This project aims to analyze customer behavior and preferences to define the most attractive reward or perk for each customer group. It involves processing customer data, calculating various indexes, segmenting customers, and determining suitable perks for each segment. The project also explores customer demographics to tailor the perks accordingly.
Data Collection: Gathering customer data, including behavioral metrics and demographic information using SQL query.
Index Calculation: Calculating various indexes like Hotel Hunter Index, Average Bags Index, Cancellation Rate, Bargain Hunter Index, Combined Booking Index, and Session Intensity Index for each customer using SQL.
Ranking Customers: Ranking customers based on their index values in descending order using Python.
Defining Perks: Defining the most attractive perk for each customer based on their minimum rank in the indexes. And segment customers into groups based on their preferred perks using Python.
Demographic Analysis: Analyzing customer segments by demographic characteristics such as age, gender, marital status, and parental status using Tableau
Visualization: Creating visualizations in Tableau to present the findings and insights from the analysis.
Conclusions and Recommendations: Summarizing the project's goals, findings, and provide recommendations based on customer segmentation. Creating slides and video presentation in canves.com