Stop Guessing: Use Machine Learning to Improve Delivery Predictions

Today’s consumers aren’t just shopping for products—they’re shopping for an experience, and timely delivery is an integral part of the journey. Tracking updates on shipped packages have become an industry standard, but they’re not enough. Customers don’t care where the package is; they want to know when they will get it. Shoppers now expect to see clear delivery estimates before they even hit the checkout button, and retailers that can’t deliver accurate predictions risk losing sales to competitors who can.

Unfortunately, traditional parcel carriers are pulling back on delivery guarantees, making it more challenging than ever for sellers to offer reliable delivery estimates. Sendflex helps bridge this gap. By leveraging machine learning to track and predict carrier historical performance, Sendflex enables shippers to provide reliable Estimated Delivery Dates (EDD) grounded in real data, helping businesses maintain their promises and keep customers coming back.

Understanding Estimated Delivery Dates

The eCommerce market continues on an upward trajectory, with over 20% of retail purchases expected to take place online in 2024. One of the key factors driving consumer behavior is confidence in delivery times, so accurate EDDs are now part of the decision-making process. Shoppers aren’t just focused on the speed of delivery anymore—they need assurance that their orders will arrive when expected. For retailers, offering precise delivery estimates is a critical driver of conversion rates and customer loyalty.

What is Estimated Delivery and Why Does it Matter?

The days of vague delivery windows are over. EDDs provide customers with a specific date they can expect their order to arrive, based on a combination of factors. These estimates aren’t arbitrary—they rely on data around carrier performance, historical shipping data, destination, and conditions like weather and traffic.

When shoppers are provided only with shipping speed or a wide delivery range, it often leads to hesitation. Many stop mid-checkout, left to guesstimate their order’s arrival date. This uncertainty frequently leads to shopping cart abandonment, especially when on-time delivery is a key concern.

To eliminate this confusion, retailers are increasingly working to present clear estimated delivery dates within the shipping selection process. Here’s why this approach works:

• Buyer confidence: Displaying a predicted delivery date allows users to confidently choose the shipping option that meets their needs without second-guessing processing times, shipping cutoff windows, or how "business days" are defined.
• Easier comparison: Showing EDDs alongside the shipping cost enables customers to make informed decisions about whether to pay extra for express delivery or opt for standard ground shipping.
• Reduces abandonment: By providing a specific delivery date, the uncertainty and burden are removed from the customer’s shoulders. Shoppers no longer need to calculate holidays, processing times, or carrier schedules—they simply select the best option for their needs and proceed to checkout.

Providing reliable predicted delivery dates not only improves the customer experience, it also directly impacts sales. Recent studies show that offering delivery timelines at checkout can increase conversion rates by as much as 10%. Retailers that consistently provide accurate delivery promises build trust, resulting in higher customer retention and stronger brand loyalty.

The Challenge of Predicting Delivery Dates

Accurately predicting delivery dates is complex, with several variables that can disrupt even the most well-intentioned calculations. Here are some of the main obstacles that retailers face:

• Processing time: The time it takes to prepare an order for shipping may vary based on order volume, inventory availability, or the time of day the order was placed. These inconsistencies make it more difficult to predict when the order will leave the warehouse.
• Shipping cutoffs: For large shippers with multiple fulfillment centers, parcel carriers may have different cutoff times for each location, making it hard to calculate when an order will be picked up.
• Hub location: Depending on where a shipper is located in relationship to the carrier’s hub will greatly impact delivery times. That’s one of the reasons why making benchmark predictions across many shippers in a region will be highly imprecise.
• Carrier reliability: While most major carriers follow a schedule, delays and service variations are bound to happen—whether due to misrouted packages, limited carrier network capacity, or operational inefficiencies. These inconsistencies can create significant gaps in delivery predictions.
• Order date and holidays: When a customer places an order late in the day or just before a holiday weekend, the expected delivery can be pushed back, especially if the cutoff time is missed. Taking these factors into account is essential for calculating accurate and reliable delivery promises.

Retailers who can overcome the complexities of estimating shipping timelines and offer accurate delivery dates are better positioned to boost sales and build a loyal customer following. As the expectations around delivery precision grow, the ability to consistently meet them is a critical market advantage.

Machine Learning: The Key to Smarter Delivery Predictions

Predicting delivery dates at the point of checkout is essential for managing customer expectations and improving conversion rates. Sendflex’s Parcel TMS platform leverages machine learning to analyze historical data, carrier performance, and shipping routes to accurately predict delivery dates before an order is even placed.

Machine learning plays a crucial role in this process by continuously learning from vast datasets of previous shipments, carrier performance records, and shipping times between specific zip code pairs. Unlike traditional methods that rely on static data, machine learning adapts in real time, refining predictions based on evolving patterns. It identifies trends in how long deliveries typically take between various locations, taking into account factors like carrier speed and delivery success rates, to make increasingly accurate predictions.

By incorporating machine learning, shippers can generate EDDs with greater precision based on their specific profile. This is done by recognizing patterns in delivery behavior and using those insights to predict how current orders will move through their fulfillment process and carrier networks. As a result, businesses can provide customers with more reliable delivery options at checkout.

Shippers can further customize these predictions to align with their own business needs. The system allows adjustments to date ranges, sensitivity to potential delays, and delivery estimates based on real-world data. This flexibility ensures that businesses can fine-tune their delivery promises, building customer trust by offering a more accurate, personalized shipping experience.

Use Cases for Predicted Delivery Dates

EDDs aren’t just a back-end feature—they directly shape the customer experience from the moment they hit the checkout button to when the package arrives at their door. Sendflex’s platform uses real-time data and machine learning to provide retailers with the tools they need to improve delivery predictions throughout the purchase-to-delivery process.

• Order creation: When consumers are at checkout, they want to know exactly when their packages will arrive. Sendflex’s high-speed optimization engine (20,000/second) provides shippers with the ability to offer accurate delivery promises upfront. This may be particularly important for urban customers, who often need to plan around delivery times to avoid porch piracy or missed deliveries.
• Order allocation: With tight integration into order management systems (OMS) and warehouse management systems (WMS), Sendflex refines delivery predictions by evaluating real-time changes in inventory availability, costs, and shipping options. By determining which distribution center is best positioned to fulfill the order and meet delivery expectations, the platform ensures that shipments are optimized for both efficiency and accuracy.
• Order fulfillment: 97% of online shoppers consider delivery tracking essential to the e-commerce experience. Sendflex continuously updates EDDs during the fulfillment and shipping execution process, enforcing intelligent decisions and ensuring customers are kept informed of any changes. This ongoing communication helps maintain customer confidence, especially when unforeseen circumstances affect delivery timelines.

Transform Your Parcel Predictions with Sendflex

Meeting delivery promises shouldn’t feel like guesswork. Sendflex takes the complexity out of parcel shipping by providing retailers with data-driven delivery predictions right from the checkout process. By leveraging advanced machine learning models, Sendflex enables businesses to streamline operations, reduce delivery uncertainty, and deliver an experience customers can rely on.

Sendflex equips modern shippers to exceed customer expectations by ensuring delivery promises are kept and customers stay informed every step of the way. Ready to simplify your parcel delivery predictions? Contact Sendflex today for a consultation and discover how to optimize your shipping operations with confidence.

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