A Retail company wants to analyze customer purchase behavior using historical shopping data collected during a major sales event. The management team aims to understand:
- Who are their primary buyers?
- What customer segments contribute the most to sales?
- Which factors influence product category purchases?
- How demographic characteristics impact spending?
The insights will support marketing strategy, product positioning, and customer targeting to boost revenue during future sale events.
- To perform an in-depth exploratory analysis of customer dataset.
- To identify key demographic groups with the highest and lowest purchase volumes.
- To analyze product category preferences by age, gender, marital status, and occupation.
- To detect purchasing patterns across repeated visits (User_ID-level insights).
- To extract actionable insights to help Walmart improve customer segmentation.
- Loaded the dataset and inspected overall structure and data types.
- Identified categorical and numerical variables and performed type-casting where needed.
- Checked for missing values and duplicates (none found).
- Explored demographic distributions: age group, gender, marital status, occupation.
- Performed EDA on product categories and purchase amounts.
- Analyzed user-level repeat purchases to understand customer retention.
- Generated visualizations to uncover trends and segment behavior.
- Interpreted EDA findings into business insights for retail decision-making.
- Frequency counts & distributions
- Boxplots, histograms, bar charts
- Groupby operations for segment-wise insights
- Data type correction
- Feature understanding
- User-level aggregation
- Age-wise behavior
- Gender-wise purchasing
- Marital status influence
- Product category preference
- Customer retention and repeat visit analysis
- Revenue contribution analysis
- Target customer persona development
- No missing values
- No duplicate records
- Many users appear multiple times → repeat visits, not unique customers
- Indicates strong customer retention during the sales period
- Majority of buyers belong to the 26–35 age group
- There are more male customers than female
- More single customers compared to married ones
- Some occupations have a significantly higher representation
- Male customers have a slightly higher average purchase amount
- The age group 26–35 contributes the most revenue
- Some product categories show strong popularity across specific demographics
- Customers often make multiple visits, showing event-based engagement
- Clear preference patterns are visible across age and gender
- Some product categories are more frequently purchased together
- Product_Category_1, 2, and 3 show varied distributions across important segments
| Segment | Key Behavior | Business Implication |
|---|---|---|
| Age 26–35 | Highest purchase volume | Target this age group more heavily in campaigns |
| Male Customers | Higher mean purchase amount | Create male-focused promotional bundles |
| Single Individuals | More active shoppers | Weekend/holiday offers may perform well |
| Repeat Customers | High frequency of visits | Implement loyalty-based promotions |
- Walmart’s peak buyers are young adults (26–35), making them the most profitable segment.
- Male customers spend more per transaction, making them valuable targets for premium bundles.
- Customers frequently revisit the store → strong opportunity for loyalty programs.
- Product categories show differentiated demand patterns — ideal for targeted upselling.
- Single customers are more likely to make impulse or discretionary purchases.
- Prioritize promotional campaigns for the 26–35 age group
- Design personalized offers for male customers who show higher spending habits
- Bundle products frequently bought together
- Offer discounts on categories with high repeat engagement
- Introduce points, cashbacks, or early-access deals for repeat customers
- Create membership tiers for high-frequency shoppers
- Provide separate campaigns for married vs. single individuals
- Use targeted ads based on occupation segments
- Analyze peak purchase times to optimize manpower and inventory
- Create flash deals for categories with stagnant demand
This analysis highlights key drivers behind Walmart’s customer purchase behavior. The 26–35 age group, male customers, and single individuals form the core revenue-generating segments. Repeated visits indicate strong customer interest that Walmart can further nurture through loyalty programs and targeted marketing.
Understanding category preferences and demographic purchase patterns enables Walmart to refine its strategy for future mega-events, optimize stock allocation, and boost revenue through data-driven decision-making.