International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 05 | May 2024
p-ISSN: 2395-0072
www.irjet.net
Predicting Customer Churn in SaaS Products using Machine Learning Pranav Khare1, Sahil Arora2 1Independent Researcher/Sr. Product Manager, AI/ML & Digital Identity Verification, Seattle, WA, USA
Independent Researcher/Staff Product Manager, AI/ML, Edge Infra & Identity, Mountain View, CA, USA -----------------------------------------------------------------------------***------------------------------------------------------------------------Abstract: The world of digital is quickly changing. In cloud-based services, the ability to quickly spot potential patterns in 2
vendor migration or client loss threats is critical.A supervised machine-learning technique was utilized to create a training dataset from actual customer, subscription service, and usage history data in order to make predictions. This research aims to explore the efficacy of machine learning models in predicting customer churn within Software as a Service (SaaS) products, offering insights to empower companies to proactively retain at-risk customers through targeted interventions. In the dynamic landscape of SaaS businesses, customer retention stands as a critical factor influencing long-term success. However, identifying and addressing churn risks among subscribers remains a formidable challenge. Leveraging historical data encompassing customer demographics, usage patterns, interactions, and subscription attributes, this study employs advanced machine learning techniques to develop predictive models capable of anticipating churn behavior. Through thorough data preprocessing, feature engineering, and model selection processes. Furthermore, model interpretation techniques shed light on the factors driving churn predictions, enabling companies to devise targeted retention strategies. By deploying the developed models into production environments and integrating them into the SaaS product lifecycle, organizations can actively monitor churn risks and implement proactive interventions, such as personalized marketing campaigns, tailored offers, and enhanced customer support initiatives. The findings of this research contribute to advancing the understanding of customer churn prediction in SaaS contexts and offer practical guidance for companies seeking to mitigate churn risks and foster long-term customer relationships.
Keywords: Digital transformation, Software as a Service (SaaS), Client loss threats, Long-term customer relationships, Customer retention and Customer churn prediction
I INTRODUCTION In recent years, businesses have been able to collect and process enormous amounts of data, and at the same time, they have come to the realization that putting the customer first is becoming an essential prerequisite in order to differentiate themselves from the competition. In point of fact, because of the saturation of markets, concentrating on Customer Relationship Management (CRM) in order to keep the customers that are already there is no longer an option; rather, it is an imperative requirement for an organization to continue to be competitive. Taking a more general approach, data-driven decision making is a method that companies can use to ensure that their subsequent action will be beneficial to both themselves and their customers. The majority of businesses, particularly those operating within the technology ecosystem, have now implemented a tracking method in order to collect information concerning the actions of their customers. When businesses adopt machine learning applications that are functional for commercial purposes, they achieve better results in predicting the amount of customers who will leave. When there is a large amount of new data of a high quality to work with in order to increase earnings, the difficulty emerges. One of the most important metrics for measuring customer happiness is the churn rate. Customers who are dissatisfied with your product or service and decide to quit company are said to have a high churn rate. Even a minute shift in the churn rate can have a cumulative effect over time, which can amount to over 12 percent of the churn rate on an annual basis. The individual business model of each firm and the problem service that they intend to address both have a role in determining the data that should be tracked accordingly. Through the process of studying how, when, and why consumers behave in a particular manner, it is possible to anticipate the next steps that they will take and have the opportunity to work on resolving difficulties in advance. Churn prediction is the process of attempting to forecast the outcome of a phenomenon that involves the loss of customers. This forecast and quantification of the risk of losing clients can be done on a global or individual level, and it is primarily utilized in regions where the product or service is promoted on a subscription basis. Generally speaking, the prediction of churn is accomplished by either researching the behavior of consumers or by observing individual behavior that suggests a danger of attrition taking place. The process involves the application of modeling and machine learning techniques, which may at times require the utilization of a substantial amount of
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