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# LLM-as-a-Judge: Call Transcript Analysis + Bias



![LLM-as-a-Judge](https://www.deepchecks.com/wp-content/uploads/2024/08/post-what-is-llm-as-judge.jpg)



In today's competitive business landscape, leveraging technology to enhance sales strategies is more critical than ever. Large Language Models (LLMs) like GPT-4 have emerged as powerful tools for analyzing sales call transcripts, offering "insights". However, biases in LLMs can significantly impact the accuracy and consistency of these analyses.



This post explores how LLM biases affect sales motion analysis, delving into the technical challenges and providing practical examples. We'll discuss why LLMs may produce inconsistent results when interpreting sales motions and offer strategies to mitigate these biases, maximizing the potential of AI in sales analytics.



---



## Understanding Sales Motion and Sales Process



Before diving into the technical intricacies, it's essential to distinguish and understand the interplay between two foundational concepts:



- **Sales Motion**: The overarching strategy or methodology a company employs to sell its products or services. It defines the approach to customer engagement, reflecting the company's philosophy and value proposition. Sales motions are often shaped by the sales process and are influenced by the company's goals and market positioning.



- **Sales Process**: The specific, repeatable steps taken during the sales cycle, such as prospecting, qualifying leads, presenting solutions, handling objections, and closing deals. The sales process operationalizes the sales motion, providing a structured path for sales representatives to follow.



### **The Dependency Relationship**



- **Sales Motion Depends on Sales Process**: The effectiveness of a sales motion is closely tied to the sales process that implements it. For example, a consultative sales motion requires a sales process that includes steps for in-depth customer needs analysis and solution tailoring.



- **Sales Process Depends on Company Strategy**: The design of the sales process is influenced by the company's overall strategy, including its target market, product offerings, competitive positioning, and growth objectives. A company focusing on rapid market penetration might adopt a transactional sales process, emphasizing speed and volume.



---



## Call Transcript Analysis using LLMs



LLMs, like GPT-4, are trained on vast amounts of text data, allowing them to understand and generate human-like language. When applied to call transcript analysis, they can perform a variety of tasks that help businesses extract valuable information from customer interactions.



### 1. Accurate Transcription and Summarization



LLMs can transcribe call recordings with high accuracy, converting spoken words into text. Beyond transcription, they can generate concise summaries of interactions, highlighting key components such as:



- **Reason for the Call**: Understanding why the customer reached out.

- **Issues Addressed**: Identifying the problems discussed and solutions offered.

- **Action Items**: Noting any follow-up actions required.



*Example:* After a 30-minute support call, an LLM-generated summary might state: "Customer called about software integration issues causing downtime. Agent provided troubleshooting steps and scheduled a follow-up appointment."



This capability reduces the need for manual summarization, allowing agents and managers to focus on enhancing customer experiences.



### 2. Sentiment and Emotional Analysis



LLMs can assess the emotional tone of both the customer and the agent during a call. By analyzing language cues and expressions, they determine sentiments such as happiness, frustration, or confusion.



*Benefits:*



- **Real-Time Insights**: Immediate understanding of customer satisfaction levels.

- **Agent Performance Evaluation**: Identifying areas where agents excel or need improvement.

- **Trend Analysis**: Spotting patterns in customer emotions over time.



*Example:* If a customer frequently uses words like "frustrated" or "disappointed," the LLM flags the call for managerial review.



### 3. Insight Extraction and Problem Identification



By processing call transcripts, LLMs can uncover specific insights and core problems that customers face. They can:



- **Synthesize Topics**: Group conversation points into main themes.

- **Identify Recurring Issues**: Highlight common problems across multiple calls.

- **Provide Actionable Recommendations**: Suggest ways to address frequent customer concerns.



*Example:* An LLM might detect that many customers are confused about a recent software update, indicating a need for better communication or training materials.



### 4. Automated Call Categorization



LLMs enable automated categorization of calls based on predefined criteria, such as:



- **Sentiment**: Positive, negative, or neutral interactions.

- **Keywords/Phrases**: Specific product mentions or common inquiries.

- **Compliance Monitoring**: Ensuring agents adhere to company policies and scripts.



*Benefits:*



- **Efficiency**: Quickly sorting large volumes of calls.

