Call Scoring: Manual vs Keyword-Based vs Generative AI-based

Quality assurance (QA) in call centers isn’t just about checking off boxes; it’s about enhancing customer satisfaction and boosting overall business performance. Did you know that 89% of consumers have switched to a competitor following a poor customer experience? With hundreds or even thousands of calls to get through daily, efficient call scoring is critical. This blog explores three primary call scoring methods: manual, keyword-based, and generative AI-based, highlighting their pros, cons, and best practices to help you choose the right approach for your organization.

Read More: What Is Contact Center Quality Management?

Manual Call Scoring for Quality Assurance

Manual call scoring is the traditional method where human evaluators listen to call recordings, assess agent performance, and assign scores based on predetermined criteria. This approach relies heavily on human judgment, providing a detailed analysis of each interaction. Evaluators focus on aspects such as customer satisfaction, politeness, and problem resolution accuracy. Manual call scoring doesn’t require any advanced tools or technologies, just trained personnel familiar with the evaluation criteria.

Use Cases

• Ideal for organizations with low call volumes.
• Suitable for specific scenarios requiring a detailed human touch.
• Effective for handling complex interactions where context and nuance are crucial.
• Useful in situations where compliance and adherence to scripts need to be thoroughly checked.

Benefits and Challenges

Benefits:

• High accuracy due to human judgment.
• No reliance on technology, eliminating technical errors.
• Personalized feedback tailored to individual agent performance.

Challenges:

• Time-consuming and labor-intensive.
• Potential for human bias affecting evaluations.
• Difficult to scale with large volumes of calls.

Best Practices

• Use customizable forms to streamline the evaluation process.
• Train evaluators thoroughly to ensure consistent and unbiased scoring.
• Regularly review and update evaluation criteria to maintain relevance and effectiveness.
• Integrate manual scoring with automated tools for a comprehensive QA approach.

Keyword-Based Call Scoring for Quality Assurance

Keyword-based call scoring utilizes artificial intelligence (AI) algorithms to analyze call transcripts for specific keywords or phrases. This method is efficient for scenarios where particular words or phrases are indicative of the call’s content. For example, checking if compliance statements have been read or if specific script lines were used.

Use Cases

• Effective for simple scenarios with specific keywords.
• Useful for compliance checks and script adherence.
• Ideal for evaluating calls at scale, ensuring every interaction meets basic criteria.
• Can quickly identify calls that need further human review.

Benefits and Challenges

Benefits:

• Can evaluate 100% of calls efficiently.
• Reduces the workload of human evaluators.
• Ensures consistent application of criteria across all calls.

Challenges:

• Limited understanding of conversation context.
• Requires constant maintenance to update keywords and phrases.
• Dependent on the accuracy of call transcripts.

Best Practices

• Regularly update keyword lists to cover all relevant phrases.
• Use advanced query expressions for more accurate evaluations.
• Combine keyword-based scoring with human reviews for comprehensive QA.
• Ensure high-quality call transcription to improve evaluation accuracy.

Generative AI-Based Call Scoring for Quality Assurance (Auto QA)

Generative AI-based call scoring leverages large language models to analyze entire conversations. Unlike keyword-based scoring, this method understands the full context of interactions, providing deeper insights into customer satisfaction and agent performance. It answers complex questions about calls, such as whether issues were resolved or if agents followed protocols properly.

Use Cases

• Ideal for analyzing long and complex conversations.
• Suitable for evaluating overall customer satisfaction and agent professionalism.
• Effective for scaling QA processes, handling large volumes of calls.
• Provides nuanced insights that keyword-based scoring might miss.

Benefits and Challenges

Benefits:

• Comprehensive analysis of entire conversations.
• Easier configuration using natural language questions.
• Generates detailed and context-rich insights.
• Reduces manual workload significantly.

Challenges:

• Requires investment in advanced voice analytics software.
• Dependent on accurate speech-to-text transcription.
• Potential for initial setup complexity and costs.

Best Practices

• Implement a feedback loop to continuously improve AI accuracy.
• Use generative AI in conjunction with human reviews for balanced evaluations.
• Ensure robust speech-to-text technology for precise transcription.
• Regularly update and refine AI models to keep up with evolving call scenarios.

Choosing the Right Quality Assurance Call Scoring Method

Manual vs. Keyword-Based vs. Generative AI-Based Call Scoring

When it comes to selecting the appropriate call scoring method for quality assurance in contact centers, it is crucial to understand the unique strengths and weaknesses of each approach. Here’s a detailed comparison of manual call scoring, keyword-based call scoring, and generative AI-based call scoring.

Manual Call Scoring

Strengths

• Personalized Feedback: Manual call scoring allows human evaluators to provide detailed and personalized feedback. Evaluators can pick up on nuances and context that automated systems might miss, such as tone, emotion, and specific interaction dynamics.
• High Accuracy for Reviewed Calls: Since human judgment is involved, the accuracy for each reviewed call tends to be higher. Evaluators can interpret complex interactions and understand subtleties that algorithms might not catch.
• Contextual Understanding: Humans can understand the broader context of a conversation, including cultural and situational nuances, making this method ideal for complex or sensitive interactions.

