Retail customer service · AI quality checks

Incorrect commitments in customer service only surface when the customer writes back

Service replies are checked against refund, warranty and delivery rules before sending. Incorrect commitments fell from around 8 to under 2 percent.

  • Retail
  • Customer service

Key metric

Replies with an incorrect commitment

8%<2%

Share of reviewed replies containing a commitment outside the rules

At a glance

Starting point: around 300 closed service cases with the reply sent and a note on any later follow-ups.

Initial problem

Outgoing service replies are only spot-checked

Solution type

Quality check

Complexity

Quick start

Key metric

Incorrect commitments: around 8% → under 2%

User group

Around 120 employees in written customer service

Implementation time

1-2 weeks

Operating model

EU data center, connected only to the ticketing system

The problem

The retailer's customer service handles around 15,000 written cases a week via email, contact form and messenger, mostly about returns, warranty claims and delivery and refund status. Around 120 employees at several locations answer these requests using text templates and a knowledge base that holds deadlines, goodwill limits and responsibilities. Before sending, team leads spot-check roughly five out of every hundred replies, mainly from new employees. All other replies go out to customers unchecked. Escalations and the monthly review regularly show that individual replies contain commitments that deviate from the applicable rules.

Because almost all replies go out without a second pair of eyes, quality depends on the individual's experience and how their day is going. Incorrect commitments rarely stem from carelessness but from special cases: different warranty periods for certain product groups, temporary goodwill rules, and different deadlines for marketplace and direct orders. Such errors are not noticed when the reply is sent, but only when the customer invokes the written commitment and writes a second time. The case then has to be walked back, escalated internally and decided again, which costs trust and takes more handling time than a correct reply the first time. The team leads' sample is too small to spot patterns early, and too late, because it only takes effect after sending.

15,000
service cases per week
5 / 100
replies checked in advance

“We only find out whether the commitment was right when the customer sends it back to us”

“Hardly anyone really understands the special rules for individual product groups”

“I see five out of a hundred replies; for the rest I just have to trust it's right”

“Walking back an incorrect refund commitment costs us a whole week”

Why it was left unsolved

The ticketing system can enforce mandatory fields, but it cannot judge whether a freely worded reply contains a commitment that should not be made. Rule-based checks fall short because the decisive statements are written in everyday language and the exceptions change several times a year. The topic was too small for a dedicated IT project, yet the team felt it every week.

The solution

Every finished reply is read immediately before sending and checked against the company's stored rule set: statutory and commercial warranty periods per product group, current goodwill limits, deadlines for returns and refunds, and responsibilities per ordering channel. If a commitment deviates, a mandatory detail is missing or the tone is inappropriate, the employee gets a short note before sending, showing the affected passage and the relevant rule. Unremarkable replies go out without any extra step. Decisions and wording stay with the person, but every reply is checked instead of only every twentieth.

Building blocks
  • Every outgoing reply is checked against deadlines, warranty periods and goodwill limits from the knowledge base
  • Note before sending with the passage highlighted and a reference to the relevant rule
  • Silent approval of unremarkable replies, so the standard case needs no extra step
  • Weekly analysis of the most frequent issues as a basis for training and text templates
Impact

Impact and effort

Period: first 3 months after launch.

MetricValueBasis
Replies with an incorrect commitmentKey metricaround 8% → under 2%Samplemeasured
Replies checked in advancearound 5% sample → all repliesSystem measurementmeasured
Confirmed issuesQuality metricaround 80% of notes confirmed by team leadsSamplemeasured
Repeat contacts due to incorrect informationnoticeably reducedUser surveycustomer-reported

Cost-effectiveness

Effort class

Quick start (< 10 person-days)

Payback period

< 3 months

Main lever

Quality and compliance

Ongoing costs

Low, mainly inference per reviewed reply

Limitation

The check only detects deviations from stored rules. It cannot assess new goodwill decisions or statements with no basis in the rule set, and the rule set has to be kept up to date.

Also works for

Checking outgoing claims correspondence in insurance before sending

Reviewing information given in bank customer service for binding commitments

Proofreading official notices in the public sector before delivery

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