Setting Service Commitments: Response Times That Are Kept

Why “as soon as possible” is not a commitment

A customer writes a question about their order on Tuesday morning. They get an answer on Thursday. For the company that was quick, because the colleague was on vacation and the stand-in had to ask around. For the customer it was slow, because they had expected an answer the same day. Nobody did anything wrong, and yet the customer is disappointed. The reason is that nobody ever said what applies.

This leads to the guiding question of this guide: How does a small company set service commitments that give customers orientation and that it actually keeps in daily work? To answer it, we first clarify the terms, then introduce the zone of tolerance and the gap model as a thinking model from service research, review the findings, weigh two perspectives, and derive commitments from your own data in five steps.

A customer forgives a long deadline that is kept more readily than a short one they have to chase.

Terms: service commitment, response time, resolution time

A service commitment, or service level, specifies which service a customer can expect within what time. If it is agreed in a contract, it is called a service level agreement, often with consequences for non-compliance. In most small companies, it is about something simpler: a public or internal commitment the team adheres to.

• Response time is the time until the first personal reply that says more than an automatic acknowledgment: who is taking care of it and what happens next.
• Resolution time is the time until the request is settled or the customer has a binding date for the solution.
• Service hours define when the clock runs, for example weekdays from 8 a.m. to 5 p.m. A four-hour deadline that starts on Friday at 4 p.m. ends on Monday at 11 a.m.
• The fulfillment rate is the share of cases in which the commitment was kept.

Distinguishing response from resolution is the most important step. Many requests cannot be resolved in four hours, but almost every one can be answered in four hours. Whoever mixes the two either commits to too little or breaks the commitment.

The thinking model: zone of tolerance and gap model

With the gap model of service quality, Parasuraman, Zeithaml, and Berry (1985) described that customers judge quality as the distance between expected and experienced service. The model names several gaps within the company that lead to this distance. For service commitments, one is particularly important: the gap between what is promised externally and what the company actually delivers. Every overly generous promise in advertising or conversation raises the expectation and thereby enlarges the disappointment.

Zeithaml, Berry, and Parasuraman (1993) added the zone of tolerance to the model. According to it, customers do not have one expectation but two: the desired service, what they hope for, and the adequate service, what they will just accept. Between the two lies the zone of tolerance. If the experienced service falls within it, the customer is satisfied. If it falls below, they are annoyed. If it lies above, they are pleasantly surprised.

A comparison from rail travel makes this tangible. A passenger who knows the train is scheduled for 8:12 a.m. is annoyed by a ten-minute delay. A passenger at a stop without a timetable is annoyed by every minute, because they do not know whether anything is coming at all. A service commitment is customer service’s timetable. It sets the lower limit of the zone of tolerance, which the customer orients by.

What research shows about expectations and responsiveness

Where the gaps are largest is shown by a meta-analysis by Çelik, Bölükbaşı, Ulaş, and a further author (2026). They analyzed 47 studies with a total of 13,581 respondents that had measured service quality in healthcare facilities in Turkey and Asia using the widely used SERVQUAL questionnaire. They found the largest gaps between expectation and experience in responsiveness, at 0.59 points, and reliability, at 0.54 points. Precisely where quick replies and kept commitments matter, expectations were missed most clearly.

That the zone of tolerance can actually be measured is shown by Kettinger and Lee (2005). They rebuilt the questionnaire so that it records desired, adequate, and perceived service separately, and tested it on two groups: 250 users of the IT services of two universities and employees of four large companies in Asia. Four dimensions with 18 questions proved robust. The authors emphasize the practical benefit: whoever knows where the adequate limit lies sees at a glance where performance falls below it and where it already exceeds what is desired, that is, where additional effort brings little.

