Using AI to accurately predict cross-border delivery times increases user trust and scales business

The modern e-commerce market is evolving at lightning speed: customers not only want a wide product range but also precise information on when they will receive their orders. This is especially critical in cross-border (international) deliveries, where shipping times depend on numerous factors—from logistics chains and customs requirements to national holidays across different countries. Artificial Intelligence (AI) and Machine Learning (ML) can address this challenge, providing highly accurate forecasts and boosting consumer trust.

1. Why Businesses Need Cross-Border Delivery Forecasting

Modern consumers have high expectations for service quality, particularly when it comes to delivery. In the past, many companies relied on simple statistical models, providing average estimates such as “30 days.” However, in reality, shipments could arrive either earlier or later than expected, leaving users frustrated with the lack of accuracy in forecasts.

Today, this approach is no longer sufficient. When users feel they have no control over the process they rely on, their trust in the service diminishes. This often leads them to seek alternatives and eventually switch to competitors. Khoros, in its 2024 report, notes that 65% of customers switch to another brand after a single bad experience. This issue is especially critical in cross-border logistics, where shipments move between countries. Unlike domestic deliveries, in international shipping, the number of influencing factors can reach into the hundreds:

• Different transportation services (airlines, rail, trucking)
• Country-specific nuances (holidays, local regulations)
• Multiple scanning checkpoints at transit hubs (distribution centers, customs)

Each of these elements can impact delivery times, creating potential delays. However, users, unaware of these nuances, often compare cross-border shipping to domestic delivery and opt for faster, more straightforward alternatives. 

2. How Delivery Prediction Models Work. Successful Еxamples. 

To ensure accurate delivery time predictions and minimize delays, marketplaces use AI/ML models that analyze vast amounts of logistics data.

• Data in Delivery Prediction

Data is the foundation of these models. Without data, reliable predictions cannot be made. The system collects information on several key parameters:

• Which shipping service is used
• Which airport the shipment departs from
• Which airlines are involved
• How long each stage of the delivery process takes

For accurate delivery time predictions, marketplaces integrate with tracking services, which provide data on each stage of a shipment’s journey. This information is transmitted through logistics companies, delivery services, and customs authorities, creating a comprehensive view of the delivery process. The deeper the level of integration, the more data the system collects, improving the accuracy of predictions.

The initiative for integration comes from the marketplace itself—it collaborates only with delivery services that are willing to provide all the necessary data. In practice, each package is assigned a QR code or barcode, which is scanned at every stage of the logistics chain. For example: 

1. First scan—when the package leaves the manufacturer.
2. Second scan—upon arrival at the distribution center.
3. Third scan—before being shipped from the airport.

This data is transmitted in real time to the marketplace, where it is used for predictive models, enabling precise tracking and accurate delivery estimates.

The Role of Explorative Data Analysis

Once the data is collected, EDA (Explorative Data Analysis) is performed to identify which parameters influence delivery times. For example, one shipping provider may operate significantly faster than another. Based on this analysis, hypotheses are formulated and tested.

Different shipping providers offer various service levels—express, standard, and free shipping. To enhance the customer experience, the system can artificially upgrade the delivery speed for certain users without informing them. While this increases short-term costs, it leads to higher order volumes and greater customer loyalty in the long run.

To improve prediction accuracy, Gradient Boosting is used, as it effectively identifies complex dependencies between factors such as delivery service type, route, customs procedures, and seasonal variations. It performs particularly well on tabular data, efficiently handling heterogeneous features (both numerical and categorical) and capturing nonlinear relationships, making it more accurate than simple statistical models.

Traditional statistical models often provide an excessive time buffer. For instance, the system may predict a one-month delivery time, but the package actually arrives in 15 days. This leads to user confusion—if the shipment arrives faster, why was a longer estimate given?

ML models analyze real logistics processes and dynamically adjust forecasts.

If the system promises delivery by the 15th, it can determine 5-10 days in advance whether this deadline will be met. If a delay is expected, the system proactively notifies the user and offers alternative solutions:

• Extending storage time at the pickup point
• Changing the delivery method
• Providing compensation

This level of transparency significantly improves the user experience. Even when an item arrives earlier than expected, it positively impacts customer satisfaction and loyalty. The golden rule is to never mislead users, whether about delays or early deliveries.

To minimize discrepancies between predictions and actual delivery times, quantile models are used. These models provide high-probability estimates, reducing cases where customer expectations do not align with reality. Customers are highly sensitive to these issues, making prediction accuracy a critical factor.

Using Quantile Models for Improving User Experience by Overpredicting

Delivery often involves multiple logistics “legs”:

• Manufacturing Country → Transit Country → Destination Country
• In some cases, the transit leg is skipped, and the item is shipped directly

Issues arise when these stages vary significantly in time and complexity. For example, delays may be caused by customs procedures, airlines, or local logistics providers.

Users do not always understand where exactly the delay occurs, leading to questions: Is the issue in the manufacturing country, the transit country, or customs clearance?

It is essential to note that neither the buyer, the seller, nor the marketplace itself can influence customs delays. However, the system can provide timely updates to the user.For example, if passport information is required for customs clearance, the system immediately notifies the user, increasing trust in the service.

Thus, AI/ML in logistics not only accurately predicts delivery times but also ensures process transparency, reducing customer frustration and increasing customer loyalty.

Successful Marketplace Examples

When it comes to the most technologically advanced marketplace, Taobao stands out. While it operates exclusively in China, its internal logistics system is exceptionally well-structured and optimized for efficiency.  

On a global scale, Amazon remains the industry leader. They have built an infrastructure that guarantees two-day deliveries, ensuring seamless operations at every stage. In 2024, Amazon reported that nearly 60% of orders for Amazon Prime members in the 60 largest metropolitan areas in the US are delivered on the same day or the next day.

3. Key Challenges and Solutions

One of the primary challenges is scaling models to handle large user volumes. When processing millions of orders daily, models must operate quickly and efficiently without slowing down the platform.

The solution involves several aspects.

One challenge is load balancing—it is possible to allocate vast computing resources to ensure smooth operation, but this is not always economically viable.

There are also architectural challenges:

• How to optimize code to ensure faster calculations?
• How to distribute resources to avoid system overload?
• How to scale computations to maintain stable performance?

This complex problem requires balancing costs and efficiency. For example, transformer models provide high accuracy but require enormous computational resources, making them impractical for real-time operations. Instead, marketplaces opt for lightweight ML models , which allow for fast predictions without overloading the system.

Another challenge is handling rare scenarios. If a user from a small town in China orders an item for delivery to a remote region in Europe, the system may not have enough historical data to make an accurate prediction. In such cases, a hybrid approach, combining statistical methods and machine learning, is used to generate the most accurate estimate possible.

Additionally, system performance remains a critical issue. Users expect instant calculations of delivery times, even when switching between item page. The system must dynamically update delivery estimates in real time, considering location, seller, logistics parameters, and other factors.

The ultimate goal is to build a resilient system capable of processing millions of orders simultaneously, maintaining high prediction accuracy, and remaining stable under peak loads. Achieving this requires continuous optimization of models, architecture, and infrastructure, enhancing efficiency while minimizing operational costs.

In recent years, delivery time prediction has become an integral part of marketplaces, enabling greater transparency and trust. AI-powered models have significantly improved accuracy, making cross-border logistics more predictable. However, there is still room for improvement, and as e-commerce evolves, precise delivery forecasting is no longer a competitive edge but a necessity for meeting modern consumer expectations.