📊 Marketing Analytics Segmentation

Course Project – Group 12
Date: 05 December 2024

Contributors:

• Heta Chavda (NF1014555)
• Krishna Patel (NF1017043)
• Bijal Panchal (NF1003964)
• Bimbo O. Solanke (NF1009888)

---

📌 Project Overview

This project applies market segmentation techniques to divide a dataset of 100 customers into actionable clusters based on demographic and behavioral data. The goal is to design segment-specific marketing strategies that improve personalization, resource allocation, and ROI.

---

🎯 Objectives

• Identify customer segments based on demographics & behaviors.
• Apply K-means clustering to classify customers.
• Generate marketing strategies (4Ps) tailored to each segment.
• Provide recommendations for improving retention and ROI.

---

📂 Dataset

• Size: 100 customers
• Features Analyzed:
  • Demographics → Age, Income, Gender, Region
  • Behavior → Purchasing patterns, Promotion engagement

---

🛠️ Methodology

• Tools Used:

  • Excel → Data cleaning & pivot tables
  • Python → K-means clustering, visualizations, strategy generation

• Steps:

  1. Data preprocessing and cleaning.
  2. K-means clustering applied → 4 clusters identified.
  3. Visual analysis of age, income, and engagement patterns.
  4. Segment-specific 4P (Product, Price, Place, Promotion) strategies created:contentReference[oaicite:0]{index=0}.

---

👥 Segments Identified

🔹 Cluster 0 – Young Professionals

• Products: Affordable, quality products with modern features
• Place: Digital platforms
• Promotion: Personalized online promotions & loyalty programs

🔹 Cluster 1 – Wealthy Seniors

• Products: Premium, high-end products
• Place: Traditional retail + hybrid online/offline options
• Promotion: Loyalty rewards & targeted offers

🔹 Cluster 2 – Moderately Engaged Professionals

• Products: Practical mid-range items
• Place: No strong platform preference
• Promotion: Value-based offers, simple pricing

🔹 Cluster 3 – Low-Income Retirees

• Products: Affordable, basic goods
• Place: Local stores & simple online access
• Promotion: Community-driven campaigns, affordability focus:contentReference[oaicite:1]{index=1}

---

📊 Visualizations (Suggested for Repo)

• 📈 Age distribution by segment
• 💰 Income distribution by segment
• 📊 Stacked bar chart → Engagement & spending patterns
• 🥧 Pie charts → 4Ps by segment

---

✅ Key Takeaways

• Focus marketing investment on Segments 0 & 1 for ROI growth.
• Use cost-effective, value-based strategies for Segments 2 & 3.
• Improve retention of retirees (Cluster 3) via local partnerships & community programs:contentReference[oaicite:2]{index=2}.

---

⚠️ Limitations & Challenges

• Small dataset (100 customers).
• Missing/inconsistent data may affect accuracy.
• Rapid market changes require frequent re-segmentation.
• High cost of personalized promotions for certain groups.

Suggestions

• Use real-time customer data for ongoing updates.
• Automate promotions with AI-based personalization tools.
• Partner with local communities to retain low-income retirees.
• Pilot strategies on smaller groups to minimize risk:contentReference[oaicite:3]{index=3}.

---

📌 Conclusion

Through K-means clustering and behavioral analysis, this project demonstrates how businesses can personalize marketing strategies for diverse customer groups. By balancing ROI-driven focus on premium clusters with inclusive strategies for lower-income groups, companies can enhance profitability, customer satisfaction, and long-term loyalty.

---