Investigating how features affect the success level of the projects
Measuring the differences between successful and failed projects
This is a project of the course MODS203, we are crowdfunding group, with: Nghia, Anna, Yukun, and Madellein.
Please contact: Mr. Nghia N.D - nghia.nguyendanh@telecom-paris.fr for more information.
This project was instructed by Prof. Ulrich LAITENBERGER, Telecom Paris.
In order to collect data from website: https://www.kisskissbankbank.com/en/discover we used BeautifulSoup and Selenium. For BeautifulSoup, it's quite simple, but with Selenium, it's more complicated and takes much time to work with. That is because, Selenium needs to open a new Chrome browser to load Javascript data. For each project, we have to open three tasks of Chrome, thus, it takes time to wait for web loading and scarping data. In general, it takes us about 6min for obtaining 9 observations with full features. There has been a little bit difference between the features we stated on the Assignment 1 due to the feedback of the professor. Now, we have these features:
'id' : ID of project
'is_successful' : project is successful of not
'percentage_fund' : Equal to 100*funded/target. If it is greater or equal than 100, the project is successful
'funded' : Amount of money the project was funded, in euros
'target' : Amount of money the project hopes to be funded, in euros
'in_2020' : If the end date of the project is in 2020, this variable will be True, and False otherwise
'backers' : The number of backers for this project
'category' : Category is one of these four categories: Music, Sport, Film, and theatre
'thumbnail_type' : Image or Video
'num_rewards' : The number of offered rewards for backers
'min_price' : The minimum price of a reward that the backers can get, in euros
'max_price' : The maximum price of a reward that the backers can get, in euros
'num_news' : The number of news of the project
'num_comments' : The number of comments of the project
'num_contributions' : The number of contributions of the project
'num_creator_projects' : The number of projects created by this project’s creator
Besides the main data above, we also collected the information about the ranges of prices of each project and the number of backers supporting each price. This means we have another data:
'id' : ID of project
'category' : Category is one of these four categories: Music, Sport, Film, and theatre
'price' : one price that backers can choose
'backers' : number of backers choosing for this price
In this folder, we created some important files:
'collect_data.ipynb': Code to collect data from website
'test_selenium.ipynb': Code to play with selenium, if you are not familiar with it
'chromedrive.exe': Driver for openning Chrome using Selenium
'data' folder: Containing the data we have obtained
Let me explain a little bit about the our main code in file 'collect_data.ipynb'. In this file, we wrote some functions:
create_url(page, category): create the url based on page number and filter parameters
param:
page: int, the page we want to scrape
category: str, the category we want to filter
return: str, the url
get_page(url): Get the page from url, using the request package
param:
url: str, the url to get page
return: bs, BeautifulSoup containing the page
get_num_of_projects(bs): get total number of projects matching the filter
param:
bs: bs, BeautifulSoup containing the page
return: int, the total number of projects matching the filter
get_num_of_pages(bs): get total number of pages matching the filter
param:
bs: bs, BeautifulSoup containing the page
return: int, the total number of pages matching the filter
go_to_project(url_pro): Go to each project and take the project's information
param:
url_pro: str, url of the project
return:
'num_rewards' : int, The number of offered rewards for backers
'min_price' : float, The minimum price of a reward that the backers can get, in euros
'max_price' : float, The maximum price of a reward that the backers can get, in euros
'num_news' : int, The number of news of the project
'num_comments' : int, The number of comments of the project
'num_contributions' : int, The number of contributions of the project
'num_creator_projects' : int, The number of projects created by this project’s creator
'prices_np' : numpy array, list of prices of the project
'backers_np' : numpy array, list of backers supporting each price of the project
collect_data(start, end, category): Collect the data and store into DataFrame
param:
start: int, the start page
end: int, the end page
category: str, the category we want to filter
return:
samples: list, containing the main data
rewards: list, containing the information about the rewards
Before running the code, make sure you had all the packages in import part. And, you also need the Chrome browser to run with Selenium. Finally, run it.
The data has been cleaned in two phases. In the first phase, we cleaned values of funded, target, end_date, and short_des. Funded and target has been splitted from the strings and changed into numbers, while we changed the end_date into Date-time format and short_des was replaced by keywords of it. In the second phase, the data was cleaned to be ready for deeper analysis. At this phase, we removed duplicates and outliers from our data, then we also made some variables in string (end_date, status, language, subcategory, thumbnail_type, title) into lower case string. Finally, we changed features that were in string or other format into numbers in order to analyze and make graphs on them.
It was a long analysis. We did it in our report for this project. If you need more information about the analysis, please find it in our report.
This paper aimed to investigate how features affect the success level of the projects and also to measure differences between successful and failed projects. The amount of collected projects is feasible, however the ratio of successful and failed projects is very unbalanced and therefore conclusions of measuring differences between successful and failed projects are weak. It was however possible to find specific features that have a higher positive impact on the success of projects. The study can confirm that project creators have the possibility to increase the success of funding by leveraging on features that are more compatible for success. Thereby project titles that consist of several letters in the title attract more backers. In addition, projects that engage for more comments, have several rewards and post relatable news will also be features that support crowdfunding projects success. In terms of the language that the project is stated in, it is found that the most popular projects are in French and the number of successful projects is also highest if French language is used, however, the average percentage funded, and the amount of funding is much higher in English (Table 4). Lastly, by lowering the target it increases the chances too. Results show that the median of these features in failed projects are low, which could indicate that failed projects have not taken these features in priority when handling a project (Table 2).
This project's conclusion is merely limited to the platform KissKissBankBank, which should be taken into consideration when assessing what features that impact the success of projects. However, there are similar variables on similar platforms which can be assumed to have similar outcomes. We will leave a more defined measurement of the differences between successful and failed projects for future research since KissKissBankBank did not have enough failed projects to give a whole and reliable outcome.
Cumming, D. J., Leboeuf, G. and Schwienbacher, A. (2014, December). Crowdfunding models: Keep-it-all vs. all-or-nothing. In Paris December 2014 finance meeting EUROFIDAI-AFFI paper (Vol. 10).
Dushnitsky, Gary, Guerini, Massimiliano, Evila, Piva, Cristina Rossi-Lamastra (2016). Crowdfunding in Europe: DETERMINANTS OF PLATFORMCREATION ACROSS COUNTRIES. Available at: https://www.researchgate.net/publication/295252763_Crowdfunding_in_Europe_Determinants_of_Platform_Creation_across_Countries (Accessed: 2021-01-18)
Massolution (2015). 2015CF: The Crowdfunding Industry Report, available at: https://www.smv.gob.pe/Biblioteca/temp/catalogacion/C8789.pdf (Accessed:2021-01-18)