- 18 Aug 2023
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Improving Conversation Quality Scores & Agent Performance
- Updated on 18 Aug 2023
- 3 Minutes to read
- Contributors
- Print
- PDF
SECTOR | GOAL | HOW | PRODUCT FEATURES |
---|---|---|---|
Call Center | Improve Conversation Quality Scores & Agent Performance | Manual evaluations based on categories were conducted using Knovvu Analytics' Automated Quality Management module. | Knovvu Analytics-Analytics-Categories & Knovvu Analytics-AQM-Forms-Manual Forms |
Problem: In order to maintain a consistent and professional image, a call center had established a standard script that customer representatives were expected to follow during their interactions with customers. The script provided guidelines on how to greet customers, address their concerns, and offer solutions effectively. However, the company lacked a reliable system to track and assess whether the representatives adhered to the script and followed the prescribed protocols.
Success Scenario: Recognizing the importance of empowering their staff and boosting their performance, the call center management decided to take action. They collaborated with data analysts and experts to design specific performance evaluation forms. These forms were designed to assess how well the customer representatives aligned with the script, how effectively they handled customer inquiries, and whether they followed the call center's best practices.
Before creating a Manual Form, categories should be generated because the category created will be selected under the Form Category section to verify compliance with the designated script. Therefore, first, analysts go to the Analytics module and select the "Categories" section. Then, they choose "Basic Category" from the Text Search section and enter the appropriate categories for the script into the interface. They select "Agent" as the Channel since the company's goal is to improve customer experience by conducting agent evaluations.
After creating the categories, analysts go to the Forms submodule within the AQM (Automated Quality Management) module and click on the Add New button. They select the Conversation Type for the form. Subsequently, a new window appears with general information about the form on the left side.
First, they enter the Form Name, and then select the Form Type, which can be Public, Private, or Private For Department. The Normalization option, if selected, normalizes the scores to a percentage out of 100 if the total score is above or below 100. If Normalization is chosen, the Max Score must be set to determine the targeted maximum total score. Lastly, to determine if customer representatives conducted their conversations in accordance with the Opening Script, the Form Category should be selected as Opening Script.
After entering the New Form information, on the right side of the screen, there are New Section and New Question to be added to the form under the Questions section. The New Section field can be modified to edit the section name, the delete button can be used to remove an existing section, and the plus button on the right can be used to add new questions or sections. The question name in the New Question field can be modified. The Hint field is not mandatory for a question; it can be used to provide a detailed explanation for the respective question. There are several question types available, including Multiple Choice, Multiple Selection, Yes/No, Acoustic Parameter-Based, and Category-Based options.
Analysts enter the necessary section names and questions to create the Opening Script Evaluation Form, and they input the scores they want agents to receive. Once the changes are completed in the draft version, they publish the form by clicking the arrow next to the Save button and click the Save and Publish button.
Reason of the problem: The absence of a proper evaluation mechanism created a sense of uncertainty and frustration among the customer representatives. They felt that their efforts were not being recognized or properly measured, which, in turn, hindered their personal growth and motivation to improve their performance. Without constructive feedback and evaluations, they found it challenging to identify areas of improvement or refine their communication skills.
Result: The decision to leverage technology and data-driven insights not only improved the customer representatives' morale but also positively impacted the overall performance of the call center. The average quality score, which was 90% before the implementation, increased to 96.4% after the analysis. As representatives received constructive feedback and gained access to development opportunities, their communication skills and problem-solving abilities showed significant improvement. This, in turn, led to enhanced customer satisfaction and loyalty, reflecting positively on the call center's reputation and success.
In conclusion, the call center's decision to address the issue of performance evaluation by using specific, tailored categories transformed the work environment for its customer representatives. By providing them with the tools they needed for personal growth and success, the call center not only empowered its staff but also strengthened its position in the competitive business landscape.