In the modern Telecommunication with the competition mounting up between the service providers, customer acquisition and retention is a considerable challenge. For the new entrants, acquiring the new customers is the highest priority, whereas for the incumbents, retaining the revenue earning customers is essential.
The telecom companies can increase profitability by creating a predictive modeling for identifying potential churn candidates and non-revenue earning customers; and can increase revenue and profitability by targeted campaigning and promotional offers which will not only retain these customers but also convert the non-revenue earning customers to profitable revenue earning customers.
This article highlights the necessity of churn and campaign management and the usage of SAS - Telecommunication Intelligence software (TIS) for the purpose. It also includes various implementation challenges for SAS - TIS in the real time scenario.
Churn Management
Customer acquisition and retention is a significant challenge in all industries. In the Telecom industry it affects profitability of the company if a customer churns before the company can earn back the investment it incurred in acquiring the customer. Therefore, it is very critical to identify the profitable customers and retain them.
With the telecom market becoming more competitive, determining the reasons of the customer leaving the service of the company is increasingly difficult. In this circumstance, it is even more difficult to predict the probability of the customer to leave in near future. It is increasingly challenging to devise a cost-effect incentive to target the right customer to convince him to stay with the company.
Predictive modeling of churn analysis and management aims at generating scores depicting the probability of the customers to churn out in future. This takes into consideration different aspects of customer's susceptibility to churn, including the history of people those who have churned in the past and build a data model that generates an easy-to-understand reference numbers (scores) assigned to each customers. These customers are then targeted with incentives to deter their cancellation. In other words, Churn analysis determines the probable reasons for a future cancellation depending on the past records which will help the companies to customize their offer. For example: if analysis reveals that many customers have churned from a particular area last month and further investigation has identified that there are frequent call drops (disruptions in service) in that exchange (or BTS area). It can be concluded that due to the technical inadequacy of that particular exchange, frequent call drops are experienced which has contributed to the customer dissatisfaction and their moving out of the company. So further technical solution for that exchange can prevent future potential churns.
Business Definition of Churn Management
Defining churn is the first and foremost activity in Churn Management designing. Different companies define churn according to their business experiences.
Churn definition differs from a Pre-paid to Post-paid scenario.
In pre-paid scenario, a customer can be considered as churned in the following cases:
a) If the customer goes out of network (deactivated)
b) If the customer is an active non user (ANU)
A customer can be considered as ANU when:
i. the customer has no outgoing or incoming usage for last (X) rolling days
ii. the customer has only incoming usage but no out-going usage for last (X) rolling days iii. If the customer's usage is below a pre-determined (business decided) amount for last (X) rolling days.
In post-paid scenario, a customer pays a rental on monthly basis. So in case of non-usage or lower-usage, the company earns fixed revenue from every post-paid customer. Therefore, the customer is considered as churned only when he/she goes out of network (Deactivated).
Churn Parameters for business analysis
After defining churn, next activity is identifying the correct parameters for the contribution of churn. The churn probability or churn scores for individual customers can be generated on the basis of following categorical details:
1. Customer demographics Customer demographics related data are used for segmenting the entire customer base depending on:
a) Age
b) Sex
c) Income
d) Customer Account Information
e) Subscription life cycle
2. Billing and Usage:
Billing and usage related information which is obtained from switch (Call Data Records) is mainly used for detection of churn probability. The following details are used:
a. Price plan
b. Monthly usage summary (Charged call count, Charged data volume, Free call & Data amount)
c. Monthly profit contribution
d. Bounced payment
e. Managing channel information
f. Recharge channel information
g. Network Product information ( Voice, Messaging, Data)
3. Technical Quality:
Quality of service is a potential churn driver as call drops or inferior service quality increases the customer dissatisfaction and therefore churn probability. In case of CDMA, as the customer is tightly coupled with the handset equipment, the aging of handset impacts the probability of the customer churn.
The following details are used:
a. Dropped call counts
b. Service quality
c. Equipment age (Handset age in case of CDMA)
4. Contract Details: At the end of the contract period or grace period, the probability of the customer leaving the connection is high, therefore it has a high impact in determination of churn. The following details are used:
a. Commitment period
b. Count of contract renewal
c. Current contract and end date
5. Event related:
Loyalty scheme or loyalty benefits are key drivers for retention. The Loyalty scheme related data is used for churn scoring.
www.made-from-india.com provides an exclusive trade directory of information on Telecommunications Equipment suppliers, and exporters, electronic, message, computer network etc. It has an amazing product gallery where customers can view online Product of Telecommunications suppliers, manufacturer & exporters along with product description and other details.
