10 Steps to Calculate First Call Resolution in Your Contact Center
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Once you have established your measurement criteria and decided how to measure First Call Resolution, it is time to calculate first call resolution (FCR). Below are steps to help with this process.
1. Decide on a Time Period
To start this process, you should decide what time period you are most interested in looking at FCR performance. This can be arbitrary (i.e. a randomly chosen month) or targeted (i.e. the week after Christmas).
2. Compile the Data
After you select a time period you should compile data on all calls within your predetermined time period. The data you include should have the time of the call, caller’s name, the reason for the call, the agent the caller spoke with, the phone number the caller dialed, department reached, the level of satisfaction with the call, etc. You should gather this information from call logs, IVR, post-call surveys, information recorded in your CRM, etc.
You should also record any outbound calls made to each caller on your list. This will allow you to know whether or not the agent had to return the call.
3. Clean the Data
Once you compile all of the data into a spreadsheet, you must make sure that you flag data that meets exclusion criteria. Any interactions that falls outside of your time frame, any calls that you deem not relevant (i.e. wrong numbers, internal calls, calls from employees, etc.), calls for which you have incorrect of incomplete data should all be flagged and removed from the master spreadsheet (and data analysis). This step is very important as clean data will help to ensure that your analysis accurately reflects your customer’s experience. “Garbage in garbage out”.
4. Calculate the Total Calls
You should next calculate how many calls you have overall in your given time frame (i.e. 1 month). This number will include repeat calls, calls abandoned in the queue, calls that were lost on transfer, outbound calls to the customers, etc. As long as they meet your inclusion criteria, you should include them.
5. Segment the Repeat Calls
Once you know how many total calls you have, you should isolate all of the repeat calls and callbacks. This should include any call that is received from the same person or placed to the same person (even if it is a different phone number) within your designated time period (i.e. 1 month).
6. Identify All Repeat Calls That Fall Within Your Contact Window
Once you have identified all repeat calls and callbacks for the time period that you are interested in, you will then want to compile a list of all repeat calls that fall within your previously defined “contact window” (i.e. 24 hours or 16 business hours). This is the list of calls that you will use for your first call resolution calculation as well as your root cause analysis.
7. Segment Repeat Calls by Type
Once your repeat calls (that fall into a predefined “contact window”) are compiled into one list, you may also want to segment the data by caller type, call type, call reason, etc. You should do this in a way that is most meaningful for you and the question you are trying to ask. For example, you can segment the calls by issue:
- Technical support issues
- Update account information
- Billing issues
- Simple support requests
- Human Resources
You can use this information to measure FCR for each segment and also to conduct a root cause analysis for each segment. It is important to know that you can repeat the segmentation and FCR analysis as many times as you would like. This will allow you to conduct a more detailed analysis.
8. Do The Math
It is now time to calculate FCR using the formula that you chose to be most relevant. You can calculate an overall FCR, an FCR for each agent, call type, team, department, time period, etc. Segmenting the data and analyzing FCR based on the segmentation will help you to quickly compare performance, identify issues, pinpoint inefficiencies, etc.
9. Take a Multi-Source Measurement Approach to Tracking FCR
FCR is a complex metric that is influenced by many different variables. Therefore, taking a one-dimensional measurement approach can be too rudimentary. To gain more accurate insights, you should account for many different factors as possible and take different approaches to measuring the same construct. Use information from surveys, agent logs, repeat call calculations, call monitoring, etc. to enhance or support your FCR analysis. You can also use this information to conduct a root cause analysis in order to identify reasons why FCR is impacted.
10. Evaluate the Results
Once you have run the data, take some time to think about the results and what they mean for your company, department, team, agent, customers, etc. You should consider the following questions:
- How do my results compare to industry standards?
- How often are my customers calling my company for each issue?
- Is there an agent, department, team, etc. that have more repeat calls than others?
- Is there a time of day, week, year that has more repeat calls than others?
- How many repeat calls does my company have during seasonal rushes? Periods of peak call volume? After a new product launches? During a promotion?
- Are there certain issues that tend to be escalated more than others?
- What factors could be contributing to the differences in FCR across departments/issues/agents/etc.?
- Are there any gaps in training, education, access to resources that could be contributing to these issues?
- Are there any issues with software, applications, hardware, plugins or equipment that may be contributing to an increase in escalated calls or callbacks?
- Is a lack of internal communication contributing to a decrease in FCR?
- For customers whose issues were not resolved on the first attempt, how many calls were required?
- Are there any issues with workforce management, scheduling or agent availability that might be contributing to the results?
Taking the time to understand your results and put them into context will allow you to have a more comprehensive understanding of which variables are contributing to your first call resolution and how you might think about enhancing positive influencers and fixing problematic ones.
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