Analysis of ‘JB Link Telco Customer Churn’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/johnflag/jb-link-telco-customer-churn on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This is a customized version of the widely known IBM Telco Customer Churn dataset. I've added a few more columns and modified others in order to make it a little more realistic.

My customizations are based on the following version: Telco customer churn (11.1.3+)

Below you may find a fictional business problem I created. You may use it in order to start developing something around this dataset.

JB Link Customer Churn Problem

JB Link is a small size telecom company located in the state of California that provides Phone and Internet services to customers on more than a 1,000 cities and 1,600 zip codes.

The company is in the market for just 6 years and has quickly grown by investing on infrastructure to bring internet and phone networks to regions that had poor or no coverage.

The company also has a very skilled sales team that is always performing well on attracting new customers. The number of new customers acquired in the past quarter represent 15% over the total.

However, by the end of this same period, only 43% of this customers stayed with the company and most of them decided on not renewing their contracts after a few months, meaning the customer churn rate is very high and the company is now facing a big challenge on retaining its customers.

The total customer churn rate last quarter was around 27%, resulting in a decrease of almost 12% in the total number of customers.

The executive leadership of JB Link is aware that some competitors are investing on new technologies and on the expansion of their network coverage and they believe this is one of the main drivers of the high customer churn rate.

Therefore, as an action plan, they have decided to created a task force inside the company that will be responsible to work on a customer retention strategy.

The task force will involve members from different areas of the company, including Sales, Finance, Marketing, Customer Service, Tech Support and a recent formed Data Science team.

The data science team will play a key role on this process and was assigned some very important tasks that will support on the decisions and actions the other teams will be taking : - Gather insights from the data to understand what is driving the high customer churn rate. - Develop a Machine Learning model that can accurately predict the customers that are more likely to churn. - Prescribe customized actions that could be taken in order to retain each of those customers.

The Data Science team was given a dataset with a random sample of 7,043 customers that can help on achieving this task.

The executives are aware that the cost of acquiring a new customer can be up to five times higher than the cost of retaining a customer, so they are expecting that the results of this project will save a lot of money to the company and make it start growing again.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Client churn rate in Telecom sector’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sagnikpatra/edadata on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context "Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs."

Content The Orange Telecom's Churn Dataset, which consists of cleaned customer activity data (features), along with a churn label specifying whether a customer canceled the subscription, will be used to develop predictive models. Two datasets are made available here: The churn-80 and churn-20 datasets can be downloaded.

The two sets are from the same batch, but have been split by an 80/20 ratio. As more data is often desirable for developing ML models, let's use the larger set (that is, churn-80) for training and cross-validation purposes, and the smaller set (that is, churn-20) for final testing and model performance evaluation.

Inspiration To explore this type of models and learn more about the subject.

--- Original source retains full ownership of the source dataset ---

Big Data Analytics In Telecom Market Outlook

The global market size for Big Data Analytics in the Telecom sector was valued at approximately USD 10 billion in 2023 and is projected to reach around USD 50 billion by 2032, exhibiting a robust CAGR of 20% during the forecast period. This impressive growth trajectory is fueled by the increasing demand for advanced analytics to optimize operations, enhance customer experience, and improve network management. The telecom sector's continuous expansion and the proliferation of connected devices are also significant contributors to this market's rapid growth.

One of the primary growth factors for this market is the exponential increase in data generation. With the advent of 5G technology, the volume of data transmitted over networks has surged, necessitating sophisticated analytics to manage and utilize this data effectively. Telecom companies are increasingly relying on big data analytics to derive actionable insights from vast datasets, which can lead to improved decision-making and strategic planning. Moreover, the integration of IoT devices and services has further amplified data traffic, making analytics indispensable for telecom operators.

Another crucial driver is the need for enhanced customer experience. Telecom operators are leveraging big data analytics to gain deeper insights into customer behavior, preferences, and pain points. This data-driven approach allows for personalized marketing strategies, better customer service, and reduced churn rates. By analyzing customer data, telecom companies can identify trends and patterns that help in developing targeted campaigns and offers, thereby increasing customer loyalty and satisfaction.

Operational efficiency is also a significant factor propelling the growth of big data analytics in the telecom market. Telecom operators are under constant pressure to improve their network performance and reduce operational costs. Big data analytics enables real-time monitoring and predictive maintenance of network infrastructure, leading to fewer outages and improved service quality. Additionally, analytics helps in optimizing resource allocation and enhancing the overall efficiency of telecom operations.

Regionally, North America holds a substantial share of the big data analytics in telecom market, driven by the presence of leading telecom companies and advanced technology infrastructure. Additionally, the Asia Pacific region is expected to witness the fastest growth rate due to the rapid digital transformation and increasing adoption of advanced analytics solutions in emerging economies like China and India. European countries are also making significant investments in big data analytics to enhance their telecom services, contributing to the market's growth.

Component Analysis

In the context of components, the Big Data Analytics in Telecom market is segmented into software, hardware, and services. The software segment is anticipated to dominate the market, as telecom operators increasingly invest in advanced analytics platforms and tools. The software solutions facilitate the processing and analysis of large datasets, enabling telecom companies to gain valuable insights and improve decision-making processes. Moreover, the software segment includes various sub-categories such as data management, data mining, and predictive analytics, each contributing significantly to market growth.

The hardware segment, although smaller compared to software, plays a critical role in the overall ecosystem. This segment includes servers, storage systems, and other hardware components necessary for data processing and storage. As data volumes continue to grow, the demand for robust and scalable hardware solutions is also on the rise. Telecom companies are investing in high-performance hardware to ensure seamless data management and analytics capabilities. The hardware segment is essential for supporting the infrastructure needed for big data analytics.

On the services front, the market is witnessing substantial growth due to the increasing need for consulting, integration, and maintenance services. Telecom operators often require expert guidance and support to implement and manage big data analytics solutions effectively. Service providers offer a range of services, including system integration, data migration, and ongoing support, which are crucial for the success

By the end of the first quarter of Vodafone's financial year 2024/25, the contract churn rate in the United Kingdom (UK) stood at 13.4 percent. This is an increase compared to the previous quarter, and yet a decrease when compared to the same quarter in the previous year. Overall, the contract churn rate at Vodafone UK has been decreasing steadily since 2014.

Forecast: TELUS Telecom Company Churn Rate in Canada 2022 - 2026 Discover more data with ReportLinker!

In the second quarter of 2024, Verizon's wireless retail churn rate was 1.63 percent. This was a marginal increase on the same period in 2024, but short of the 1.73 percent churn rate reported for the final quarter of 2023.

Forecast: Bell Mobility Telecom Company Churn Rate in Canada 2022 - 2026 Discover more data with ReportLinker!

Bell Canada reported a monthly wireless postpaid churn rate of 1.63 percent for the fourth quarter of 2023, with this figure having remained below the one percent mark for the majority of 2020 and 2021. The prepaid churn rate was 6.15 percent during the same period, resulting in a blended churn rate of 2.03 percent.

Turkey Churn Rate: Period End: Operators: Vodafone data was reported at 23.300 % in Jun 2018. This records an increase from the previous number of 2.500 % for Mar 2018. Turkey Churn Rate: Period End: Operators: Vodafone data is updated quarterly, averaging 3.160 % from Mar 2010 (Median) to Jun 2018, with 34 observations. The data reached an all-time high of 23.300 % in Jun 2018 and a record low of 2.100 % in Sep 2017. Turkey Churn Rate: Period End: Operators: Vodafone data remains active status in CEIC and is reported by Information and Communication Technologies Authority . The data is categorized under Global Database’s Turkey – Table TR.TB003: Telecommunication Statistics.

****Business Problem Overview**** Let us say that Reliance Jio Infocomm Limited approached us with a problem. There is a general tendency in the telecom industry that customers actively switch from one operator to another. As the telecom is highly competitive, the telecommunications industry experiences an average of 18-27% annual churn rate. Since, it costs 7-12 times more to acquire a new customer as compared to retaining an existing one, customer retention is an important aspect when compared with customer acquisition which is why our clients, Jio, wants to retain their high profitable customers and thus, wish to predict those customers which have a high risk of churning. Also, since a postpaid customer usually informs the operator prior to shifting their business to a competitor’s platform, our client is more concerned regarding its prepaid customers that usually churn or shift their business to a different operator without informing them which results in loss of business because Jio couldn’t offer any promotional scheme in time, to prevent churning. As per Jio, there are two kinds of churning - revenue based and usage based. Those customers who have not utilized any revenue-generating facilities such as mobile data usage, outgoing calls, caller tunes, SMS etc. over a given period of time. To determine such a customer, Jio usually uses an aggregate metrics like ‘customers who have generated less than ₹ 7 per month in total revenue’. However, the disadvantage of using such a metric would be that many of Jio customers who use their services only for incoming calls will also be counted/treated as churn since they do not generate direct revenue. In such scenarios, revenue is generated by their relatives who also uses Jio network to call them. For example, many users in rural areas only receive calls from their wage-earning siblings in urban areas. The other type of Churn, as per our client, is usage based which consists of customers who do not use any of their services i.e., no calls (either incoming or outgoing), no internet usage, no SMS, etc. The problem with this segment is that by the time one realizes that a customer is not utilizing any of the services, it may be too late to take any corrective measure since the said customer might already switched to another operator. Currently, our client, Reliance Jio Infocomm Limited, have approached us to help them in predicting customers who will churn based on the usage-based definition Another aspect that we have to bear in mind is that as per Jio, 80% of their revenue is generated from 20% of their top customers. They call this group High-valued customers. Thus, if we can help reduce churn of the high-value customers, we will be able to reduce significant revenue leakage and for this they want us to define high-value customers based on a certain metric based on usage-based churn and predict only on high-value customers for prepaid segment. Understanding the Data-set The data-set contains customer-level information for a span of four consecutive months - June, July, August and September. The months are encoded as 6, 7, 8 and 9, respectively. The business objective is to predict the churn in the last (i.e. the ninth) month using the data (features) from the first three months. To do this task well, understanding the typical customer behavior during churn will be helpful. Understanding Customer Behavior During Churn Customers usually do not decide to switch to another competitor instantly, but rather over a period of time (this is especially applicable to high-value customers). In churn prediction, we assume that there are three phases of customer lifecycle: 1) The ‘good’ phase: In this phase, the customer is happy with the service and behaves as usual. 2) The ‘action’ phase: The customer experience starts to sore in this phase, for e.g. he/she gets a compelling offer from a competitor, faces unjust charges, becomes unhappy with service quality etc. In this phase, the customer usually shows different behavior than the ‘good’ months. Also, it is crucial to identify high-churn-risk customers in this phase, since some corrective actions can be taken at this point (such as matching the competitor’s offer/improving the service quality etc.) 3) The ‘churn’ phase: In this phase, the customer is said to have churned. You define churn based on this phase. Also, it is important to note that at the time of prediction (i.e. the action months), this data is not available to you for prediction. Thus, after tagging churn as 1/0 based on this phase, you discard all data corresponding to this phase. In this case, since you are working over a four-month window, the first two months are the ‘good’ phase, the third month is the ‘action’ phase, while the fourth month is the ‘churn’ phase. Data Dictionary  The data-set is available in a csv file named as “Company Data.csv” and the da...

Forecast: Rogers Communications Telecom Company Churn Rate in Canada 2022 - 2026 Discover more data with ReportLinker!

In the first quarter of Vodafone's financial year 2024/2025, the firm's total churn rate in Germany was 16.2, the lowest of its European markets. African countries had the highest churn rate at 66.6 percent, while the United Kingdom reported the highest churn rate within Europe, with 29.8 percent. This figure was driven by exceptionally high prepaid churn in the UK.

BPNNs’ parameters and experimental results comparison table.

Rogers Communications reported a monthly wireless postpaid churn rate of 1.1 percent during the first quarter of 2024. The firm's prepaid churn rate for the same period was 3.9 percent, the lowest rate since the fourth quarter of 2017.