Introduction
In the contemporary Telecommunication with the competition mounting up between the service providers, customer acquisition and holding is a critical challenge. For the new entrants, acquiring the new customers is the highest priority, whereas for the incumbents, retaining the wage earning customers is essential.
Related Sas - company brain - Churn & Campaign supervision clarification For Telecom industry.
The telecom fellowships can growth profitability by creating a predictive modeling for identifying possible churn candidates and non-revenue earning customers; and can growth wage and profitability by targeted campaigning and promotional offers which will not only retain these customers but also change the non-revenue earning customers to profitable wage earning customers.
Recommend Sas - company brain - Churn & Campaign supervision clarification For Telecom industry.
This description highlights the necessity of churn and campaign administration and the usage of Sas - Telecommunication brain software (Tis) for the purpose. It also includes various implementation challenges for Sas - Tis in the real time scenario.
Churn Management
Customer acquisition and holding is a critical challenge in all industries. In the Telecom commerce it affects profitability of the business if a customer churns before the business can earn back the speculation it incurred in acquiring the customer. Therefore, it is very critical to recognize the profitable customers and retain them.
With the telecom shop becoming more competitive, determining the reasons of the customer leaving the service of the business 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 consuming to devise a cost-effect incentive to target the right customer to convince him to stay with the company.
Predictive modeling of churn pathology and administration aims at generating scores depicting the probability of the customers to churn out in future. This takes into notice distinct aspects of customer's susceptibility to churn, together with the history of citizen 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 pathology determines the probable reasons for a hereafter cancellation depending on the past records which will help the fellowships to customize their offer. For example: if pathology reveals that many customers have churned from a singular area last month and supplementary investigation has identified that there are frequent call drops (disruptions in service) in that replacement (or Bts area). It can be fulfilled, that due to the technical inadequacy of that singular exchange, frequent call drops are experienced which has contributed to the customer discontentment and their consuming out of the company. So supplementary technical solution for that replacement can prevent hereafter possible churns.
Business Definition of Churn Management
Defining churn is the first and prominent activity in Churn administration designing. distinct fellowships 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 thought about 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 thought about 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) estimate 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 business earns fixed wage from every post-paid customer. Therefore, the customer is thought about as churned only when he/she goes out of network (Deactivated).
Churn Parameters for business analysis
After defining churn, next activity is identifying the strict parameters for the gift of churn. The churn probability or churn scores for personel customers can be generated on the basis of following categorical details:
1. customer demographics customer demographics associated data are used for segmenting the entire customer base depending on:
a) Age
b) Sex
c) Income
d) customer inventory Information
e) Subscription life cycle
2. Billing and Usage:
Billing and usage associated data 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 overview (Charged call count, charged data volume, Free call & Data amount)
c. Monthly behalf contribution
d. Bounced payment
e. Managing channel information
f. Recharge channel information
g. Network goods data ( Voice, Messaging, Data)
3. Technical Quality:
Quality of service is a possible churn driver as call drops or inferior service ability increases the customer discontentment 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. Tool age (Handset age in case of Cdma)
4. Compact Details: At the end of the Compact duration 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 Compact renewal
c. Current Compact and end date
5. Event related:
Loyalty scheme or loyalty benefits are key drivers for retention. The Loyalty scheme associated data is used for churn scoring.
Identifying the source systems:
After deciding the Churn parameters, next step is to recognize the source systems from where the respective data will be extracted.
For example:
Cusomer details from Crm system
Usage & Billing associated details from Billing system
Technical ability from replacement & CellSite
Activation details from Provisioning system
Data Management
Data administration is the foundation for a business analysis. strict data should be gift in strict place.
Data administration has three parts:
Extraction: Involves extracting of data from source system and loading to data interchange layer
Transformation: Involves validation of the extracted data (eg: Validation for unique keys), creation of joining conditions among the tables, cleaning of invalid data etc.
Load: Involves loading the data in the business brain Data Warehouse
Data Modeling and Churn Score generation
Once the authenticated data is available in the data warehouse, the data modeling is performed. It is an iterative process. The ability of the model is accessed and the model which returns the best business value is considered. This model provides results in the form of churn score of personel customers which can be used for determining campaign targets.
Using the churn scores for holding Campaigns
The data model generates personel customer's churn score which ranges from 0 to 1.
0 - Signifies least probability of the customer to churn
1 - Signifies highest probability of the customer to churn.
These scores are weighted components of various parameters, such as
Usage information
Balance information
Recharge information
Decrement (Promotional and Core) information
Handset feature
Network coverage
Quality of service
Customer service/complaints
Price plan sensitivity
Business decision needs to be taken to resolve an upper threshold of the churn score. The customers above this threshold need to be analyzed supplementary (eg: customers with score 0.7 and above). The top two parameters contributing to the churn score to be generated on personel customer level (for customers having churn scores greater than the threshold). Depending on these parameters holding campaign can be carried out. The parameters can be as follows:
Usage statistics: The usage behavior can be derived from the aggregate of decrement (promo and core), equilibrium and recharge information. The customer who has higher score in "lesser usage" can be targeted with promotional price plan offers to improve his/her usage and change that customer from non-revenue earning to wage earning.
Higher Off-net usage: The higher score on "off-net usage" signifies that the singular customer has called very frequently to other networks. A targeted campaign can be performed with the price plan beneficial to call other networks. A supplementary pathology of the called off-net numbers can succeed in identifying frequently called off-net numbers which can be targeted by campaigns as a candidate of acquisition.
Handset Features: The handset used by the customer can be old and be lacking the contemporary features. In this case, the probability of the customer to change to a newer handset is high and there is a critical susceptibility of that customer to move to other service victualer having bundled handset offer. A holding campaign can be targeted (to this group of customers having high Handset churn score) with new service offer bundled with handset.
