When you improve First Contact Resolution (FCR), you improve customer service quality, cost efficiency, and customer experience, all directly. Improvement in First Contact Resolution brings improvements in both efficiency and effectiveness of the contact center operations. It’s a number one indicator of customer satisfaction because customers want their issues to be resolved on the primary contact, regardless of the channel they prefer to contact.
By increasing the FCR rate, organizations can substantially bring down their operational costs. Taken together, these factors increase the importance of FCR multiple times as a contact center operational metric.
However, accurately measuring FCR doesn’t appear to be a simple task for contact centers. This is often mostly due to their failure to gather data from good sources and correlate diverse data sets to urge a much bigger picture of FCR. Customer surveys are commonly employed by organizations to directly collect customer feedback and measure FCR on the idea of survey scores. But, a majority of consumers don’t answer survey requests. Moreover, survey scores by themselves don’t clarify the basis causes of low FCR rates.
An interaction analytics solution can help organizations drill down into data to get the decision types that aren’t getting resolved within the first contact and diagnose the basis causes of the repeat contact volume. Then, it can help determine which call types are often resolved within the first contact and the way. With deeper and meaningful insights, organizations can boost their first call resolution performance. This might require process restructuring and automation, optimization or adding more self-service options, and targeted training to agents.
Here are five simple analytics-enabled steps which will help organizations measure and improve FCR efficiently and effectively.
The step of defining FCR will broadly include the subsequent activities:
- Use filters to pick the proper metadata to spot the repeat callers.
- Decide how the repeat call data will be reported—for instance, agent-wise, month-wise, date-wise, etc.
The step of measuring FCR will essentially involve the subsequent activities:
- Collect data from diverse sources like customer interactions, feedback surveys, and other enterprise-wide information systems
- Associate repeat calls from individual customers with their IDs or ANIs. This may enable the system user to spot multiple calls from an equivalent customer and drill down into the basic causes.
- Identify and build sub-queries and use them as a measure for FCR.
The step of analyzing FCR will largely include the subsequent activities:
- Evaluate data to spot repeat call drivers and categorize repeat call types
- Recognize repeat calls
- Perform root cause analysis
- Examine the basis causes to acknowledge why certain call types aren’t getting resolved within the first contact
- Recognize process inefficiencies, agent knowledge gaps, customer behavior, impacting the FCR performance
- Calculate the share of deflectable Call volume
The step of improving FCR will mostly embrace the subsequent activities:
- Evaluate alternative options to enhance FCR
- Select the foremost suitable option and calculate the potential ROI
- Execute the chosen choice to improve FCR
The step of controlling FCR will essentially include the subsequent activities:
- Utilize reports to live improvements and realized ROI
- Re-analyze data for extra improvements