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A MODEL FOR MAPPING MOBILE
AVERAGE REVENUE PER USER
TO PERSONAL INCOME VIA HOUSEHOLD
INCOME EXPENDITURE SURVEYS
DR. ASOKA KORALE, C.ENG. MIET & MIESL
INTRODUCTION AND BACKGROUND TO MAPPING ARPU TO INCOME
Slide | 2
1. Household Income Expenditure Survey (HIES )2012-2013 conducted by the
Department of Census and Statistics has provided some estimates on household
expenditure on telecommunication expenses incurred by households that could be used
to extract information needed for planning and programming marketing strategies.
2. The survey had revealed that the average expenditure incurred by households on
communication amounts to Rs.892 /= per household. Of this expenditure Rs.567/ was
spent on the use of mobile telephones. The survey had also reported that the average
household size was 3,8 while the number of income receivers averaged 1.8 thus it is
possible to use 2 as the average number of mobile telephone users and as 2 or more
particularly in respect of higher income deciles.
3. Based on the data on total household expenditure and its components that are
reported for the districts it is possible to disaggregate the estimates to derive on the
expenditure incurred on telecomm services and mobile telephone usage for the districts
in order to rank and select districts for the marketing campaigns.
Slide | 3
4. As should be expected there is a wide variation in the amounts spent on
communication (including fixed, mobile, postage ect…) across the districts with Colombo
taking the lead spot with Rs. 1807/ per household.
5. In order to examine the variation in household expenditure on communication
and mobile telephones the amounts spent across the expenditure decile groups were
examined. There is a close match between household expenditure and household income.
The Table -1 shows that the average household expenditure by expenditure decile
groups and corresponding expenditure on communication and mobile telephone usage
along with household income based on expenditure decile groups Table -2.
6. The table shows the increase in amounts spent on mobile telephone usage which
has amounted to Rs135/ x 567/892= Rs. 86/- in the first expenditure decile rising to
Rs2731 x 567/892 = Rs 1736/=in the 10th decile. This rise in mobile usage expenditure
along with increase in household expenditure and income, makes it possible to identify
users based on expenses on mobile phone usage to treat it as a proxy for estimating
household income. Thus, where mobile usage expenditure can be extracted and used to
derive the income profile of the income receivers then it will be possible to identify the
households on the basis of household incomes and group such households into desired
income categories target the those households which had expended over targeted
amounts.
INTRODUCTION AND BACKGROUND TO MAPPING ARPU TO INCOME
Slide | 4
7. The form and pattern of increase of household income from the estimated
mobile usage expenditure can be plotted against estimated household income using the
estimates from the 10 decile values to derive the form of the curve / path and then use it
derive estimates for other points along its path. Alternatively regression method using the
data set can used to derive income estimates for the needed mobile phone usage
expenses.
8. With four persons as the average household size as reported in the survey and
with 2 income receivers per household the estimated expenses in the use of mobile
telephones is estimated to increase form Rs 86/2 = Rs 43 in the lowest decile to Rs 868/ in
the 10th decile/
9. A review of this data source indicates that the average / per person per
household expenditure on mobile usage within the districts will also basically follow a
similar pattern as that disclosed for the whole country. Thus the same regression equation
/ graph will provide estimates for the income variable for reported mobile phone usage
expenses.
INTRODUCTION AND BACKGROUND TO MAPPING ARPU TO INCOME
Slide | 5
INTRODUCTION AND BACKGROUND TO MAPPING ARPU TO INCOME
10. The survey estimates establish the relationship between household income/
expenditure with the amounts spent on mobile phone usage. The factors that are
established for the whole country, the sectoral breakdowns and the districts can be used
to identify and group the subscribers based on the amounts spent on mobile phone
expenses to income ranges and groups. Using other information available on geographic
locality, age and sex data it will be possible to prepare marketing campaigns using better
and more objective variables .
