Customer Classifier

😫 The Pain Point

You have a sales Excel file. You want to send a “Thank You” gift to customers who spent over $5000, and a “Discount Code” to those who haven’t bought anything in 6 months. Filtering manually in Excel is tedious and prone to errors.

🚀 Agentic Solution

Automated Classification: Apply business rules to segment 10,000 customers in seconds.

Key Features:

• RFM Analysis: Classifies based on Recency (Last buy), Frequency (Count), and Monetary (Total spend).
• Tagging: Adds a “Segment” column to your Excel file.

⚔️ Phase 1: Commander (Quick Fix)

For a one-time segmentation.

Prompt:

“I have sales.xlsx with ‘Customer’, ‘Amount’, and ‘Date’. Calculate total spending per customer. If Total > 5000, label them ‘VIP’. If Total > 1000, label ‘Regular’. Otherwise ‘New’. Save the result to segmented_customers.xlsx.”

Result: A tagged customer list.

🏗️ Phase 2: Architect (Permanent Tool)

For Marketing Managers.

Engineering Prompt:

**Role:** Python Data Analyst
**Task:** Create a "Customer Segmentation App".
**Requirements:**
1.  **GUI:**
    *   Select Sales Data File.
    *   Input Thresholds: e.g., VIP Limit ($), Risk Days (days since last buy).
    *   "Segment" button.
2.  **Logic:**
    *   Group by Customer ID.
    *   Calculate Sum(Amount) and Max(Date).
    *   Apply conditional logic to assign tags: 'VIP', 'Loyal', 'At Risk', 'Lost'.
    *   Export Report.
3.  **Deliverables:** `classifier.py`, `run.bat` (Windows), `run.sh` (Mac).

🧠 Prompt Decoding

• RFM Model: This is the gold standard in retail marketing. Even a simple Python script implementing RFM is more powerful than hours of looking at spreadsheets.

🛠️ Instructions

1. Copy Prompt -> Paste -> Run.
2. Load Sales Data -> Segment.