How to measure and track FCR or First Call Resolution rate?
The First Call Resolution rate is a key indicator for evaluating the effectiveness of call centers and customer satisfaction. It measures the ability to resolve customer issues on their first contact, thereby reducing the need for follow-ups and improving the customer experience.
In this article, we will explain how to calculate this rate, the different measurement methods available, and the advantages and disadvantages of each.
Why track the First Call Resolution rate in a contact center?
FCR is a relevant KPI that can have benefits on all other key performance indicators such as customer satisfaction, costs and sales.
According to SQM, for every 1% improvement in first contact resolution, this leads to:
- A CSAT improvement of +1%
- A decrease in operational costs of -1%
- An improvement in employee satisfaction of +1 to +5%
Also according to this same study, when a situation is resolved, the customer conversion rate increases by +20% and 98% of customers will remain loyal to the brand in the event of first contact resolution.
This virtuous circle of customer satisfaction helps to understand that the FCR metric is interconnected with other essential customer relationship center metrics such as CSAT, ESAT, costs or even sales.
How to calculate FCR?
The First Call Resolution rate is calculated by dividing the number of requests resolved on the first call by the total number of calls. Then the result is multiplied by 100 to obtain a percentage.
For example, if a call center receives 200 calls and manages to resolve 150 issues on the first call, the calculation is as follows:
150 / 200 * 100 = 75%
A good FCR score is between 70 and 80%.
Measuring First Call Resolution from External Data
By "external data," we mean data based on direct customer feedback to evaluate issue resolution on the first contact.
It is agreed that this is the best source of information for accurately measuring FCR and assessing the quality of the customer experience.
However, the response rate varies, which can limit the representativeness of the data. In addition, customers may be influenced by emotional or contextual factors when responding, which can affect objectivity.
Here are the main ways to collect this customer feedback:
Post-call telephone survey
The post-call telephone survey involves contacting customers by telephone after their initial interaction to ask them if they believe their issue was resolved during the first call. This method allows for gathering direct and immediate feedback on the effectiveness of the resolution.
Advantages:
- The customer determines the FCR/resolution of their request
- High reliability
- Allows for identifying dissatisfied customers
Disadvantages:
- High cost
- Reduced sample size due to customers' willingness to respond
- Processing time >1 day after the call
Post-call email survey
The post-call email survey involves sending an email to customers after their initial interaction, asking them to answer questions to assess whether their issue was resolved on the first contact. This method allows for collecting detailed feedback asynchronously.
Advantages:
- The customer determines the FCR / resolution of their request
- Low cost
- Allows for identifying dissatisfied customers
Disadvantages:
- Average cost (related to processing)
- Reduced sample size due to customers' willingness to respond and the availability of the email address
- Limited feedback (reduced number of questions to maximize responses)
- Processing time >1 day after the call
Post-call Outbound IVR (Interactive Voice Response) Survey
The post-call outbound IVR survey uses an interactive voice response (IVR) system to call customers after their initial interaction and ask them automated questions about the resolution of their issue.
This method allows for the rapid and automated collection of feedback.
Advantages:
- The customer determines the FCR / resolution of their request (reliable unitary declaration).
- Enables the identification of dissatisfied customers (can be linked to CSAT).
- Low cost (automated process).
- Rapid information availability.
Disadvantages:
- Reduced sample size due to customers' willingness to respond
- Customers rarely appreciate contact with a robot.
Post-call Inbound IVR (Interactive Voice Response) Survey
The post-call inbound IVR survey invites customers to answer a questionnaire via an interactive voice response (IVR) system immediately after their initial interaction, typically by staying on the line or calling a specific number.
This method allows for the collection of instant and automated feedback on issue resolution.
Advantages:
- The customer determines the FCR / resolution of their request (reliable unitary declaration).
- Enables the identification of dissatisfied customers (can be linked to CSAT).
- Low cost (automated process).
- Rapid information availability.
Disadvantages:
- Unreliable and sampled.
- Can be manipulated (offered by the advisor only in the event of successful calls).
- Limited feedback (reduced number of questions to maximize responses)
- Customers rarely appreciate contact with a robot.
Survey by the advisor during the call
The survey by the advisor during the call consists of the advisor directly asking the customer, before ending the call, whether they believe their issue has been resolved.
