Customer service cost per ticket ranges from $2.70 for retail e-commerce to $60 for B2B support. The biggest hidden cost driver is repeat contacts - a 2.3 contact-per-issue average means real cost per issue is 2.3x your cost-per-contact figure.
Customer service cost per ticket ranges from $2.70 for simple retail interactions to $60 or more for complex B2B support cases. The wide range reflects differences in handle time, channel, issue complexity, and agent cost. Understanding your cost per ticket - and what's driving it - is the starting point for any serious support cost reduction effort. In 2026, Lorikeet and other AI-native platforms have made it possible to reduce cost per ticket by 40-60% for eligible ticket types without degrading resolution quality.
Industry benchmarks: retail e-commerce $2.70-$5.60 per ticket, SaaS support $18-$35, B2B support $30-$60, per LiveChatAI's 2025 industry analysis.
Self-service resolution costs $0.50-$2.37 per issue - but only when content genuinely resolves, not when it deflects without solving.
The biggest hidden cost multiplier is repeat contacts: a 2.3 contact-per-issue rate means your real cost per issue is 2.3x your cost-per-contact benchmark.
First-contact resolution rate is a stronger predictor of total support cost than handle time - because FCR determines how many contacts each issue generates.
Cost per ticket is one of the most widely tracked metrics in customer support - and one of the most commonly misread. The number most teams see is cost per contact. The number that matters for budget planning is cost per issue resolved, which accounts for how many contacts each problem requires. Until you track both, you're optimising for the wrong unit.
This guide covers what drives cost per ticket, how your numbers compare to industry benchmarks, and the levers with the highest impact on reduction.
What Is Cost Per Ticket in Customer Service?
Cost per ticket is the total cost of handling one customer support interaction, including agent time, overhead, tooling, and management. It's calculated by dividing total support operating costs by total ticket volume over a period. For most teams, it's the primary unit cost metric - but it only tells part of the story when analysed in isolation from resolution quality.
The calculation includes direct costs (agent salaries, benefits, tooling licences) and indirect costs (management overhead, training, quality review). BMC's analysis of service desk cost structures shows that agent labour typically represents 70-80% of total cost per ticket, with tooling and overhead making up the remainder. This means cost reduction strategies that don't address labour efficiency - by either automating eligible ticket types or improving resolution speed - have limited impact on the headline number.
What Are the Industry Benchmarks for Cost Per Ticket?
Cost per ticket varies substantially by industry, ticket complexity, and channel. Knowing your benchmark category helps you understand whether your costs are typical or whether there's significant room to improve.
By industry
According to LiveChatAI's 2025 cross-industry analysis, benchmarks by sector are: retail and e-commerce at $2.70-$5.60 per ticket, SaaS and software support at $18-$35, high-tech product support at $28-$35, B2B enterprise support at $30-$60, and telecom and utilities at $20-$30. The global baseline across all industries sits around $6-$7 per contact. B2B costs are high because of longer handle times, more complex issues, and higher agent specialisation requirements.
By channel
Channel has a major impact on cost per ticket. Phone support is consistently the most expensive channel, with costs 3-5x higher than email or chat for the same issue type, because handle time is longer and agents can only manage 1 call at a time. Live chat agents can handle 3-5 concurrent sessions, which reduces effective cost per ticket significantly. AI-handled tickets cost $0.50-$2.37 per resolution when AI takes full ownership - comparable to self-service, but with resolution quality more like an experienced agent.
What Drives Cost Per Ticket Higher Than Benchmarks?
When cost per ticket exceeds benchmark for your industry, the causes are almost always found in one of 4 areas: high repeat contact rates, misrouting, channel mix, or resolution quality issues that generate escalations.
Repeat contacts. If 30% of your issues require 2 or more contacts to resolve, your effective cost per issue is significantly higher than your cost per contact. A ticket averaging 2.3 contacts costs 2.3x your stated cost-per-ticket figure. Tracking contact-per-issue rate reveals this multiplier - and targeting it is the highest-leverage cost reduction available to most teams.
Channel misrouting. When a simple password reset lands in a queue with a 2-hour SLA behind more complex cases, you're paying for queue position and agent overhead that aren't necessary. Intelligent routing that matches ticket type to the right resolution path - AI, self-service, or specialist agent - eliminates this overhead.
