresses this by breaking down cost per shipment, per courier, and even per destination. Advanced platforms can detect hidden fees and identify cost-saving opportunities across the delivery network. This empowers logistics leaders to take corrective action, simulate courier mix adjustments, and optimise total spend.
Fluctuating order volumes, seasonality, and unpredictable external disruptions—such as strikes or adverse weather—make it difficult to plan shipping capacity efficiently. Overestimating leads to underutilisation and wasted spend, while underestimating causes backlogs and SLA breaches.
Modern analytics platforms use historical trends and AI-based models to improve forecasting accuracy. By identifying demand patterns and anomalies early, logistics directors can align courier allocation, staff planning, and warehouse throughput accordingly. This not only reduces bottlenecks but also protects delivery reliability and cost-efficiency during peak periods.
Discover how AI is reshaping logistics strategy in our article on AI and logistics innovation.
Shipping analytics platforms have evolved far beyond simple reporting tools. They now offer predictive insights, cost simulations, and automated recommendations powered by AI. Below is a curated overview of the most relevant platforms in 2025, tailored for logistics leaders managing large shipping volumes across multiple couriers.
Best for: End-to-end analytics with strong EU courier coverage
ShippyPro Optimizer is designed for logistics directors who need full visibility across multi-courier shipping networks.
ShippyPro Optimizer for shipping analytics offers:
With strong integrations and coverage across UK, EU, and international couriers, ShippyPro Optimizer is a solid choice for businesses looking to scale intelligently and can also be interated via API.
Best for: Data visibility and courier cost control for retail supply chains
Senvo is a logistics data intelligence platform designed to provide centralised visibility across transportation, delivery performance, and parcel costs. Built for retail and DTC brands, it enables logistics leaders to unify and act on disparate courier data for better decision-making and strategic cost reductions.
Key Features:
Best for: SMEs and retailers focused on European last-mile optimisation
Sendcloud's analytics suite focuses on delivery performance across Europe, offering real-time tracking dashboards and delivery success insights. Its strength lies in post-purchase analytics and returns efficiency, making it ideal for growing e-commerce brands.
Main analytics features:
Best for: courier contract optimisation for large-volume US shippers
Reveel specialises in uncovering courier overcharges and suggesting renegotiation levers. It offers AI-driven “Savings Opportunities” reports and deep courier cost analysis.
Key features:
Best for: Multi-courier performance benchmarking across global networks
Known for its comprehensive tracking engine, Parcel Perform provides delay predictions, courier scorecards, and exception analytics. It's particularly effective for brands shipping internationally with complex supply chains. Advanced users can build custom dashboards for SLA enforcement and transit time benchmarking.
Best for: Historical rate analysis and cost forecasting
ShipSigma is a strong choice for logistics leaders focused on long-term cost reduction. It enables users to run “what-if” simulations and build future spend models based on historical behaviour.
Key Features:
Lojistic is known for its simplicity and accessibility. It focuses on invoice audits and basic operational cost insights, making it ideal for mid-sized teams without large internal BI resources.
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While not a traditional operations analytics platform, Aftership adds value by giving logistics directors insight into delivery outcomes and customer satisfaction trends post-shipping.
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Optimind is a SaaS platform originating from Japan that provides advanced logistics analytics, route optimisation, and performance visualisation tailored to last‑mile delivery operations.
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Tip: When evaluating these platforms, always assess the ability to integrate with your OMS, WMS, or ERP systems, and ensure that your core couriers are fully supported.
The chosen shipping analytics platform must support long-term scalability, integrate seamlessly with the existing tech stack, and deliver insights that drive measurable outcomes. Below are the key criteria to consider during the evaluation process.
Shipping analytics isn’t just a reporting tool — it’s a competitive advantage. By tracking the right KPIs, using AI to predict and optimise, and choosing tools built for scale, logistics leaders can reduce costs, increase delivery success, and better serve their customers.
What is shipping analytics?
It's the practice of using data to track, assess, and improve shipping operations.
What KPIs should I monitor?
Delivery speed, costs, courier performance, first-attempt delivery rate, and sustainability metrics.
Is AI used in shipping analytics?
Yes, many modern tools use AI to forecast demand, flag anomalies, and recommend cost-saving actions.
How to analyse shipping data?
Analysing shipping data involves collecting, structuring, and interpreting key delivery and cost metrics to optimise logistics performance. The process typically combines historical analysis, real-time monitoring, and predictive forecasting.