The dataset is about IOT information based on the Devices used by the customers and the revenue generated by them in USD.

**CRM ATTRIBUTE DESCRIPTION**      
msisdn : Unique identification number assigned to each mobile number
gender : sex of the customer using the mobile service
year_of_birth : year of birth of the customer
system_status : indicates the status of the mobile service being used by the customer
mobile_type : Customers can choose their service as prepaid or postpaid
value_segment : Segmentation based on how well the customer matches the business goals

**IOT DEVICES ATTRIBUTE DESCRIPTION**
msisdn: Unique identification number assigned to each mobile number
imei_tac: Unique identification number assigned to the location of the mobile service
brand_name: The brand of the mobile
model_name: The model of the mobile
os_name: The Operating System of the mobile
os_vendor: The company of the mobile operating system

**REVENUE ATTRIBUTE DESCRIPTION**
msisdn : Unique identification number assigned to each mobile number
week_number : Week number for the particular year
Revenue_usd : Revenue generated in that week in US dollars

Business problem overview In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition.

For many incumbent operators, retaining high profitable customers is the number one business goal.

To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.

In this project, you will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

Understanding and defining churn There are two main models of payment in the telecom industry - postpaid (customers pay a monthly/annual bill after using the services) and prepaid (customers pay/recharge with a certain amount in advance and then use the services).

In the postpaid model, when customers want to switch to another operator, they usually inform the existing operator to terminate the services, and you directly know that this is an instance of churn.

However, in the prepaid model, customers who want to switch to another network can simply stop using the services without any notice, and it is hard to know whether someone has actually churned or is simply not using the services temporarily (e.g. someone may be on a trip abroad for a month or two and then intend to resume using the services again).

Thus, churn prediction is usually more critical (and non-trivial) for prepaid customers, and the term ‘churn’ should be defined carefully. Also, prepaid is the most common model in India and Southeast Asia, while postpaid is more common in Europe in North America.

This project is based on the Indian and Southeast Asian market.

Definitions of churn There are various ways to define churn, such as:

Revenue-based churn: Customers who have not utilised any revenue-generating facilities such as mobile internet, outgoing calls, SMS etc. over a given period of time. One could also use aggregate metrics such as ‘customers who have generated less than INR 4 per month in total/average/median revenue’.

The main shortcoming of this definition is that there are customers who only receive calls/SMSes from their wage-earning counterparts, i.e. they don’t generate revenue but use the services. For example, many users in rural areas only receive calls from their wage-earning siblings in urban areas.

Usage-based churn: Customers who have not done any usage, either incoming or outgoing - in terms of calls, internet etc. over a period of time.

A potential shortcoming of this definition is that when the customer has stopped using the services for a while, it may be too late to take any corrective actions to retain them. For e.g., if you define churn based on a ‘two-months zero usage’ period, predicting churn could be useless since by that time the customer would have already switched to another operator.

In this project, you will use the usage-based definition to define churn.

High-value churn In the Indian and the Southeast Asian market, approximately 80% of revenue comes from the top 20% customers (called high-value customers). Thus, if we can reduce churn of the high-value customers, we will be able to reduce significant revenue leakage.

In this project, you will define high-value customers based on a certain metric (mentioned later below) and predict churn only on high-value customers.

Understanding the business objective and the data The dataset 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 behaviour during churn will be helpful.

Understanding customer behaviour 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 :

The ‘good’ phase: In this phase, the customer is happy with the service and behaves as usual.

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 behaviour than the ‘good’ months. Also, it is crucial to...

****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...

Problem Statement

Business problem overview In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition.

For many incumbent operators, retaining high profitable customers is the number one business goal.

To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.

In this project, you will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

Understanding and defining churn There are two main models of payment in the telecom industry - postpaid (customers pay a monthly/annual bill after using the services) and prepaid (customers pay/recharge with a certain amount in advance and then use the services).

In the postpaid model, when customers want to switch to another operator, they usually inform the existing operator to terminate the services, and you directly know that this is an instance of churn.

However, in the prepaid model, customers who want to switch to another network can simply stop using the services without any notice, and it is hard to know whether someone has actually churned or is simply not using the services temporarily (e.g. someone may be on a trip abroad for a month or two and then intend to resume using the services again).

Thus, churn prediction is usually more critical (and non-trivial) for prepaid customers, and the term ‘churn’ should be defined carefully. Also, prepaid is the most common model in India and Southeast Asia, while postpaid is more common in Europe in North America.

This project is based on the Indian and Southeast Asian market.

Definitions of churn There are various ways to define churn, such as:

Revenue-based churn: Customers who have not utilised any revenue-generating facilities such as mobile internet, outgoing calls, SMS etc. over a given period of time. One could also use aggregate metrics such as ‘customers who have generated less than INR 4 per month in total/average/median revenue’.

The main shortcoming of this definition is that there are customers who only receive calls/SMSes from their wage-earning counterparts, i.e. they don’t generate revenue but use the services. For example, many users in rural areas only receive calls from their wage-earning siblings in urban areas.

**Usage-based churn: **Customers who have not done any usage, either incoming or outgoing - in terms of calls, internet etc. over a period of time.

A potential shortcoming of this definition is that when the customer has stopped using the services for a while, it may be too late to take any corrective actions to retain them. For e.g., if you define churn based on a ‘two-months zero usage’ period, predicting churn could be useless since by that time the customer would have already switched to another operator.

In this project, you will use the usage-based definition to define churn.

High-value churn In the Indian and the Southeast Asian market, approximately 80% of revenue comes from the top 20% customers (called high-value customers). Thus, if we can reduce churn of the high-value customers, we will be able to reduce significant revenue leakage.

In this project, you will define high-value customers based on a certain metric (mentioned later below) and predict churn only on high-value customers.

Understanding the business objective and the data The dataset 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 behaviour during churn will be helpful.

Understanding customer behaviour 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 :

The ‘good’ phase: In this phase, the customer is happy with the service and behaves as usual.

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 dif...

This this dataset represents a portion of combined Ghana telecom industry data collected from open datasets and annual reports which can used to analyze factors influencing customer churn, assess customer satisfaction, and identify trends based on demographics and service usage. Key variables include:

AgeGroup: The age range of customers. Gender: The gender identity of customers. Education: Educational attainment of customers. EmploymentStatus: Employment status, indicating whether customers are employed, retired, or self-employed. TelecomCompany: The telecommunications provider each customer uses. ReasonForChoosing: The primary reason customers selected their telecom provider, such as network coverage or customer service. DurationWithCompany: How long customers have been with their telecom provider. PlanType: The type of service plan customers are subscribed to (e.g., postpaid or prepaid). MonthlyCharges: The monthly fees customers pay for their service. Churn: A binary indicator of whether the customer has churned (left the service). ChurnReason_NetworkCoverage: Indicates if network coverage was a reason for churn. ChurnReason_CustomerService: Indicates if customer service was a reason for churn. ChurnReason_Pricing: Indicates if pricing was a reason for churn.