Research Article
BibTex RIS Cite

Do Machine Learning and Business Analytics Approaches Answer the Question of ‘Will Your Kickstarter Project be Successful?

Year 2021, , 255 - 274, 07.10.2021
https://doi.org/10.26650/ibr.2021.50.0117

Abstract

Kickstarter is one of the popular crowdfunding platforms used to implement business ideas on the web. The success of crowdfunding projects such as Kickstarter is realized with future financial support. However, there is no platform where users can get decision support before presenting their projects to supporters. To solve this problem, a platform where users can test their projects is required. Within this scope, a business intelligence model that works on the web has been developed by combining business analytics and machine learning methods. The data used for business analytics has been brought to a state that can provide inferences through visualization, reporting and query processes. Within the scope of machine learning, various algorithms were applied for success classification and the best results were given by 91% Random Forest, 85% Decision Tree, 84% K-Nearest Neighbors (KNN) algorithms. F1-Score, Recall, Precision, Mean Squared Error (MSE), Kappa and AUC values were analyzed to determine the most successful models. Thus, Kickstarter users will be able to see their shortcomings and have a prediction about success before presenting their projects to their backers.

References

  • Laudon, K. C. (2007). Management Information Systems: Managing the Digital Firm. Pearson Education India.
  • Cheng, C., Tan, F., Hou, X., & Wei, Z. (2019, August). Success Prediction on Crowdfunding with Multimodal Deep Learning. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China (pp. 10-16).
  • Chen, K., Jones, B., Kim, I., & Schlamp, B. (2013). Kickpredict: Predicting Kickstarter Success. Technical report, California Institute of Technology.
  • Kindler, A., Golosovsky, M., & Solomon, S. (2019). Early Prediction of the Outcome of Kickstarter Campaigns: Is the Success due to Virality? Palgrave Communications, 5(1), 1-6.
  • Huber, S., Wiemer, H., Schneider, D., & Ihlenfeldt, S. (2019). DMME: Data mining methodology for engineering applications–a holistic extension to the CRISP-DM model. Procedia CIRP, 79, 403-408.
  • Schäfer, F., Zeiselmair, C., Becker, J., & Otten, H. (2018, November). Synthesizing CRISP-DM and Quality Management: A Data Mining Approach for Production Processes. In 2018 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD) (pp. 190-195). IEEE.
  • Mouillé, M. (2018). Kickstarter Projects Dataset, Kaggle. More than 300,000 kickstarter projects (Version 7). Access address: https://www.kaggle.com/kemical/kickstarter-projects
  • Chung, J., & Lee, K. (2015, August). A Long-Term Study of a Crowdfunding Platform: Predicting Project Success and Fundraising Amount. In Proceedings of the 26th ACM Conference on Hypertext & Social Media (pp. 211-220).
  • Rao, H., Xu, A., Yang, X., & Fu, W. T. (2014, April). Emerging dynamics in crowdfunding campaigns. In International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (pp. 333-340). Springer, Cham.
  • Etter, V., Grossglauser, M., & Thiran, P. (2013, October). Launch hard or go home! Predicting the success of Kickstarter campaigns. In Proceedings of the first ACM conference on Online social networks (pp. 177-182).
  • Jensen, L. S., & Özkil, A. G. (2018). Identifying challenges in crowdfunded product development: a review of Kickstarter projects. Design Science, 4.
  • Du, Q., Fan, W., Qiao, Z., Wang, G., Zhang, X., & Zhou, M. (2015). Money talks: a predictive model on crowdfunding success using project description.
  • Zvilichovsky, D., Inbar, Y., & Barzilay, O. (2015). Playing both sides of the market: Success and reciprocity on crowdfunding platforms. Available at SSRN 2304101.
  • Bi, S., Liu, Z., & Usman, K. (2017). The influence of online information on investing decisions of reward-based crowdfunding. Journal of Business Research, 71, 10-18.
