DM: Re: Mobile Churn

[wang yongjiang]

Dear Mr Rassy,
 
We are a specialist consultancy company providing marketing 
advice to  telecommunications operators. Telecoms network churn is a 
complex, and for the most part, poorly understood phenomenon, and
there are other factors which could explain your findings, namely: 

D.) In most western countries, new mobile subscriptions 
show a marked  annual seasonal pattern (possibly different from the
pattern you mention in Hypothesis A.), with the largest portion of
sales in any year usually occurring between Thanksgiving and New
Year, and the lowest portion in the next 3 months (Jan through March).
Non-western countries tend to exhibit similar patterns based around
seasonal holidays (e.g. Lunar New  Year in Asia, after Ramadam in
Islamic countries, etc).

As well as  being different in number, customers at different times
of the 
year tend  to be different in type (e.g. there are more gift buyers 
before Christmas), and hence may churn off the network for different 
reasons. 

E.) Customers may churn more readily when their contracts 
(if they have such) come up for renewal. Contract renewal dates may 
differ by month  independently of any seasonal purchase patterns
(Factor D) because, for  instance, of the dates of income tax years;
of sales tax quarters;  payment periods for sales dealer commissions;
etc. 

F.) More or fewer customers, and customers of different types, 
may churn 
 at a particular time as a reaction to marketplace events - e.g. 
new competitors launching networks or services; new price plans 
being introduced; the launch of new handsets; new services being 
offered (e.g. 
 pre-pay services); the opening of new channels to market; the 
withdrawal 
 of any of these; changes to the quality of service of 
after-sales  customer care.
 
G.) Churn also arises because of underlying economic cyles - for 
many customers, mobile phone service is discretionary, and hence is 
discarded  as a recession strikes. A new tax can result in a sharp,
sudden rise in  churn. Again, not all customers react similarly
to these stimuli. 

H.) Customers may stop using the phone well before they 
officially "off the network." Any churn analysis needs to deal
coherently and consistently with those customers whose usage
levels were zero in a  month, but who paid any monthly access fee.
These customers may or may  not be motivated by the same factors
as those customers who passively stop using the service and stop
paying the monthly fee, or 
those  customers who call in to cancel their service. 

I.) There may be a usage-age component to churn. Most 
networks witness some form of "first bill shock" - some customers
are 
surprised at how  large their first bill is, and some of these cancel
the service. These  customers are qualitatively different from those
who churn after longer periods with the network. 

J.) Finally, churn generally differs according to the size 
of the total  customer base and its rate of growth. Fast growing 
networks in their early life typically have many logistical, process
and operational  elements in flux, and this impacts the customer
experience, potentially  resulting in greater churn than in the
case of older, more 
stable networks. For example, many new networks experience churn 
from  customers who expect network coverage to be greater than it 
typically is  in the early years of network operation. If the total 
customer base  grew (or shrank) markedly between the first 3-month
period and the second, this may result in different causes of churn,
and hence different types of customers churning in the two 
periods.
 
Analysis and prediction of mobile network churn is not generally 
a matter of a simple statistical analysis!
 
 Best regards, 
 Peter McBurney 
 Redwing Consulting Ltd 
 
 Hallberg Rassy wrote: 
    
   Dear Friend 
    
   I have now a problem and would like to share with you and 
to receive, if 
   possible your opinion. 
   Hereinafter the picture of situation: 
   1.I'm presently involved in a customer profiling project 
for a large mobile operator. 
   2.The goal is to set up a system able to anticipate the 
likelyhood of churn of customers 
   3.As a pilot step I extracted call records for 10000 active 
customers 
   plus 4000 churned 
   4.Using SPSS neural connection I made up a neural network 
based on a set 
   of 4000 active+4000 churned 
   5.The data was: calling patterns of july, agoust and 
september the 
   target was: churn/no churn situation in december 
   6.The results was promising: 90% of real churn anticipated, 
with a cut-off probability of 80% 
   7.The same network was used on october, nov, dec. data to 
anticipate march churn the results dropped to a terrific 11% 
with the same cut-off of 80%: totally useless 
    
   I have formulated some hypotheses 
   A.The low time span (three month) is affected by 
seasonality 
   B.The data used are not sufficient to build a reliable 
network 
   C.The tool (SPSS Neural Connection) is not reliable 
    
   Could you give your opinion? 
   Many thanks in advance 
    
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