Measuring the success of an AI phone assistant: The 7 key metrics that matter

Measure the success of a AI phone assistant using seven metrics: acceptance rate, resolution rate, transfer rate, abandonment rate, booked appointments or cases, time saved in processing, and caller satisfaction. They only become meaningful when compared with a baseline value — which is why a short measurement before launch is part of the process.

Before you start: record the baseline

For two weeks, note how many calls come in, how many go unanswered, and what people are calling about. Many phone systems already provide call volumes and missed calls as a report. Without this baseline, it will later be impossible to prove what has improved.

The seven metrics

1. Answer rate

The share of calls that are answered — instead of going unanswered. With Assistent, it should be close to 100%. Comparing it with the previous value shows how many contacts were previously lost.

2. Resolution rate

Share of calls that the assistant handles completely on its own, for example by booking an appointment or providing information. It is the most important metric for relieving the team. If it increases over the first few weeks, the knowledge base refinement is working.

3. Transfer rate

Share of calls handed over to humans. A high rate is not automatically bad – for sensitive topics, it is intentional. What matters are the reasons: Is knowledge missing, is an interface missing, or is the request simply individual?

4. Abandonment rate

Share of callers who hang up during the conversation. Frequent drop-offs at the same point indicate messages that are too long, pauses, or unclear questions.

5. Scheduled appointments and created cases

The metric with the most direct business relevance: How many appointments, orders, or qualified inquiries are generated through the assistant – especially outside business hours?

6. Saved processing time

Estimate the average duration of a standard call including after-call work and multiply it by the number of resolved calls. This makes it clear how much time your team regains. A template for such calculations is provided by the Automation Calculator.

7. Caller satisfaction

A short question at the end of the call or sample reviews of call logs are often sufficient. Pay particular attention to repeated complaints about the same situation.

Typical patterns in the first few weeks

• High forwarding rate at the beginning: normal. The knowledge base is expanded every week.
• Drop-offs immediately after the greeting: shorten the announcement, and keep the AI notice brief and friendly.
• Many callback requests on the same topic: this usually indicates a missing interface or rule.

How often should it be evaluated?

Weekly during the first four weeks, then monthly. Define who performs the evaluation and who approves changes to the assistant — otherwise insights will go unused.

Which analyses our AI phone assistant includes and how the rollout works is also described in Setting up the AI phone assistant. The Introduction of an AI phone assistant can be supported as a consulting project: through INQA-Coaching the federal government covers 80% of the coaching costs nationwide. The support applies to the consulting, not the software itself. The funding checkshows in just a few minutes what is suitable for your business.

Example of a simple monthly dashboard

One page is enough. This structure has proven effective:

• Header: total calls, of which outside business hours
• Quality:Resolution rate, transfer rate, abandonment rate – each compared with the previous month
• Result: booked appointments, created cases, callback requests
• Time: estimated hours saved in the team
• Top 5 open issues:Topics the assistant had to hand off – the to-do list for next month

How to derive actions from the numbers

1. Sort open issues:Is knowledge, a rule, or an interface missing?
2. Add knowledge:record missing answers in the knowledge base.
3. Refine rules: When should a call be transferred, when should a callback be offered?
4. Plan interfaces: Recurring requests that can only be resolved with system access are candidates for the next expansion stage.
5. Measure again: Has the change improved the resolution rate?

Typical mistakes in measuring success

• Looking only at overall figures: The reasons behind redirects and drop-offs are what matter.
• Judging too early: The first few days reflect initial teething problems.
• Overestimating time savings:Calculate conservatively and only with calls that are actually resolved.
• Ignore satisfaction:A high resolution rate is of little use if callers are annoyed.

From practice

In our own projects, the same pattern keeps emerging: the benefit comes less from the voice than from the clear task. At Blumenhaus am Pfaffenteich the assistant takes orders while customers are being advised in the store; at easy Parken it answers questions about opening hours and rates; at ELLI-Bus passengers book their ride directly by phone.

In all three cases, the first evaluation after just a few weeks was the moment when the biggest improvements emerged – because real calls showed which questions had not been considered during preparation.

Weight metrics by industry

• Trades: Acceptance rate and created orders – missed calls are missed orders.
• Practices and care:Resolution rate for appointment-related requests and proper forwarding of urgent matters.
• Hospitality and gastronomy:Requests outside reception hours and booked reservations.
• Service providers:Quality of pre-qualification – how many callbacks lead to an order?

Data protection in the evaluation

Evaluations should, wherever possible, work with aggregated figures. Individual call logs are important for troubleshooting, but they should be accessible only to authorized persons and only for the defined retention period. Define who may view the logs and document this in the record of processing activities.

Frequently asked questions

Which metric is most important for an AI phone assistant?

The resolution rate: It shows how many calls the assistant handles completely on its own and, therefore, how much the team is actually relieved.

From when are the metrics meaningful?

After about four weeks of operation, once the first refinement of the knowledge base has been completed. The first few days mainly reflect initial teething problems.

What is a good transfer rate?

There is no universally valid target value — it depends on the share of individual requests. More important than the level is that the reasons for transfers are known and that avoidable ones decrease.

What data do I need to calculate the time savings?

The number of fully resolved calls and a realistic estimate of how long such a call used to take, including follow-up work. Multiplied together, this gives the time saved.

As of October 2026.