The telecom companies can increase profitability by creating a predictive modeling for identifying potential churn candidates and non-revenue earning customers; and can increase revenue and profitability by targeted campaigning and promotional offers which will not only retain these customers but also convert the non-revenue earning customers to profitable revenue earning customers.
This article highlights the necessity of churn and campaign management and the usage of SAS - Telecommunication Intelligence software (TIS) for the purpose. It also includes various implementation challenges for SAS - TIS in the real time scenario.
Churn Management
Customer acquisition and retention is a significant challenge in all industries. In the Telecom industry it affects profitability of the company if a customer churns before the company can earn back the investment it incurred in acquiring the customer. Therefore, it is very critical to identify the profitable customers and retain them.
With the telecom market becoming more competitive, determining the reasons of the customer leaving the service of the company is increasingly difficult. In this circumstance, it is even more difficult to predict the probability of the customer to leave in near future. It is increasingly challenging to devise a cost-effect incentive to target the right customer to convince him to stay with the company.
Predictive modeling of churn analysis and management aims at generating scores depicting the probability of the customers to churn out in future. This takes into consideration different aspects of customer's susceptibility to churn, including the history of people those who have churned in the past and build a data model that generates an easy-to-understand reference numbers (scores) assigned to each customers. These customers are then targeted with incentives to deter their cancellation. In other words, Churn analysis determines the probable reasons for a future cancellation depending on the past records which will help the companies to customize their offer. For example: if analysis reveals that many customers have churned from a particular area last month and further investigation has identified that there are frequent call drops (disruptions in service) in that exchange (or BTS area). It can be concluded that due to the technical inadequacy of that particular exchange, frequent call drops are experienced which has contributed to the customer dissatisfaction and their moving out of the company. So further technical solution for that exchange can prevent future potential churns.
Business Definition of Churn Management
Defining churn is the first and foremost activity in Churn Management designing. Different companies define churn according to their business experiences.
Churn definition differs from a Pre-paid to Post-paid scenario.
In pre-paid scenario, a customer can be considered as churned in the following cases:
a) If the customer goes out of network (deactivated)
b) If the customer is an active non user (ANU)
A customer can be considered as ANU when:
i. the customer has no outgoing or incoming usage for last (X) rolling days
ii. the customer has only incoming usage but no out-going usage for last (X) rolling days iii. If the customer's usage is below a pre-determined (business decided) amount for last (X) rolling days.
In post-paid scenario, a customer pays a rental on monthly basis. So in case of non-usage or lower-usage, the company earns fixed revenue from every post-paid customer. Therefore, the customer is considered as churned only when he/she goes out of network (Deactivated).
Churn Parameters for business analysis
After defining churn, next activity is identifying the correct parameters for the contribution of churn. The churn probability or churn scores for individual customers can be generated on the basis of following categorical details:
1. Customer demographics Customer demographics related data are used for segmenting the entire customer base depending on:
a) Age
b) Sex
c) Income
d) Customer Account Information
e) Subscription life cycle
2. Billing and Usage:
Billing and usage related information which is obtained from switch (Call Data Records) is mainly used for detection of churn probability. The following details are used:
a. Price plan
b. Monthly usage summary (Charged call count, Charged data volume, Free call & Data amount)
c. Monthly profit contribution
d. Bounced payment
e. Managing channel information
f. Recharge channel information
g. Network Product information ( Voice, Messaging, Data)
3. Technical Quality:
Quality of service is a potential churn driver as call drops or inferior service quality increases the customer dissatisfaction and therefore churn probability. In case of CDMA, as the customer is tightly coupled with the handset equipment, the aging of handset impacts the probability of the customer churn.
The following details are used:
a. Dropped call counts
b. Service quality
c. Equipment age (Handset age in case of CDMA)
4. Contract Details: At the end of the contract period or grace period, the probability of the customer leaving the connection is high, therefore it has a high impact in determination of churn. The following details are used:
a. Commitment period
b. Count of contract renewal
c. Current contract and end date
5. Event related:
Loyalty scheme or loyalty benefits are key drivers for retention. The Loyalty scheme related data is used for churn scoring.
www.made-from-india.com provides an exclusive trade directory of information on Telecommunications Equipment suppliers, and exporters, electronic, message, computer network etc. It has an amazing product gallery where customers can view online Product of Telecommunications suppliers, manufacturer & exporters along with product description and other details.
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