This method allows for the collection of immediate and direct feedback on the quality of the resolution.
Advantages:
- Cost diluted in the customer relationship process.
- Enables the advisor to be involved in FCR monitoring.
- Can be linked to the Quality Management (QM) process.
Disadvantages:
- Advisors do not appreciate scripted questions.
- The survey can negatively impact the overall experience.
- Can be manipulated (offered by the advisor only in the event of successful calls).
- Customers may not dare to be honest.
- Limited feedback (the number of questions is reduced to maximize responses).
Measure First Call Resolution from internal data
Measuring FCR using internal data involves leveraging information gathered through internal software and systems. These methods rely on analyzing interactions and call data to evaluate problem resolution.
These methods have the advantage of being more objective because they are less subject to the emotional biases of customers. Furthermore, this data can be collected continuously and in real time, providing a constant view of performance.
However, internal data may not fully reflect customer satisfaction or perception. Moreover, the analysis of this data can be complex as it requires sophisticated tools and advanced analytical skills.
Here are the main methods used to analyze FCR with internal data:
Automatic detection of multiple calls
Automatic detection of multiple calls uses software to identify customers who contact customer service multiple times for the same issue.
Advantages:
- Automated process
- Enables a quantitative approach
- Allows selection of calls to be listened to for a targeted QM approach
- Based on significant data volumes
Disadvantages:
- Based on deterministic and subjective resolution rules
- May differ from the actual FCR rate (+/- 10 to 20%)
- Limited feedback (no qualitative information)
Quality Management
Quality Management involves the evaluation of interactions between agents and customers by supervisors or automated tools such as Batvoice to verify whether issues have been correctly resolved during the first call.
This method ensures high quality standards and identifies areas needing improvement.
Advantages:
- Based on existing quality management processes
- Enables coaching of advisors on the calls listened to
- Costs diluted in the quality process
Disadvantages:
- High cost (related to listening and processing)
- Small sample. Based on deterministic and subjective resolution rules (evaluator)
- May underestimate or overestimate the actual FCR rate (+/- 10 to 20%)
- May seem intrusive to advisors
CRM and ticket management
This method involves using a Customer Relationship Management (CRM) system to track and analyze support tickets. It determines whether customer issues are resolved on first contact by checking the history and updates of the tickets.
Advantages:
- Based on existing systems
- Enables a quantitative approach
- Low costs (checkbox in a drop-down menu)
- Identifies unresolved requests
Disadvantages:
- Based on deterministic and subjective resolution rules
- Can be skewed by advisors (subjective declaration)
- May differ from the actual FCR rate
- Limited feedback (little qualitative information in self-assessment)
Which method should you choose to measure your customer service's FCR?
To manage the activities of your CRC (customer relationship center), it is essential to select one or more measurement methods in order to have a complete view of your performance and to balance the advantages and disadvantages.
The question is how to reconcile :
- Data exhaustiveness and reliability for a statistical approach
- Qualitative content (customer feedback) to identify priority areas for improvement
- Automation to streamline implementation without changing processes
- Return on investment
The best of both worlds: Speech Analytics and automatic Quality Management
Artificial intelligence provides elements of response, with Speech Analytics applied to the voice of the customer and quality management processes. This technology consists of analyzing all calls from a contact center and extracting both quantitative and qualitative data.
Advantages:
- Exhaustive analysis (all recorded calls)
- Instant and continuous call processing
- High reliability
- Identifies dissatisfied customers (irritated, very angry) and associates automated actions with them
- Obtains spontaneous customer feedback without additional steps thanks to the Voice of Customer
- Identifies areas for advisor improvement and continuously monitors their progress (quality management)
- Automates the identification of repeat calls and reasons for repeat calls
Disadvantages:
- Requires the involvement of teams around the voice of the customer (VOC) to launch actions on cross-functional company processes
- Additional costs are sometimes to be expected for support in the form of consulting
- Analysis models are sometimes generic (depending on the specific Speech Analytics solutions) and may lack precision depending on the business sectors
- ROI is difficult for the company to measure and requires expertise from the Speech Analytics provider
- Generic AI models are sometimes offered: these are less efficient than specific models (Specific calibrated AIs require more effort and cooperation for optimal performance)
