AI and e-commerce packaging: Designing the delivery experience with data

AI is changing how shoppers discover and choose products online. At delivery, packaging connects the promise on screen with the physical experience. For Vietnamese businesses, a practical starting point is to use data to guide packing decisions, then validate those decisions with real orders.

1. Standardise the data before choosing technology

Build a packing profile for each SKU: measured dimensions, weight, damage sensitivities, accessories and common order combinations. Distinguish the product's dimensions from its dimensions after protective materials have been added.

Missing or inaccurate inputs can turn a plausible software recommendation into an unsuitable parcel. Businesses with a small catalogue can start with simple rules. Consider AI only after defining the problem and establishing reliable data.

2. Select packaging for the order, not just the product

An item shipped alone may need a different packing arrangement when combined with other items. A packaging catalogue should state usable dimensions, application limits and tested cushioning arrangements.

Compare materials, packing time, actual freight charges and damage-handling costs together. The smallest box or cheapest material may not deliver the lowest total cost. A retail paper bag should not automatically be treated as shipping packaging.

3. Make testing part of approval

Test a software recommendation with the actual product and packing configuration. Transit hazards can include vibration, shock, compression and environmental conditions. Select a programme that fits the distribution route rather than applying one test to every shipment.

Before testing, define product damage, acceptable package deformation and the inspection method. Record materials, closures, cushioning and results so the approved configuration can be reproduced on the next order.

4. Design for opening and returns

Evaluation should continue beyond dispatch. Ask someone unfamiliar with the package to open it, remove the item and find the relevant instructions. Use points of confusion to improve the opening feature or guidance.

If the business offers returns, specify which components the customer can reuse, how to reseal them and where to find instructions. Test that scenario before claiming reusability. An attractive package does not automatically make returns convenient.

5. Make material information specific and actionable

Replace broad descriptions such as “green” with verifiable information: material composition, components to separate and disposal instructions appropriate to local collection arrangements. Paper content alone does not establish that the complete package is recyclable in every market.

For cross-border sales, adapt instructions to each language and market context. Product pages, packaging and customer support should communicate consistently so they do not create conflicting expectations.

6. Run a pilot with clear decision criteria

Select a stable group of orders, document the current configuration and change one variable at a time. Compare orders under similar conditions. A few positive comments or an appealing unboxing video are not enough to establish performance.

• Record packing time and material consumption per order.
• Track damage and packaging-related returns, with specific causes.
• Compare total fulfilment costs rather than bag or box prices alone.
• Collect feedback on opening, product removal and disposal.

AI is valuable when it supports decisions that can be tested and improved. It does not replace every assessment step. Keeping product data, packing methods and customer feedback up to date gives businesses a stronger basis for choosing packaging for each order.