Logical Reasoning vs. Pattern Matching: Which AI is Better for Contact Center Quality Assurance?

Logical Reasoning vs. Pattern Matching: Which AI is Better for Contact Center Quality Assurance? a group of business colleages consider what's on a computer screen while another has a graph on their screen.

With advances in artificial intelligence, many contact center leaders are analyzing the differences between logical reasoning vs. pattern matching. Beginning this review often leads to the initial question of which AI is better for contact center quality assurance.

Choosing the right AI-powered quality assurance (AIQA) methodology is a critical decision. Monitoring and assessing agent performance and interaction with customers is not enough; you must also use the right approach.

That right approach should leverage the tools that will give you the most valuable insights and data about your contact center.

To do so, and to determine which approach is most suited for your AIQA needs, you need to know more about both logical reasoning and pattern matching, as well as how they work together. 

What is Logical Reasoning?

Logical reasoning, in the context of AI, involves systems that use rules-based algorithms (including if/then, and/or, and other formulas) to analyze and interpret complex scenarios and human interactions.

These systems aim to emulate human decision-making processes by understanding the context, logic, and relationships within data.

In contact center quality assurance, logical reasoning AI evaluates customer interactions based on predefined rules and criteria, considering factors such as language semantics, conversation flow, and problem-solving capabilities. This approach enables AI systems to provide detailed insights and actionable feedback to improve agent performance and customer satisfaction.

What is Pattern Matching?

Alternatively, pattern-matching AI uses systems that identify and recognize patterns within large datasets or input data. This approach relies on statistical algorithms and machine learning techniques to detect recurring patterns, similarities, and anomalies in the data.

In contact center quality assurance, pattern-matching AI evaluates customer interactions by comparing them to predefined patterns or benchmarks. This approach allows AI systems to assess performance consistently, identify trends, and flag deviations from expected behaviors.

While pattern-matching AI excels in efficiency and scalability, it may lack logical reasoning AI’s contextual understanding and problem-solving capabilities.

AIQA Example Using Logical Reasoning vs. Pattern Matching

Here’s a brief example of the difference between logical reasoning and pattern matching …

Scoring Category: Greeting

  • Scoring Criterion: Proper and polite greeting and introduction.
  • Transcript: “Hello, thank you for calling XYZ Company. This is John speaking.”
  • AIQA Scoring with Pattern Matching methodology: Matches greeting phrases to predefined patterns. High scores are given if patterns like “hello” and “thank you for calling” are detected.
  • AIQA Scoring with Logical Reasoning methodology: Analyzes the tone and context of the greeting, understanding politeness and professionalism and potentially recognizing subtle nuances.

Scoring Category: Understanding the Issue

  • Scoring Criterion: Clear understanding and acknowledgment of the customer’s issue.
  • Transcript: “I understand that your internet connection is unstable…”
  • AIQA Scoring with Pattern Matching methodology: Checks for keywords like “understand” and “internet connection unstable.” Scores are based on the presence of these keywords.
  • AIQA Scoring with Logical Reasoning methodology: Interprets the agent’s understanding of the issue in context and evaluates how accurately and empathetically the issue is addressed.

Scoring Category: Product Knowledge

  • Scoring Criterion: Demonstrating knowledge about the product or service.
  • Transcript: “To reset your router, you’ll need to locate the small reset button…”
  • AIQA Scoring with Pattern Matching methodology: Checks for technical keywords and accurate procedural language. High score for matching technical terms and instructions.
  • AIQA Scoring with Logical Reasoning methodology: Understands the relevance and accuracy of the information provided and the agent’s ability to convey it clearly.

These examples show how pattern matching focuses on detecting specific words or phrases, while logical reasoning goes deeper, understanding context, subtleties, and the overall sentiment in the conversation.

Both methods have their strengths and weaknesses.

Pros and Cons of Logical Reasoning

Some of the strengths that logical reasoning AI brings to contact center quality assurance include the following:

Contextual Understanding. Logical reasoning AI systems can comprehend the context of customer interactions, enabling them to interpret complex scenarios and nuanced language more accurately.

Flexibility. These systems are flexible and can handle diverse types of inquiries and conversations, making them suitable for contact centers with varied customer bases and service offerings.

Problem-solving Capability. Logical reasoning AI can identify and address underlying issues or root causes behind customer concerns, providing actionable insights for process improvements.

Empowerment of Agents. By providing detailed feedback and explanations about why specific scores were given, logical reasoning AI empowers agents to understand the rationale behind QA evaluations and improve their performance over time.

On the other hand, logical reasoning AI can present the following challenges:

Complexity. Developing logical reasoning AI systems require extensive training data and complex algorithms, making them resource-intensive and challenging to implement for some organizations trying to do so in-house.

Subjectivity. Interpretation of customer interactions may vary among evaluators, leading to subjective judgments and inconsistencies in QA assessments.

Limited Scalability. Logical reasoning AI systems may struggle to scale effectively to handle large volumes of customer interactions, particularly during peak periods or rapid business growth.

High Maintenance. Maintaining and updating logical reasoning AI models requires ongoing effort and expertise, as they need to adapt to evolving customer behaviors and service requirements.

Pros and Cons of Pattern Matching

Some of the benefits of pattern-matching AI include the following:

Efficiency. Pattern-matching AI systems excel in processing large volumes of data quickly and efficiently, making them ideal for contact centers with high call volumes or fast-paced environments.

Consistency. These systems provide consistent evaluations based on predefined patterns and criteria, reducing variability and ensuring uniformity in QA assessments.

Scalability. Pattern-matching AI can scale effortlessly to handle increasing volumes of customer interactions without compromising performance or accuracy.

The challenges with pattern-matching AI include:

Limited Contextual Understanding. Pattern-matching AI may struggle to grasp the context of customer interactions and may miss subtle nuances or complex issues that require deeper analysis.

Rigid Evaluation Criteria. These systems rely on predefined patterns and criteria, which may not capture the full spectrum of customer interactions or account for unique scenarios.

Potential for False Positives. Pattern-matching AI may produce false positives or inaccuracies in QA evaluations, particularly when evaluating unconventional or novel customer interactions.

Reduced Agent Engagement. Due to the standardized nature of evaluations, pattern-matching AI may provide less actionable feedback to agents, potentially leading to reduced engagement and motivation.

Which AI — Logical Reasoning vs. Pattern Matching — is Better for YOUR Contact Center Quality Assurance?

The choice between logical reasoning and pattern-matching AI for contact center quality assurance depends on your specific needs and circumstances. Some factors include the complexity of your customer interactions, your organizational resources, and your scalability requirements.

While logical reasoning AI offers deeper contextual understanding and problem-solving capabilities, pattern matching AI excels in “just the facts” efficiency, consistency, and scalability.

Ultimately, the goal is to leverage AI technologies effectively to drive continuous improvement, enhance agent performance, and deliver exceptional customer experiences in the contact center environment. As with any new business practice, the best way to achieve that goal depends on your organization’s context.

What About Both?

At ARC, we specialize in human-driven AI quality assurance, which combines the human element into AI and offers a tailored combination of logical reasoning and pattern matching based on unique needs.  

We invite you to book a meeting with the experts at ARC to discuss logical reasoning vs pattern-matching AIQA in more detail within the specific context of your contact center.

To get in touch, use this form to book a call, or you can reach us directly at 1-866-798-0488. If you prefer email, you can contact us at info@arcqs.com.