Customer-Segmentation-Analysis

Customer Segmentation Analysis

Introduction

Company Overview

Zomato is a multinational restaurant aggregator and food delivery company, committed to revolutionizing the way people experience dining and food services. With a vast network of restaurants and a user-friendly platform, Zomato aims to connect food enthusiasts with diverse culinary experiences while providing convenient and efficient food delivery services. Zomato has embarked on a strategic initiative to enhance its understanding of customer demographics and purchasing behavior.

Objective

The overarching objective of this project is to dissect Zomato’s customer base through detailed segmentation analysis. By scrutinizing demographic attributes and purchasing behaviors, the aim is to extract actionable insights that will guide decision-making and drive sustainable business growth. The primary goal is to unravel insights from extensive datasets, empowering Zomato to optimize marketing strategies, refine customer targeting, and elevate the overall user experience.

Data Set

• Users table (https://github.com/chiangsuanne/Customer-Segmentation-Analysis/blob/main/users.csv)
• Orders table (https://github.com/chiangsuanne/Customer-Segmentation-Analysis/blob/main/orders.csv)

Tools

Tableau for joining and cleaning data for analysis and visualization

Project Analysis

Customer Segmentation Insights

• Objective: Identify and categorize customers based on Recency, Frequency, and Monetary metrics.
• Outcome: Highlight key customer segments, including Best Customers, Big Spenders, and Loyal Customers.
• Actionable Insights: Inform targeted marketing strategies and enhance customer engagement.

Purchasing Behavior Analysis

• Objective: Analyze purchasing behavior patterns through a Time Series Line Chart, focusing on order trends by yearly quarter.
• Outcome: Identify peak order quarters to optimize promotional activities and resource allocation.
• Actionable Insights: Enable strategic planning for promotions and enhance resource efficiency.

Demographics Insights Integration

• Objective: Integrate demographic breakdown (age, gender) visualization.
• Outcome: Understand the age and gender distribution of Zomato’s customer base.
• Actionable Insights: Tailor marketing campaigns to specific demographic groups, enhancing personalized user experiences.

Total Sales Value for Best Customers:

• Objective: Compute the total sales value for the top Best Customers segment, sorted in descending order.
• Outcome: Identify high-value customers contributing significantly to Zomato’s revenue.
• Actionable Insights: Tailor promotional efforts and loyalty programs to maximize engagement with high-value customers.

Dashboard

View dynamic dashboard

Executive Summary

By synthesizing insights from the RFM analysis, purchasing behavior, demographic breakdowns, geographic analysis, and total sales value computation, Zomato can formulate a holistic strategy. The company can refine marketing efforts, allocate resources effectively, and foster personalized engagement, thereby solidifying its market leadership and ensuring sustainable growth in the competitive food delivery and restaurant aggregation industry.

Conclusion

1. RFM Customer Segments:
   • Visualization Strength: The RFM Customer Segments effectively illustrate the strength of customer groups in terms of customer counts. Occasional Buyers, Loyal Customers, and Big Spenders emerge as the top three prominent groups, with the Best Customers being particularly noteworthy.
   • Insights from RFM Segment Details: Delving into each segment reveals valuable insights. Best Customers, characterized by an average spend of $46,909, four orders, and a recency of 87 days, stand out as the most lucrative and engaged group.
2. RFM Segment Sales Over Time:
   • Trend Analysis: The RFM Segment Sales Over Time visualization unveils trends in both spending and customer counts. Notably, Best Customers and Recent Buyers show an increase from 2019 Q4 to 2020 Q1. Conversely, Loyal Customers experience a decline in both spending and count during the same period. Stability characterizes other segments from 2018 Q1 to 2019 Q3, with exceptions in Big Spenders and Loyal Customers.
   • Potential Actionable Insights: Tailoring strategies to retain Loyal Customers and understanding the factors driving the increase in Best Customers could optimize marketing efforts.
3. Demographics Insights:
   • Top Demographic Group: Females in the 21-25 age group, with a monthly income higher than $50,000, emerge as the highest average sales contributors.
   • Second Top Demographic Group: Females in the 26-30 age group, with a monthly income below $10,000, constitute the second-highest average sales contributors.
   • Potential Actionable Insights: Tailoring promotions and marketing messages to cater specifically to these demographic groups could yield higher returns.
4. Total Sales Value for Best Customers:
   • Revenue Optimization: The computation of the total sales value for the top Best Customers segment, sorted in descending order, offers a deep understanding of high-value customers. Tailoring loyalty programs and personalized engagement for this segment could maximize revenue.

Suggestions for Further Improvement and Business Outcomes

1. Targeted Marketing Campaigns:
   • Suggestion: Develop personalized marketing campaigns tailored to the top-performing RFM segments—Occasional Buyers, Loyal Customers, and Big Spenders. Leverage insights from the demographic analysis to create targeted promotions that resonate with specific age and income groups.
   • Expected Outcome: Increase customer engagement, loyalty, and overall satisfaction by tailoring promotions, discounts, and marketing messages to the unique preferences and behaviors of each segment.
2. Enhanced Best Customer Engagement:
   • Suggestion: Develop a comprehensive strategy to engage and retain Best Customers, incorporating insights from the total sales value computation.
   • Expected Outcome: Maximize revenue from high-value customers by offering exclusive benefits, personalized recommendations, and loyalty programs, fostering a deeper connection with the brand.
3. Dynamic Pricing Strategies:
   • Suggestion: Implement dynamic pricing strategies based on the purchasing power of different demographic segments. Adjust pricing, discounts, or promotions to align with the spending capacity and preferences of females in the 21-25 age group with higher incomes and those in the 26-30 age group with lower incomes.
   • Expected Outcome: Optimized pricing structures that resonate with the target audience, resulting in improved conversion rates and increased average sales amounts.
4. Retention Strategies for Declining Segments:
   • Suggestion: Develop targeted retention strategies for declining segments, such as Loyal Customers. Implement personalized communication, exclusive offers, or loyalty incentives to re-engage customers and mitigate the observed declining trends.
   • Expected Outcome: Strengthen customer loyalty by addressing factors contributing to the decline in spending. This may involve exclusive offers, loyalty programs, or personalized communications to re-engage and retain this valuable customer segment.
5. Regular Monitoring and Feedback Loop:
   • Suggestion: Establish a framework for continuous monitoring of RFM segments and demographic trends. Regularly update strategies based on evolving customer behavior, ensuring the company remains agile in responding to market dynamics.
   • Expected Outcome: Timely identification of shifts in customer preferences and the ability to proactively adapt marketing, pricing, and retention strategies for sustained business growth.

Implementing these suggestions will contribute to a more customer-centric approach, enhancing the overall effectiveness of marketing and sales efforts. By tailoring strategies to the unique characteristics of each segment and demographic group, the company can foster long-term customer relationships, drive revenue growth, and maintain a competitive edge in the market.