{"id":24248930,"url":"https://github.com/chaaalistaa/thelookecommerce---project","last_synced_at":"2026-04-17T18:02:23.519Z","repository":{"id":272413005,"uuid":"895410590","full_name":"chaaalistaa/Thelookecommerce---Project","owner":"chaaalistaa","description":"Analysis \"TheLook\" eCommerce with highlight goals such as identifying sales trends, understanding customer behaviors, enhancing customer retention, and driving repeat purchases.","archived":false,"fork":false,"pushed_at":"2024-12-02T04:17:24.000Z","size":19,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-14T09:44:14.621Z","etag":null,"topics":["big-data-analytics","bigquery","data-analytics","data-visualization","looker-studio","sql"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/chaaalistaa.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-28T06:47:55.000Z","updated_at":"2024-12-02T04:20:34.000Z","dependencies_parsed_at":"2025-01-14T09:44:18.942Z","dependency_job_id":"7ee99334-19ce-4421-a2a9-9edfc2b8dfaf","html_url":"https://github.com/chaaalistaa/Thelookecommerce---Project","commit_stats":null,"previous_names":["chaaalistaa/thelookecommerce---project"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chaaalistaa%2FThelookecommerce---Project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chaaalistaa%2FThelookecommerce---Project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chaaalistaa%2FThelookecommerce---Project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chaaalistaa%2FThelookecommerce---Project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/chaaalistaa","download_url":"https://codeload.github.com/chaaalistaa/Thelookecommerce---Project/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241884761,"owners_count":20036819,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["big-data-analytics","bigquery","data-analytics","data-visualization","looker-studio","sql"],"created_at":"2025-01-15T00:48:32.026Z","updated_at":"2026-04-17T18:02:23.467Z","avatar_url":"https://github.com/chaaalistaa.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Comprehensive Analysis and Optimization Strategies for TheLook E-Commerce\n\n# **1. Introduction**\n- 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.\nObjective: Highlight goals such as identifying sales trends, understanding customer behaviors, enhancing customer retention, and driving repeat purchases.\n\n# **2. Dataset \u0026 Tools Details:**\n- Data source: https://console.cloud.google.com/marketplace/product/bigquery-public-data/thelook-ecommerce\n- Available tables and key fields, such as:\n  - Users: Customer demographics and traffic sources.\n  - Orders: Purchase details, order status, and timestamps.\n  - Events: Web traffic and session activity.\n  - Products: Product categories, prices, and distribution details.\n  - Order Items: Purchase-level granularity.\n  - Distribution Centers: Inventory and shipment data.\n\n**Tools** \n- BigQuery: For querying and analysis.\n- Visualization Tools: Integration with Looker for data visualization.\n  \n# **3. Analysis Objectives**\n**A. E-commerce A Trends (2019-2024):**\n  - In-Depth InsightsE-commerce A has demonstrated remarkable growth from 2019 to 2024, particularly in revenue, profit, and order volume.\n  - Gender distribution is evenly split, with 50% male and 50% female, reflecting the platform’s broad appeal.\n\n**B. Customer Profile**\n  - Demographics\n    - Age and Gender: The primary customer base consists of young adults and middle-aged individuals (20-59 years old).\n      \n- Geographical Distribution:\n  - Users are spread across 15 countries, with the top 5 countries being:\n    1. China\n    2. USA\n    3. Brazil\n    4. South Korea\n    5. France\n\n  - User Growth Over Time:\n    - 2019: Started with approximately 16,500 users.\n    - 2021-2023: Experienced a decline due to market competition and global challenges (e.g., pandemic).\n    - 2024: Witnessed a surge, driven by innovative products and enhanced marketing strategies.\n\n\u003cimg width=\"371\" alt=\"{3BFF1BD0-9D7E-46BC-99AD-2B9118142D07}\" src=\"https://github.com/user-attachments/assets/112291d5-09b3-4c31-b042-4545fb9431d4\"\u003e\n\n**C. Traffic Insights:**\n\n  - Traffic Sources:\n    - The majority of visitors come from email campaigns, attracting over 1 million users. Other significant sources includes:\n      1. AdWords\n      2. YouTube ads\n      3. Facebook campaigns\n      4. Organic search\n        \n  - Visit Timing:\n    - Peak visiting hours are between 1:00 AM and 8:00 AM, primarily on Thursdays, Fridays, and Saturdays.