Weaknesses

• Time-Consuming: Reviewing calls manually is extremely time-consuming. Each call must be listened to in its entirety, which can be impractical with large volumes of calls.
• Potential for Human Bias: Human evaluators might introduce personal biases into their assessments. Factors such as mood, fatigue, and subjective opinions can influence the scoring.
• Scalability Issues: Scaling manual call scoring is challenging. As call volumes increase, it becomes impractical to maintain the same level of detailed review without significantly increasing the number of evaluators.

Example

Consider a scenario where a contact center handles a small volume of high-value customer interactions, such as a luxury brand’s customer service. Manual call scoring is suitable here because the detailed feedback and personal touch are crucial for maintaining high service standards and customer satisfaction.

Keyword-Based Call Scoring

Strengths

• Efficiency and Scalability: Keyword-based call scoring can quickly analyze large volumes of calls, searching for specific keywords or phrases. This method is highly efficient and scalable, capable of evaluating 100% of calls.
• Consistency: Automated systems provide consistent scoring without the variability introduced by human judgment. This ensures that the same criteria are applied uniformly across all calls.
• Compliance and Script Adherence: This method is particularly effective for checking compliance with legal requirements and adherence to call scripts. It can quickly identify whether specific phrases were used, such as “This call is being recorded.”

Weaknesses

• Limited Context Understanding: Keyword-based systems have a narrow focus, often missing the broader context of the conversation. They cannot interpret the overall tone or sentiment effectively.
• Maintenance Requirements: These systems require regular updates to the keyword list to ensure they capture all relevant phrases. Changes in call scripts or compliance requirements necessitate ongoing adjustments.
• Dependency on Transcription Accuracy: The effectiveness of keyword-based scoring relies heavily on the accuracy of call transcripts. Errors in transcription can lead to incorrect scoring.

Example

A contact center handling routine customer service inquiries for a telecom company can benefit from keyword-based call scoring. The system can efficiently check if agents are following scripts and ensuring compliance with regulatory requirements, such as informing customers about call recordings.

Generative AI-Based Call Scoring

Strengths

• Comprehensive Analysis: Generative AI-based call scoring uses advanced language models to understand entire conversations, providing a deep and context-rich evaluation. It can answer complex questions about interactions, such as assessing overall customer satisfaction or determining if an issue was fully resolved.
• Ease of Configuration: These systems can be configured using natural language, making setup easier compared to keyword-based systems. Managers can input evaluation criteria in plain language without needing to define specific keywords.
• Scalability and Efficiency: Generative AI can analyze large volumes of calls quickly and efficiently, making it suitable for scaling QA processes in large contact centers.
• Reduced Manual Workload: By automating comprehensive evaluations, these systems significantly reduce the need for manual review, freeing up human evaluators to focus on the most critical interactions.

Weaknesses

• Initial Investment: Implementing generative AI-based call scoring requires a substantial initial investment in advanced voice analytics software and highly accurate speech-to-text transcription.
• Technical Complexity: These systems can be complex to set up and maintain, requiring technical expertise to ensure optimal performance.
• Potential Lag: There might be a slight delay in generating scores due to the speech-to-text transcription process, although this is generally minimal.

Example

A large e-commerce company dealing with a high volume of customer service calls can greatly benefit from generative AI-based call scoring. The system can analyze the full context of interactions, providing insights into customer satisfaction, identifying trends, and pinpointing areas for improvement, all at scale.

Best Practices for Choosing the Right Method

1. Assess Your Call Volume:
   • For low call volumes, manual call scoring might be sufficient.
   • For high call volumes, consider keyword-based or generative AI-based scoring for scalability.
2. Identify Your Evaluation Needs:
   • For detailed, nuanced feedback, manual scoring is ideal.
   • For compliance and script adherence checks, keyword-based scoring is effective.
   • For comprehensive, context-rich insights, generative AI-based scoring is the best choice.
3. Consider Your Budget and Resources:
   • Manual scoring requires investment in human resources.
   • Keyword-based and generative AI-based scoring require investment in technology and training.
4. Evaluate Integration Possibilities:
   • Combining methods can leverage the strengths of each approach.
   • Use generative AI to pre-score calls and focus manual reviews on critical interactions.
5. Regularly Review and Update Processes:
   • Ensure continuous improvement by regularly reviewing and updating your QA processes and criteria.

Selecting the right call scoring method for quality assurance depends on your specific needs, call volume, and available resources. By understanding the strengths and weaknesses of manual, keyword-based, and generative AI-based call scoring, you can make an informed decision that enhances your contact center’s performance and customer satisfaction. For comprehensive quality assurance, consider integrating these methods to leverage their unique advantages, ensuring a balanced and effective approach to call scoring.