- **Focused Analysis**: Allowing teams to concentrate on specific categories of interest.

- **Performance Tracking**: Monitoring metrics like upsell rates or cancellation requests.



*Example:* Calls mentioning "upgrade" or "new features" can be grouped for the sales team to analyze potential upselling opportunities.



### 5. Privacy and Data Protection



LLMs can detect and redact sensitive information from transcripts, such as:



- **Personal Identifiable Information (PII)**: Names, addresses, passwords, credit card numbers.

- **Confidential Data**: Proprietary business information.



*Importance:*



- **Regulatory Compliance**: Meeting standards like GDPR or HIPAA.

- **Customer Trust**: Protecting customer data enhances brand reputation.



*Example:* An LLM automatically replaces credit card numbers mentioned during a call with "[REDACTED]" in the transcript.



### 6. Enhancing Sales and Customer Support Strategies



In sales contexts, LLMs can analyze transcripts to:



- **Identify Key Themes**: Understanding what customers care about most.

- **Detect Objections**: Recognizing common hesitations or concerns.

- **Classify Sales Motions**: Determining the sales strategies used during interactions.



*Example:* By analyzing a sales call, an LLM might identify that the agent employed a "Solution Selling" approach, focusing on how the product addresses the customer's specific needs.



---



## The Challenge: LLMs and Subjectivity



### **Subjectivity and Context Dependence**



LLMs, despite their sophistication, face challenges when interpreting subjective and context-dependent concepts like "effective" sales practices. This difficulty arises from:



- **Cultural and Individual Variations**: What constitutes an effective sales motion can vary widely across different industries, cultures, and individual customer preferences. The LLM must navigate these variations to provide accurate analyses.



- **Company-Specific Strategies**: Each company may have unique sales processes tailored to its strategy, affecting how sales motions are executed. LLMs trained on general data may not account for these specifics.



- **Contextual Nuance**: The same sales technique might be appropriate in one context but ineffective or even detrimental in another. Understanding the dependency between sales motion, sales process, and company strategy is essential.



---



## An Experiment in Sales Motion Analysis



To illustrate these challenges, consider an experiment where an LLM analyzes the same sales call transcript multiple times to identify the sales motion used.



### **The Sales Call Transcript**



A conversation between a sales representative and a prospective client discussing a SaaS solution to enhance productivity. The dialogue includes elements like:



- Identifying customer challenges.

- Presenting tailored solutions.

- Addressing concerns about integration and support.

- Proposing next steps with a personalized demo.



### **The LLM's Task**



The model is prompted with:



```plaintext

What is the `sales motion`?

```



### **Variable Outputs**



Despite using the same transcript, the LLM provides different answers across multiple runs:



1. **Solution Selling**

2. **Consultative Selling**

3. **Problem-Agitate-Solve (PAS)**

4. **Addressing Customer Challenges Proactively**

5. **Showcase Benefits and Build Trust**



---



## Analyzing the Variability



### **Reasons for Inconsistent Outputs**



1. **Randomness in Generation**



   - **Temperature Settings**: LLMs use a parameter called *temperature* to control randomness. A higher temperature results in more varied outputs, while a lower temperature makes the model more deterministic.

   - **Impact**: LLM temperature has a direct impact on response variability.



2. **Ambiguity in Prompts**



   - **Open-Ended Questions**: The prompt "What is the `sales motion`?" is broad, allowing the model to interpret it in various ways.

   - **Lack of Contextual Guidance**: Without information on the company's specific sales process or strategy, the LLM lacks the necessary context to make an accurate assessment.

   - **Impact**: The LLM may focus on different aspects of the conversation each time.



3. **Biases in Training Data**



   - **Overrepresentation**: The model might favor certain sales methodologies prevalent in its training data. If the training data overemphasizes methods like "Solution Selling," the LLM is more likely to identify that motion.

   - **Underrepresentation**: Less common or company-specific sales motions may not be adequately recognized. If a company's unique sales process isn't reflected in the training data, the LLM may misclassify it.



### **Technical Explanation**



LLMs generate text by predicting the next word in a sequence based on learned probabilities. Without constraints and specific context, minor differences in initial conditions can lead to divergent paths due to the vast possibility space in language generation. The dependency of sales motion on sales process and company strategy adds layers of complexity that the LLM must navigate.