  • Kuppuswamy, V., & Bayus, B. L. (2018). Crowdfunding creative ideas: The dynamics of project backers. In The economics of crowdfunding (pp. 151-182). Palgrave Macmillan, Cham.
  • Mortensen, S., Christison, M., Li, B., Z hu, A., & Venkatesan, R. (2019, April). Predicting and Defining B2B Sales Success with Machine Learning. In 2019 Systems and Information Engineering Design Symposium (SIEDS) (pp. 1-5). IEEE.
  • Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217-222.
  • Xu, J., Zhang, Y., & Miao, D. (2020). Three-way confusion matrix for classification: a measure driven view. Information Sciences, 507, 772-794.
  • Yang, T. L., Lin, C. H., Chen, W. L., Lin, H. Y., Su, C. S., & Liang, C. K. (2019). Hash Transformation and Machine Learning-based Decision-Making Classifier Improved the Accuracy Rate of Automated Parkinson’s Disease Screening. IEEE Transactions on Neural Systems and Rehabilitation Engineering.
  • Sasikala, B. S., Biju, V. G., & Prashanth, C. M. (2017, May). Kappa and accuracy evaluations of machine learning classifiers. In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 20-23). IEEE.
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37-46.
  • Landis, J. R., & Koch, G. G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics, 159-174.
  • Berhane, T. M., Lane, C. R., Wu, Q., Autrey, B. C., Anenkhonov, O. A., Chepinoga, V. V., & Liu, H. (2018). Decision-tree, rule-based, and random forest classification of high-resolution multispectral imagery for wetland mapping and inventory. Remote sensing, 10(4), 580.
  • Leiva, R. G., Anta, A. F., Mancuso, V., & Casari, P. (2019). A Novel Hyperparameter-Free Approach to Decision Tree Construction That Avoids Overfitting by Design. IEEE Access, 7, 99978-99987.
  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 1165-1188.
  • Wang, J., Wu, X., & Zhang, C. (2005). Support vector machines based on K-means clustering for real-time business intelligence systems. International Journal of Business Intelligence and Data Mining, 1(1), 54-64.
  • Cook, A., Wu, P., & Mengersen, K. (2015). Machine learning and visual analytics for consulting business decision support. In 2015 Big Data Visual Analytics (BDVA) (pp. 1-2). IEEE.
  • Fahmy, A. F., Mohamed, H. K., & Yousef, A. H. (2017). A data mining experimentation framework to improve six sigma projects. In 2017 13th International Computer Engineering Conference (ICENCO) (pp. 243-249). IEEE.
  • Mitra, T., & Gilbert, E. (2014, February). The language that gets people to give: Phrases that predict success on kickstarter. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (pp. 49-61).
  • Greenberg, M. D., Pardo, B., Hariharan, K., & Gerber, E. (2013). Crowdfunding support tools: predicting success & failure. In CHI'13 Extended Abstracts on Human Factors in Computing Systems (pp. 1815-1820).
  • Kickstarter Projects Stats (2020). Access address: https://www.kickstarter.com/help/stats
  • Fawcett, T. (2004). ROC graphs: Notes and practical considerations for researchers. Machine learning, 31(1), 1-38.
  • Adeniyi, D. A., Wei, Z., & Yongquan, Y. (2016). Automated Web Usage Data Mining and Recommendation System Using K-Nearest Neighbor (KNN) Classification Method. Applied Computing and Informatics, 12(1), 90-108.
  • Jain, M., Narayan, S., Balaji, P., Bhowmick, A., & Muthu, R. K. (2020). Speech Emotion Recognition Using Support Vector Machine. arXiv preprint arXiv:2002.07590.
  • Saritas, M. M., & Yasar, A. (2019). Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification. International Journal of Intelligent Systems and Applications in Engineering, 7(2), 88-91.
  • Statistica (2020). Overview of projects and dollars on crowdfunding platform Kickstarter as of July 2019, Access address: https://www.statista.com/statistics/251727/projects-and-dollars-overview-on-crowdfunding-platform-kickstarter/
  • Szmigiera, M. (2019). Crowdfunding - Statistics & Facts, Statistica. Access address: https://www.statista.com/topics/1283/crowdfunding/
Year 2021, , 255 - 274, 07.10.2021
https://doi.org/10.26650/ibr.2021.50.0117

