IET_Estimating_market_share_through_mobile_traffic_analysis linkedin

Transactions of the IEE Sri Lanka October 2011 A. J. M. Korale
ABSTRACT
This paper describes a model by which the market
shares of mobile operators can be estimated by
analyzing the intra and inter operator traffic. A
gravity model is sufficiently general and is employed
as the framework on which the model is based.
Certain general assumptions that are necessary to
make this estimate from call data records and inter
connection traffic are presented.
The model is derived for the case of an example
mobile network consisting of five operators and the
method by which the market shares are estimated is
presented. Example results are given for the case of
the operator Dialog Axiata to demonstrate the
workings of the model. A validation of the technique
is performed by presenting traffic patterns that are
predicted by the model that should be evident if the
data fits the derived model. These results are also
used to confirm the validity of the underlying
assumptions.
The analysisproceeds by apportioning the Dialog to
other operator outgoing traffic in to pre and post
paid segments. Each segment is then scaled by a
factor determined by the effective dialog to dialog
and dialog to other operator tariffs. The ratio of this
total scaled traffic to each operator to the total
dialog to dialog total traffic gives the proportion of
market share of each operator expressed in terms of
Dialog subscribers. A further analysis is presented
utilizing the same model but analyzing traffic
destined to other operators and intra net traffic on a
call by call basis that helps to validate the scheme.
1. INTRODUCTION
In this paper we derive a model that enables
a network operator to estimate its share of
the mobile market through some commonly
available traffic measurements. Estimating
market share from Call Data Records
(CDR) and inter operator connection traffic
require certain general assumptions to be
made due to the very few measurements
available to make the estimate. A gravity
model is sufficiently general and is
employed here.
We begin by stating several general
assumptions that form the basis of the
model. These assumptions relate to the
propensity of a network to originate a
certain number of calls depending on the
size of its subscriber base. They also relate
the propensity of a network to attract a
certain number of calls depending on the
same criteria. By virtue of these
assumptions the model also predicts certain
traffic patterns that will be manifest if the
proposed model is valid.
We next present an example network
consisting of five operators making calls in
a busy hour. The model is derived in the
context of this example network by viewing
the network operator space (total market)
from the point of view of a single operator,
or the host operator (Dialog in this case).
In this example network one assumes
perfect knowledge of the subscriber bases of
all participating operators. The traffic flows
between the host operator and other
operators in the network space are
calculated using the model and the resulting
patterns presented. The market shares are
then estimated and compared to the actual
figures.
The derivation of the model proceeds by
calculating the expected traffic between the
host operator and each of the other operators
in the network space. It will be shown that
there is a certain symmetry in the traffic
between two operators due to the nature of
the assumptions. In other words it will be
shown that the number of calls going to and
from the host operator to a particular
operator in the network space is expected to
be approximately similar. This result will
also be used to validate the model when real
Estimating Market Share through Mobile Traffic Analysis
Asoka J. M. Korale Ph.D. C.Eng. MIET

inter operator traffic is analyzed from
CDRs.
Once the symmetry is established another
key assumption is introduced, which states
that the duration of a call is inversely
proportional to the tariff for that call. It is
typically the case that outgoing calls to
other networks are charged at a higher rate
than outgoing calls to one’s own home
network. Thus in this case the calls to other
networks will be of a proportionately
shorter duration than calls to one’s own
home network. Thus it becomes necessary
to adjust the traffic volume going out to
other networks as it would be understated
when compared to the traffic destined to the
host network by the host network (on net or
intra net traffic).
Thus the outgoing traffic to other networks
is understated and it will be shown that it is
understated by a factor given by the ratio
between the “off network” tariff to the “on
network” tariff. In the subsequent sections
the outgoing traffic to other networks is
scaled and related to the “on net” traffic of
the host network. As the subscriber base of
the host network and the amount of traffic
attracted by the host network are known, the
outgoing traffic (or traffic attracted to other
networks) gives an indication of the size of
those networks in terms of the size of the
host network.
In conclusion the model is validated by
analyzing CDRs and calculating the on net
and off net traffic volumes destined to other
operators of the host network. The expected
symmetry in the traffic is demonstrated. The
market shares are computed over several
months and a stable (over the short term)
result is presented. These results are based
on long term aggregate traffic
measurements and monthly averages for the
off net and on net tariffs.
Validations of these results are next
performed by observing the individual intra
and inter network calls in a busy hour over a
seven day period. The individual tariffs as
specified in a subscriber’s package details
are utilized in this estimate to scale the off
net traffic to other operators. These short
term analytical results showed a very close
correspondence with those market share
values obtained over longer periods. One
can conclude that these short term estimates
converge to those obtained by analyzing
long term aggregate traffic.
2. THE ASSUMPTIONS
1). All subscribers are uniform across all of
the networks, and they are all equally likely
to make a call to any other subscriber in any
network. Under this assumption of
uniformity all subscribers are also equally
likely to receive a call from any other
subscriber in any network.
2) The number of calls originated by a
particular network is directly proportional to
the number of subscribers belonging to that
network.
3) The number of calls “attracted” to
(terminated) in a particular network is also
directly proportional to the number of
subscribers belonging to that network.
*Needless to state that all subscribers are
not uniform due to the effect of the various
subscription packages and we make
adjustments based on whether they are
prepaid or post paid when using the call
statistics.
3. EXAMPLE NETWORK VIEWED
FROM DIALOG’S POSITION
Consider the following example network
presented in Figure 1, which has 10,000
subscribers that make 1000 calls in a busy
hour.
Thus Dialog will originate
(1000/10000)*6000 = 600 calls, uniformly
to all subscribers.

