retail-ecommerce_customer-voice-sales-and-behaviour-prediction
🥈SilverE-commerce quick prototyping for customer segmentation, sentiment analysis and review processing automation. Includes presentation in video and PowerPoint together with the ERD of the database used. Serves as Business Case example impacting several business units and providing opportunities to improve Customer Success, Sales, Marketing and Supply Chain.
400Updated 2mo ago
Intermediate30min to implementmarketing
Saves ~60 min per use
Quick InstallView Source
git clone https://github.com/DataRaul/retail-ecommerce_customer-voice-sales-and-behaviour-prediction.gitWorks with:
Claude
Overview
About This Skill
This skill provides a complete MVP for analyzing customer behavior and sales patterns in e-commerce using Python, NLP, and unsupervised learning. It automates customer voice analysis through Natural Language Processing and segments customers using machine learning to identify actionable patterns. The project includes a 10-minute video presentation, PowerPoint deck, and Entity-Relationship Diagram that document the methodology and business case across customer success, sales, marketing, and supply chain functions. Built on anonymized retail e-commerce data, it demonstrates how to extract geographic and revenue insights at postal code level and uncover customer segmentation opportunities within a one-year planning horizon.
How to Use
Use Python 3.x in a Jupyter Notebook with the provided data files from the data folder. Follow along with the video presentation (two parts at YouTube links provided) and reference the PowerPoint deck and ERD diagram to understand the database schema and methodology. The project demonstrates rapid MVP prototyping suitable for presenting business cases to stakeholder audiences.
Use Cases
Automated customer sentiment analysis from review data
Customer segmentation for targeted marketing campaigns
Geographic revenue analysis by postal code area
Sales behavior prediction for business case development
Tags
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/DataRaul/retail-ecommerce_customer-voice-sales-and-behaviour-predictionRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Act as an e-commerce business intelligence consultant. Analyze the following [DATA] from [COMPANY], an online retailer in the [INDUSTRY] sector. Perform customer segmentation, sentiment analysis, and review processing. Identify sales and behavior patterns. Present findings in a PowerPoint and video format, including an ERD of the database used. Highlight opportunities to improve Customer Success, Sales, Marketing, and Supply Chain.
Example Output
# E-commerce Customer Insights Analysis ## Executive Summary Based on the analysis of [COMPANY]'s customer data, three distinct segments have been identified: 1. **Loyal Enthusiasts** (35% of customers) - High purchase frequency - Positive sentiment in reviews - Interested in premium products 2. **Budget Conscious Shoppers** (40% of customers) - Price-sensitive - Moderate purchase frequency - Mixed sentiment in reviews 3. **Impulse Buyers** (25% of customers) - Low purchase frequency - Positive sentiment in reviews - Interested in trending products ## Key Findings - **Sales Patterns**: Loyal Enthusiasts contribute to 60% of total sales. - **Behavior Trends**: Budget Conscious Shoppers are most active during sales events. - **Sentiment Analysis**: Overall customer sentiment is positive, with a net promoter score of 75. ## Recommendations - **Customer Success**: Implement a loyalty program for Loyal Enthusiasts. - **Sales**: Target Budget Conscious Shoppers with personalized discounts. - **Marketing**: Create campaigns highlighting customer reviews and testimonials. - **Supply Chain**: Stock more premium products to meet Loyal Enthusiasts' demand. ## ERD Overview The database schema includes tables for Customers, Orders, Products, Reviews, and Segments. Relationships are established to link customer data with their purchase history and reviews.
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