The Customer Shopping Preferences Data Analysis project seeks to reveal key insights into consumer behavior and purchasing trends through an extensive dataset. The dataset includes a variety of customer attributes such as age, gender, purchase history, preferred payment methods, frequency of purchases, and more. By examining this information, businesses can make well-informed decisions to refine their product selections, enhance marketing efforts, and boost overall customer satisfaction.
Dataset Link: https://www.kaggle.com/datasets/iamsouravbanerjee/customer-shopping-trends-dataset/
- Age Group Purchasing Behavior: Analyzing the purchasing behavior of the most popular age groups, including their preferred items and payment methods.
- Gender Distribution: Analyzing the ratio of male to female customers to develop strategies for attracting more female customers.
- Category Popularity: Determining the most and least popular product categories to inform inventory decisions.
- Popular Items: Identifying the most frequently purchased items to enhance product placement and promotions.
- Seasonal Trends: Analyzing purchase data across different seasons to optimize seasonal marketing campaigns.
- Payment Methods: Identifying the most popular payment methods to streamline the checkout experience.
- Shipping Preferences: Understanding the most preferred shipping types to improve logistics.
Step 1: Ask
- Defining Problem : Obtaining the understanding of the consumer behavior and purchasing patterns
- Focus: Concentrating on identifying key trends and actionable insights that could directly impact business strategies.
Step 2: Prepare
- Metrics to Measure: Gender distribution, age groups, product categories, popular items, seasonal purchase trends, payment methods, and shipping preferences.
- Data Location: shopping_trends_updated dataset.
- Data Security: Implemented necessary security measures to protect customer data, ensuring compliance with data protection regulations.
Step 3: Process
- Cleaning Data: We used SQL functions to identify and correct errors, such as removing duplicates, correcting incorrectly entered data, and checking for extra spaces.
- Bias Check: We examined the dataset for potential biases that could skew our analysis.
Step 4: Analyze
- Data Sorting and Formatting: We sorted and formatted data to facilitate calculations, combined data from multiple sources where necessary, and created tables for clearer results.
- Key Findings:
- Gender Distribution: The ratio of male to female customers is about 68:32.
- Age Groups: Identified the most common female age group as 46-59.
- Product Categories: Determined the most and least popular product categories.
- Popular Items: Found the top items purchased by customers.
- Seasonal Trends: Analyzed purchase counts by season.
- Payment Methods: Identified the most popular payment methods.
- Shipping Preferences: Determined the most popular shipping types.
Step 5: Share
- Visualization Tools: We used Tableau to create interactive dashboards and graphs.
Step 6: Act Recommendations based on our findings:
- Gender Targeting: Develop marketing strategies specifically aimed at attracting -more female customers, given the 68:32 male-to-female ratio.
- Age Group Focus: Tailor products and marketing strategies for the 46-59 age group, which is the most common among female customers.
- Product Optimization: Focus on stocking and promoting the most popular product categories and items.
- Seasonal Campaigns: Plan marketing campaigns around seasons with higher purchase counts.
- Payment Method Optimization: Ensure the most popular payment methods are easy and efficient for customers.
- Shipping Enhancements: Improve logistics to prioritize the most popular shipping types.
Dashboard using tableau
Link: https://public.tableau.com/shared/F575ZNXCQ?:display_count=n&:origin=viz_share_link