---



## Impact of LLM Biases



### **Business Implications**



- **Misclassification of Sales Strategies**: Inaccurate identification of sales motions can lead to ineffective training and strategy development. If the LLM doesn't account for how the sales process implements the company's strategy, its analysis may be off-target.



- **Inconsistent Performance Metrics**: Variability in analysis affects the reliability of performance evaluations and KPIs. Sales teams may be unfairly assessed based on misinterpreted data.



- **Decision-Making Risks**: Relying on biased outputs may result in misguided business decisions, such as shifting focus away from successful strategies that the LLM fails to recognize.



### **Examples**



- A sales manager might implement unnecessary training programs if the LLM incorrectly suggests that sales reps are not using the prescribed sales motion aligned with the company's strategy.



- Inconsistent analyses could lead to confusion among sales teams about which strategies are most effective, especially if the LLM doesn't consider the company's unique sales process.



---



## Mitigating LLM Biases



### **1. Fine-Tuning the Model**



**Action**: Train the LLM on domain-specific data, including company-specific sales interactions, processes, and terminology.



**Benefit**: The model becomes more attuned to the nuances of your sales motions and processes, and less influenced by general biases.



**Example**: By incorporating transcripts of successful sales calls that exemplify your company's sales motion and process, the LLM learns to recognize and classify similar patterns accurately, reflecting your strategic objectives.



### **2. Providing Clear and Specific Prompts**



**Action**: Use detailed instructions to guide the LLM's focus, including context about your company's sales process and strategy.



**Improved Prompt**:



```plaintext

Based on the transcript, identify the sales motion used. Choose from the following options that align with our company's sales process:



- Solution Selling

- Consultative Selling

- Transactional Selling

- SPIN Selling



Please provide your answer using one of the above terms.

```



**Benefit**: Reduces ambiguity and limits the model's response scope, leading to more consistent outputs that are relevant to your company's context.



### **3. Adjusting Model Parameters**



**Action**: Set the temperature parameter to zero for deterministic outputs.



**Benefit**: Eliminates randomness, ensuring the same input yields the same output every time.



**Technical Note**: In models like GPT, `temperature=0` makes the model choose the highest probability token at each step, enhancing consistency.



### **4. In-Context Learning: Incorporating Company Strategy into Prompts**



**Action**: Include information about your company's sales process and strategic goals in the prompt.



**Enhanced Prompt**:



```plaintext

Our company focuses on a consultative selling approach, emphasizing deep understanding of customer needs and tailored solutions. Based on the transcript, analyze how the sales representative implements this sales motion.



[Please limit your response to 100 words.]

```



**Benefit**: Provides the LLM with necessary context to align its analysis with your company's strategy, improving accuracy.



### **5. Implementing a Human-in-the-Loop**



**Action**: Combine AI analysis with human expertise for validation.



**Benefit**: Human reviewers can catch and correct misclassifications, enhancing overall accuracy and ensuring alignment with company strategy.



**Example**: Sales managers review the LLM's output to confirm the identified sales motion aligns with the company's sales process and strategic objectives.



### **6. Updating the Model Regularly**



**Action**: Continuously retrain the model with new data to reflect changes in sales strategies, processes, and market dynamics.



**Benefit**: Keeps the model's understanding current, reducing the risk of outdated biases and ensuring it reflects your evolving company strategy.



---



## Demonstrating the Solution



### **Updated Prompt and Parameters**



- **Prompt**:



  ```plaintext

  Based on the transcript and considering our company's focus on a consultative selling approach, identify the sales motion used, choosing from: Solution Selling, Consultative Selling, Transactional Selling, or SPIN Selling.

  ```



- **Temperature**: Set to 0 for deterministic output.



### **Expected Consistent Output**



The LLM now consistently identifies the sales motion as:



```plaintext

Run 0: Result = Consultative Selling

Run 1: Result = Consultative Selling

Run 2: Result = Consultative Selling

Run 3: Result = Consultative Selling

Run 4: Result = Consultative Selling

```



### **Analysis**



- **Alignment with Content**: The sales representative engages in understanding the client's challenges and offers tailored solutions, characteristic of consultative selling, which aligns with the company's strategy.



