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Customer Support ROI Calculator

Enter five numbers you already know. The calculator works out how many handling hours your ticket volume consumes, what share automation can realistically reach, and what that time costs you today.

How much can AI save on customer support?

The honest answer is that it depends almost entirely on your intent mix, not on the AI. Support automation reaches the repetitive, data-answerable share of your tickets — typically order status, account queries, policy questions and status chasing. If that share is large and your systems expose the data, automation can absorb a substantial part of the handling load. If your volume is mostly judgement, complaints or bespoke cases, it will not. This calculator sizes the addressable portion from your own numbers rather than quoting an industry percentage.

Methodology

How this is calculated

Published in full, so you can disagree with it. A tool that hides its model is a lead form.

Handling hours = tickets × average handling time. This is contact-handling time only; it excludes breaks, meetings, training and admin, so your real support cost is higher than the figure shown.
Repetitive hours = handling hours × the repetitive share you entered.
Addressable hours = repetitive hours × 0.65. The 0.65 is a deliberate discount: automation rarely resolves 100% of even a well-defined intent, some contacts escalate, and a share of deflected volume returns as a second contact.
Illustrative cost = addressable hours × blended hourly cost, where hourly cost = annual loaded salary ÷ (12 × 160 hours).
Utilisation = handling hours ÷ (staff × 160 hours). Above roughly 85% a team has no slack, which is why exceptions and complaints get handled badly during peaks.

Assumptions and limits

160 working hours per month per full-time person.
The 0.65 addressability discount is a conservative planning figure from deployment experience, not a measured constant. Your figure is established by classifying a month of real tickets.
The cost figure is the value of time currently spent — an opportunity size. It is not a saving forecast, because most teams redeploy capacity rather than reduce headcount.
Automation running cost, build cost and integration effort are not modelled here. Use the AI Project Cost Estimator for that side.

Worked examples

A 3-person team on 1,200 tickets
At 9 minutes average handling and 55% repetitive volume, roughly 180 hours a month sit in automatable intents — around one full-time role of capacity, without reducing the team.

A small team where the case is weak
At 250 tickets a month and 25% repetitive, the addressable time is a few hours a week. Integration effort would not repay it. The calculator will say so.

FAQ

Questions about this tool

Because a repetitive intent is not a fully solved intent. Edge cases escalate, some customers insist on a person, and a proportion of automated resolutions produce a follow-up contact. Planning on 100% is how automation projects end up disappointing everyone.

It means the time exists. Most teams we work with redeploy it — onto exceptions, complaints, proactive contact and quality — because that work was previously being squeezed. Whether you convert it to a cost reduction is a business decision, not a technical one.

Export a month of tickets and classify them by intent. It takes a few hours and it is the single most useful piece of preparation before any automation project. If the top three intents are under 30% of volume, be sceptical of any vendor promising a big number.

The calculation runs on our server and returns the result. Nothing is stored against you unless you choose to enter your email to receive the report, in which case the inputs and the result are saved with the enquiry so we can discuss it sensibly.

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