Integration and Complementary Use of Call Scoring Methods

To achieve the highest quality assurance in contact centers, integrating manual, keyword-based, and generative AI-based call scoring methods can provide optimal results. By leveraging the strengths of each approach, you can ensure comprehensive evaluations that enhance agent performance and customer satisfaction. Here’s an extensive look at how these methods can be effectively combined.

Combine Manual, Keyword-Based, and Generative AI-Based Methods for Optimal Results

Holistic Quality Assurance

Integrating multiple call scoring methods creates a robust QA system that covers all bases. Manual call scoring provides detailed insights and personal feedback, keyword-based scoring ensures compliance and script adherence, and generative AI offers comprehensive, context-rich evaluations.

Enhanced Accuracy and Consistency

Using all three methods together enhances the accuracy and consistency of evaluations. While AI and keyword-based methods handle large volumes of calls efficiently, manual reviews add a layer of human understanding and context, ensuring that no important detail is overlooked.

Example Integration Workflow

1. Initial Screening with Keyword-Based Scoring: Automatically screen all calls for compliance and script adherence using keyword-based scoring.
2. Comprehensive Analysis with Generative AI: Apply generative AI to analyze call context, customer satisfaction, and agent performance.
3. Detailed Review with Manual Scoring: Identify calls that need further scrutiny based on AI and keyword results, and have human evaluators perform detailed reviews.

Use AI to Pre-Score Calls and Identify Those Needing Detailed Human Review

Efficient Pre-Screening

AI can efficiently pre-score calls, identifying those that meet specific criteria and flagging ones that require further human review. This reduces the workload for human evaluators and ensures they focus on calls that need detailed attention.

Prioritizing High-Impact Calls

By pre-scoring calls, AI helps prioritize high-impact interactions that could significantly affect customer satisfaction and business outcomes. Human evaluators can then focus their efforts on these critical calls, providing in-depth analysis and actionable feedback.

Example Use Case

A financial services contact center uses AI to pre-score all customer interactions. Calls that involve complex financial advice or show signs of customer dissatisfaction are flagged for detailed human review. This ensures that critical interactions receive the attention they deserve while routine calls are efficiently handled by AI.

Leverage Keyword-Based Scoring for Compliance Checks and Script Adherence

Automated Compliance Monitoring

Keyword-based scoring is particularly effective for ensuring compliance with regulatory requirements. It can automatically check if agents are using mandatory phrases and adhering to legal guidelines, such as informing customers about call recordings.

Script Adherence

Ensuring agents follow prescribed scripts is crucial for maintaining consistency and quality in customer interactions. Keyword-based scoring can quickly identify whether agents are sticking to the script, providing immediate feedback for training and improvement.

Example Use Case

In a healthcare contact center, keyword-based scoring is used to ensure agents comply with HIPAA regulations by checking for phrases like “This call is being recorded for quality purposes.” It also verifies that agents are following the prescribed script when discussing sensitive health information with patients.

Utilize Generative AI for Overall Customer Satisfaction and Performance Insights

Deep Contextual Analysis

Generative AI excels at understanding the full context of conversations, providing insights into overall customer satisfaction and agent performance. It can analyze entire call transcripts to identify trends, sentiment, and key performance indicators.

Comprehensive Feedback

AI-driven insights offer a comprehensive view of agent performance, highlighting strengths and areas for improvement. This helps managers provide targeted coaching and support, enhancing overall team performance.

Example Use Case

A retail contact center uses generative AI to analyze customer interactions, assessing satisfaction levels and identifying common issues. The AI provides detailed reports on agent performance, including suggestions for training and improvement. This enables managers to make data-driven decisions to enhance customer service quality.

Best Practices for Integrating Call Scoring Methods

Develop a Hybrid QA Strategy

Create a hybrid QA strategy that leverages the strengths of manual, keyword-based, and generative AI-based scoring. Define clear roles for each method and ensure they complement each other.

Train and Support Evaluators

Provide thorough training for human evaluators to minimize bias and ensure consistent scoring. Equip them with the necessary tools and support to effectively integrate AI and keyword-based insights into their evaluations.

Continuous Improvement

Regularly review and refine your QA processes to adapt to changing needs and technologies. Use feedback loops to continuously improve AI accuracy and update keyword lists and evaluation criteria.

Conclusion

Efficient call scoring is essential for maintaining high-quality customer interactions in contact centers. Manual call scoring offers detailed insights but is labor-intensive, keyword-based scoring is efficient but limited in scope, and generative AI-based scoring provides comprehensive evaluations but requires significant investment. By integrating these methods, you can leverage the strengths of each approach, ensuring thorough and effective quality assurance. Explore these methods to find the best fit for your organization, and consider combining them for a holistic QA strategy that maximizes both efficiency and effectiveness.