Of these 600 calls, (600/10000)*4000 = 240
calls will be to other networks and
(600/10000)*6000 = 360 calls will be to
Dialogs own (D2D) subscribers.
Of the 240 outgoing calls, (240/4000)*500
or 30 calls will be to Airtel. Similarly under
the uniform assumption we can determine
the number of calls (120 to Mobitel, 45 to
Etisalat and Hutch) that are destined to each
network depending on how many
subscribers belong to that network.
Now Dialog will also attract a certain
number of calls in proportion to its actual
subscriber base. Thus it will attract a total of
(1000/10,000)*6000 = 600 calls. Of these
600 calls (600/10000)*4000 = 240 calls will
come from other networks while
(600/10000)*6000 = 360 calls will be from
Dialogs own subscribers (D2D). So there is
symmetry that comes from the assumptions.
4. EXAMPLE NETWORK VIEWED
FROM AIRTEL’S POSITION
We now consider the network from Airtel’s
position as depicted in Figure 2.
Thus Airtel will originate
(1000/10000)*500 = 50 calls uniformly to
all subscribers.
Of these 50 calls, (50/10000)*9500 = 47.5
calls will be to other networks and
(50/10000)*500 = 2.5 calls will be to
Airtel’s own (Airtel2Airtel) subscribers.
Of the 47.5 outgoing calls,
(47.5/9500)*6000 = 30 calls will be to
Dialog. Similarly under the uniform
assumption we can determine the number of
calls that are destined to each network
depending on how many subscribers belong
to that network. Thus it can be shown that
Airtel originates 10 calls to Mobitel, and
3.75 calls each to Etisalat and Hutch.
So due to the uniform assumption we find
that while Dialog originates 30 calls to
Airtel, Airtel too originates 30 calls to
Dialog.
This symmetry is observed with respect to
the traffic going to and from Dialog to the
other operators in the network space as well.
Dialog
6000 subs
Mobitel
2000 subs
Etisalat
750 subs
Hutch
750 subs
360 calls
240 calls
30 calls
120 calls
Airtel
500 subs
45 calls
45 calls
Figure 1

5. PRELIMINARY ESTIMATE
Thus under the stated assumptions we can
make an estimate for the market share of
each network by using the measurements of
D2D calls and Dialog to other network
(D2ND) calls that are available through the
call data records of the host operator.
Thus in the case of Airtel we can reason that
if 6000 subscribers at Dialog attract 360
calls (D2D), then 30 calls between Dialog
and Airtel imply that Airtel has
(6000/360)*30 = 500 subscribers. Using the
same principal the number of subscribers of
the other networks can also be estimated.
Thus we are able to make an estimate of the
subscriber base of each network utilizing
the Dialog to Dialog D2D and Dialog to
other operator (D2ND) traffic
measurements.
This is a preliminary calculation made
under the assumption of uniform tariffs for
both off net and on net calls. In practice
there are different tariffs and an adjustment
is made as described in the next section.
6. ADJUSTING FOR INTRA AND
INTER NETWORK TARIFFS
The revision of tariffs and the presence of
widely different rates for intranet (D2D) and
extranet (D2ND) calls requires us to take in
to account the applicable tariffs as it
determines the propensity of a subscriber to
make calls of a certain length. Thus we
apply different treatment based on whether
the calls are Dialog to Dialog (D2D), Dialog
to non Dialog (D2ND) and whether the
subscribers are pre or post paid.
In the above example if the applicable tariff
for a D2ND call is higher than the
applicable tariff for a D2D call one would
expect a particular subscriber to speak
proportionately less on a D2ND call than he
would spend on a D2D call. Thus even
though the number of calls attracted to (or
emanating from) is proportional to the
number of subscribers present in the
network we may further assume that the
amount of time spent on each call is
inversely proportional to the applicable
tariff for that call.
Airtel
500 subs
Mobitel
2000 subs
Etisalat
750 subs
Hutch
750 subs
2.5 calls
47.5 calls
30 calls
10 calls
Dialog
6000 subs
3.75 calls
calls
3.75 calls
Figure 2

