Make Better Delivery Promises with Transit Data

Anurag Allena
June 17, 2024

Introduction

Most digital customer experience leaders already know that offering a competitive delivery promise is a proven way to increase cart conversion and average order value (AOV). However, it’s critical that your shipping operations teams can keep your promise at a reasonable cost for the business. Too often, digital experience teams and shipping ops teams are disconnected, especially when it comes to the sharing of important data that can inform a more accurate delivery promise.

A key (and often overlooked) aspect of making a delivery promise is understanding the carriers and service methods that your ops team is able to leverage — and not just the SLAs that each service method offers.

Limitations of static delivery promises

If you’re basing your delivery promises on static criteria like carrier SLAs or exclusively on historical performance for predefined routes, you’re leaving accuracy and value on the table.

When delivery promises aren’t truly data-driven, you risk one of two outcomes:

• Missing a delivery date, which negatively impacts customer experience and brand perception.
• Meeting a date by upgrading to premium service methods or making similar concessions that increase costs and/or involve manual intervention.

For instance — many shipments that have date constraints tied to them don’t flow through economy service methods, because those methods don’t guarantee a delivery date. In many instances, they may actually meet your date constraint — but without the right data leverage, you’d never think to select them.

In reality, time-in-transit is impacted by a number of factors including seasonal variations in demand, weather patterns, fuel prices and more. With the right technology, you can factor these criteria into your delivery promise and close the data gap between your digital and shipping teams — allowing you to meet your dates at the lowest possible cost.

How to make more accurate delivery promises with transit data

To make a dynamic delivery promise, you need to account for all the relevant data that exists within your company’s systems of record (ecommerce platform, OMS, WMS, & TMS), as well as the macro-conditional data described above.

Here are the most important factors to consider.

Fixed Network Criteria

These are properties specific to your network that only change when you make changes to them. Think origins/nodes, operating days and times, carrier pickup schedules, and customizable shipment options (e.g., excluding air modes, setting cost-based limits). Once declared, these properties will influence the additional, more dynamic layers of the model.

Dynamic, Real-Time Factors

These are the broader conditions that we touched on in the prior section. Factors include the time of day, day of the week, seasonal variations, and broader macro-conditions like unexpected weather events, fluctuations in fuel prices, or labor issues. While it may seem obvious that these types of factors influence transit performance, they’re not considered nearly as often as they should be.

Predictive Factors

As mentioned above, you’ll need the right shipping technology (whether built in-house or bought) that enables you to generate ML-powered insights using your existing data. These include insights such as projected demand increases during specific periods, trends in shipping volumes to particular ZIP codes, and more.

Historical transit data

It’s important to go beyond SLAs to understand the true performance trends for each carrier and service method that you’re using. Knowing how carriers perform under different conditions (ex. different geographies) and how well their provided SLAs align with true performance is essential. When you weigh transit performance correctly, you can establish your own dynamic carrier SLAs with confidence.

Gain further control with Shipium's Shipment Planning API

To further enhance your ability to make precise and cost-effective delivery promises, Shipium offers a Shipment Planning API, which allows you to proactively manage your customer delivery experience by defining your tolerance for how early or late a shipment can arrive.

How the Shipment Planning API Works

The Shipment Planning API works by taking your desired delivery date and other configuration preferences, then providing a recommended ship-by date to ensure that goal is met. This involves a few key steps:

• Fulfillment Network Configuration: Set up all your warehouse or store origins and their schedules, as well as the expected number of days to ship from each origin.
• Time in Transit (TNT) Calculation Hierarchy: Work with Shipium to determine the preferred hierarchy for TNT calculations.
• Historical Shipment Data Upload: Upload your organization's historical shipment data, typically 12 months' worth, which will be combined with Shipium’s platform-wide data.

When you call the API, you can specify various parameters, and the API then returns crucial information, including the drop-by date and ship-by date.

The benefits of a transit data-powered delivery promise

Operational benefits

• Improved promise accuracy: Dynamic modeling using transit data provides more accurate time-in-transit predictions.
• Enhanced flexibility: Real-time adjustments based on current network conditions allow for responsive logistics management processes.
• Optimal route planning: Detailed network properties can help identify the most efficient routes to send deliveries through.
• Improved inventory management: Dynamic time-in-transit modeling enables better inventory allocation throughout your network.

Overall business benefits

• Increased customer satisfaction: Accurate and reliable delivery promises enhance customer trust and satisfaction.
• Competitive advantage: Offering precise delivery estimates differentiates your brand from competitors.
• Cost savings: Improved efficiency and reduced delays translate to cost savings across transportation, labor, and inventory management.
• Better resource utilization: Dynamic time-in-transit modeling ensures optimal use of key resources.

Wrapping up

Offering delivery promises rather than estimated date ranges is becoming essential in the ecommerce world. To do this effectively without increasing costs or creating inefficiencies, you need to invest in advanced analytics capabilities to establish accurate and trustworthy SLAs based on various data factors.

By leveraging data and closing the gap between digital experience and shipping operations, you can better meet customer expectations and improve financial performance.