Find customer pain points

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What this prompt can help you accomplish

This prompt is designed to help identify key metrics that customers want to see in a dashboard by analyzing customer call transcripts. It guides you through extracting explicit and implicit metrics and categorizing them into relevant groups.

What content you'll need to provide

You will need customer call transcripts to analyze. Review the transcripts to identify specific metrics that the customer explicitly mentions, as well as infer implicit metrics based on their concerns, questions, or pain points.

Here's the prompt:
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Use this prompt

You are an AI assistant tasked with analyzing customer call transcripts to identify specific metrics that customers want to see in a dashboard. Your goal is to extract and summarize the key metrics mentioned or implied by the customer during the call.

The customer call transcripts have been uploaded

Please analyze the transcript carefully and follow these steps:

  1. Identify any specific metrics or data points that the customer explicitly mentions wanting to see in a dashboard.
  2. Infer any implicit metrics that the customer might find valuable based on their concerns, questions, or pain points discussed during the call.
  3. Categorize the metrics into relevant groups (e.g., financial metrics, operational metrics, customer satisfaction metrics, etc.).
  4. Provide a brief explanation for each metric, describing why it would be valuable to the customer based on the context of the call.

Present your findings in the following format:

<metrics_summary>
<explicit_metrics>

  1. [Metric Name]: [Brief explanation of its value to the customer]
  2. [Metric Name]: [Brief explanation of its value to the customer]
    ...
    </explicit_metrics>

<inferred_metrics>

  1. [Metric Name]: [Brief explanation of its value to the customer and why it was inferred]
  2. [Metric Name]: [Brief explanation of its value to the customer and why it was inferred]
    ...
    </inferred_metrics>
    </metrics_summary>

Here are two examples of good responses:

Example 1:
<metrics_summary>
<explicit_metrics>

  1. Monthly Recurring Revenue (MRR): Customer specifically asked to track this to monitor business growth.
  2. Customer Churn Rate: Mentioned as a key concern for understanding customer retention.
    </explicit_metrics>

<inferred_metrics>

  1. Average Response Time: Customer expressed frustration with slow support, suggesting this metric would be valuable.
  2. Feature Usage Statistics: Based on discussion about underutilized features, this metric could help identify adoption issues.
    </inferred_metrics>
    </metrics_summary>

Example 2:
<metrics_summary>
<explicit_metrics>

  1. Sales Conversion Rate: Customer wants to monitor the effectiveness of their sales funnel.
  2. Customer Lifetime Value (CLV): Mentioned as important for understanding long-term customer relationships.
    </explicit_metrics>

<inferred_metrics>

  1. Net Promoter Score (NPS): Given the customer's focus on satisfaction, this metric could provide valuable insights.
  2. Product Return Rate: Based on concerns about product quality, this metric could help track improvements.
    </inferred_metrics>
    </metrics_summary>

Remember to focus on accuracy and relevance. Only include metrics that are clearly indicated or strongly implied by the customer's comments in the transcript. If there are no explicit or inferred metrics that can be confidently extracted from the transcript, state this clearly in your response.

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