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The Sixth “European Students Meeting”The Sixth European Students MeetingESM 2011
Forecasting in ICTForecasting in ICT
Mladen SokeleMladen SokeleHrvatski Telekom d.d.
Opatija, 2011-03-22
Business Forecasting
The objective of Business forecasting is to enable reliable business decisions
Application: mid-term and long-term business planning or particular business challenges /opportunities:
• New products / services• New products / services• New markets or new conditions on existing market (competition, technology
changes, ...)
Result of efficient forecasting should be:• The most probable value of observed indicator• The interval in which the value of the observed indicator has a particular probability
of being in (confidence interval & confidence level)
2
Forecasting in ICT
Views: operator, service provider, content provider, vendors (telco systems, PC, CPE, handsets, SIMs, …), customers (KA, LA,…), regulator,…
Starting point: demand forecasting – customers growth dynamicsPlanning of resources: human potentials, equipment, space, sales, marketing, g p , q p , p , , g,call centers, provisioning, fault-repair, etc.Planning of finances: CapEx, OpEx, revenue, EBITDA
Literature: Fild R T l i ti d d f ti i htt // t d t / t /M k ti R h/T l i ti F ti dfFildes, R.: Telecommunications demand forecasting – a review http://www.cc.nctu.edu.tw/~etang/Marketing_Research/TelecommunicationsForecasting.pdfTelektronikk magazine: Telecommunications Forecasting
http://www.telenor.com/telektronikk/volumes/index.php?page=overview&id1=27&select=allhttp://www.telenor.com/no/innovasjon/forskning/publikasjoner/telektronikk/volume/telektronikk-3-4-2008
ITU Telecommunication Development Sector (ITU-D) - Adjusting Forecasting Methods to the Needs of the Telecommunication Sectorhttp://www itu int/ITU D/finance/work cost tariffs/events/expert dialogues/forecasting/presentations html
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http://www.itu.int/ITU-D/finance/work-cost-tariffs/events/expert-dialogues/forecasting/presentations.htmlTelecommunications forecasting - http://en.wikipedia.org/wiki/Telecommunications_forecasting
Forecasting Methods - Qualitative Methods
Qualitative methods rely exclusively on the intuition of experts, while the statistical analysis of available data is not taken into account. The most important among them are:
Judgmental method – based on the experience of experts who forecast future Judgmental method based on the experience of experts who forecast future conditions. The results of forecasting can also be numerically expressed, but are not an outcome of applying analytical or statistical models.
Delphi method – also based on expert knowledge, but with a detailed procedure of reconciling independent predictions of future state, with consensus as a goal. Useful WEB tool: http://armstrong wharton upenn edu/delphi2/Useful WEB tool: http://armstrong.wharton.upenn.edu/delphi2/
Scenario method – based on a set of terms that regulate the predicting of future events Changing conditions results with several possible outcomes concerning an events. Changing conditions results with several possible outcomes concerning an individual case. Taking it all into account, the experts choose the most probable scenario.
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Forecasting Methods - Quantitative Methods
Quantitative methods are based on analytical and statistical models of the observed phenomenon. It is presumed, for the forecasting purposes, that the developed models will also be valid for the phenomenon description in the future.
The most important methods are:The most important methods are:
Time series methods – predict the future based on the extrapolation of the available past informationpast information.
Causal methods – recognize the relations between the variables which are to be forecasted and the independent variables which can be interpreted Their elements are forecasted and the independent variables which can be interpreted. Their elements are regression models and various techniques for the evaluation of their applicability, as well as the reliability of forecasting results.
Literature:Armstrong J S (Eds): Principles of Forecasting: A Handbook for Researchers and Practitioners Kluwer Academic Publishers 2001
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Armstrong, J. S. (Eds): Principles of Forecasting: A Handbook for Researchers and Practitioners, Kluwer Academic Publishers, 2001 Mostly available on Forecasting principles portal: http://www.forecastingprinciples.com/
Evolution of number of ICT customers
N1+N2+N3+N4+N5
N6 - Number of ...N1+N2+N3+N4+N5
N5 - Number of individuals N4 - Number of SoHo + householdsN3 - Number of SME + ‘wealthy’ householdsN2 - Number of large enterprisesN1 - Number of governmental customers
N1+N2+N3+N4
N1 Number of governmental customers
N +NN1+N2+N3
Diffusion of innovation and new technology, subscription services, market adoption of
N1
N1+N2
1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
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Diffusion of innovation and new technology, subscription services, market adoption of consumer durables, and allocations of restricted resources have S-shaped (sigmoidal) growth.
ICT service life-cycle (SLC)T1 Service is unique and new on the market. Its
market capacity M1 is identical to the current total market capacity. Customer growth can me
d l d b i l S d l
N(t)
M2 M4 modeled by simple S-curve models.