The limits are clear. The meta-analysis concerns healthcare facilities; the measurement of the zone of tolerance, IT services. What transfers to a trade business or an engineering office is the direction: customers pay particular attention to responsiveness and reliability, and a commitment works where it clarifies the expectation. This fits the finding of the meta-analysis by Gelbrich and Roschk (2011) described in the article on bundling customer requests: a fair, prompt procedure shapes overall satisfaction at least as strongly as the outcome.

Commit ambitiously or reliably? Two perspectives

The first view says: short commitments are a sales argument. Whoever promises a reply within an hour while competitors promise nothing stands out, and the commitment disciplines the team. Better to commit ambitiously and then do everything to make it.

The second view says: a commitment that is broken is worse than none at all. According to the gap model, it raises the expectation and enlarges the disappointment. The guide to lead handover from marketing to sales puts it in one sentence for inquiries: a four-hour deadline that is kept is better than a five-minute one that is broken.

I consider the second view the more robust one, with a condition from the first. The commitment may be ambitious, but it must come from your own data. The same rule applies that the guide to measuring on-time delivery describes for delivery dates: commit to what your data show you keep in nine out of ten cases. Whoever wants to commit to shorter times first improves the process and then the commitment. Deliberately understating your own performance so that you always exceed it is not a good solution either: it pushes the commitment below some customers’ zone of tolerance, and they then choose another provider.

Setting service commitments in five steps

1. Measure the current state

   For four weeks, record receipt, first personal reply, and resolution for every request. A ticket system provides this automatically; otherwise a list is enough.

2. Sort requests by urgency

   Three to four categories are enough, such as malfunction, complaint, inquiry, and general question. Each gets its own times.

3. Derive the commitment from the data

   Sort the measured times per category and read off which time was kept in nine out of ten cases. That is the commitment, rounded to a simple number.

4. Set service hours and cover

   Define when the clock runs and who takes over during vacation and illness. A commitment that depends on one person lasts until that person’s first vacation.

5. Publish and measure

   Put the commitments on the website, in quotes, and in the automatic acknowledgment. Measure the fulfillment rate per category every month.

The times in the example are aligned with the other guides. The deadlines for complaints match the guide to handling complaints, and those for e-mail and callbacks the channel map from the article on organizing distributed teams. Whoever lives by different deadlines internally than they promise externally opens exactly the gap the model warns against.

Keeping commitments: measure, warn, adjust

A commitment holds not because it is written down but because someone watches it. Three things suffice: a monthly fulfillment rate per category, a warning before a deadline expires, and a rule for when it breaks anyway.

• Warn instead of count: a ticket system or a colored column in the list shows requests whose deadline expires within an hour. Whoever only counts at the end of the month can only report.
• Report early: if it is foreseeable that a resolution time will not hold, the customer gets a new, reliable date and the reason before it expires. In the fulfillment rate, the case still counts as missed.
• Secure cover: most broken commitments arise during absences. How to arrange cover is described in the guide to arranging stand-ins.
• Adjust monthly: if a category is below 90 percent for two months, the process is reviewed or the commitment adjusted, openly toward customers.

Conclusion: how does a company set service commitments that hold?

We set out to ask
a small company sets service commitments that give customers orientation and are kept in daily work. The answer begins with separating response from resolution and with fixed service hours. The commitments themselves come from your own data: commit to what was kept in nine out of ten cases, per category of request, with cover arranged. The fulfillment rate is measured monthly, and warnings come before a deadline expires, not after.

As the zone of tolerance and the gap model show, a commitment works mainly by clarifying the expectation. Research adds that responsiveness and reliability in particular show the largest gaps between expectation and experience. The sober observation is: customers measure a company not by its fastest reply but by its slowest, and a commitment is the only way to make even the slowest one reliable.

The next step

• Define three to four categories of customer requests and your service hours.
• From Monday, record receipt, first reply, and resolution for every request.
• Check who takes over the requests in each category during absences.
• Rewrite the automatic acknowledgment: who is taking care of it and by when.
• After four weeks, derive the commitments using the nine-out-of-ten rule and publish them.