An essential process for any call center or contact center, call calibration involves ensuring that all call center agents are providing consistent and high-quality customer experience during customer interactions.
At its core, call calibration is the discussion between contact center managers and third-party call tracking and evaluation partners to review call evaluation results and ensure accuracy.
Conducting call calibration sessions regularly can lead to consistently good customer experience.
In a call calibration session, the majority of the time is spent listening to an agent’s call and gauging the quality of experience served to the customer. The meeting proceeds towards grading those calls, where these questions are addressed:
Call calibration is essential for a variety of reasons. These sessions:
Effective call calibration requires a structured approach. Here are some best practices to follow:
The first step in effective call calibration is to define quality standards for customer experience. These standards should be based on the company's goals and should be clear and measurable.
For example, standards could include the length of time an agent spends on a call, the number of calls answered per hour, and the quality of the agent's communication skills. The baseline should be established before starting the call calibration sessions.
Once quality standards have been defined, agents should be trained to meet these standards. This training should include communication skills, product knowledge, and customer service techniques. Agents should also be trained on how to handle difficult customers and how to de-escalate situations.
Deep insights are critical because they allow you to make informed decisions, but they need to be accurate. The way you achieve that accuracy is by making sure that your scoring is calibrated properly and that evaluations are correct. Otherwise, you’re basing your decisions on inaccurate or incomplete data and reports. As the saying goes: If you put garbage in, you get garbage out.
The best way to ensure you’re using good data and producing accurate reports is through automation. Observe.AI’s conversation intelligence platform ensures that 100% of calls are monitored, transcribed, and analyzed, meaning insights are gleaned from accurate, complete data that can be reviewed and referenced quickly and easily.
This allows for more targeted coaching down the line and more opportunity for success in hitting these quality standards.
Generative AI can supercharge both agent efficiency and the call calibration process by providing real-time guidance, automated call summaries, and coaching tips to help agents check all the boxes of your quality standards.
Observe.AI’s Generative AI suite is built on a proprietary LLM that has been meticulously trained on extensive contact center data. This LLM, in conjunction with customized intents tailored to specific businesses, ensures unparalleled data privacy, control, and accuracy for contact center teams, setting new industry standards.
And unlike legacy solutions, which are black-box solutions that rely on hard-coded logic that can’t be updated, Observe.AI allows QA talent to customize automation rules to the unique requirements of your business. This means QA teams can test, fine tune, and calibrate machine-driven automation to achieve the optimal balance of human efficacy and AI-powered efficiency.
“When we launch a new product or feature, we have a full framework that allows the customer to calibrate the machine and make sure the outputs are in line with what they expect,” explains Swapnil Jain, Observe.AI’s CEO. “You can give feedback to the machine and the machine learns and improves, which builds trust. We don’t let AI just go out there and start doing things on their own.”
At the end of the day, human talent should be kept in the loop for strategic areas where they can make the greatest impact, while letting the machine take over the tedious, manual QA tasks.
Managers and the QA team should analyze the reports and look for trends and common issues that agents may be facing. This analysis can help identify areas where agents need additional training or support—as well as surface positive behaviors that lead to success.
Providing actionable feedback to call center agents can help improve their performance while also boosting the overall customer experience.
However, it’s important to make sure that you also create a channel for agents to get involved in the process. This two-way street ensures both supervisors and agents have a fair chance to share feedback and drive improvements in the QA process. That’s why Observe.AI included the option to share QA results directly with agents. Not only can managers track when agents have received evaluations and reviewed the feedback embedded in them, but agents also have the opportunity to raise disputes when they don’t agree with a certain evaluation or score, which helps improve your calibration process and boost trust among agents, QA evaluators, and their supervisors.
Managers and the QA team should follow up with call center agents and establish ground rules for the call calibration process. This should include setting up a scorecard and evaluation form to track agent performance.
They should also establish a program to ensure that the call center quality assurance process is consistent. Setting ground rules helps ensure that the call calibration process is fair and free from favoritism. Follow-up sessions can help identify areas where agents need additional training or support.
There’s no doubt about it: Call calibration is an essential process. By following the best practices outlined above, call centers can improve their customer service and meet their goals for customer satisfaction. Establishing a quality program and following these best practices can help to improve the overall quality management of the call center.
With Conversation Intelligence, you can automatically analyze 100% of customer conversations to get valuable insights about agent behavior and customer requests.
Observe.AI makes call calibration easy to ensure our AI and automation tools are properly evaluating agent performance—and then take action on it with Real-Time AI and Generative AI tools to give agents what they need to improve.
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