Exploring Influencer Profiles in Customer-to-Customer E-Commerce
Analyzing Success Factors for Influencers
By Jeffrey Mvutu Mabilama [source]
About this dataset
This dataset contains data about user profiles on a C2C e-commerce store, giving us key insights into what makes a successful influencer. The data includes information such as the number of products sold, number of followers, and number of follows along with the seller's profile description (mood). Through exploring this data, we can gain invaluable knowledge about what it takes to become an influencer.
The goal is to understand how the profile mood messages relate to their level of success. Is there something special in those status updates that leads a seller to great heights or is it just luck? With this dataset, you can discover patterns and success markers that could give aspiring influencers the opportunity they need.
Our focus should be not only on discovering these markers but also finding out which ones make them stand apart from other sellers. Through exploring different levels of 'success', we gain insight into what makes for an eye-catching profile that attracts buyers and followers alike! This can then help you craft your own profile status message for maximum impact! So dive in and find out - What does it take for one to become an e-commerce influencer?
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How to use the dataset
This dataset provides valuable insights into the profiles of successful influencers in C2C eCommerce and can be used to uncover the secrets behind influencer success. The dataset contains information about the seller's profile, including number of products sold, number of followers, number of follows, and their profile description (also known as mood). With this data you can explore what makes a successful influencer by segmenting users according to the number of products sold.
In order to use this dataset effectively for your analysis project, it is important to understand which columns are relevant for exploring influence profiles in C2C e-commerce. The principal columns that should be looked at include: statistics__productsSold (the number of products sold by a seller), social__nbFollows (the amount people followed by a seller), mood (the description written by the seller on their profile) , soldprodslowerbound and soldprodsupperbound (the lower/upper bounds for the total product sold respectively), nbusers (The total amount users in data set ), meannbwords(Average NoOfWords In userprofileDescription ), meanmoodlength(Average Length Of UserProfileDescription In Words ), meanProdsSold(Average Products Sold By The User ),meanfollowers(average no Of Followers Of A particular User ),meanfollowed(avg no Of Users followed By particular user ) ,totalProductsold ,totalfollowerstotalUsersFollowed.,totalfollowed.
To begin with your analysis project you need to decide what constitutes success as an influencer so that you have an accurate way measure it within your dataset. Once defined some basic analysis techniques such as correlation structure or clustering could be used investigate different aspects related to successful influencers such like who has more followers? Which age group is more active? And which service does user prefer etc... Additionally if you want use advanced analytics techniques for better understanding towards decision making processes like Predictive model or using Bayesian Optimization Algorithm then you should look out for feature engineering part develop models for predicting best possible Influencer score based on their recent activities .However care should be taken while applying any supervised learning algorithms given that we don’t have enough data points available here !! All above mentioned approaches will help us discovering better key factors which make Influencers stand out from crowd hence enabling us draw useful insights and take informed decisions at end
Happy Data Analyzing !!
Research Ideas
- Analyzing the correlation between total product sold, total followers and words used in profile description to identify the key factors that make a successful influencer.
- Using Natural Language Processing techniques to analyze profile descriptions and uncover patterns in the language used by successful influencers compared to those with fewer sales or followers.
- Conducting A/B testing experiments with different combinations of words, phrases or images in seller profiles to see which ones yield better results for influencers depending on their target audience or goals as sellers
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: seller-moods_8m4x.csv
| Column name |
Description |
| statistics__productsSold |
The total number of products sold by each seller. (Integer) |
| social__nbFollowers |
The total number of followers for each seller. (Integer) |
| social__nbFollows |
The total number of people following each seller. (Integer) |
| mood |
The description provided by sellers for their storefront or themselves. (String) |
File: Segmentation-of-Users-per-Number-of-Sales.csv
| Column name |
Description |
| soldprodslowerbound |
Lower bound range for calculating average product sales per individual. (Integer) |
| soldprodsupperbound |
Upper bound range for calculating average product sales per individual. (Integer) |
| nbusers |
Number of users in the dataset. (Integer) |
| meannbwords |
Average word count per user. (Integer) |
| meanmoodlength |
Average length of profile descriptions. (Integer) |
| meanprodssold |
Average products sold per user. (Integer) |
| meanfollowers |
Average followers per seller. (Integer) |
| meanfollowed |
Average followed per seller. (Integer) |
| totalproductssold |
Total products sold by all users. (Integer) |
| totalfollowers |
Total followers of all users. (Integer) |
| totalfollowed |
Total followed of all users. (Integer) |
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.