Why FCR continues to be a Top-priority Metric for Contact Centers
Improving First Call Resolution (FCR) is crucial for organizations to keep their operational costs low and enhance customer experience. Organizations always measure FCR from a contact center’s metric point of view, however it is equally important to measure FCR from customers’ perspective. FCR as a metric is directly linked with customer satisfaction and its poor performance can lead to a significant rise in customer dissatisfaction and churn rate. Irrespective of the channel customers choose to contact, they always want their issue to be resolved the first time they connect. That’s why FCR is also known as First Time Resolution (FTR).
There are several definitions of repeat contacts as every organization defines it differently. For example, some organizations define repeat contacts as the second contact for the same reason within 7 days, whereas some organizations consider the second contact within 3 days (irrespective of the call type) or 3 contacts within 7 days as the repeat contact. However, understanding the exact definition of repeat contacts is important for organizations to ensure the success of FCR project undertaken to reduce the repeat contact volume.
Despite being the most traced down metric, when it comes to accurately measure FCR, contact centers find it a challenging task. This is primarily because of their inability to collect data from all relevant data sources and correlate them for the accurate measurement of FCR. When customers’ issues are not resolved in the first contact, it leads to repeat contacts, and thus high call volume and cost per call in a contact center. Though the repercussions of low FCR could be numerous, Figure 1 depicts some of its major drawbacks, and thus the need for organizations to focus on FCR improvement.
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Improving FCR with Customer Interaction Analytics
Discovering the key drivers of repeat contact volume is the essential step towards FCR improvement. This can be efficiently achieved with a customer interaction analytics solution. A solution that combines the capabilities for speech analytics, text analytics, social media analytics, big data and predictive analytics can be the ideal one to precisely measure and improve FCR. This is because for a 360-degree FCR view , data needs to be collected and analyzed from multiple sources, including phone, email, chat, customer feedback surveys, IVR, CRM, ACD, and other information systems.
One such solution is R Systems’ Anagram that not only analyzes FCR, but also predicts it, prescribes remedial actions and evaluates and monitors results for FCR continuous improvements. As a complete data analytics solution, Anagram allows extracting and analyzing data from all possible data sources to help achieve targeted business goals.
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After extracting data from multiple sources, Anagram performs call driver analysis to identify call types leading to repeat call volume. Afterward, root cause analysis is carried out to detect the reasons of repeat contacts by spotting frequently used words and phrases in customer interactions, as well as analyzing self-help channels usage patterns. Evaluation of data from self-help channels like IVR helps identify the issues that drive customers to escalate to live representatives, as well as the opportunities to improve the self-help effectiveness for higher FCR performance.
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Given below are two essential and comprehensive steps to improve FCR through repeat call volume reduction.
  • Understanding and Predicting Customer Contact Behavior Patterns
  • A proactive approach is required by contact centers to resolve customers’ inquiries on the first contact. For this, a deep understanding is required around customers’ potential issues, needs or reasons that can drive them to contact a call center throughout their interaction journey. Besides, contact centers need to be familiar with customers contact behavior patterns so as to make out which customers will contact through which medium, when and why and how to best help them.
  • Some customers are found to be relatively tougher to be satisfied on the first contact. They display more repeat contact behavior patterns. Such customers can be directly routed to more experienced agents with expertise in FCR. Besides, organizations can supervise agents to more carefully handle such customers’ contacts and provide them with adequate information in the first interaction. Insights on the customer contact behavior can be gained by analyzing customer data, including customer interactions, profiles, and transactions.
  • Detecting and Fixing the Process Glitches and Agents’ Knowledge Gaps and Behavioral Issues
  • Apart from customer insights, contact centers need to identify process glitches and agents’ knowledge gaps or behavioral issues, causing repeat contacts. To get to the bottom of the repeat call volume, data has to be collected and analyzed from a number of sources.
  • o Optimizing the agent FCR performance: As analyzing customer profiles and contact history helps better understand customers’ contact behavior, similarly evaluating agents’ profiles and interaction history can help optimize agents’ FCR performance. This will help identify agents’ knowledge or skills gaps, obstructing the successful closure of customers’ cases on the first time. With to-the-point insights, contact center managers can provide more targeted trainings to agents with poor FCR scores. At the same time, customer interaction analytics can help detect agents with expertise in FCR resolution for more effective routing and FCR best practices identification.
  • o Fixing the process issues: Analytics-enabled root-cause analysis can reveal the prevailing process issues. To reduce misdirected or transferred calls from the IVR systems, IVR prompts can be personalized or new options can be added to address common issues that force customers to talk to live agents. Similarly, web knowledgebase and other self-help options can be optimized for repeat calls reduction.
To learn more on how to improve FCR and other contact center operational metrics, read the R Systems’ white Paper “Cost of Delaying the Adoption of Speech Analytics for Contact Centers”.
About Sheetal Kumari
Sheetal is an MBA with 6.5 years of experience in the publishing and the IT industry. She is currently working as an Assistant Manager in the RSI Marcom department.

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