Slide | 6
AVERAGE MONTHLY HOUSEHOLD EXPENDITURE AND ON COMMUNICATION
Table - 1
AVERAGE MONTHLY HOUSEHOLD EXPENDITURE VS. INCOME
Slide | 7
Table - 2
AVERAGE MONTHLY HOUSEHOLD INCOME
Slide | 8
AVERAGE MONTHLY HOUSEHOLD EXPENDITURE ON COMMUNICATION
Slide | 9
Slide |
10
BREAKDOWN OF HOUSEHOLD & PER PERSON COMMUNICATION EXPENDITURE
Slide |
11
DIALOG BLENDED ARPU AND UNADJUSTED ARPU – INCOME MAP
0 100 200 300 400 500 600 700 800 900
0
20
40
60
80
100
120
140
Per Inc Earner spend on Mobile vs Mean HH Income
Per Inc earner spend on mobile (ARPU)
MeanHHIncome(000)
300
400
500
600
700
800
Colombo
Gampaha
Mannar
Kilinochchi
Mullaitivu
Jaffna
Vavuniya
Puttalam
Kalutara
Kandy
Trincomalee
NuwaraEliya
Matale
Kurunegala
Galle
Batticaloa
Badulla
Polonnaruwa
Ampara
Kegalle
Monaragala
Ratnapura
Matara
Anuradhapura
Hambantota
• These are unadjusted values that need to be shifted to align with Mobile
ARPU in each district
• The shift amount is the difference in blended ARPU for that district and
the ARPU predicted by the survey ta the mean income level of that
district
Slide |
12
ADJUSTED ARPU – INCOME MAP - COLOMBO
• Mapping results calculated for each district and available with BI
• Result for
Colombo
calculated
by shifting
plot on
slide 11 by
247
• Required
shift
amounts
for each
district
found on
slide 13
Slide |
13
BLENDED ARPU VS. AVERAGE INCOME IN DISTRICT
So districts on the line 1, 10, 12, 19 (as are points that fall on line 20, 18, 14, 9) largely follow
the mapping as their shifts are similar. Each district adjusted separately and independently of
the others
The others can use a different mapping on a case by case basis or clustering close
Points to determine degree of offset
Slide |
14
NOTABLE POINTS
• The proportion of fixed lines will vary from district to district so the factor of 567/892 or
146/230 used to compute the proportion of mobile expense from total communication
expense will change from place to place
• The proportion of pre paid to post paid will also similarly change from district to district
and as the Dialog revenues differ widely in this respect the
• It can be expected that in those districts where the fixed line penetration is very low
most of the communication expense will go towards mobile
• There may also be a relationship between post paid connections and a fixed line in a
household.
• There may be also be the possibility that pre paid connections are mainly prevalent in
households that don’t usually have a fixed line connection.
• To examine this one would have to correlate the fixed line penetration across
districts with pre and post split
• The average district spend on mobile per income-expenditure decile from the survey is
a blended value
• it may be possible to estimate pre and post components by studying dialog mobile
district distribution
• Due to tax and other reasons respondents to the survey may under report both income
and expenditure

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Mapping Mobile Average Revenue per User to Personal Income level via Household Income & Expenditure Surveys

  • 1. A MODEL FOR MAPPING MOBILE AVERAGE REVENUE PER USER TO PERSONAL INCOME VIA HOUSEHOLD INCOME EXPENDITURE SURVEYS DR. ASOKA KORALE, C.ENG. MIET & MIESL
  • 2. INTRODUCTION AND BACKGROUND TO MAPPING ARPU TO INCOME Slide | 2 1. Household Income Expenditure Survey (HIES )2012-2013 conducted by the Department of Census and Statistics has provided some estimates on household expenditure on telecommunication expenses incurred by households that could be used to extract information needed for planning and programming marketing strategies. 2. The survey had revealed that the average expenditure incurred by households on communication amounts to Rs.892 /= per household. Of this expenditure Rs.567/ was spent on the use of mobile telephones. The survey had also reported that the average household size was 3,8 while the number of income receivers averaged 1.8 thus it is possible to use 2 as the average number of mobile telephone users and as 2 or more particularly in respect of higher income deciles. 3. Based on the data on total household expenditure and its components that are reported for the districts it is possible to disaggregate the estimates to derive on the expenditure incurred on telecomm services and mobile telephone usage for the districts in order to rank and select districts for the marketing campaigns.