\n    - This pattern suggests that users prefer browsing or shopping outside regular working hours, likely after completing their daily routines.\n    - Peak visiting hours are between 1:00 AM and 8:00 AM, primarily on Thursdays, Fridays, and Saturdays.\n    - This pattern suggests that users prefer browsing or shopping outside regular working hours, likely after completing their daily routines.\n\n  - Purpose of Visits:\n    - Most visitors are drawn to the platform by product promotions disseminated through these traffic sources.\n   \n  ![image](https://github.com/user-attachments/assets/f8fc5682-7643-4594-af9f-0467acde4db7)\n\n    \n**D. Purchase Behavior:**\n- Shopping Habits:\n  - Average products per order: 1 product.\n  - Average spending per order: $86.66.\n  - Repeat purchases typically occur every 1 year.\n    \n- Repeat Customer Frequency:\n  - Currently, 29,814 customers have made more than one purchase within a 5-year period.\n  - Challenge: This represents a low percentage compared to the total user base, indicating a need for strategies to boost customer loyalty and retention.\n\n \u003cimg width=\"646\" alt=\"{39155B12-94C7-446B-9254-13ABC4CE5C9C}\" src=\"https://github.com/user-attachments/assets/7b60ab22-9bad-4a8b-b3d2-e0f9c0ad1e77\"\u003e\n\n**E. Highest Revenue:**\n\n\u003cimg width=\"451\" alt=\"{78AEFE6C-CB5D-449A-AA71-5F190076026D}\" src=\"https://github.com/user-attachments/assets/eed67d5b-6ce1-4056-a63e-d14f6cadbc58\"\u003e\n\n- Traffic Source:\n  - The highest revenue is generated by purchases originating from search traffic, followed closely by organic traffic.\n  - These two sources are the most significant drivers of revenue, showcasing the importance of optimizing visibility in search engines and maintaining strong organic presence.\n\n**F. Loyalty and Churn Customer**\n1. Most Spending Customers Insights from the Data:\n  - A small percentage of customers contribute disproportionately to overall revenue.\n\n2. Returning vs. Churning Customers\n  -  Returning customers: Represent a consistent revenue stream but make up a relatively small portion of the overall user base.\n  -  Churning customers: A significant number of users do not return after their first purchase, contributing to revenue leakage.\n\n3. Non-Purchasing Users:\n  - A considerable number of users browse the platform without completing a purchase.\n  - This segment represents untapped potential, likely driven by barriers such as pricing, lack of trust, or unclear value propositions.\n\n\u003cimg width=\"449\" alt=\"{6718CEA7-2B63-4391-93DA-464B3077C883}\" src=\"https://github.com/user-attachments/assets/d4965d20-2d2c-4ea2-a8b2-5a3dca52fc3b\"\u003e\n\n# **4. Recommendations**\n1. Enhance Customer Loyalty: Develop rewards programs or loyalty incentives to encourage repeat purchases.\n2. Optimize Promotional Timing: Focus marketing efforts during peak visiting hours and days to maximize engagement.\n3. Personalize Email Marketing: Use demographic and behavioral data to craft more relevant and appealing campaigns.\n4. Enhancing search and organic traffic channels through refined SEO and high-impact content marketing.\n5. Capitalizing on high-revenue geographies by tailoring campaigns and product launches to top-performing cities.\n6. Leveraging best-selling product categories and strong brand partnerships to drive customer loyalty and maximize profitability.\n7. Reactivation Campaigns: Implement win-back strategies for churning customers, such as offering limited-time discounts or personalized incentives.\n8. Post-Purchase Engagement: Send follow-up communications (thank-you emails, satisfaction surveys) to maintain interest and drive repeat purchases.\n9. Enhanced Onboarding: Improve the first-purchase experience to leave a lasting impression, reducing the likelihood of churn.\n\n**Marketing Recommendations:**\n1. Collaborative Promotions: Partner with these brands to create co-branded campaigns, offering limited-edition products or bundled deals.\n2. Seasonal Campaigns: Leverage the popularity of outerwear and sweaters during colder months through targeted promotions.\n3. Highlight Premium Quality: Use storytelling and visuals to emphasize the quality and uniqueness of these brands, appealing to aspirational shoppers.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchaaalistaa%2Fthelookecommerce---project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchaaalistaa%2Fthelookecommerce---project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchaaalistaa%2Fthelookecommerce---project/lists"}