Order exceptions for an online retailer

The situation

An online retailer ships a few hundred orders a day. Most flow straight through. About one in ten needs a decision: a risky payment, an item out of stock, a split shipment, a courier delay. Staff review these in a morning pass through three systems, then email customers one at a time. By then some customers have already written in.

What we would map first

• Every exception type for a month, with how often it occurs and how long it takes to resolve.
• Time from the moment an exception happens to the moment the customer hears about it.
• Which exceptions are routine and which need a judgement call.

The e-commerce map shows the stock and delivery loops involved.

What we would change

• Detect stock and risk exceptions when the order arrives, not the next morning.
• Prepare each case with the order, customer history and options on one screen, so the agent decides without searching.
• Send a standard message for routine cases: split shipment, short delay, backorder date.
• Watch courier events and message customers when a parcel is delayed, before they ask.

What stays manual

Fraud decisions above a threshold, goodwill gestures for upset customers and any high-value order with partial stock. The system prepares the options. A person chooses.

What we measure

Orders needing manual attention per 100, time from exception to customer informed, where-is-my-order contacts per 100 orders and share of exceptions resolved in one touch.

Typical outcome targets

Fewer inbound status contacts and a shorter morning queue. Figures are set with you after measuring your own order mix.

This case study describes a typical project of this kind. It is not a named client story, and the targets are planning figures we would agree with you after a free review, not results we are claiming.

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