Customer Service/Complaints: The higher score in customer service/Complaints signifies that the customer has called the customer care frequently and probability of that customer dissatisfied with the service is higher. supplementary investigation to the customer call interaction details can communicate the cause of frequently calling to customer service. After the execution of campaigns on the basis of the churn score and churn drivers, the campaign response needs to be captured and fed into the database for pathology of successfulness of campaigns.
Implementing Churn administration solution Implementation Steps
The following phases are complex in Churn administration solution implementation:
1. Requirement Analysis: In this phase, the business requirements are gathered and analyzed and business definitions for churn are decided
2. solution Assessment: In this phase, the business brain solutions are assessed with the high level requirement of the implementing company. The feasibility test is done depending on the high level business requirement and data availability.
3. Detailed Analysis/Detailed design: In this stage, the business requirements for the Churn administration scheme are analyzed in depth for design, improvement and enhancement of the project. An exercise is performed to understand the availability/unavailability of data required to fulfill the business requirements and data mapping from source system.
4. Data pathology - Etl: In this stage, the data is extracted from the source system, transformed (cleaned/modified for missing fields and data ability is analyzed) and then loaded into Data storehouse of the business brain tool.
5. Data Modeling: In this stage, the analytical data models are created by statistical methods (eg: Logistic regression method) on historical data for churn score prediction and Analytical Base tables are populated by data.
6. Reporting: The churn score (0-1: 0 - means less probability of churn, 1 - Maximum probability of churn) is generated at each customer/account/subscription level and corresponding description is generated.
7. User Acceptance Test and Roll-out: On completion of successful Uat, the software is rolled out for the business users.
Implementation Challenges
There are several challenges when a business brain solution is implemented in a huge scale of millions of customers.
The major time of the implementation is consumed by data management. Data administration utilizes 75% of the total implementation time. Data administration includes:
Identification of source systems from where data needs to be extracted:
Due to the involvement of many source systems (Crm, Provisioning system, Billing, Mediation systems etc.), it becomes increasingly difficult to recognize the strict source system for various data fields. Identification of the strict data source and mapping to Dil fields consumes majority of the implementation time. If the data source mapping is wrong, then the subsequent steps of implementation (modeling, analysis) will also be erroneous. Therefore, extra care needs to be taken during the data convention exercise.
Data Quality: Data obtained from the source systems need to be of high ability and error free. The major challenge in implementing a business analytics solution is obtaining a high ability data. Cleaning up of data and filling the missing fields consume critical estimate of implementation time.
Change management: With the implementation of a Bi solution, the users need to change the way they used to conduct churn prediction and campaign management. Therefore, user adaptability and user awareness needs to be built up through permissible training sessions
To make the business brain system operational: After the implementation, specific organizational structure for handling the Bi operations needs to be planned and the resources need to be trained in the required areas.
Sas in business analytics
Sas is a prominent business analytics software and service victualer in the business brain domain. It has delivered proven solutions to way relevant, reliable, consistent data throughout the organizations assisting them to make the right decisions and accomplish sustainable execution improvement as well as mitigate risks.
Sas has an extended ability of handling data of large scale (with the help of Sas-Spds - scalable execution data server). This combined with strong programming language and enriched graphical interface has differentiated it from the other analytical tools available in the market. This makes Sas perfectly convenient for business usage where it demands handling of huge data stores.
Sas - Telecommunication brain solution (Tis)
Sas has several industy specific solutions. Sas has packaged their business analytics knowledge in the form of models, processes, business logic, queries, reports and analytics.
Tis is the telecom commerce specific business analytic solution which has been built specific to telecom commerce needs. This solution assists the telecom service providers with specific modules, for example:
Sas Campaign administration for Telecommunication
Sas customer segmentation for Telecommunication
Sas customer holding for Telecommunication
Sas Strategic execution administration for Telecommunication
Sas Cross sell and Up sell for Telecommunication
Sas cost risk for Telecommunication
Sas churn administration and campaign administration solution includes Segmenting the entire customer base
Detecting the causes of churn
Scoring the personel customer on the basis of their churn probability
This churn score is supplementary used as an input for campaign management.
Sas Data flow (Architecture)
The data needs to be collected from various source systems.
Crm system: Customer/Account/Subscription associated data
Provisioning system: Activation date, Tool (Handset) age Billing System: Billing data
Mediation System: Call description details
The data is collected in the Data Interchange Layer (Dil). The data is then extracted, transformed and loaded into Detailed Data Store (Dds).
The data is used for:
1. Dimensional Data Modeling: This is used for query, reporting and Olap (Online Analytical Processing)
2. Abt (Analytical Base Table): This is the solution specific model developed which can be used for a singular analysis. For example: The Abt for churn model.
3. Campaign Data Mart: This data is used for targeting specific customer segments for targeted campaign.
Conclusion
Therefore, it is imperative that churn administration is an critical challenge in the contemporary day Indian telecommunication industry. Detecting the permissible think of churn and predicting churn in develop can save the business from expansive wage loss.
Business brain tools help the telecom service providers to accomplish data pathology and to predict churn probability of a singular customer. Apart from churn predictive analysis, the tools can be used for various other pathology to help the business decisions.
Sas has a possible to deal with huge volume of data. As a business brain tool, Sas empowers the business to efficiently deal with expansive volume of data and accomplish pathology on the available data for millions of customers. Moreover, Sas with its telecommunication specific solution (Tis - Telecom brain Solution) assists in construction the data storehouse to hold the required parameters for supplementary analysis.
Therefore, Sas-Tis can be an effective tool for business brain activities in the telecom industry.
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