🏆 TravelTide Rewards

This project segments TravelTide customers into clear personas to enable targeted allocation of personalized perks.

---

🎯 Objectives

• Segmentation: Identify customer groups based on booking behavior and engagement.
• Personalization: Assign one single tailored perk per persona.
• Optimization: Derive data-driven recommendations for marketing and loyalty programs.

---

🚀 Methodology

1. 📊 Data Preparation: Filtered to 5,998 active users (≥7 sessions since Jan 4, 2023).

   WITH sessions_2023 AS (
     SELECT *
     FROM sessions
     WHERE session_start > '2023-01-04'
   ),
   filtered_users AS (
     SELECT user_id
     FROM sessions_2023
     GROUP BY user_id
     HAVING COUNT(session_id) > 7
   )

2. 🔎 EDA: Analyzed booking frequency, spending patterns, and perk engagement using Python & Tableau.

3. 🤖 Clustering: Optimized at 8 clusters (Silhouette ≈ 0.19), based on booking behavior, cancellations, spending, and discounts.

---

👥 TravelTide Cluster Personas & Perks

Cluster	Persona-Name	Profile (Key Traits)	✨ Assigned Perk
0	Inactive Users	Few bookings, almost no spend, low engagement	🎁 10% Welcome Discount
1	Family Travelers	Group trips, baggage-heavy, moderate spend	🛄 Free Checked Bag
2	Discount Hunters	Price sensitive, high discount usage	💸 Exclusive Discounts
3	Dormant Accounts	No spending, almost no flights	🏨 1 Night Free Hotel
4	Hotel Loyalists	High hotel engagement, balanced spending	🍽️ Free Hotel Meal
5	Premium Elites	Ultra-premium flyers, luxury spend	🏨 1 Night Free Hotel
6	Low-Value Users	Low/no spend, occasional corporate bookings	🛡️ No Cancellation Fees
7	Family Package Deals	Group & family-oriented, above-average spending	👨‍👩‍👧 Family Package Deals

---

📊 Cluster Overview (Key Metrics)

Cluster	Users	Avg Flights	Avg Hotels	Avg Spend (USD)	Revenue Share (%)	Cancel Rate	Discount Ratio	Key Perk
0	363	0.26	0.00	125	0.6	0.0	0.13	Welcome Discount
1	1490	2.12	2.25	928	18.5	2.53	0.31	Free Checked Bag
2	1532	1.70	1.89	776	15.9	0.07	0.58	Exclusive Discounts
3	184	0.01	2.41	0	0.0	0.0	0.0	1 Night Free Hotel
4	1554	4.01	3.97	1760	36.7	0.13	1.04	Free Hotel Meal
5	61	2.62	2.28	12,959	10.6	0.9	2.15	1 Night Free Hotel
6	274	0.00	1.27	0	0.0	0.0	0.21	No Cancellation Fees
7	540	3.62	3.52	2,436	17.6	0.76	2.34	Family Package Deals

---

📈 Visual Insights

Revenue Share by Segment

Customer Value vs Segment Size

---

🔎 Deep Dives per Segment

Each segment can also be visualized with detailed plots (spending distribution, age profile, destinations, revenue share).

Example:

Hotel Loyalists

---

💡 Key Insights

• 70% of revenue comes from only 3 segments → Hotel Loyalists, Premium Elites, Family Package.
• Low-value segments (0, 3, 6) need activation strategies to increase engagement.
• Discount Hunters represent a price-sensitive group → can be retained via promotions.
• Family & Hotel travelers are highly engaged → perks strengthen loyalty.

---

📑 Full Report

👉 Download Full PDF Presentation

---

🛠️ Tools Used

• Python: pandas, seaborn, matplotlib, scikit-learn
• SQL: Feature engineering & filtering
• Visualization: Custom dashboards, Tableau
• Output: PowerPoint-ready CSVs, PDF report

---

👤 Author: @42KIKO | Date: Sep 27, 2025