In the cutthroat competitive arena, it is a very challenging task for any enterprise to make a balance between retaining its existing loyal customers and attracting new customers. It is a tedious task to find the right segment of active customers and understand the reason behind churn numbers. It is said that it is five times more costly to attract new customers as compared to retaining existing customers. The main aim of this chapter is to understand the customer churn application, which is one of the most useful applications of AI and ML in the customer analytics domain. Researchers further studied the most significant factors affecting customer behavior and responsible for increasing the customer churn rate. The study mainly targeted the semiconductor supply chain industry, which is one of the most complex industries and foundation of advanced technology and penetrated the walks of human life.
TopIntroduction
In the data driven era, Artificial Intelligence (AI) and Machine Learning (ML) techniques can play a crucial role in terms of extracting useful business information from raw data. Customer Segmentation, Behavior modeling, fraud detection, sentiment analysis, customer churn analytics, predicting customer wallet shares and understanding of customer loyalty are different use cases of AI and ML in customer support and analytics domain. Nowadays, it is very easy for any organization to collect and analyze vast amounts of data related to customer purchase, demographic, social media, supply chain performance issues, customer preference and reviews data. These data can help organizations to understand customer behavior to some extent and help to see a clear picture of the target audience including customers suppliers and academic world. In academic world, students, researchers and scholars can understand use case of AI and ML and its implications on day-to-day business activities.
By using raw data, firms can better understand about customers 360 visibility, check spending patterns and predict future behaviors of customers. Similarly, companies would be able to collect supplier related information in an appropriate manner. There are different organizations across the world including H20.ai, Odaia, Predicted Layer, Rulex, Tizamo which incorporated Artificial Intelligence to businesses for developing smart churn prediction applications. For business leaders, managers, researchers, practitioners and academician, this study could be useful in term of understand use case of AI/ML and its roles in data-driven decision-making in their respective areas. By evaluating methodologies, students belong to various universities and institutes would be able to get familiar with AI and ML applications from data science perspectives.
The overall problem talks about churn which means customers who stop using products & services of a firm due to certain reasons. Many enterprises across the world are facing customer churn problems and it is very difficult for them to make the right balance between customer satisfaction and revenue. In modern arena, artificial Intelligence based techniques could be beneficial for the companies to manage large amounts of customers data and understand their behavior in an appropriate manner (Sullivan, 2022). By adopting these advanced techniques, firms belong to various industries including finance & banking, retail, marketing and telecom etc. would be able to solve the problem of churn which is not good for any business. Today, many businesses are emphasizing the root cause why customers might leave in future (Alboukaey, Joukhadar & Ghneim, 2020). It is a tedious job to find the right segment of active customers and understand the reason behind churn numbers.
It is said that it is five times more costly to attract new customers as compared to retaining existing customers. In the big data era, mostly companies are more emphasizing on deliberate churners that bring more profit to business. Further, the chapter explores customer trends and identifies factors which are responsible for churn. The data has been collected from secondary data sources such as books, published journals, previous research papers and articles related to churn analytic to better understand the appropriate use of ML algorithms in customer support and analytics domain. The present study emphasizes the implications of machine learning models that can predict customer churn based on available data and help firms to take appropriate action. Researchers studied how different types of Machine learning algorithms including Logistic regression, Random Forest and Gradient boosted tree algorithms could be applied for classifying churned customers in the semiconductor supply chain industry. Confusion matrix, Recall, and AUC evaluation metrics could be useful to measure the performance of the ML models.
The present chapter has been written to target semiconductor supply chain industry which is one of the most complex industries to understand root causes behind churns and application of AI and ML to tackle those challenges. Across all major industries including media and entertainments, telecommunication, manufacturing, transportations and personal health and hygiene, semiconductors are used as foundations of advanced technology products. However, the results of the paper could be taken as reference by various companies belonging to telecom, marketing, semiconductor and supply chain and banking to identify high risk churned customers and develop win back strategies to retain existing profitable customers and reduce cost of maintaining customers.