Viral marketing in telecoms alpha customers in phone networks by tls14265

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									 D1 Solutions AG

 a Netcetera Company

Viral marketing in telecoms: alpha customers in phone networks

Stamatis Stefanakos

October 2008

Marketing through social networks?

Some possibilities:
 Word-of-mouth product penetration

 Campaign targeting through hub (“alpha”) selection

 Customer risk assessment by social-ties analysis

 Goal: Not a graph-theoretic study of social networks but a hands-on
  evaluation of the possibilities of using social networking information
  in CRM and marketing.

A case study in telecoms

The company:

   Number 2 in Switzerland
   Mobile & wireline
   1.5M mobile „customers“


Marketing and CRM face one main challenge:

 „Know your customers“

Main activities:

   Design of new products (what do our customers need and how
    much are they willing to pay for it?)
   Launch of promotional campaigns (what should we offer and to
   Customer retention (which customers are considering to leave us,
    what can we do to stop them?)

Available tools

The company has a plethora of data available:

   Usage & revenues
   Customer socio-demographics
   Product-customer relationship history
   Network logs (call detail records)

Traditional actions

   Design of new products
     Guesstimates

     Ad-hoc analyses of customer behaviour

   Launch of promotional campaigns
     Guesstimates

     Selections of target groups based on revenue, products, socio-
     Evaluation of previous campaigns

   Customer retention
     Churn models based on revenue, products, socio-demographics

How can social networking help with this?

   Design of new products
     Analyze social behaviour of customers (e.g. sizes of cliques for
       determining the structure of „family“ products)
   Launch of promotional campaigns
     Identify and target „alpha“ customers (viral marketing)

   Customer retention
     Viral churn models

Call Detail Records

Every call originating from or terminating in the company‘s network
generates a call detail record (CDR). The CDR contains information on:
 Type of call
 Originating MSISDN, terminating MSISDN
 Date / time
 Duration
 Completion code
 Cell (Starting / Ending)
 Volume (data calls)
 Handset
 ….

CDRs are available for mobile and wireline calls. Terminating wireline
CDRs are currently not available (these are handled in the network of
the incumbent operator).

The call (sms) graph

The call graph

Is a directed / undirected graph:
 Nodes are subscribers.

 Edges are calls between subscribers.

In the undirected case calls have to be two-directional in a certain
period of time.

To limit the size we only add an edge if a minimum number of calls with
a required minimum duration is made between two nodes.

The call graph obeys a power law degree distribution and contains a
giant connected component with almost all nodes ( suitable for viral

The call graph: computational limits

The size of the call graph is the main obstacle in any analysis:

As there are millions of calls handled daily, the size of the call graph
can grow beyond computational feasibility.

   Voice graph (for one month):
     No restrictions: 11M nodes, 35.2M edges

     Edges with > 4 calls, 120sec total duration: 3.8M nodes, 7.8M
   SMS graph (for one month):
     No restrictions: 6.8M nodes, 26.2M edges

     Edges with >4 SMS: 2.7M nodes, 6.8M edges

Alpha customers

The company bases all customer communication and product offers on
four revenue-based segments.

We could extend this using social-networking information:
 Social-behaviour based segments.

 Alpha customers: the „hubs“ of the network.

 Large 1st and 2nd in-degrees.

Example campaign: 1000 SMS Special Promo

   An offer of 1000 SMS for a very cheap fix price is sent per SMS to
    ~200K prepaid customers.
   A customer that wants to take the offer has to send a keyword to a
    specific number.
   An amount is deducted from the customer‘s balance and 1000 SMS
    can be sent at no additional cost.
   12571 customers signed up.
   652 (5% of all takers) were not targeted by the campaign.
   Obviously the campaign spread beyond the initial targeted group via
     (Some customers though might have more than one

1000 SMS Special Promo: Analysis

   Out of the 652 takers that were not targeted by the campaign 47 had
    another subscription that was in the campaign.
   605 (4.8% of all takers) learned about the campaign via word-of-
   Out of the 605 customers, 339 had social links to the takers of the
    campaign (social links = call in the CDRs over a period of a month).
     We only consider takers, not the whole campaign group.

   The links in the takers group had an “alpha degree” of 194.
   The takers group in total had an “alpha degree” 117.

Customer churn

Churn rate = measure of customer attrition, defined as the number of
customers that discontinue a service during a certain time period.

Churn control is currently the main focus in CRM in the European
markets: high (mobile) penetration and low barriers to switching
providers make it cheaper to retain customers than to acquire new

Typical yearly telecom churn rates:
 Postpaid: 10-15%

 Prepaid: 30-40%

Prepaid churn prediction

Prepaid churn is defined solely by usage; there are no binding
 Similar to consumer churn in everyday brands?

Available information on prepaid customers is limited
 No contract  bad data

 Socio-demographics are not always available

 The phone might be used by someone else than the one that
   registered it

Many prepaid promotions are based on cheaper or free calls/sms within
  the providers network.
 Social behaviour might be an influencing factor in prepaid churn.

Prepaid churn prediction by social ties

Social tie = A calls B and B calls A, 5 or more calls, in Nov 2007.

Viral propagation of churn:
 Churned in June 2008:

     13.1% of their social ties had churned before they did.

 Active in June 2008:

     9.3% of their social ties had churned.

Marketing through social networks?

   Telecom companies “own” social network information.
   Our simple analyses show that word-of-mouth can be quantitatively
   Our experience indicates that social-networking analysis can be
    very useful for improving marketing and CRM activities.
     Gain knowledge about your customer base.

     Understand the value of your customers.

     Implement more effective campaign targeting.

     Improve customer risk assessment.


   Outside the telecom world:
     Do phone networks resemble human social networks?

     How about other networks (email, facebook, …)?

     Can we measure word-of-mouth and product churn rates in other
       social networks?


   From the algorithmic point of view:
     How do we deal with massive data sets?

        (find cliques / clustering / …)

     How to maximize word-of-mouth spreading?

     How about dynamic networks (over time…)?

        (rate of friend acquisition / friend loss)

     How do we integrate business specific information into our graph
 D1 Solutions AG

 a Netcetera Company

Stamatis Stefanakos,,
phone: +41 44 435 10 03 | mobile: + 41 78 634 09 24

D1 Solutions AG
Zypressenstrasse 71
CH-8040 Zürich

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