Let us assume that an average D2D call is 4
minutes and the D2D tariff is 1 Rs per min
and D2ND tariff is 2 Rs per min. Then a
subscriber who spends 4 minutes on a D2D
call will spend ½ the time on a D2ND call
or two minutes. In this calculation the actual
tariff experienced by the subscriber could be
used as that is what will determine the D2D
talk time and D2ND minutes of use (MOU).
Thus when considering the inter operator
traffic (D2ND minutes) we need to scale the
traffic by the ratio between the D2ND tariff
and D2D tariff. The example (Figure 3) then
presents us with 4*360 (1440) MOUs for
D2D traffic. While the MOUs to Mobitel
will be 120*2 (240). The adjusted MOUs to
Mobitel will then be 240*D2ND tariff /
D2D tariff = 480. (what this implies is that
the D2ND MOUs are understated by a
factor determined by the ratio between the
two tariffs.)
Then we may estimate the Mobitel base as
having a proportion 480/1440 = 1/3 (or
2000 subscribers). This leads to a market
share of 25% for Mobitel and 75% for
Dialog in this particular example of a two
operator market. Thus we may use the same
principal to estimate the market shares of
the other operators by considering the D2D
minutes of use and the outgoing traffic to
other operators in the network space.
Due to the different opinions as to what
constitutes an operator’s subscriber base, it
is prudent to consider proportions (and
market shares) and not the absolute numbers
of subscribers when making comparisons.
Typically the subscriber base is taken to be
the revenue generating base or the base of
subscribers who have made a call in the last
30 days. This method gives widely differing
values for the subscriber base depending on
the time window used. For example an
operator that has a subscriber base of 3.5
million when a window of one month is
considered can have up to 7.5 million when
the window is extended to three months.
Thus it is prudent to use proportions and
market shares as opposed to absolute
subscriber numbers when making relative
comparisons.
7. ESTIMATE VIA AGGREGATE
MOU ANALYSIS
In applying the derived model to the
available call data records at Dialog we
analyze the data according to the natural
D2D: 360 calls => 360*4 = 1440MOUs
Share Mobitel = 1/3/(1+1/3) = 25%
Dialog
6000 subs Mobitel
2000 subs
120 calls => 120*2 MOUs
Adjusted MOUs = 240 * (D2ND tariff/ D2D tariff) = 480 MOUs
Proportion Mobitel = 480/1440 = 1/3
Share Dialog = 1/(1+1/3) = 75%
Figure 3

split that is commonly applied at network
operators. Thus we determine the pre paid
(Prep MOU) and post paid (PoP MOU) split
in the minutes of use outgoing towards a
particular operator and scale each quantity
by the ratio between the off net to on net
tariff to arrive at the total post paid and pre
paid adjusted minutes of use given by
“ToT_PoP_Adj” and “ToT_PreP_Adj”
respectively. In figure 4, the prepaid off net
tariff and on net tariff is denoted “PreP
D2ND tariff” and “PreP D2D tariff”
respectively. The post paid off net and on
net tariffs are denoted PoP D2ND tariff and
PoP D2D tariff respectively.
Once the adjusted MOUs have been
determined for each operator the
proportions are determined as a fraction of
the total Dialog on net traffic “ToT_D2D”
from which the market share is estimated as
discussed in the previous section following
the scheme depicted in Figure 3.
Table 1 presents the market shares
estimated via the model for three
consecutive months. Table 2 presents the
number of calls and MOUs going to and
from each operator to Dialog, illustrating
the symmetry in the traffic to within +/-
10% as predicted by the model.
March April May
Share Airtel 0.0780 0.0765 0.0747
Share Hutch 0.0164 0.0173 0.0181
Share Mobitel 0.2526 0.2534 0.2542
Share Etisalat 0.1863 0.1865 0.1864
Share Dialog 0.4664 0.4660 0.4663
Table 1, Market shares estimated via the model
Dialog
6000 subs
Mobitel
2000 subs
ToT_D2D: 360 calls => 360*4 = 1440MOUs
120 calls => 120*2 MOUs
ToT_PreP_Adj = PreP MOU*(PreP D2ND tariff/ PreP D2D tariff)
Proportion = (ToT_PoP_Adj + ToT_PreP_Adj)/ToT_D2D
ToT_PoP_Adj = PoP MOU *(PoP D2ND tariff/PoP D2D Tariff)
Figure 4

