Customer Service
Proactive delivery issue communications
Identify orders likely to miss their promised delivery date based on carrier data and automatically draft personalised apology and update messages. Reduces inbound contacts from frustrated customers.
20–30%
reduction in delivery-related inbound contact volume
Opportunity assessment
Moderate impact. Meaningful cost or revenue improvement, likely noticeable at function level but modest relative to total business scale.
Relatively straightforward. Off-the-shelf tools handle the core task. Implementation is well-understood with good examples to follow.
Moderate data needs. Works with data most businesses hold, but will likely need consolidation, cleaning, or reformatting before use.
Moderate risk. Some customer or external exposure. Errors create rework or reputational impact but are recoverable.
Moderate people impact. Part of someone's working day changes. Requires training and some adjustment time, but roles remain broadly the same.
Tooling required
Things to consider
Everlane achieved a 400% increase in deflection rate using AI-powered proactive customer communication, with 25% increase in agent productivity (Kustomer).
Carrier data feeds vary in reliability and refresh frequency — a carrier API showing a four-hour lag may result in customers being alerted about issues that have already resolved.
The AI drafts the message; a human should define and approve the templates and the conditions under which they send. Fully automated sends without template approval add brand risk.
Compensation thresholds (when to include a discount code, when to escalate to a personal call) need to be defined as rules, not left to the AI to determine.
Tone is critical for delivery failure messages — a customer whose order is three days late doesn't want a chirpy AI message. Define the tone register explicitly in the template and test with real agents before automating.
Experiment starter: Over four weeks, manually identify all orders with a delivery exception flag from your carrier portal. Draft and send proactive messages to half the affected customers and measure inbound contact volume from both groups in the 48 hours following the exception. A significant difference in contact rates justifies automating the identification and drafting step.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — The Tooling LandscapeThis is an automation opportunity — see the relevant playbook section for how to approach it.More in Customer Service
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