Global C2C Fashion Store User Behaviour Analysis
Analyzing Buyer and Seller Profiles across Countries
By Jeffrey Mvutu Mabilama [source]
About this dataset
Welcome to an exciting exploration of global C2C fashion store user behaviour! This dataset seeks to serve as a benchmark by providing valuable insights into e-commerce users, enabling you to make informed decisions and effectively grow your business. Let's dive right into the data!
This dataset contains records on over 9 million registered users from a successful online C2C fashion store launched in Europe around 2009 and later expanded worldwide. It includes metrics such as country, gender, active users, top buyers/sellers/ratio*, products bought/sold/listed* and social network features (likes/follows). Furthermore this is just a preview of much larger data set which contains more detailed information including product listings, comments from listed products etc.
E-commerce has become an essential part of our lives - people are now accustomed to buying anything with a few clicks online. With so many unknown elements that come with not only selling but also providing good customer service - understanding user behavior is key for success in this domain. By utilizing this dataset you can answer questions such as 'how many customers are likely to drop off after years of using my service?,' 'are my users active enough compared to those in this dataset?,” or “how likely are people from other countries signing up in a C2C website?' In addition, if you think this kind odf dataset may be useful don't forget do show your support or appreciation by leaving an upvote or comment on the page!
My Telegram bot will answer any queries regarding the datasets as well allow you see contact me directly if necessary; also please don't forget check out the *[data.world page](https://data.world/jfreex/e-commerce-users-of-a-french-c2c
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How to use the dataset
This dataset provides a useful overview of global users' behavior in an online C2C fashion store. The data includes metrics such as buyers, top buyers, top buyer ratio, female buyers and their respective ratios, etc., per country. This dataset can be used to gain insights into how global audiences interact with the store and draw conclusions from comparison between different countries.
In order to make use of this dataset, one must first familiarize themselves with the various metrics included in it. These include: country; number of overall buyers; number of top buyers; ratio(s) of them (top buyer to total buyer); female-related data (buyers, top female buyers); bought-to-wish/like ration (top and non-top separately); overall products bought/wished/liked; total products sold by tops sellers in the same country versus what they sold outside the country; mean value for product stats (sold/listed/etc...) from looking at the whole population or just users that make those actions multiple times; average days for user offline /lurking around on the site without posting anything or buying anything etc.; mean follower(s) count(s).
Using this data one could generate reports about user behavior within particular countries either manually by computing all statistics or by using libraries like Pandas or SQL with queries made toward this datasets which consists of columns representing individual countries with all values necessary to answer any questions you might have regarding how many people buy something out there per region and what type they are –– Are they Top Buyer? Female? Etc.
Further potential work could involve utilising machine learning tools such as clustering algorithms to group similar customers together based on certain traits like age group, profession etc., so that personalised marketing promotions can be targetted at these customer clusters rather than aiming more generic ads at everyone!
Finally combined with other related product datasets which is available upon request via JfreexDatasets_bot provided by Jfreex team , this dataset can become another powerful tool providing you actionable insights into customers today — allowing you build better strategies towards improving customer experience tomorrow!
Research Ideas
- Analyzing the conversion rate of users on a website - Comparing user metrics like the overall number of buyers, female buyers, top buyers ratio and top buyer gender can help determine if users in certain countries are more or less likely to convert into customers. Additionally, comparing average metrics like products bought or offline days can help identify areas of improvement and optimization to maximize conversion rates.
- Characterizing customer behavior by country - Using this dataset, businesses can get an idea of how their customers are behaving in different countries based on factors such as mean followers/following, shopping patterns and product likes/wishes ratio which could then be used for personalized marketing campaigns for those particular markets.
- Making better inventory decisions - By analyzing the number of products listed by sellers from different countries based on factors like best sold ratio and mean products sold per seller from each country, businesses will have a good idea about demand for certain products and necessities to make efficient inventory decisions to avoid overstocking or undersupplying goods
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: Buyers-repartition-by-country.csv
| Column name |
Description |
| country |
Country of origin of the user. (String) |
| buyers |
Total number of buyers in the country. (Integer) |
| topbuyers |
Total number of top buyers in the country. (Integer) |
| topbuyerratio |
Ratio of top buyers to total buyers in the country. (Float) |
| femalebuyers |
Total number of female buyers in the country. (Integer) |
| topfemalebuyers |
Total number of top female buyers in the country. (Integer) |
| topmalebuyers |
Total number of top male buyers in the country. (Integer) |
| femalebuyersratio |
Ratio of female buyers to total buyers in the country. (Float) |
| topfemalebuyersratio |
Ratio of top female buyers to total buyers in the country. (Float) |
| boughtperwishlistratio |
Ratio of products bought to products wished for in the country. (Float) |
| boughtperlikeratio |
Ratio of products bought to products liked in the country. (Float) |
| topboughtperwishlistratio |
Ratio of top products bought to top products wished for in the country. (Float) |
| topboughtperlikeratio |
Ratio of top products bought to top products liked in the country. (Float) |
| totalproductsbought |
Total number of products bought in the country. (Integer) |
| totalproductswished |
Total number of products wished for in the country. (Integer) |
| totalproductsliked |
Total number of products liked in the country. (Integer) |
| toptotalproductsbought |
Total number of top products bought in the country. (Integer) |
| toptotalproductswished |
Total number of top products wished for in the country. (Integer) |
| toptotalproductsliked |
Total number of top products liked in the country. (Integer) |
| meanofflinedays |
Average number of days offline for users in the country. (Float) |
| topmeanofflinedays |
Average number of days offline for top users in the country. (Float) |
| meanfollowers |
Average number of followers for users in the country. (Float) |
| meanfollowing |
|
File: Countries-with-Top-Sellers-(Fashion-C2C).csv
| Column name |
Description |
| country |
Country of origin of the user. (String) |
| meanofflinedays |
Average number of days offline for users in the country. (Float) |
| topmeanofflinedays |
Average number of days offline for top users in the country. (Float) |
| meanfollowers |
Average number of followers for users in the country. (Float) |
| meanfollowing |
|
| sellers |
Total number of sellers in the country. (Integer) |
| topsellers |
Total number of top sellers in the country. (Integer) |
| topsellerratio |
Ratio of top sellers to total sellers in the country. (Float) |
| femalesellersratio |
Ratio of female sellers to total sellers in the country. (Float) |
| topfemalesellersratio |
Ratio of female top sellers to total top sellers in the country. (Float) |
| topmalesellers |
Total number of male top sellers in the country. (Integer) |
| countrysoldratio |
Ratio of products sold in the country to total products sold. (Float) |
| bestsoldratio |
Ratio of products sold by top sellers in the country to total products sold. (Float) |
| toptotalproductssold |
Total number of products sold by top sellers in the country. (Integer) |
| toptotalproductslisted |
Total number of products listed by top sellers in the country. (Integer) |
| topmeanproductssold |
Average number of products sold by top sellers in the country. (Float) |
| topmeanproductslisted |
Average number of products listed by top sellers in the country. (Float) |
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Jeffrey Mvutu Mabilama.