- **Reduced Variability**: By constraining the response options, providing company-specific context, and eliminating randomness, the LLM provides consistent and accurate analyses.



---



## Defining the `sample_function`



```python

def sample_function(question="", max_word=5):

    sample_call = """

    **Call Transcript**

    .........

    **End of Call**

    ======================

    """

    response = ollama.chat(model='llama3.2:3b', messages=[

        {

            'role': 'system',

            'content': f'You are a helpful AI assistant who analyzes call transcripts in the context of our company\'s consultative selling strategy. [important! response must always be max {max_word} words]'

        },

        {

            'role': 'user',

            'content': f'[call]\n{sample_call}\n[/call]\n\n\n{question} [important! max {max_word} words]',

        },

    ])

    print(response['message']['content'])

    ai_msg = response['message']['content']

    return ai_msg

```



### Breakdown



- **Function Purpose**: Analyzes a sales call transcript using an LLM to answer a specific question, considering company-specific context.



- **Enhancements**:



  - Included a more detailed `sample_call` that reflects the company's consultative selling process.



  - Modified the system message to include the company's strategy.



---



## Running the Function in Parallel with `run_function_in_parallel`



```python

def run_function_in_parallel(func: Callable, N: int, question: str, max_word: int) -> Dict[int, Any]:

    # Initialize Ray if it hasn't been initialized yet

    if not ray.is_initialized():

        ray.init(ignore_reinit_error=True)



    # Define a remote version of the input function

    @ray.remote

    def remote_func():

        return func(question, max_word)



    # Launch N parallel tasks

    futures = [remote_func.remote() for _ in range(N)]



    # Wait for all tasks to complete and gather results

    results = ray.get(futures)



    # Create a dictionary mapping run indices to results

    result_dict = {i: result for i, result in enumerate(results)}



    return result_dict

```



### Breakdown



- **Function Purpose**: Executes `sample_function` `N` times in parallel using Ray, ensuring consistency in the LLM's output.



- **Enhancements**:



  - Ensured that the same context and parameters are used across all runs to test for consistency.



---



## Main Execution Block



```python

if __name__ == "__main__":

    # Number of parallel runs

    N = 5



    # Run the function in parallel

    results = run_function_in_parallel(sample_function, N, question="What is the `sales motion`?", max_word=5)



    # Print the results

    for run_id, result in results.items():

        print(f"Run {run_id}: Result = {result}")

    # Shutdown Ray (optional)

    ray.shutdown()

```



## Conclusion



LLMs hold significant potential for enhancing sales motion analysis by automating the interpretation of sales interactions. However, inherent biases and technical limitations can lead to inconsistent and inaccurate outputs. By understanding these challenges and implementing targeted strategies—such as fine-tuning models, crafting precise prompts with company context, adjusting parameters, and involving human expertise—we can mitigate biases and unlock the full value of LLMs in sales analytics.



**Key Takeaways**



- **Recognize Dependencies**: Acknowledge that sales motion depends on the sales process, which is influenced by company strategy, and ensure this context is included in LLM analysis.



- **Address Training Data Biases**:



  - **Overrepresentation**: Be aware that LLMs may favor sales methodologies that are overrepresented in their training data. Fine-tuning the model with company-specific data can balance this bias.



  - **Underrepresentation**: Recognize that less common or company-specific sales motions may not be adequately recognized by the LLM. Providing relevant training data mitigates this issue.



- **Leverage Technical Adjustments**: Fine-tuning and parameter control significantly improve model performance and consistency.



- **Combine AI with Human Insight**: A collaborative approach enhances accuracy and reliability, ensuring analyses align with company strategy.



- **Embrace Continuous Improvement**: Regular updates and training keep the model aligned with current practices and evolving strategies.



By thoughtfully integrating LLMs into the sales analysis process, businesses can gain deeper insights, make more informed decisions, and ultimately drive better sales outcomes.



---



**Unlock the Potential**



Embracing these strategies not only addresses the challenges of LLM biases but also positions your organization at the forefront of AI-driven sales optimization. The synergy of advanced technology and human expertise can transform how you understand and improve your sales motions, delivering a competitive edge in a rapidly evolving marketplace.



---