Abstract

References

  • Laudon, K. C. (2007). Management Information Systems: Managing the Digital Firm. Pearson Education India.
  • Cheng, C., Tan, F., Hou, X., & Wei, Z. (2019, August). Success Prediction on Crowdfunding with Multimodal Deep Learning. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China (pp. 10-16).
  • Chen, K., Jones, B., Kim, I., & Schlamp, B. (2013). Kickpredict: Predicting Kickstarter Success. Technical report, California Institute of Technology.
  • Kindler, A., Golosovsky, M., & Solomon, S. (2019). Early Prediction of the Outcome of Kickstarter Campaigns: Is the Success due to Virality? Palgrave Communications, 5(1), 1-6.
  • Huber, S., Wiemer, H., Schneider, D., & Ihlenfeldt, S. (2019). DMME: Data mining methodology for engineering applications–a holistic extension to the CRISP-DM model. Procedia CIRP, 79, 403-408.
  • Schäfer, F., Zeiselmair, C., Becker, J., & Otten, H. (2018, November). Synthesizing CRISP-DM and Quality Management: A Data Mining Approach for Production Processes. In 2018 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD) (pp. 190-195). IEEE.
  • Mouillé, M. (2018). Kickstarter Projects Dataset, Kaggle. More than 300,000 kickstarter projects (Version 7). Access address: https://www.kaggle.com/kemical/kickstarter-projects
  • Chung, J., & Lee, K. (2015, August). A Long-Term Study of a Crowdfunding Platform: Predicting Project Success and Fundraising Amount. In Proceedings of the 26th ACM Conference on Hypertext & Social Media (pp. 211-220).
  • Rao, H., Xu, A., Yang, X., & Fu, W. T. (2014, April). Emerging dynamics in crowdfunding campaigns. In International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (pp. 333-340). Springer, Cham.
  • Etter, V., Grossglauser, M., & Thiran, P. (2013, October). Launch hard or go home! Predicting the success of Kickstarter campaigns. In Proceedings of the first ACM conference on Online social networks (pp. 177-182).
  • Jensen, L. S., & Özkil, A. G. (2018). Identifying challenges in crowdfunded product development: a review of Kickstarter projects. Design Science, 4.
  • Du, Q., Fan, W., Qiao, Z., Wang, G., Zhang, X., & Zhou, M. (2015). Money talks: a predictive model on crowdfunding success using project description.
  • Zvilichovsky, D., Inbar, Y., & Barzilay, O. (2015). Playing both sides of the market: Success and reciprocity on crowdfunding platforms. Available at SSRN 2304101.
  • Bi, S., Liu, Z., & Usman, K. (2017). The influence of online information on investing decisions of reward-based crowdfunding. Journal of Business Research, 71, 10-18.
  • Kuppuswamy, V., & Bayus, B. L. (2018). Crowdfunding creative ideas: The dynamics of project backers. In The economics of crowdfunding (pp. 151-182). Palgrave Macmillan, Cham.
  • Mortensen, S., Christison, M., Li, B., Z hu, A., & Venkatesan, R. (2019, April). Predicting and Defining B2B Sales Success with Machine Learning. In 2019 Systems and Information Engineering Design Symposium (SIEDS) (pp. 1-5). IEEE.
  • Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217-222.
  • Xu, J., Zhang, Y., & Miao, D. (2020). Three-way confusion matrix for classification: a measure driven view. Information Sciences, 507, 772-794.
  • Yang, T. L., Lin, C. H., Chen, W. L., Lin, H. Y., Su, C. S., & Liang, C. K. (2019). Hash Transformation and Machine Learning-based Decision-Making Classifier Improved the Accuracy Rate of Automated Parkinson’s Disease Screening. IEEE Transactions on Neural Systems and Rehabilitation Engineering.
