Cost-per-resolution
Cost-per-resolution (CPR) is a customer service metric that measures the cost an organization incurs to fully resolve a customer issue. Unlike cost-per-call or cost-per-ticket, CPR focuses on outcomes rather than activity. It answers a simple but powerful question: How much does it actually cost us to solve a customer’s problem?
CPR includes all resources involved in resolution, such as agent time, technology costs, overhead, and follow-up work. Because it reflects end-to-end effort, it provides a more accurate picture of operational efficiency than surface-level metrics.
How cost-per-resolution works
Cost-per-resolution is calculated by dividing total support costs by the number of issues successfully resolved within a given period. Total costs may include salaries, benefits, software licenses, telecom fees, and management overhead.
What matters most is consistency. Organizations must clearly define what “resolution” means, whether it’s first-contact resolution, confirmed customer satisfaction, or ticket closure without reopening.
In AI-based environments, CPR calculations often include automation costs alongside human labor, reflecting blended workflows.
Why cost-per-resolution matters more than traditional metrics
Many teams track handle time or cost-per-call, but those metrics can be misleading. A fast call that doesn’t solve the problem increases repeat contacts and total cost. Cost-per-resolution shifts focus toward effectiveness. It rewards systems that resolve issues correctly the first time, even if individual interactions take slightly longer.
For AI-based customer service, CPR highlights where automation truly adds value and where it creates hidden costs through rework or escalation.
How AI influences cost-per-resolution
AI tools like chatbots, AI voice agents, and predictive dialers directly affect CPR. When AI handles simple issues accurately, CPR drops. When AI misroutes or misunderstands, cost-per-resolution increases due to follow-ups.
Advanced teams use AI to predict resolution complexity, route issues intelligently, and surface context to agents. These improvements reduce wasted effort and improve CPR over time.
Common drivers that raise CPR
Cost-per-resolution often increases gradually, making it easy for teams to miss the underlying causes. Understanding what pushes CPR upward helps organizations move beyond surface-level fixes and address root causes that affect both cost and customer experience.
- Poor routing that sends customers to the wrong agent
- Incomplete customer context, leading to repetition
- Over-automation without clear escalation paths
- Knowledge gaps or outdated documentation
Monitoring CPR helps teams spot these systemic issues early.
Practical ways teams use CPR
Cost-per-resolution is most valuable when it is used as a decision-making tool rather than a passive reporting metric.
Teams commonly use cost-per-resolution to:
- Evaluate ROI on AI investments
- Compare channels (voice vs chat vs SMS)
- Identify training or tooling gaps
- Support staffing and capacity planning
CPR ties cost to outcomes, so it’s especially useful for executive-level decision-making.
Considerations for using cost-per-resolution responsibly
CPR should never be optimized in isolation. Driving CPR too low can encourage rushed interactions or under-servicing customers. The healthiest approach balances CPR with experience metrics like customer satisfaction score (CSAT) and first-contact resolution (FCR). AI systems should be evaluated for cost reduction and for whether they reduce total effort across the customer journey.
