https://github.com/chaaalistaa/thelookecommerce---project
Analysis "TheLook" eCommerce with highlight goals such as identifying sales trends, understanding customer behaviors, enhancing customer retention, and driving repeat purchases.
https://github.com/chaaalistaa/thelookecommerce---project
big-data-analytics bigquery data-analytics data-visualization looker-studio sql
Last synced: 2 months ago
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Analysis "TheLook" eCommerce with highlight goals such as identifying sales trends, understanding customer behaviors, enhancing customer retention, and driving repeat purchases.
- Host: GitHub
- URL: https://github.com/chaaalistaa/thelookecommerce---project
- Owner: chaaalistaa
- Created: 2024-11-28T06:47:55.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-02T04:17:24.000Z (over 1 year ago)
- Last Synced: 2025-01-14T09:44:14.621Z (over 1 year ago)
- Topics: big-data-analytics, bigquery, data-analytics, data-visualization, looker-studio, sql
- Language: Jupyter Notebook
- Homepage:
- Size: 18.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Comprehensive Analysis and Optimization Strategies for TheLook E-Commerce
# **1. Introduction**
- Dataset Overview: The "TheLook" eCommerce dataset hosted on Google BigQuery is designed to analyze customer behavior, sales trends, and operational insights for a fictional clothing retailer.
Objective: Highlight goals such as identifying sales trends, understanding customer behaviors, enhancing customer retention, and driving repeat purchases.
# **2. Dataset & Tools Details:**
- Data source: https://console.cloud.google.com/marketplace/product/bigquery-public-data/thelook-ecommerce
- Available tables and key fields, such as:
- Users: Customer demographics and traffic sources.
- Orders: Purchase details, order status, and timestamps.
- Events: Web traffic and session activity.
- Products: Product categories, prices, and distribution details.
- Order Items: Purchase-level granularity.
- Distribution Centers: Inventory and shipment data.
**Tools**
- BigQuery: For querying and analysis.
- Visualization Tools: Integration with Looker for data visualization.
# **3. Analysis Objectives**
**A. E-commerce A Trends (2019-2024):**
- In-Depth InsightsE-commerce A has demonstrated remarkable growth from 2019 to 2024, particularly in revenue, profit, and order volume.
- Gender distribution is evenly split, with 50% male and 50% female, reflecting the platform’s broad appeal.
**B. Customer Profile**
- Demographics
- Age and Gender: The primary customer base consists of young adults and middle-aged individuals (20-59 years old).
- Geographical Distribution:
- Users are spread across 15 countries, with the top 5 countries being:
1. China
2. USA
3. Brazil
4. South Korea
5. France
- User Growth Over Time:
- 2019: Started with approximately 16,500 users.
- 2021-2023: Experienced a decline due to market competition and global challenges (e.g., pandemic).
- 2024: Witnessed a surge, driven by innovative products and enhanced marketing strategies.
**C. Traffic Insights:**
- Traffic Sources:
- The majority of visitors come from email campaigns, attracting over 1 million users. Other significant sources includes:
1. AdWords
2. YouTube ads
3. Facebook campaigns
4. Organic search
- Visit Timing:
- Peak visiting hours are between 1:00 AM and 8:00 AM, primarily on Thursdays, Fridays, and Saturdays.
- This pattern suggests that users prefer browsing or shopping outside regular working hours, likely after completing their daily routines.
- Peak visiting hours are between 1:00 AM and 8:00 AM, primarily on Thursdays, Fridays, and Saturdays.
- This pattern suggests that users prefer browsing or shopping outside regular working hours, likely after completing their daily routines.
- Purpose of Visits:
- Most visitors are drawn to the platform by product promotions disseminated through these traffic sources.
**D. Purchase Behavior:**
- Shopping Habits:
- Average products per order: 1 product.
- Average spending per order: $86.66.
- Repeat purchases typically occur every 1 year.
- Repeat Customer Frequency:
- Currently, 29,814 customers have made more than one purchase within a 5-year period.
- Challenge: This represents a low percentage compared to the total user base, indicating a need for strategies to boost customer loyalty and retention.
**E. Highest Revenue:**
- Traffic Source:
- The highest revenue is generated by purchases originating from search traffic, followed closely by organic traffic.
- These two sources are the most significant drivers of revenue, showcasing the importance of optimizing visibility in search engines and maintaining strong organic presence.
**F. Loyalty and Churn Customer**
1. Most Spending Customers Insights from the Data:
- A small percentage of customers contribute disproportionately to overall revenue.
2. Returning vs. Churning Customers
- Returning customers: Represent a consistent revenue stream but make up a relatively small portion of the overall user base.
- Churning customers: A significant number of users do not return after their first purchase, contributing to revenue leakage.
3. Non-Purchasing Users:
- A considerable number of users browse the platform without completing a purchase.
- This segment represents untapped potential, likely driven by barriers such as pricing, lack of trust, or unclear value propositions.
# **4. Recommendations**
1. Enhance Customer Loyalty: Develop rewards programs or loyalty incentives to encourage repeat purchases.
2. Optimize Promotional Timing: Focus marketing efforts during peak visiting hours and days to maximize engagement.
3. Personalize Email Marketing: Use demographic and behavioral data to craft more relevant and appealing campaigns.
4. Enhancing search and organic traffic channels through refined SEO and high-impact content marketing.
5. Capitalizing on high-revenue geographies by tailoring campaigns and product launches to top-performing cities.
6. Leveraging best-selling product categories and strong brand partnerships to drive customer loyalty and maximize profitability.
7. Reactivation Campaigns: Implement win-back strategies for churning customers, such as offering limited-time discounts or personalized incentives.
8. Post-Purchase Engagement: Send follow-up communications (thank-you emails, satisfaction surveys) to maintain interest and drive repeat purchases.
9. Enhanced Onboarding: Improve the first-purchase experience to leave a lasting impression, reducing the likelihood of churn.
**Marketing Recommendations:**
1. Collaborative Promotions: Partner with these brands to create co-branded campaigns, offering limited-edition products or bundled deals.
2. Seasonal Campaigns: Leverage the popularity of outerwear and sweaters during colder months through targeted promotions.
3. Highlight Premium Quality: Use storytelling and visuals to emphasize the quality and uniqueness of these brands, appealing to aspirational shoppers.