Customer churn occurs when customers or subscribers stop doing business with a company or service. Also known as customer attrition, customer churn is a critical metric because it is much less expensive to retain existing customers than it is to acquire new customers – earning business from new customer’s means working leads all the way through the sales funnel, utilizing your marketing and sales resources throughout the process.

1. ANALYSIS OF CHURN CUSTOMERS USING DATA
MINING TECHNIQUE IN TELECOMMUNICATION
INDUSTRIES
BY
DR.BECHOO LAL
DEPT. OF IT, WESTERN COLLEGE,
UNIVERSITY OF MUMBAI

2. INTRODUCTION
 Customer churn occurs when customers or subscribers
stop doing business with a company or service.
 Also known as customer attrition, customer churn is a
critical metric because it is much less expensive to
retain existing customers than it is to acquire new
customers – earning business from new customer’s
means working leads all the way through the sales
funnel, utilizing your marketing and sales resources
throughout the process.
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3. INTRODUCTION3

4. INTRODUCTION
It is also possible to do this with your customer churn in
a number of ways, so that you can find out:
 The total number of lost customers within a specific
time frame.
 The percentage of lost customer within a specific
time frame.
 The recurring business value that is lost.
 The percentage of the recurring business value that
is lost.
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5. REASONS: CHURN CUSTOMERS
 SLOW IMPLEMENTATION
 POOR UTILIZATION/SERVICES
 NO RECOGNIZED VALUE
 LACK OF FEATURES
 NEW MANAGEMENT
 PRODUCT ISSUES
 CHANGE IN NEEDS
 NOT A FIT
 THE PEOPLE FACTORS
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6. RELATED STUDY
 Chris Rygielskia et. al(2002):through data mining—the
extraction of hidden predictive information from large
databases—organizations can identify valuable
customers, predict future behaviours, and enable firms to
make proactive, knowledge-driven decisions.
 Miguel A.P.M. Lejeune, (2001):Churn management is a
fundamental concern for businesses and the emergence of
the digital economy has made the problem even more
acute. Companies’ initiatives to handle churn and
customers’ profitability issues have been directed to more
customer‐oriented strategies.
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7. RELATED STUDY…
 John Haddenaet et. al(2007): research has been
invested into new ways of identifying those customers
who have a high risk of churning.
 Scott A. Neslin et.al (2006): Given a descriptive
analysis of how methodological factors contribute to
the accuracy of customer churn predictive models. The
researcher observed that predictive accuracy across
submissions could change the profitability of a churn
management campaign by hundreds of thousands of
dollars.
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8. PROBLEM STATEMENT
Customer churn is one of the significant research issues
in Telecommunication Industries that significantly
impedes growth, so companies should have a defined
method for calculating customer churn in a given period
of time.
By being aware of and monitoring churn rate,
organizations are equipped to determine their customer
retention success rates and identify strategies for
improvement.
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9. RESEARCH OBJECTIVES
The most commonly used methods for calculating customer
churn is to divide the total number of clients a
telecommunication industries have at the beginning of a
specified time period by the number of customers lost during
the same period. Some of the research objectives are stated
below by the researcher:
 To study the churn customer patterns
 To identify the factors which are responsible for churning
rate.
 To develop a methodology to reduce the churning
customer rate.
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10. CONCEPTUAL FRAMEWORK OF
THE RESEARCH STUDY
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11. RESEARCH METHODOLOGY11

12. PROPOSED RESEARCH WORK12

13. STATISTICS AND DISCUSSION13

14. STATISTICS AND DISCUSSION14

15. CHURN PREDICTION MODEL
IN TELECOMMUNICATION
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16. METHOD TO PROTECT CHURN
CUSTOMERS
 Analyze why churn occurs. ...
 Engage with your customers. ...
 Educate the customer. ...
 Know who is at risk. ...
 Define your most valuable customers. ...
 Offer incentives. ...
 Target the right audience. ...
 Give better service.
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17. SIGNIFICANT ISSUES OF CHURN
CUSTOMERS
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18. CONCLUSION
Today’s customers expect an easy-to-use interface across
all channels, an exciting in-store experience, and fast
service 24/7.
Yet many operators, especially incumbents, struggle to
meet these expectations because of slow design
processes, limited customer input, and rigid legacy IT
systems.
They need to overcome these barriers and invest in
effective customer-relationship-management systems to
track customers’ digital footprints, reduce costs, boost
customer satisfaction, and improve brand advocacy and
differentiation.
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19. REFERENCES
 YayaXieaXiuLiaE.W.T.NgaibWeiyunYingc(2008),’ Customer churn prediction using improved balanced random
forests’, Expert Systems with Applications, Volume 36, Issue 3, Part 1, April 2009, Pages 5445-5449,
Copyright © 2008 Elsevier Ltd. https://doi.org/10.1016/j.eswa.2008.06.121
 Shin-Yuan,HungaDavid, and C.YenbHsiu-YuWangc(2006),’ Applying Data Mining To Telecom Churn
Management ’, Expert Systems with Applications, Volume 31, Issue 3, October 2006, Pages 515-524,
Copyright © 2008 Elsevier Ltd. https://doi.org/10.1016/j.eswa.2005.09.080.
 Hyunseok Hangman Taesoo Jung EuihoSuh(2004),’ An LTV model and customer segmentation based on
customer value: a case study on the wireless telecommunication industry ’, Expert Systems with
Applications,Volume 26, Issue 2, February 2004, Pages 181-188, Copyright © 2008 Elsevier Ltd,
https://doi.org/10.1016/S0957-4174(03)00133-7
 Miguel A.P.M. Lejeune, (2001) ‘Measuring the impact of data mining on churn management’, Internet
Research, Vol. 11 Issue: 5, pp.375-387, https://doi.org/10.1108/10662240110410183
 Chih-Ping,WeiaI-Tang Chiub(2002),’ Turning telecommunications call details to churn prediction: a data
mining approach ’, Expert Systems with Applications,Volume 23, Issue 2, August 2002, Pages 103-112,
Copyright © 2008 Elsevier Ltd, https://doi.org/10.1016/S0957-4174(02)00030-1.
 John Haddena, Ashutosh Tiwari Raj Kumar, and Roya Dymitr Rutab(2007),’ Computer assisted customer
churn management: State-of-the-art and future trends ’, Computers & Operations Research, Volume 34,
Issue 10, October 2007, Pages 2902-2917, Copyright © 2008 Elsevier Ltd,
https://doi.org/10.1016/j.cor.2005.11.007.
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20. THANKS !!!
QUESTIONS & SUGGESTIONS
AT
BLAL@WCCBM.AC.IN
BLAL2K7@GMAIL.COM
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