{ "fetched_at": "2026-10-20T00:20:18Z", "source_url": "https://www.kaggle.com/competitions/analytics-competition-havaianas-ec-data-2017/data", "pages": [ { "name": "data-description", "content": "## File descriptions\n* **ECommerce_data_train.csv** - the released data for analytics\n* **Column Description.xlsx** - supplemental column information about the data", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "## Goal of the Competition\nThe goal of this competition is to analyze Havaianas ECommerce data for several months of 2017. You will be given the real retail shopper data of platform [Shopee](https://shopee.sg/) and [Qoo10](https://www.qoo10.sg/).\n\nYour work will help Havaianas and [Aroodai](https://www.aroodai.com/) better understand their business. With better insights and visualization, they can apply these tactics into business growth. And you will also have the oppotunity to win the prizes sponsored by Aroodai.\n
\n## Havaianas Introduction\n- Havaianas is the largest sandal brand in the world, selling over 200 million pairs a year.\n- We are sharing 10 years of actual data from our Havaianas Singapore business from 2009 to 2018, including our 24 retail shops, 65 distribution points, warehouses, mobile app, 25 virtual stores and 8 eCommerce stores, totalling over 10 million pairs of Havaianas sold, making Singapore the biggest Havaianas distributor per capita in the world.\n- Actual Data includes: Sales & Cash-register data, transcribed Product data, product images, camera & IOT data, Marketing data, eCommerce data, CRM data, and much more.\n- We have also built a Havaianas deep-learning color-and-pattern recognition model using product photos, transcribed catalog data & our sales data to predict future high-selling sandal designs.\n
\n## Credential of Aroodai\n- AROODAI PTE. LTD. (Singapore Company 201539281E) started as our own in-house data-analytics company to service our own retail & restaurant & partners businesses in Singapore. We also worked closely with the Singapore Government, the Singapore Agency for Science, Technology and Research (A*STAR) and Deloitte Digital for their own Retail & Restaurant clients.\n- Our team built A.I. models to predict product sales, pricing & discounting, eCommerce traffic, customer behavior, marketing budgets & merchandise planning.\n- Since the Pandemic’s adverse effect on the retail & restaurant industry, we have closed our own retail businesses, and are now sharing our data & predictive models with outside retailers, restaurants and the data-science community at large by empowering a culture of sharing & knowledge.\n- Here, you’ll find our datasets on which much of our sample codes are built. We are encouraging the community to explore these datasets and, hopefully, make use of them towards your own learning journey in data science.\n- If you are a retailer, restaurant or data-scientist interested in what we do, or need more free actual data and models from our stores, outlets, warehouses and ecommerce sites, please let us know at contact@aroodai.com.\n
\n## What You Should Notice\n- [Shopee](https://shopee.sg/) and [Qoo10](https://www.qoo10.sg/) are ECommerce platforms like Amazon. Havaianas runs online shops on Shopee and Qoo10. Customers buy Havaianas products via online payment, and Shopee and Qoo10 will deliver the package of products through express delivery.\n- We released a lot of data or features, like purchase details, delivery information, inventory data and SKU styles, etc.\n- In this competition, you are free to conduct any kinds of data analysis or visualization, which you think will add value to Havaianas or Aroodai. You should follow the [Rules of this competition](https://www.kaggle.com/competitions/analytics-competition-havaianas-ec-data-2017/rules). Your solution will be evaluated based on the [metrics](https://www.kaggle.com/competitions/analytics-competition-havaianas-ec-data-2017/overview/evaluation) here.\n- **SPECIAL NOTICE:** This competition is an **Analytics Competition**. You **do not** need to submit any solution to Leaderboard. You **only** need to create **notebook** in [Code](https://www.kaggle.com/competitions/analytics-competition-havaianas-ec-data-2017/code).\n- We hold the rights to make some changes on the rules and arrangement of the competition.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "## One account per participant\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\n## No private sharing outside teams\nPrivately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.\n\n## Team Limits\nThis competition is an analytics competition. It emphasizes personal creativity about data analytics. It is not applicable on Team work. Any notebook created by more than one people will be judged non-qualified.\n\n## Submission Limits\nThere is no daily submission limit. Only the most recent submission by a team will be evaluated after the challenge deadline.\n\n## Competition Timeline\nCompetition Timeline dates (including Entry Deadline, Final Submission Deadline, Start Date, and Team Merger Deadline, as applicable) are reflected on the competition’s Overview > Timeline page.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Timeline", "content": "- January 10, 2023 - Start Date.\n\n- July 31, 2023 - Final Submission Deadline.\n\nAll deadlines are at 11:59 PM UTC on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Instructions", "content": "## Submission Instructions\nYou can make as many public notebooks as you like, but we will only consider your latest commit before the deadline of the copetition.\n\nAll team members must accept the competition rules before the submission deadline.\n\nA valid submission or notebook will include a Notebook Analysis. All notebooks submitted must be made public on or before the submission deadline to be eligible.\n\n**SPECIAL NOTICE:** This competition is an **Analytics Competition**. You **do not** need to submit any solution to Leaderboard. You **only** need to create **notebook** in [Code](https://www.kaggle.com/competitions/analytics-competition-havaianas-ec-data-2017/code).", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Prizes", "content": "## Prizes for Philippine Participants\nFor Philippine Participants, we are awarding Havaianas Online Gift Vouchers (Gift Vouchers valid only in the Philippines, delivered to winners via email)\n* 1st Prize - P10,000\n* 2nd Prize - P5,000\n* 3rd Prize - P2,500\n\n## Prizes for Participants that can accept Amazon Gift Cards in their country\nWinning recipient must live in a country that Amazon allows them to receive Gift Cards in their country's Amazon website. Not all countries comply with this Amazon restriction, please check Amazon.com for more information. If the winning participant cannot receive an Amazon gift card in their country, we cannot issue prizes in that country, and the winner will not receive a prize.\n* 1st Prize - USD$200 Amazon gift card\n* 2nd Prize - USD$100 Amazon gift card\n* 3rd Prize - USD$50 Amazon gift card", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "## Overview\n\nYour challenge is to generate actionable, practical, and novel insights from Havaianas ECommerce data.\n\n
\n\n## Scoring\n\nAn entry to the competition consists of a Notebook submission that is evaluated on the following five components, where 0 is the low score and 10 is the high score, and the Upvotes together as following formula:\n\ntotal\\_score = evaluation\\_average\\_score + Upvotes\n\nSubmissions will be judged based on how well they address:\n\nInnovation:\n\n- Are the proposed findings actionable?\n- Is this a way of looking at tracking data that is novel?\n- Is this project creative?\n\nAccuracy:\n\n- Is the work correct?\n- Are claims backed up by data?\n- Are the statistical models appropriate given the data?\n\nRelevance:\n\n- Does the analysis help for the business potential growth?\n\nClarity:\n\n- Evaluate the writing with respect to how clear the writer(s) make findings.\n\nData visualization/tables:\n\n- Are the charts and tables provided accessible, interesting, visually appealing, and accurate?\n\nJudges will be Aroodai managers and Data Scientists. Scores will be averaged so that each of the components above are weighed equally.\n\nThe top eight scoring papers will be selected as finalists.\n\n
\n\n## Notebook requirements\n\nNotebooks should consist of no more than 2,000 words and no more than 10 tables/figures. Submissions will not be penalized for any number of words or figures under this limit. Participants are encouraged to show statistical code if it helps readers better understand their analyses; most, if not all code, however, should be hidden in the Appendix.\n\nAll notebooks submitted must be made public on or before the submission deadline to be eligible.\n\nAny notebook that does not use the player tracking data will not be scored.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }