IET_Estimating_market_share_through_mobile_traffic_analysis linkedin

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 analysis proceeds 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

Transactions of the IEE Sri Lanka October 2011 A. J. M. Korale

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.


Figure 145 calls

45 calls

Airtel500 subs

120 calls

30 calls

240 calls

360 calls

Hutch750 subs

Etisalat750 subs

Mobitel2000 subs

Dialog6000 subs

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 tomake 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.


Figure 23.75 calls

3.75 calls

Dialog6000 subs

10 calls

30 calls

47.5 calls

2.5 calls

Hutch750 subs

Etisalat750 subs

Mobitel2000 subs

Airtel500 subs

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


Figure 3

Share Dialog = 1/(1+1/3) = 75%

Proportion Mobitel = 480/1440 = 1/3

Adjusted MOUs = 240 * (D2ND tariff/ D2D tariff) = 480 MOUs

120 calls => 120*2 MOUs

Mobitel2000 subs

Dialog6000 subs

Share Mobitel = 1/3/(1+1/3) = 25%

D2D: 360 calls => 360*4 = 1440MOUs

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.


Figure 4

ToT_PoP_Adj = PoP MOU *(PoP D2ND tariff/PoP D2D Tariff)

Proportion = (ToT_PoP_Adj + ToT_PreP_Adj)/ToT_D2D

ToT_PreP_Adj = PreP MOU*(PreP D2ND tariff/ PreP D2D tariff)

120 calls => 120*2 MOUs

ToT_D2D: 360 calls => 360*4 = 1440MOUs

Mobitel2000 subs

Dialog6000 subs

Table 1, Market shares estimated via the model

March April MayShare Airtel 0.0780 0.0765 0.0747Share Hutch 0.0164 0.0173 0.0181Share Mobitel 0.2526 0.2534 0.2542Share Etisalat 0.1863 0.1865 0.1864Share Dialog 0.4664 0.4660 0.4663

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.

MayShare Airtel 0.0763Share Hutch 0.0187Share Mobitel 0.2599Share Etisalat 0.1787Share 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


Table 2, Total incoming and outgoing calls in a busy hour over 7 day period (in 105)

Total Incoming

MOUs

Total outgoing MOUs

Total incoming calls

Total outgoingcalls

Airtel 3.5475 3.3206 2.173 2.06Etisalat 6.8092 8.3425 4.407 4.881Hutch 0.8331 0.8114 0.461 0.472Mobitel 10.371 11.0604 5.449 5.756

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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IET_Estimating_market_share_through_mobile_traffic_analysis linkedin