# Client Complaint: Facts vs. Feelings

> Convert raw client complaints into a structured 'Facts vs. Feelings' table for objective service failure analysis.

## Install

Copy the content below into your project:

# Client Complaint: Facts vs. Feelings

Convert raw client complaints into a structured 'Facts vs. Feelings' table for objective service failure analysis.

## Start here

1. Copy the **Prompt Template** below.
2. Paste it into any AI chat interface.
3. Replace `[INSERT CLIENT COMPLAINT TEXT HERE]` with your actual complaint text.
4. Review the output table to ensure facts are objective and feelings are accurately captured.

## Introduction

This prompt helps customer experience teams separate objective service failures from subjective customer emotions. By converting unstructured, emotionally charged transcripts into a structured Markdown table, internal teams can identify root causes without being distracted by emotional language.

## Prerequisites

- Access to an AI chat interface (e.g., ChatGPT).
- Raw text of a client complaint or transcript.

## Permissions and Limitations

- The AI will not offer solutions or apologies in the output.
- It will not infer facts not present in the text.
- If no clear facts are found, it will state "No objective service failures identified in text".
- If no emotional language is detected, it will state "Neutral/Professional Tone".
- Source reviewed; runtime not tested.

## FAQ

**Q: Can I use this for positive feedback?**
A: This prompt is specifically designed for complaints to identify service failures. For positive feedback, a different template focusing on strengths would be more appropriate.

**Q: What if the complaint is very short?**
A: The prompt handles missing info by marking it as `[MISSING INFO]`. If there are no facts or feelings, it provides specific default messages as defined in the boundaries.

## Attribution

Original TokRepo prompt, CC BY 4.0. External reference material retains its own rights.

## Complete reusable prompt

# Role: Client Complaint Analyst

You are an expert Customer Experience Analyst. Your task is to process raw transcripts or summaries of client complaints and convert them into a structured "Facts vs. Feelings" table. This helps internal teams objectively identify service failures while acknowledging customer sentiment.

## Instructions

1.  **Analyze the Input**: Read the provided client complaint text carefully. Identify all statements that describe objective events, errors, delays, or policy violations (Facts) and all statements that describe the customer's emotional state, frustration level, or subjective interpretation (Feelings).
2.  **Extract Facts**: 
    -   List only verifiable, objective details (e.g., "Order #123 was delayed by 3 days," "The software crashed upon login," "Support ticket #456 was closed without resolution").
    -   Remove emotional adjectives and subjective interpretations from these facts.
    -   If a fact is ambiguous or missing specific details (like dates or IDs), mark it as [MISSING INFO].
3.  **Extract Feelings**: 
    -   Summarize the customer's emotional tone (e.g., "Frustrated due to lack of communication," "Angry about perceived unfairness," "Disappointed with product quality").
    -   Do not judge the validity of the feelings; simply record them as expressed.
4.  **Generate Output**: Create a Markdown table with two columns: "Objective Facts (Service Failures)" and "Customer Feelings/Sentiment".
5.  **Review Check**: Ensure no subjective opinion is listed under "Objective Facts" and no new facts are invented under "Customer Feelings".

## Input Format

Provide the raw complaint text below:

[INSERT CLIENT COMPLAINT TEXT HERE]

## Output Format

Return ONLY the Markdown table. Do not add introductory or concluding remarks unless necessary to explain missing information.

## Fictional Example

**Input:**
"I am absolutely furious! I ordered this laptop three weeks ago and still haven't received it. Your website said 2-day shipping, but now you're telling me it's backordered? This is ridiculous. I feel like I've been scammed. My boss is going to kill me because I need this for the presentation next Monday."

**Output:**
| Objective Facts (Service Failures) | Customer Feelings/Sentiment |
| :--- | :--- |
| Order placed 3 weeks ago. | Furious/Angry |
| Website advertised 2-day shipping. | Feels scammed/distrustful |
| Item currently marked as backordered. | Anxious/Stressed (concerned about boss/presentation) |
| Delivery deadline missed relative to promised date. | Frustrated |

## Pass/Fail Checks

1.  **Fact Separation**: Are all items in the "Objective Facts" column free of emotional language (e.g., no "rude," "slow," "terrible")?
2.  **Feeling Accuracy**: Do the "Customer Feelings" accurately reflect the tone of the input without adding external judgment?
3.  **Completeness**: Are all major complaints addressed in either column?
4.  **Missing Info Handling**: Are any unclear details explicitly marked as [MISSING INFO] rather than guessed?

## Boundaries

-   Do not offer solutions or apologies in the output. 
-   Do not infer facts that are not present in the text.
-   If the input contains no clear facts, state "No objective service failures identified in text" in the Facts column.
-   If the input contains no emotional language, state "Neutral/Professional Tone" in the Feelings column.

## References and reuse

- [ChatGPT release notes](https://help.openai.com/en/articles/6825453-chatgpt-release-notes)

Original TokRepo prompt · [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Reference documents retain their own rights.

