Did you know that 83% of agents do not feel their quality assurance program helps them improve customer satisfaction, and 7 out of 10 companies believe their QA program is broken for monitoring and improving call quality service?
Customer Satisfaction (CSat) has always been the most popular metric for call centers, serving as the primary indicator of call quality service and CX delivery. However, accurately predicting CSat has been a challenge for most organizations.
While traditional methods, like post-call surveys, provide valuable insights, they often have limitations, such as small sample sizes, low response rates, delayed feedback, and a lack of real-time actionable data.
However, SQM Group has developed a revolutionary solution: the Post-Call CSat Prediction QA Model. By leveraging advanced techniques such as AI, machine learning, natural language processing (NLP), and regression analysis, this model can predict customer satisfaction with exceptional accuracy—up to 95% match to survey-based ratings.
Forecasting CSat outcomes enables call centers to evaluate an agent's customer experience (CX) delivery more accurately and fairly than surveys and traditional QA.
This blog explores how SQM's Post-Call CSat Prediction QA Model works, its key components, and how it is a game changer for QA in call centers. As call centers face increasing pressure to deliver high-quality service while managing agent performance, this model ensures a more data-driven, efficient approach to measure and improve customer satisfaction.
Why is SQM's Post-Call CSat Prediction Model a Game Changer for Call Center QA?
Measuring and improving CSat is essential for any call center looking to enhance its customer service. Traditionally, call centers have relied on post-call surveys to gauge customer sentiment. These surveys typically ask customers to rate their interaction on a scale (e.g., 1 to 5), and this feedback is then used to calculate an agent's CSat score. While the post-call survey method provides valuable insights, it has limitations, such as:
Firstly, agents' biggest concern with surveys is the small sample size, which might not represent their overall quality of service, and the fact that some customers are not accurate or fair in the agent's CX delivery.
Secondly, response rates for post-call surveys are often low, meaning that only a small fraction of customers provide feedback. As a result, the data may not represent the experiences of all customers, leading to skewed insights.
Thirdly, there can be an inability to link feedback to actions. Survey feedback is often not tied directly to agent performance or specific issues, especially in cases where other departments are responsible for fulfillment.
These challenges highlight the need for a more accurate, fair, timely, and data-driven approach to predicting CSat. Using advanced AI and machine learning models, call centers can move beyond traditional survey-based methods and gain deeper insights into customer satisfaction levels.
SQM's Post-Call CSat Prediction Model uses a combination of AI, machine learning, natural language processing, and regression analysis to forecast customer satisfaction with exceptional accuracy and agent fairness for evaluating their CX delivery.
At its core, SQM's Post-Call CSat Prediction QA Model leverages advanced techniques to predict CSat and determine customer sentiment. By analyzing various data points from customer interactions, the model predicts how customers are likely to rate their experience based on objective call center metrics and subjective behavioral patterns observed during the call.
The high level of predictive accuracy is essential for improving call center operations because it allows managers to take a more proactive approach to QA. Instead of waiting for survey results, the model gives managers real-time insights into customer sentiment, allowing them to take immediate action.
The model is powered by AI and machine learning, which analyze vast amounts of data to uncover hidden patterns and predict customer satisfaction with remarkable precision. These technologies continually refine the model over time, improving its accuracy as more data is processed.
Additionally, natural language processing enables the model to assess the nuances of customer-agent conversations, evaluating factors such as tone, language, sentiment, and intent. This makes the predictions far more accurate and fair than traditional survey-based methods.
Beyond its predictive capabilities, SQM's Post-Call CSat Prediction QA Model empowers call centers to improve their quality assurance processes. By identifying areas where agents may need additional coaching or training, the model provides actionable insights that help teams optimize performance. The model uncovers relationships between key performance indicators (KPIs) and CSat through regression analysis, ensuring that agents' performance aligns with customer expectations.
SQM's Post-Call CSat Prediction QA Model is a feature in our mySQM™ QA & CX analytics tool, which G2 has ranked as the #1 tool for user CSat for auto QA solution.
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What are the 10 Key Components of SQM's Post-Call CSat Prediction QA Model?
Let's delve into the 10 key components that power this game-changing CSat prediction QA model and explain how each element contributes to its ability to accurately predict customer satisfaction, benchmark, and improve overall call center performance.
From rubric QA scoring to proprietary prediction formulas, these components work together to create a more efficient, data-driven approach to quality assurance and customer experience management.
SQM's Post-Call CSat Prediction QA Model stands out because it uses ten integrated components that work together to deliver highly accurate and actionable predictions of CSat.
Statistical regression is at the core of SQM's Post-Call CSat Prediction QA Model, a statistical technique that helps establish relationships between various call center metrics and CSat outcomes. By analyzing historical data, the model identifies patterns that can be used to predict future customer satisfaction outcomes.
Regression is useful for understanding how different variables, such as call resolution, agent behavior, or specific service issues, can impact overall customer satisfaction.
Speech recognition, NLP (Natural Language Processing), LLMs (Large Language Models), statistical regression, and AI technologies play significant roles in evaluating calls by analyzing customer interactions, determining QA scores, and extracting actionable insights. Here's how they work together to contribute:
- Speech Recognition converts the audio recording into a textual transcript.
