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Prediction of customer churn in telecom sector
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Introduction
Most of telecommunication companies consider the customer as the most important asset for them. For that reason, nowadays, a challenging problem that encounters telecommunication companies is when the customer leaves the company to another service provider for a reason or another [1]. In most cases, this churn can happen in rates which seriously affect the profitability of the companies since it is easy for the customers to switch companies.
In market, where the competition between the telecommunication companies grows rapidly, companies have shifted their focus from acquiring new customers to retain their existing ones [1–3]. Basically, churn is one of these significant problems and companies started to seek new Business Intelligence (BI) applications that predict churn customers. When the company is aware of the percentage of customers who leave for another company in a given time period, it wouldbe easier to come up with a detailed analysis of the causesfor the churn rate and understand the behavior of customersthat unsubscribe and move to other business competitor. Thishelps in planning effective customer retention strategies for that company [4].
Among many approaches developed in the literature for predicting customer churn, supervised Machine Learning (ML) techniques are the most widely investigated [5–9]. Supervised ML concerns the developing of models whichcan learn from labeled data. ML includes a wide rangeof algorithms such as Decision trees, k-nearest neighbors,Linear regression, Naive Bayes, Neural networks, Supportvector machines (SVM), Genetic Programming and many others.
For example, in [5] authors conducted a comparative analysis of linear regression and two machine learning techniques; neural netwo...
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Introduction. Customer loyalty is basically defined as a deep held commitment to re-buy or re-patronize a chosen product/service consistently in the future, thereby causing repetitive same-brand or same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior (Oliver, 1997). It is a main driver for customer retention, which, in its turn, represents a basic force that accumulates a customer base for the company. As the experience suggests, the presence of the customer base is a valuable asset, because a lot of statistical data and marketing researches have proved that it is harder and much more expensive to acquire a new customer rather than retain an existing one. In this aspect, any business without a focus on customer retention is left on market’s mercy: any market movements will affect the sales in a more intense manner. There is also a risk that your competitor may eventually satisfy the existing customer’s needs and take away a part of your market niche. Moreover, customer loyalty gives a sort of discretion to the company’s R&D policy and marketing strategy: you can try to introduce different features to your products, experiment with different types of ads, and no matter what the results would be, — the customers will stay stick to your production line. Of course, an organization does not have an absolute control over the loyalty of its customers, bec...
RBC Financial Group uses a customer relationship management (CRM) strategy that provides a variety of services for a variety of clients. The strategy allows for individual customers to trust RBC and develop a personal relationship with each and every client. One major factor that allows CRM to operate effectively is the use of technologies and analytics to help classify each client’s financial situation. These customer profitability-based techniques allowed RBC to categorize their clients into A, B, and C groups so that the sales teams could optimize their efforts in catering to these different clients. This strategy holds the following strengths: optimizing sales efforts to different customers, easily accessible electronic sales leads, centralized and standardized financial decisions, and building personalized and sustainable customer relationships. There are a few weaknesses to the system though including the complexity in predicting future positions of companies despite the use of analytics as well as the complexity in creating consistency when using these
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Other alternatives to predictive modeling in businesses are churn prediction and customer retention. Where the ability to anticipate their decisions; with for example, loyalty programs, ono-to-one marketing (personalized solution) or complaints management. Will allow companies to increase their profitability in industries such as Telecommunications where retention costs are lower as compared to new customers acquisition (Ngai et al., 2009).
Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
Consumers churn for several reasons, including price and customer service. Therefore, it varies based on the sector.
Ken Kwong-Kay Wong. Fighting Churn with Rate Plan Right-Sizing: A Customer Retention Strategy for the Wireless Telecommunications Industry. Vol. 30 Routledge, 2010. doi:10.1080/02642060903295669. http://0-search.ebscohost.com.ignacio.usfca.edu/login.aspx?direct=true&db=bth&AN=55027838&site=eds-live&scope=site.
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The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.
switching behaviour. Literature reveals about the factors of switching cost, better customer care services, networking coverage, advertising , etc for switching behaviour of customers. The researchers identify the problem of industry that if one firm gains then other must be losing a customer. Number of network providers are increasing calling for an intense competition. Empowerment of technology has led to growth of mobile industry more economical.
However it is not important the loyal customer management would lead you to profits. Loyalty and profitability link can be advocated simultaneously. It can be achieved by maximizing and measuring CLV i.e. customer life value. Using CLV exemplar helps companies in making logical decisions about customers and various presumptions to retain and acquire, and additionally ordain the level of reserves that can be bleared on several micro-segments.