  • 3. Slide | 3 4. As should be expected there is a wide variation in the amounts spent on communication (including fixed, mobile, postage ect…) across the districts with Colombo taking the lead spot with Rs. 1807/ per household. 5. In order to examine the variation in household expenditure on communication and mobile telephones the amounts spent across the expenditure decile groups were examined. There is a close match between household expenditure and household income. The Table -1 shows that the average household expenditure by expenditure decile groups and corresponding expenditure on communication and mobile telephone usage along with household income based on expenditure decile groups Table -2. 6. The table shows the increase in amounts spent on mobile telephone usage which has amounted to Rs135/ x 567/892= Rs. 86/- in the first expenditure decile rising to Rs2731 x 567/892 = Rs 1736/=in the 10th decile. This rise in mobile usage expenditure along with increase in household expenditure and income, makes it possible to identify users based on expenses on mobile phone usage to treat it as a proxy for estimating household income. Thus, where mobile usage expenditure can be extracted and used to derive the income profile of the income receivers then it will be possible to identify the households on the basis of household incomes and group such households into desired income categories target the those households which had expended over targeted amounts. INTRODUCTION AND BACKGROUND TO MAPPING ARPU TO INCOME
  • 4. Slide | 4 7. The form and pattern of increase of household income from the estimated mobile usage expenditure can be plotted against estimated household income using the estimates from the 10 decile values to derive the form of the curve / path and then use it derive estimates for other points along its path. Alternatively regression method using the data set can used to derive income estimates for the needed mobile phone usage expenses. 8. With four persons as the average household size as reported in the survey and with 2 income receivers per household the estimated expenses in the use of mobile telephones is estimated to increase form Rs 86/2 = Rs 43 in the lowest decile to Rs 868/ in the 10th decile/ 9. A review of this data source indicates that the average / per person per household expenditure on mobile usage within the districts will also basically follow a similar pattern as that disclosed for the whole country. Thus the same regression equation / graph will provide estimates for the income variable for reported mobile phone usage expenses. INTRODUCTION AND BACKGROUND TO MAPPING ARPU TO INCOME
  • 5. Slide | 5 INTRODUCTION AND BACKGROUND TO MAPPING ARPU TO INCOME 10. The survey estimates establish the relationship between household income/ expenditure with the amounts spent on mobile phone usage. The factors that are established for the whole country, the sectoral breakdowns and the districts can be used to identify and group the subscribers based on the amounts spent on mobile phone expenses to income ranges and groups. Using other information available on geographic locality, age and sex data it will be possible to prepare marketing campaigns using better and more objective variables .
  • 6. Slide | 6 AVERAGE MONTHLY HOUSEHOLD EXPENDITURE AND ON COMMUNICATION Table - 1
  • 7. AVERAGE MONTHLY HOUSEHOLD EXPENDITURE VS. INCOME Slide | 7 Table - 2
  • 8. AVERAGE MONTHLY HOUSEHOLD INCOME Slide | 8
  • 9. AVERAGE MONTHLY HOUSEHOLD EXPENDITURE ON COMMUNICATION Slide | 9
  • 10. Slide | 10 BREAKDOWN OF HOUSEHOLD & PER PERSON COMMUNICATION EXPENDITURE
  • 11. Slide | 11 DIALOG BLENDED ARPU AND UNADJUSTED ARPU – INCOME MAP 0 100 200 300 400 500 600 700 800 900 0 20 40 60 80 100 120 140 Per Inc Earner spend on Mobile vs Mean HH Income Per Inc earner spend on mobile (ARPU) MeanHHIncome(000) 300 400 500 600 700 800 Colombo Gampaha Mannar Kilinochchi Mullaitivu Jaffna Vavuniya Puttalam Kalutara Kandy Trincomalee NuwaraEliya Matale Kurunegala Galle Batticaloa Badulla Polonnaruwa Ampara Kegalle Monaragala Ratnapura Matara Anuradhapura Hambantota • These are unadjusted values that need to be shifted to align with Mobile ARPU in each district • The shift amount is the difference in blended ARPU for that district and the ARPU predicted by the survey ta the mean income level of that district
  • 12. Slide | 12 ADJUSTED ARPU – INCOME MAP - COLOMBO • Mapping results calculated for each district and available with BI • Result for Colombo calculated by shifting plot on slide 11 by 247 • Required shift amounts for each district found on slide 13
  • 13. Slide | 13 BLENDED ARPU VS. AVERAGE INCOME IN DISTRICT So districts on the line 1, 10, 12, 19 (as are points that fall on line 20, 18, 14, 9) largely follow the mapping as their shifts are similar. Each district adjusted separately and independently of the others The others can use a different mapping on a case by case basis or clustering close Points to determine degree of offset
  • 14. Slide | 14 NOTABLE POINTS • The proportion of fixed lines will vary from district to district so the factor of 567/892 or 146/230 used to compute the proportion of mobile expense from total communication expense will change from place to place • The proportion of pre paid to post paid will also similarly change from district to district and as the Dialog revenues differ widely in this respect the • It can be expected that in those districts where the fixed line penetration is very low most of the communication expense will go towards mobile • There may also be a relationship between post paid connections and a fixed line in a household. • There may be also be the possibility that pre paid connections are mainly prevalent in households that don’t usually have a fixed line connection. • To examine this one would have to correlate the fixed line penetration across districts with pre and post split • The average district spend on mobile per income-expenditure decile from the survey is a blended value • it may be possible to estimate pre and post components by studying dialog mobile district distribution • Due to tax and other reasons respondents to the survey may under report both income and expenditure