- NLP identifies customer sentiment and evaluates the agent's responses.
- LLMs analyze the dialogue for adherence to CX standards and effectiveness in resolution.
- Statistical Regression predicts CSat based on adherence to CX standards and resolution score.
- AI assigns QA scores and CSat prediction to generate actionable feedback for the agent.
- Results are delivered through the mySQM™ Auto QA tool for an agent to review.
The rubric QA scoring component introduces a standardized, objective framework for evaluating call center interactions. According to predefined standards, the rubric measures various factors such as communication skills, problem resolution, empathy, helpfulness, and knowledge.
The model can generate an AI-powered QA score by assessing each agent interaction against this rubric. This consistency ensures that QA score and CSat predictions are accurate and aligned with best practices in customer service, creating a fair and transparent evaluation process for agents.
Agent coaching and training can significantly influence the model's ability to predict CSat. In addition, the model can also assess the agents' training and coaching effectiveness and determine specific skills development opportunities.
Agent coaching and training on the right CX behavioral standards are pivotal in ensuring accurate customer satisfaction prediction.
Call center QA call calibration is a process used to ensure accuracy, consistency, and fairness in evaluating agent CX delivery for customer interactions. SQM's calibration process is unique because it uses post-call surveys, QA evaluators, and AI to ensure that agent QA scores help accurately predict customer satisfaction.
Call center QA calibration aims to align post-call surveys, QA evaluators, and AI to a common understanding of the QA criteria and scoring methods to accurately, consistently, and fairly determine agent QA scores and predict customer satisfaction.
CX behavioral standards are guidelines that define what constitutes excellent customer service. The CX behavioral standards are based on SQM benchmarking over 500 leading call centers on the best practices for agents to use for delivering world class customer satisfaction.
These standards encompass a range of factors, including empathy, helpfulness, and the ability to resolve issues on the first call. The model integrates SQM's gold standard behavioral standards into its predictions by evaluating how well an agent adheres to them during each customer interaction.
When agents consistently meet these standards, their CSat scores are higher. By including CX behavioral standards in its predictions, the model ensures that the focus remains on delivering exceptional customer satisfaction.
Despite the shift toward predictive modeling, post-call survey ratings remain an important piece of the puzzle. Post-call survey ratings help calibrate QA metrics based on AI-generated QA scores for accuracy. By comparing predicted CSat scores with actual post-call survey ratings, the model's algorithms can be refined, increasing the accuracy of future predictions.
Post-call surveys are particularly useful for confirming that the model's predictions align with the real-world customer experience and identifying any discrepancies that may need further adjustments.
To ensure CSat prediction accuracy, the model utilizes external call center metrics such as post-call survey call resolution, source of error, and customer satisfaction. External call center metrics are used for QA calibration and to help predict customer satisfaction.
In addition to external metrics, the model also incorporates internal call center metrics, such as empathy, caring, listening, communication, helping, call length, reason for the call, and resolution. In addition, these internal performance metrics are directly linked to QA scores and help predict customer satisfaction.
The internal metrics used have been chosen because they represent key moments of truth that a customer experiences using a call center. Also, it is important to emphasize that because we use a standardized approach for evaluating QA scores, all the QA metrics and scores can be benchmarked against leading North American call centers.
The QA scoring ranges component ensures consistency and reliability in quality assurance assessments. These ranges help define what constitutes excellent, good, needs improvement, and unacceptable performance in terms of agent behavior and interaction CSat outcomes.
Incorporating these ranges into the model can predict how likely an agent is to achieve a high CSat rating based on their QA scores. QA scoring range and corresponding sub-metrics (e.g., CX sentiment, resolution) help align agent behavior with customer satisfaction goals, offering actionable insights into areas where agents may need improvement.
Finally, SQM's proprietary CSat prediction formula is the secret sauce that ties the 10 components together. SQM's proprietary formula combines all the factors into statistical regression, AI and NLP analysis, rubric QA scores, external and internal metrics, and more into a cohesive predictive CSat model.
The formula is continually refined based on new data, ensuring that CSat predictions remain accurate as customer expectations and call center dynamics evolve. The proprietary formula helps integrate complex data points into a single, easy-to-interpret CSat prediction score, offering call centers the ability to take proactive steps in measuring, benchmarking, and improving customer satisfaction.
In closing, SQM's Post-Call CSat Prediction QA Model will displace your traditional QA program and customer satisfaction surveying with a more accurate, consistent, and fair evaluation of your agents' CX delivery.
In addition, the SQM auto QA tool will transform your call center with AI-powered QA & CX analytics. You can analyze 100% of customer calls with our auto QA tool, benchmark your QA metrics to top-performing call centers, and predict customer satisfaction with up to 95% accuracy for every call.
As we stated in the blog's beginning, the bottom line is that most call center practitioners believe that QA is broken for accurately monitoring and improving call quality service. We strongly believe that SQM's Post-Call CSat Prediction QA Model is the best practice for evaluating, benchmarking, and improving call quality service and customer satisfaction.