Business Analytics: Consumer Segmentation & Financial Success

Problem Statement

A leading tech company, aims to understand the relationship between consumer segmentation and its financial success. By analyzing various consumer attributes and their correlation with revenue and product sales, I seek to identify key factors influencing the company's growth and develop targeted strategies for different consumer segments.

Data Extraction and Analysis Methodology

I collected and analyzed data from the company's customer relationship management (CRM) system and financial records. The data extraction process involved:

• Utilizing Python libraries such as pandas and numpy for data cleaning and preprocessing
• Employing Excel for initial data exploration and pivot table analysis
• Using SQL to query and merge data from various database sources
• Applying scikit-learn for customer segmentation using K-means clustering

The analysis focused on key variables including consumer age, account age, revenue generated, total products sold, demographics, and location data spanning the last five years.

Interactive Visualizations

Key Findings

My analysis revealed significant insights into the company's consumer base and its impact on financial performance:

• Consumer age groups 25-34 and 35-44 contribute the highest revenue, with a strong correlation to product diversity
• Account longevity shows a positive relationship with both revenue and product adoption rates
• Urban consumers generate 60% more revenue compared to rural counterparts, but rural markets show higher growth potential
• Demographic factors such as education level and occupation significantly influence product preferences and spending patterns