  • Sasikala, B. S., Biju, V. G., & Prashanth, C. M. (2017, May). Kappa and accuracy evaluations of machine learning classifiers. In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 20-23). IEEE.
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37-46.
  • Landis, J. R., & Koch, G. G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics, 159-174.
  • Berhane, T. M., Lane, C. R., Wu, Q., Autrey, B. C., Anenkhonov, O. A., Chepinoga, V. V., & Liu, H. (2018). Decision-tree, rule-based, and random forest classification of high-resolution multispectral imagery for wetland mapping and inventory. Remote sensing, 10(4), 580.
  • Leiva, R. G., Anta, A. F., Mancuso, V., & Casari, P. (2019). A Novel Hyperparameter-Free Approach to Decision Tree Construction That Avoids Overfitting by Design. IEEE Access, 7, 99978-99987.
  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 1165-1188.
  • Wang, J., Wu, X., & Zhang, C. (2005). Support vector machines based on K-means clustering for real-time business intelligence systems. International Journal of Business Intelligence and Data Mining, 1(1), 54-64.
  • Cook, A., Wu, P., & Mengersen, K. (2015). Machine learning and visual analytics for consulting business decision support. In 2015 Big Data Visual Analytics (BDVA) (pp. 1-2). IEEE.
  • Fahmy, A. F., Mohamed, H. K., & Yousef, A. H. (2017). A data mining experimentation framework to improve six sigma projects. In 2017 13th International Computer Engineering Conference (ICENCO) (pp. 243-249). IEEE.
  • Mitra, T., & Gilbert, E. (2014, February). The language that gets people to give: Phrases that predict success on kickstarter. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (pp. 49-61).
  • Greenberg, M. D., Pardo, B., Hariharan, K., & Gerber, E. (2013). Crowdfunding support tools: predicting success & failure. In CHI'13 Extended Abstracts on Human Factors in Computing Systems (pp. 1815-1820).
  • Kickstarter Projects Stats (2020). Access address: https://www.kickstarter.com/help/stats
  • Fawcett, T. (2004). ROC graphs: Notes and practical considerations for researchers. Machine learning, 31(1), 1-38.
  • Adeniyi, D. A., Wei, Z., & Yongquan, Y. (2016). Automated Web Usage Data Mining and Recommendation System Using K-Nearest Neighbor (KNN) Classification Method. Applied Computing and Informatics, 12(1), 90-108.
  • Jain, M., Narayan, S., Balaji, P., Bhowmick, A., & Muthu, R. K. (2020). Speech Emotion Recognition Using Support Vector Machine. arXiv preprint arXiv:2002.07590.
  • Saritas, M. M., & Yasar, A. (2019). Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification. International Journal of Intelligent Systems and Applications in Engineering, 7(2), 88-91.
  • Statistica (2020). Overview of projects and dollars on crowdfunding platform Kickstarter as of July 2019, Access address: https://www.statista.com/statistics/251727/projects-and-dollars-overview-on-crowdfunding-platform-kickstarter/
  • Szmigiera, M. (2019). Crowdfunding - Statistics & Facts, Statistica. Access address: https://www.statista.com/topics/1283/crowdfunding/
There are 37 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Articles
Authors

Murat Kılınç 0000-0003-4092-5967

Can Aydın 0000-0002-0133-9634

Çiğdem Tarhan 0000-0002-5891-0635

Publication Date October 7, 2021
Submission Date September 29, 2020
Published in Issue Year 2021

Cite

APA Kılınç, M., Aydın, C., & Tarhan, Ç. (2021). Do Machine Learning and Business Analytics Approaches Answer the Question of ‘Will Your Kickstarter Project be Successful?. Istanbul Business Research, 50(2), 255-274. https://doi.org/10.26650/ibr.2021.50.0117

For more information about IBR and recent publications, please visit us at IU Press.