Insufficient AI resolution rate. AI that deflects rather than resolves doesn't reduce cost - it moves contacts around. AI that takes actions end-to-end reduces cost per resolution for eligible ticket types by 40-60% by eliminating agent involvement entirely for those interactions.
Escalation rate from AI to agents. If AI is escalating 50%+ of tickets to human agents, the combined AI + agent cost per issue often exceeds pure agent handling. Monitor escalation rates as a cost efficiency signal - high escalation usually means AI scope needs tightening, not that AI can't help.
What Results Can You Expect From Cost Per Ticket Reduction?
The size of the improvement depends on where you're starting from and which levers you pull. Teams closest to benchmark have smaller room to move; teams significantly above benchmark often find multiple overlapping causes that, fixed together, produce compounding savings.
Automating the top 20% of ticket types by volume (typically the simplest, most repeatable issues) reduces cost for those categories from $18-$35 per ticket to $0.50-$2.37 per resolution - a reduction of 85-95% for that segment. Improving FCR from 55% to 70% eliminates 25-30% of repeat contacts, reducing total contact volume without any change to ticket eligibility for automation. Teams implementing AI-assisted QA to review 100% of tickets identify knowledge inconsistencies that generate repeat contacts - typically finding 5-10% of ticket categories where agent answers are inconsistent, generating unnecessary follow-up contacts. Combined, these 3 levers typically reduce total support operating cost by 20-35% within 6-12 months.
The key insight: cost reduction through resolution improvement is durable. Cost reduction through deflection erodes as customers find alternative contact paths or churn.
Lorikeet's Take on Cost Per Ticket
At Lorikeet, we see cost-per-ticket discussions miss the repeat contact problem constantly. Teams celebrate a 20% reduction in cost-per-contact while contact-per-issue quietly climbs, leaving total support cost flat or higher. Lorikeet's approach focuses on cost-per-issue-resolved as the primary unit, because that's the metric that actually reflects operational efficiency. The teams we work with that achieve the largest cost reductions do so by raising FCR and resolution quality - not by deflecting contacts or cutting agent capacity. See how Lorikeet approaches resolution-first cost reduction in practice.
Key Takeaways
Cost per ticket ranges from $2.70 (retail) to $60 (B2B support) - benchmark first to know where you stand relative to your industry.
Repeat contact rate is the largest hidden cost multiplier - a 2.3 contact-per-issue average means real cost per issue is 2.3x your cost-per-contact figure.
AI handling eligible ticket types end-to-end reduces cost per resolution by 85-95% for those categories vs. agent-assisted handling.
Improving FCR from 55% to 70% eliminates 25-30% of repeat contact volume - the highest-leverage cost reduction available without automation investment.
Frequently Asked Questions
How do you calculate cost per ticket?
Divide total support operating costs (agent labour, tooling, overhead, management) by total ticket volume for the same period. For a more accurate view, also calculate cost per issue resolved: divide total operating cost by number of unique issues resolved in the period. The gap between cost-per-contact and cost-per-issue-resolved reveals your repeat contact overhead - which is where most hidden cost lives.
What is a good cost per ticket for SaaS companies?
Industry benchmarks put SaaS support cost per ticket at $18-$35 for human-handled interactions. Teams using AI to handle eligible ticket types (account management, billing inquiries, basic troubleshooting) can drive blended cost per ticket to $8-$15 when AI resolution rates exceed 40%. The best-performing SaaS support teams achieve blended costs closer to $5-$10 through high AI resolution rates combined with strong FCR on the remaining human-handled tickets.
Does automating tickets actually reduce cost per ticket?
Yes - when automation handles full resolution, not just triage. AI that routes tickets to human agents reduces cost marginally. AI that resolves tickets end-to-end (no agent involvement) reduces cost for those tickets by 85-95%. The critical question is AI resolution rate: automation that hands 60% of tickets to agents after initial triage has a much smaller cost impact than automation that resolves 60% of tickets without agent involvement.
Cost per ticket is a useful operational metric, but it becomes genuinely powerful when tracked alongside contact-per-issue rate and first-contact resolution rate. Those 3 numbers together tell you not just what each contact costs, but how many contacts each problem generates and how efficiently your team resolves issues when customers do get in touch.
Most support cost problems are resolution problems. Fix the resolution quality, and cost per ticket follows.
If your cost per ticket is above benchmark or isn't falling despite automation investment, see how Lorikeet diagnoses and reduces support unit costs through resolution-focused AI deployment.