8. VALIDATION
With the aim of performing a finer analysis,
the MOUs from individual D2D and D2ND
calls were studied in relation to the model
derived in section 6. The tariff applicable to
each call depending on the originating
subscriber’s package was also utilized
instead of an average on net / off net and pre
and post tariff that was employed in the
previous analysis.
The call data consisted of the busy hour
traffic from seven consecutive days of the
week. Thus the total on net traffic was
calculated by summing the post and prepaid
on net MOUs in the seven hours of call
data. The on and off net tariffs
corresponding to each package was
calculated and utilized to determine the
adjusted MOUs for the off net traffic, by
scaling the outgoing MOUs to each operator
by the ratio of the off net to on net tariff for
each call.
May
Share Airtel 0.0763
Share Hutch 0.0187
Share Mobitel 0.2599
Share Etisalat 0.1787
Share Dialog 0.4663
Table 3, Market share estimated via call by call
analysis
The market share results obtained from this
analysis is given in Table 3, and bears a
close correspondence with the results
obtained via the analysis using aggregate
MOUs and average tariffs utilized in section
7. Thus we may conclude that the results
utilizing the aggregate MOUs and average
tariffs is validated and that the symmetry
predicted by the model is also evident in the
tabulated traffic results of Table 2.
9. CONCLUSION
The market share of each operator is
estimated by calculating a weighted average
of the inter operator traffic taking account
the tariffs applicable to intranet and extranet
calls of pre pay and post paid subscribers.
As lower levels of extranet traffic is
observed with regard to the smaller
operators a window of one month was used
in aggregating traffic to increase the
reliability of the estimates.
A similar number of calls and MOUs are
observed between each mobile operator and
Dialog as the model predicts except in the
case of Etisalat where there is a slight
deviation. This may be due to Dialog
subscribers churning to Etisalat and we
would expect this difference to reduce with
the progress of time.
Ideally an analysis of each intranet and
extranet call could be used with the tariffs
applicable to each subscriber’s package in
this model. This would however require a
Total
Incoming
MOUs
Total
outgoing
MOUs
Total
incoming calls
Total outgoing
calls
Airtel 3.5475 3.3206 2.173 2.06
Etisalat 6.8092 8.3425 4.407 4.881
Hutch 0.8331 0.8114 0.461 0.472
Mobitel 10.371 11.0604 5.449 5.756
Table 2, Total incoming and outgoing calls in a busy hour over 7 day period (in 105)

very large capacity to analyze calls over a
period of a month in order to fairly assess
the contribution of traffic of the smaller
operators and have a reasonable level of
confidence in their market share estimate.
The market share estimates appear stable
over past several months despite the
variation in the interconnect traffic and intra
network traffic which may be due to
cyclical and seasonal variations indicating
the robustness of the model.
The size of the data files and processing
times (due to RAM limitations on the PCs)
limit the amount of call data that can be
processed in a reasonable amount of time,
and requires splitting files and rather
complex operations to recombine results.
Thus only a sample of individual call data
corresponding to the busy hour was studied
in the validation of results.
The call by call traffic analysis gives market
share results that are very close to the
results obtained by the traffic analysis
utilizing aggregate MOUs and we could
conclude that the short term results
converge to the average values obtained
over the longer period.
10. REFERENCES
[1] Daily Performance Analysis Spreadsheet, Dialog
– Axiata, May 2011.
[2] Interconnection details voice, Analysis
Spreadsheet, Dialog- Axiata, May 2011

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