Customer Analytics SQL Project

📌 Project Overview

This project analyzes customer behavior, churn prediction, marketing effectiveness, and sales performance using SQL.
The goal is to help businesses improve customer retention, optimize marketing campaigns, and increase revenue by understanding customer purchase behavior.

🛠️ Skills Used: SQL (CTEs, Joins, Window Functions, Aggregations)

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📊 Business Insights & Analysis

** Customer Segmentation & Profiling**

Goal:

Categorize customers based on spending behavior & recency to personalize marketing strategies.

Key Insights:

VIP Customers (High Spending, Recent Buyers) should be targeted with loyalty programs.
At-Risk Customers (Haven't bought in 90+ days) need re-engagement campaigns.
Low Spenders churn the most, meaning they might need discounts to stay engaged.

** Business Recommendation:**

Offer personalized discounts to At-Risk Customers to improve retention.

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** Customer Churn & Retention Analysis**

Goal:

Identify churned customers & develop retention strategies.

Key Insights:

Low-income customers have the highest churn rate (9.19%), while higher-income customers churn slightly less (8.89%).
Lower spenders churn more, while high-value customers are more loyal.
Medium & high spenders rarely churn, meaning they have stronger engagement.

** Business Recommendation:**

Increase engagement for low-income & low-spending customers with personalized promotions.

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** Marketing Campaign Effectiveness**

Goal:

Analyze which marketing campaigns performed best.

Key Insights:

Campaign 3 had the highest acceptance rate, meaning customers engaged the most.
Low Spenders engaged the most with marketing, showing they respond well to promotions.
VIP Customers didn’t engage as much, meaning they don’t rely on discounts to buy.

** Business Recommendation:**

Optimize future marketing efforts based on Campaign 3’s strategy.
Use different marketing strategies for VIPs vs. Low Spenders.

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** Sales Performance by Purchase Channels**

Goal:

Identify whether customers prefer web, store, or catalog purchases.

Key Insights: Store purchases generate the most revenue per order, meaning in-store experiences matter.

Web purchases are frequent but have lower revenue per order, meaning online promotions should focus on volume.
Catalog purchases are less frequent but have high revenue per order, meaning businesses should continue catalog marketing.

** Business Recommendation:**

Invest in in-store experiences & online promotions based on shopping behavior.

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** RFM Analysis (Recency, Frequency, Monetary)**

Goal:

Identify the most valuable customers.

Key Insights:

High RFM customers should be prioritized for premium services & exclusive offers.
Low RFM customers need engagement strategies to become loyal buyers.

** Business Recommendation:**

Create a VIP loyalty program for high RFM customers.

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** Cross-Sell & Product Affinity Analysis**

Goal:

Identify which products are frequently bought together to boost cross-sell opportunities.

Key Insights:

Wine & Meat are the most commonly bought together → Opportunity for bundle deals.
Gold & Wine Buyers are a separate premium segment → Target them with luxury promotions.

** Business Recommendation:**

Launch bundled deals (Wine + Meat, Gold + Wine) to increase sales.

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** High-Value Customer Prediction**

Goal:

Predict future VIP customers based on spending & frequency.

Key Insights:

Potential VIP customers exist & should be nurtured before they churn.
They shop frequently but haven’t reached VIP spending yet.

** Business Recommendation:**

Offer personalized incentives to transition potential VIPs into high-value customers.

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** Customer Lifetime Value (CLV) Estimation**

Goal:

Predict long-term revenue from each customer.

Key Insights:

Some customers contribute more long-term revenue than others.
Low CLV customers need engagement campaigns to improve retention.

** Business Recommendation:**

Use CLV scores to allocate marketing budgets effectively.

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📂 SQL Scripts Included

All SQL scripts for the analysis are available in this repository:

📜 01_Customer_Segmentation.sql
📜 02_Customer_Churn.sql
📜 03_Marketing_Campaigns.sql
📜 04_Sales_Performance.sql
📜 05_RFM_Analysis.sql
📜 06_Cross_Sell_Analysis.sql
📜 07_High_Value_Customer_Prediction.sql
📜 08_CLV_Estimation.sql

Click on any script above to view the full SQL queries in GitHub.

📌 Database Schema (ER Diagram)

This diagram represents the structure of the fooddb table used for customer analytics.

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🚀 About Me

👋 Hi, I'm AJAYI OLUWASEYI, a Data Analyst passionate about using SQL to solve business problems.
📧 Contact: [email protected]
💼 LinkedIn: www.linkedin.com/in/ajayi-oluwaseyi-865a35248

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