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SQM Post-Call CSat Prediction QA Model
CSat Score
SQM Group has developed the Post-Call CSat Prediction Model – this model can predict CSat with up to 95% exceptional accuracy match to survey-based ratings.
VERNON, BRITISH COLUMBIA, CANADA, January 15, 2025 /EINPresswire / -- 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?
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.
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. 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.
Beyond its predictive capabilities, SQM's Post-Call CSat Prediction QA Model empowers call centers to improve their quality assurance processes.
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.
1. Statistical Regression
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.
2. AI and Natural Language Processing (NLP)
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
.NLP
.LLMs
.Statistical Regression
.AI
.Results are delivered through the mySQMTM QA tool for an agent to review.
3. Rubric QA Scoring
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.
4. Agent Coaching & Training
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.
5. External Call Center Metrics
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.
6. CX Behavioral Standards
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.
7. Post-Call Survey Ratings
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.
8. Internal Call Center Metrics
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.
9. QA Scoring Ranges
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.
10. Proprietary CSat Prediction Formula
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.
SQM Marketing
SQM Group Inc.
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