Fashion E-commerce User and Country Trends

Columns

• identifierHash: A unique hash identifying each entry.
• type: The category of the user.
• country: The user's country of residence.
• language: The preferred language of the user.
• socialNbFollowers: The count of social media followers the user has.
• socialNbFollows: The count of social media accounts the user follows.
• socialProductsLiked: The count of social media products liked by the user.
• productsListed: The total number of products listed by the user.
• productsSold: The total number of products sold by the user.
• productsPassRate: The pass rate associated with the user's products.
• productsWished: The count of products added to the user's wishlist.
• productsBought: The total number of products bought by the user.
• gender: The user's gender.
• civilityGenderId: An identifier for the user's civility gender.
• civilityTitle: The civility title of the user.
• hasAnyApp: Indicates if the user possesses any mobile application.
• hasAndroidApp: Indicates if the user has an Android application.
• hasIosApp: Indicates if the user has an iOS application.
• hasProfilePicture: Indicates if the user has a profile picture.
• daysSinceLastLogin: The number of days elapsed since the user's last login.
• seniority: The user's seniority level.
• seniorityAsMonths: The user's seniority expressed in months.
• seniorityAsYears: The user's seniority expressed in years.
• countryCode: The country code corresponding to the user's country.

• country: The name of the country.
• buyers: The overall count of buyers within the country.
• topbuyers: The count of top-tier buyers in the country.
• topbuyerratio: The ratio of top buyers to the total number of buyers in the country.
• femalebuyers: The count of female buyers in the country.
• malebuyers: The count of male buyers in the country.
• topfemalebuyers: The count of top female buyers in the country.
• topmalebuyers: The count of top male buyers in the country.
• femalebuyersratio: The ratio of female buyers in the country.
• topfemalebuyersratio: The ratio of top female buyers in the country.
• boughtperwishlistratio: The ratio of products bought per wishlist item in the country.
• boughtperlikeratio: The ratio of products bought per liked product in the country.
• topboughtperwishlistratio: The ratio of products bought per wishlist for top buyers in the country.
• topboughtperlikeratio: The ratio of products bought per liked product for top buyers in the country.
• totalproductsbought: The overall count of products bought in the country.
• totalproductswished: The overall count of products wished for in the country.
• totalproductsliked: The overall count of products liked in the country.
• toptotalproductsbought: The overall count of products bought by top buyers in the country.
• toptotalproductswished: The overall count of products wished for by top buyers in the country.
• toptotalproductsliked: The overall count of products liked by top buyers in the country.
• meanproductsbought: The average number of products bought per buyer in the country.
• meanproductswished: The average number of products wished per buyer in the country.
• meanproductsliked: The average number of products liked per buyer in the country.
• topmeanproductsbought: The average number of products bought per buyer for top buyers in the country.
• topmeanproductswished: The average number of products wished per buyer for top buyers in the country.
• topmeanproductsliked: The average number of products liked per buyer for top buyers in the country.
• meanofflinedays: The average number of offline days for buyers in the country.
• topmeanofflinedays: The average number of offline days for top buyers in the country.
• meanfollowers: The average number of social media followers for buyers in the country.
• meanfollowing: The average number of social media accounts followed by buyers in the country.
• topmeanfollowers: The average number of social media followers for top buyers in the country.
• topmeanfollowing: The average number of social media accounts followed by top buyers in the country.

Distribution

• type: Has a single unique value, 'user', for all entries.
• country: Features 200 unique country values, with France (25%) and Etats-Unis (21%) being the most frequent.
• language: Contains 5 unique language preferences, predominantly 'en' (52%) and 'fr' (27%).
• gender: Shows 2 unique values, with 'F' accounting for 77% and 'M' for 23%.
• civilityTitle: Displays 3 unique titles, with 'mrs' at 77% and 'mr' at 23%.
• hasAnyApp: Shows that 74% of users do not have any mobile app, while 26% do.
• hasAndroidApp: Reveals that 95% of users do not have an Android app.
• hasIosApp: Indicates that 78% of users do not have an iOS app.
• hasProfilePicture: An overwhelming 98% of users have a profile picture.
• Numerical columns such as socialNbFollowers (mean 3.43), socialNbFollows (mean 8.43), productsListed (mean 0.09), productsSold (mean 0.12), productsWished (mean 1.56), and productsBought (mean 0.17) show relatively low average engagement or activity per user, suggesting a skewed distribution with many users having zero or low values.

Usage

• Analysing user behaviour patterns in fashion e-commerce to identify key trends and engagement drivers.
• Understanding country-level e-commerce trends to inform market entry strategies or localised marketing efforts.
• Developing predictive models for buyer engagement, product listing success, or purchase likelihood.
• Investigating the influence of social media metrics (followers, follows, product likes) on user activity and purchasing decisions.
• Segmenting users based on demographics, app usage, and activity levels for targeted marketing campaigns.

Coverage

License

Who Can Use It

• Data Scientists and Machine Learning Engineers: To build and evaluate models for customer segmentation, churn prediction, or sales forecasting in e-commerce.
• Marketing Analysts: For identifying market segments, developing targeted campaigns, and assessing the effectiveness of social media strategies.
• E-commerce Businesses: To gain actionable insights into customer behaviour, optimise product offerings, and refine user engagement tactics.
• Academics and Researchers: For studying online consumer behaviour, the dynamics of social commerce, and cross-country comparisons in e-commerce.

Dataset Name Suggestions

• Fashion E-commerce User and Country Trends
• Online Fashion Behaviour Analytics
• Global E-commerce User Data
• Digital Fashion Consumer Insights

Attributes