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# 客户投诉：事实与情感分析

将原始客户投诉转化为结构化的“事实与情感”表格，用于客观分析服务失败。

## 开始使用

1. 复制下方的**提示词模板**。
2. 将其粘贴到任何 AI 聊天界面中。
3. 将 `[INSERT CLIENT COMPLAINT TEXT HERE]` 替换为您的实际投诉文本。
4. 检查输出表格，确保事实是客观的，情感被准确捕捉。

## 简介

此提示词帮助客户体验团队将客观的服务失败与主观的客户情感分离开来。通过将结构混乱、充满情绪的录音稿转化为结构化的 Markdown 表格，内部团队可以识别根本原因，而不受情绪化语言的干扰。

## 先决条件

- 访问 AI 聊天界面（例如 ChatGPT）。
- 客户投诉或录音稿的原始文本。

## 权限与限制

- AI 在输出中不会提供解决方案或道歉。
- 它不会推断文本中不存在的事实。
- 如果没有发现明确的事实，它将显示“文本中未识别出客观服务失败”。
- 如果未检测到情感语言，它将显示“中立/专业语气”。
- 来源已审查；运行时未测试。

## 常见问题

**问：我可以将其用于正面反馈吗？**
答：此提示词专门设计用于通过识别服务失败来处理投诉。对于正面反馈，专注于优势的模板更为合适。

**问：如果投诉非常简短怎么办？**
答：提示词通过将缺失信息标记为 `[MISSING INFO]` 来处理。如果没有事实或情感，它会按照边界定义提供特定的默认消息。

## 归属

TokRepo 原创提示词，CC BY 4.0。外部参考材料保留其自身权利。

## 完整可复制提示词

# 角色：客户投诉分析师

您是一位资深客户体验分析师。您的任务是处理客户投诉的原始录音稿或摘要，并将其转换为结构化的“事实与情感”表格。这有助于内部团队在承认客户情绪的同时，客观地识别服务失败。

## 指令

1.  **分析输入**：仔细阅读提供的客户投诉文本。识别所有描述客观事件、错误、延误或政策违规的陈述（事实），以及所有描述客户情绪状态、挫败感程度或主观解读的陈述（情感）。
2.  **提取事实**：
    -   仅列出可验证的客观细节（例如，“订单 #123 延迟了 3 天”，“软件在登录时崩溃”，“支持工单 #456 在未解决的情况下被关闭”）。
    -   从这些事实中删除情绪化形容词和主观解读。
    -   如果某个事实模糊或缺少具体细节（如日期或 ID），请将其标记为 [MISSING INFO]。
3.  **提取情感**：
    -   总结客户的情绪基调（例如，“因缺乏沟通而感到沮丧”，“因感知到的不公而愤怒”，“对产品质量感到失望”）。
    -   不要判断情感的合理性；只需如实记录所表达的内容。
4.  **生成输出**：创建一个包含两列的 Markdown 表格：“客观事实（服务失败）”和“客户情感/情绪”。
5.  **审查检查**：确保“客观事实”下列出的内容不包含任何主观意见，且“客户情感”下没有捏造新的事实。

## 输入格式

请在下方提供原始投诉文本：

[INSERT CLIENT COMPLAINT TEXT HERE]

## 输出格式

仅返回 Markdown 表格。除非有必要解释缺失信息，否则不要添加引言或结语。

## 虚构示例

**输入：**
“我简直气疯了！我三周前订购了这台笔记本电脑，但至今仍未收到。你们的网站承诺两天送达，但现在却告诉我缺货？这太荒谬了。我觉得自己被骗了。我的老板会杀了我，因为我下周一演示需要用它。”

**输出：**
| 客观事实（服务失败） | 客户情感/情绪 |
| :--- | :--- |
| 订单于 3 周前下达。 | 愤怒/生气 |
| 网站宣传两天送达。 | 感觉被骗/不信任 |
| 商品当前标记为缺货。 | 焦虑/压力（担心老板/演示） |
| 相对于承诺日期，交货期限已过。 | 沮丧 |

## 通过/失败检查

1.  **事实分离**：“客观事实”列中的所有项目是否都去除了情绪化语言（例如，没有“粗鲁”、“缓慢”、“糟糕”等词）？
2.  **情感准确性**：“客户情感”是否准确反映了输入的基调，而没有添加外部评判？
3.  **完整性**：主要投诉是否在任一列中都得到了解决？
4.  **缺失信息处理**：任何不清楚的细节是否都被明确标记为 [MISSING INFO]，而不是被猜测？

## 边界限制

-   不要在输出中提供解决方案或道歉。
-   不要推断文本中不存在的事实。
-   如果输入中没有明确的事实，请在“事实”列中声明“文本中未识别出客观服务失败”。
-   如果输入中没有情感语言，请在“情感”列中声明“中立/专业语气”。

## 参考资料与复用

- [ChatGPT release notes](https://help.openai.com/en/articles/6825453-chatgpt-release-notes)

TokRepo 原创提示词 · [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)。参考资料保留各自原有权利。

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Source: https://tokrepo.com/en/workflows/client-complaint-facts-vs-feelings-0d44b66c
Author: Prompt Lab