Spa Analysis_20160212_V3.0

Spa Data Analysis Overview

  • Conducted by Indian Institute of Management Bangalore (IIMB).

  • Focuses on analyzing spa customer data and understanding trends.

Study Objective

  • Understand customer demographics and visit patterns.

  • Segment customers based on service usage and location.

  • Analyze revenue and visit patterns for various services.

  • Evaluate customer lifetime value and churn behavior.

Methodology Used

  • Descriptive data analysis for insights generation.

  • Cluster analysis for customer segmentation.

  • Discrete Time Markov Chains and RFM analysis to identify loyal customers.

  • Calculation of Customer Lifetime Value (CLV) to identify valuable customers.

Data Description

  • 12 months of transaction data from September 2014 to January 2015.

  • Total of 6725 unique customers in the period.

  • 1290 unique customers with at least one transaction in September 2014 are used for Markov Chain analysis.

Variables Used for Analysis

  • Payment Type: Mode of payment (e.g., Card, Cash).

  • Number of Months Visited: Total months visited in the analysis year.

  • Number of Times Visited: Total visits within the year.

  • Customer Type: Male, Female, or both services.

  • Number of Services: Variety of services availed by the customer.

  • Membership Status: Whether the customer is a member.

  • Value of Services: Average spending on services.

Descriptive Analysis

  • Customer Segmentation:

    • 88% Existing Customers (5943): Visited prior to August 2014.

    • 12% New Customers (782): First visit between August 2014-August 2015.

  • Visit Trends:

    • 53.5% of customers visited only once within the year.

    • Close to 20% of customers visited 5 or more times.

Customer Visit Patterns

  • Analysis of frequency distributions:

    • Majority (50.9%) visited only once, with 8.4% visiting at least 12 times.

  • Services Usage by Gender:

    • Female services constituted 65% of total visits.

    • Male services were primarily hair services noted for an upward trend.

Revenue Analysis

  • Revenue increased consistently post-September 2014.

  • Peak Revenue: Observed spikes during December 2014.

  • Daily Revenue Trends: Significant spikes noted on weekends, predominantly Sundays and Saturdays.

Customer Segmentation Insights

  • Cluster Analysis:

    • Identified potential segments such as Casual Buyers, Family, and Highly Loyal customers.

  • Loyalty Trends:

    • Customer loyalty remained relatively consistent but varied month-to-month.

Churn Analysis

  • Defined churn based on a 4-month inactivity period using Markov Chain analysis.

  • Churn Behavior:

    • Majority of customers churning after extended periods of inactivity.

    • Recency state analysis indicated that the likelihood of returning customers diminishes over time.

Key Takeaways

  • Female-focused services dominate the spa industry.

  • Top 3 Services: Skin, Hair, and Hands & Feet.

  • Recommendations to improve male service offerings and investigate reasons for increased visitation during week 4.

Customer Segmentation and Implementation

  • Customer segmentation rules need to be established based on behavior and spending.

  • Implement strategies tailored to various customer segments for improved retention and increased revenue.

Conclusion

  • Insightful data analysis highlights essential areas for spa improvement and customer engagement strategies.

  • Recommendations can drive marketing strategies to target specific demographics.

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