Abstract
This work is devoted to the consideration of the issues of increasing the development of start-up projects with using modern methods of artificial intelligence. As a rule, such projects are based on innovations and their implementation requires registration as independent enterprises. Operating in market conditions, most IT companies are forced to develop new innovative ideas and present them in the form of startups At the same time, small and medium-sized businesses interact with many external independent potential customers. Such users of information systems may subsequently become clients of such enterprises. The growth and development of these SaaS enterprises relies heavily on the average customer, which is the topic of this article, with the goal of reducing customer churn. The direction of such research is addressed in many of the rules of thumb for SaaS metrics. In these conditions, it is important to take into account the completeness of the functional interaction of SaaS customers, the authors proposed a hypothesis on the possibility of using intelligent methods for predicting customer churn using deep learning neural networks. At the same time, the needs of the stakeholders of such projects should be taken into account, the satisfaction of which occurs when interacting with an innovative IT product. To describe the interactions, the authors consider mathematical models, and also propose modeling methods. To conduct chain training, Python functionality is used, with the processing of user activity datasets. In this paper, a section of the conclusion is proposed in which the results obtained are discussed and evaluated.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
https://js.tensorflow.org/api/latest/#metrics.binaryCrossentropy
Customer acquisition vs. retention costs - statistics and trends. https://www.invespcro.com/blog/customer-acquisition-retention/
Four pathways to digital growth that work for b2b companies. https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/four-pathways-to-digital-growth-that-work-for-b2b-companies
Global software as a service (SaaS) market report 2020. https://www.prnewswire.com
Interviews with experts of the Ukrainian internet market. https://adsider.com/ua/kejsi/intervyu
Public cloud trends in 2019 and beyond. https://community.spiceworks.com/blog/3208-public-cloud-trends-in-2019-and-beyond
Results of the 2013 SaaS small business conversion survey. https://www.groovehq.com/blog/saas-conversion-survey-results
SaaS spend to double by 2020. Will you be ready? https://www.blissfully.com/blog/saas-spending-to-double-by-2020-will-you-be-ready/
SaaS spending hits \$100 billion annual run rate. Microsoft extends its leadership. https://www.srgresearch.com/articles/saas-spending-hits-100-billion-annual-run-rate-microsoft-extends-its-leadership
Startup statistics - the numbers you need to know. https://smallbiztrends.com/2019/03/startup-statistics-small-business.html
Startup statistics - the numbers you need to know, small business trends. https://smallbiztrends.com/2019/03/startup-statistics-small-business.html
Startupranking. https://www.startupranking.com
The top 20 reasons startups fail. https://www.cbinsights.com/research/startup-failure-reasons-top/
What is software as a service (SaaS): A beginner’s guide - salesforce. https://www.salesforce.com/in/saas/
Cloud computing market by service, deployment model, organization size, workload, vertical and region - global forecast. Technical report (2020)
Blank, S.: The Startup Owner’s Manual: The Step-By-Step Guide for Building a Great Company Hardcover, p. 608. KandS Ranch (2020)
Chatfield, A., Reddick, C.: Customer agility and responsiveness through big data analytics for public value creation. A case study of Houston 311 on-demand services. Gov. Inf. Q. 35(2), 336–347 (2018)
Coussement, K., VandenPoel, D.: Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques. Expert Syst. Appl. 34, 313–327 (2008). https://doi.org/10.1016/j.eswa.2006.09.038
Coussement, K., VandenPoel, D.: Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers. Expert Syst. Appl. 6127–6134 (2009). https://doi.org/10.1016/j.eswa.2008.07.021
Euler, T.: Churn prediction in telecommunications using MiningMart. In: 2005 Workshop on Data Mining and Business, pp. 1–2 (2005)
Gogunskii, V., Kolesnikov, O., Oborska, G., Harelik, S.L.D.: Representation of project systems using the Markov chain. Eastern-Eur. J. Enterp. Technol. (85), 25–32 (2017). https://doi.org/10.15587/1729-4061.2017.97883
Hiziroglu, A., Seymen, O.: Modelling customer churn using segmentation and data mining. Front. Artif. Intell. Appl. 259–271 (2014). https://doi.org/10.3233/978-1-61499-458-9-259
Madden, G., Savage, S., Coble-Neal, G.: Subscriber churn in the Australian ISP market. Inf. Econ. Policy 11, 195–207 (1999)
Morozov, V., Kalnichenko, O., Kolomiiets, A.: Research of the impact of changes based on external influences in complex it projects. In: 2019 IEEE International Conference on Advanced Trends in Information Theory, pp. 481–488 (2019). https://doi.org/10.1109/ATIT49449.2019.9030441
Morozov, V., Mezentseva, O., Proskurin, M.: Trainable neural networks modelling for a forecasting of start-up product development. In: 2020 IEEE International Conference on Data Stream Mining and Processing, pp. 55–60 (2020). https://doi.org/10.1109/DSMP47368.2020.9204264
Morozov, V., Mezentseva, O., Steshenko, G., Proskurin, M.: Product development of start-up through modeling of customer interaction based on data mining. In: Babichev, S., Peleshko, D., Vynokurova, O. (eds.) Data Stream Mining & Processing. DSMP 2020. Communications in Computer and Information Science, vol. 1158, pp. 399–415. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61656-4_27
Neslin, S.: Defection detection: measuring and understanding the predictive accuracy of customer churn models. J. Mark. Res. Am. Mark. Assoc. 43, 204–211 (2006)
Popp, K.: Software industry business models. IEEE Softw. 28, 26–30 (2011)
Qi, J., Liu, Y.: Adtreeslogit model for customer churn prediction. Ann. Oper. Res. 168(1), 247–265 (2009). https://doi.org/10.1007/s10479-008-0400-8
Sherstyuk, O., Olekh, T., Kolesnikova, K.: The research on role differentiation as a method of forming the project team. Eastern-Eur. J. Enterp. Technol. 80(2), 259–271 (2016). https://doi.org/10.15587/1729-4061.2016.65681
Teslia, Y., Khlevnyi, A., Khlevna, Y.: Control of informational impacts on project management. In: 2016 IEEE International Conference on Data Stream Mining and Processing, pp. 387–391 (2016). https://doi.org/10.1109/DSMP.2016.7583584
Wei, C., Chiu, I.: Turning telecommunications call details to churn prediction: a data mining approach. Expert Syst. Appl. 103–112 (2002). https://doi.org/10.1016/S0957-4174(02)00030-1
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Morozov, V., Mezentseva, O., Kolomiiets, A., Proskurin, M. (2022). Predicting Customer Churn Using Machine Learning in IT Startups. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_45
Download citation
DOI: https://doi.org/10.1007/978-3-030-82014-5_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82013-8
Online ISBN: 978-3-030-82014-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)