Customer Behavior Analysis

Customer behaviour analysis refers to the process of examining and understanding how customers interact with your product, service, or brand. It involves collecting, interpreting, and analysing data related to customer actions to gain insights into their behaviour throughout the entire customer journey.

💡 Business Problem

Uncover key insights into customer purchase patterns, segment customers based on their behavior, and provide actionable recommendations to enhance marketing strategies, improve customer retention, and increase sales.

💡 Data and Tools

• Dataset : Data
• Tools & Technologies : Python
• Installations: Numpy, Pandas, Scipy, Matplotlib and Seaborn

💡Major Results

1. The majority of customers fall within the 36-50 age group. This age group is also the target for frequent purchases.
2. The male population accounts for 68% of customers, indicating a significant gender imbalance in the customer base.
3. Clothing (44.5%) and Accessories (31.8%) are the most popular categories. These categories should be the focus for inventory and promotional efforts.
4. The average review rating is 3.75, with a significant proportion (40.41%) giving ratings between 4-4.9. High-rated products should be highlighted in marketing materials.
5. Male customers generate $157,890 in revenue, significantly higher than the $75,191 generated by female customers.

💡 Description

The steps involved in building this healthcare analytics projects are mentioned below:

• Data Preparation

  1. Imported essential libraries for data manipulation and visualization.
  2. Cleaned the data to handle missing values, correct data types, and removed inconsistencies.

• Descriptive Analysis

  1. Analyzed the general features of the dataset to understand customer demographics and behavior, including age distribution, top locations, gender distribution, product categories, payment methods, and shipping types.

• Product Analysis

  1. Conducted a thorough analysis of product categories and items purchased to understand purchase patterns and preferences.
  2. Examined popular product categories and items by age group and gender, seasonal product trends, and top-selling items.

• Customer Engagement Metrics

  1. Analyzed key performance indicators (KPIs) such as review ratings, subscription status, discount usage, and promo code usage.
  2. Explored correlations between these features and examined metrics like average review ratings, subscription proportions, and the impact of discounts and promo codes.

• Customer Revenue Analysis

  1. Investigated revenue generation through purchase amounts and frequency of purchases.
  2. Analyzed average purchase amounts, total revenue by product category, and correlations between purchase amounts and other KPIs.
  3. Explored purchase patterns by season, gender, and age group.

💡 Recommendations

1. Targeting and Personalization:
   Focus marketing efforts on the 36-50 age group and the male demographic to maximize engagement and sales.
2. Subscription Services:
   Develop strategies to encourage more female customers to opt for subscription services, possibly through tailored incentives and promotions.
3. Promotional Strategies:
   Emphasize the benefits of discounts and promo codes to increase their usage across all demographics, particularly targeting the female segment.
4. Product and Inventory Management:
   Prioritize stocking and promoting high-selling items in Clothing and Accessories, especially during Spring and Fall.
5. Revenue Optimization:
   Analyze ways to boost spending among customers who frequently use discounts and promo codes while maintaining their purchase frequency.

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