claude-data-analysis-ultra-main

🥈Silver

AI-powered data analysis for non-technical users. Enables internet, e-commerce, and other businesses to perform complex data analysis with simple commands. Connects to existing workflows and tools for user analysis, A/B testing, and more.

174230Updated 1w ago

Intermediate30min to implementautomation

Saves ~90 min per use

Quick InstallView Source

git clone https://github.com/liangdabiao/claude-data-analysis-ultra-main.git

Works with:

Claude

Overview

About This Skill

How to Use

1. **Gather Your Data**: Export your customer behavior data (e.g., from Shopify, Google Analytics, or your CRM) into a CSV or connect your data source directly to the AI tool. Ensure the data includes metrics like conversion rates, cart abandonment, and customer segments. 2. **Customize the Prompt**: Replace the placeholders in the prompt template with your specific details (e.g., [E-COMMERCE_PLATFORM], [TIME_PERIOD], [KEY_METRIC]). For example, if you're analyzing cart abandonment for a clothing brand, your prompt might start with: *Analyze the customer behavior data for [MyClothingBrand] over the past [3 months]. Focus on [cart abandonment rates]...* 3. **Run the Analysis**: Paste the customized prompt into your AI tool (e.g., Claude, ChatGPT, or a data analysis platform like Tableau with AI integration). Specify any additional constraints or preferences, such as focusing on a particular customer segment or time frame. 4. **Review and Refine**: Review the AI-generated insights for accuracy and relevance. Use the actionable recommendations to inform your next steps, such as A/B tests or marketing campaigns. If the AI suggests data you don’t have, note the gap and adjust your data collection strategy. 5. **Iterate and Automate**: For recurring analyses (e.g., monthly reports), save the prompt as a template in your AI tool or set up an automated workflow (e.g., using Zapier or Make) to pull fresh data and generate reports automatically. This ensures consistency and saves time.

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/liangdabiao/claude-data-analysis-ultra-main

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Analyze the customer behavior data for [E-COMMERCE_PLATFORM] over the past [TIME_PERIOD]. Focus on [KEY_METRIC] trends, such as conversion rates, cart abandonment, or repeat purchase rates. Identify the top 3 customer segments with the highest [KEY_METRIC] and the top 3 with the lowest. For each segment, provide actionable insights on how to improve performance, including potential A/B test ideas. Use data from [DATA_SOURCE] if available.

Example Output

### Customer Behavior Analysis Report for ShopEase (June 2023 - August 2023)

**Key Metrics Analyzed:**
- Conversion Rate: 2.8% (down from 3.1% in May)
- Cart Abandonment Rate: 72% (stable YoY)
- Repeat Purchase Rate: 18% (up from 15% in Q1)

**Top 3 Customer Segments by Conversion Rate:**
1. **High-Value Repeat Buyers (10% of customers, 6.2% conversion)**
   - Behavior: Primarily purchases premium products ($150+ average order value).
   - Insight: These customers are highly engaged but may benefit from exclusive early access to new products.
   - A/B Test Idea: Send a "VIP Preview" email 3 days before product launch to this segment.

2. **Mobile Shoppers (35% of customers, 3.5% conversion)**
   - Behavior: 85% of sessions occur on mobile, but mobile conversion lags desktop by 22%.
   - Insight: Checkout flow may be cumbersome on mobile devices.
   - A/B Test Idea: Simplify the mobile checkout process by reducing form fields and adding one-click payment options.

3. **Seasonal Shoppers (12% of customers, 4.1% conversion)**
   - Behavior: Purchases peak during holiday seasons (November-December).
   - Insight: These customers respond well to urgency-driven campaigns.
   - A/B Test Idea: Test a countdown timer in the shopping cart for seasonal shoppers.

**Top 3 Customer Segments by Cart Abandonment Rate:**
1. **First-Time Visitors (22% of sessions, 85% abandonment)**
   - Insight: Lack of trust or unclear return policy may be deterring purchases.
   - A/B Test Idea: Add a "30-Day Return Guarantee" banner in the checkout flow.

2. **Discount Seekers (15% of sessions, 80% abandonment)**
   - Insight: These users often abandon carts to search for coupon codes.
   - A/B Test Idea: Pre-load a 10% discount code in the cart for first-time visitors.

3. **International Shoppers (8% of sessions, 78% abandonment)**
   - Insight: High shipping costs and long delivery times are major barriers.
   - A/B Test Idea: Offer free shipping on orders over $100 for international customers.

**Recommendations:**
- Prioritize mobile checkout optimization, as it impacts 35% of your customer base.
- Focus on reducing cart abandonment for first-time visitors, as they represent the largest untapped revenue opportunity.
- Leverage high-value repeat buyers for product launches to drive initial sales momentum.

*Data Source: ShopEase Analytics Dashboard (exported 09/01/2023)*