T2 New market opportunities for that service emerge (economical or technological). Its market capacity and current total market capacity are increased to M1
M3
p yM2
T3 Service is confronted with the first competition in unchanged market capacity. Number of customers N(t) decreases and service market capacity
M5
M N(t) decreases and service market capacity declines to M3 level
T4 Counter-attack of observed service provider occurs – certain number of customers are coming b k d/ t t d S i
Typical market adoption of service
0 M6
T1 T2 T3 T4 T5 T6 Time
back and/or new customers are captured. Service market capacity is increased to M4.
T5&T6 Further attacks from competitive service(s) lead to the number of users N(t) and market capacity M
yduring entire SLC
N(t) - number of the users, Mi - market capacities
( ) p ydecrease. Competitive service can be identical service but offered by other provider(s), or similar, but technologically more advanced service(s). The last part of SLC is characterized with service
During the whole service life-cycle (SLC), market capacity changes in hops and resembles a series of stairs.
7
pobsolesce, substitution by new technology and service disappearance form the market
ICT service life-cycle - Examples
30 000
30 000
10 000
20 000
10 000
20 000
0
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
0
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
USO
Number of telex subscribers in Portugal
1 1 1 1 1 1 1 1 1 1 1 1 2 2
Number of public payphones in Finland
1 1 1 1 1 1 1 1 1 1 2 2
Number of telex subscribers in Portugal1976-2003
Number of public payphones in Finland1980-2003
8
Growth ModelsGrowth Models- Modification of growth models for forecasting purposes
Determination of optimal model parameters- Determination of optimal model parameters
Literature:
Makridakis, S., S. Wheelwright, R. Hyndman: Forecasting: Methods and Applications (3rd edition), Wiley, 1998
Meade, N. and T. Islam: Modelling and forecasting the diffusion of innovation – A 25-year review, International Journal of Forecasting, Vol 22, No. 3 (2006), pp 519-545
Growth models – Modification for forecasting purposes
Modifications:to accept external variables as model
t
Time series history
{γ i})Time series (t, N(t)) {αi}
{β }
parameters: • explanatory marketing variables,• business operations information,• environmental variables;
elal
Business operations
Explanatory parameters
t;{α i
},{β
i},
Business op.
Explanatory variables {βi}Growth/decline of each segment of service
life-cycle is S shaped.
{αi} Set of model parameters resulting from fit of time series history
{βi} Set of explanatory parameters - resulting
Mode
Judg
men
tafo
reca
st
Environ-
Forecastoperations
information
Y(t)
=f(
t
Quali
tativ
efo
reca
stin
g
Environmental
Resultp
information
{βi}{βi} Set of explanatory parameters resulting
from qualitative forecasting; e.g. ts – time of launch; te, N(te) – target point in the future; M – (local) market capacity of service; tm –ti f k f l t
mental variables
Auxiliary parameters
Mode
lQ fEnvironmentalvariables
Auxiliary parameters {γ }time of peak of sales, etc{γi} Set of auxiliary parameters which allows
forecasting practitioner to adapt model to her/his specific needs
Auxiliary parameters Auxiliary parameters {γi}
10
her/his specific needs.
Growth models – Modification for forecasting purposes
Environmental variables (BI – business intelligence):• Customers• Competition• Influence of similar services• TechnologyTechnology• Macroeconomics• Regulation
Business operations information (internal knowledge):• Strategy
P d l d (fi i l HW/SW HR )• Present and planned resources (financial, HW/SW systems, HR, space, ...)• Planned date of service launch / service cancellation• Service provision and activation ability• Ability of sales and marketing• Ability of vendors and partners• IT - CRM / DWH
11
IT CRM / DWH• …
Growth models – Determination of optimal parameters
Number of customers modeling:),...,,;()( 21 kaaatfty =
k free parameters – at least k known data points: (ti, N(ti))k
Case: Exactly n = k data points are availableSystem of equations : kiaaatftN kii ,...,1,0),...,,;()( 21 ==−
Case: Available n, n > k data points are available:Weighted least squares method Weighted least squares method
Objective is to minimize sum of squared difference between data points and model evaluated points:p
[ ] 221
1
)(),...,,;( iki
n
ii tNaaatfwS −⋅= ∑
=
12
Growth models – Determination of optimal parametersOrdinary least squares method (OLS) Weighted least squares method
80
100
80
100
40
60
40
60
20
40
20
40
00 20 40 60 80 100
00 20 40 60 80 100
[ ]∑n
tNtf 2)()(min [ ]∑n
tNtf 2)();(min[ ]∑=
−i
ikiaa tNaatfk
1
21}...{ )(),...,;(
1
min
Values obtained for parameters are statistically smoothed, i.e. the influence of particular
Introduction of weights wi focus can be set on the time interval near the last
[ ]∑=
−⋅i
ikiiaa tNaatfwk
11}...{ )(),...,;(
1
min
13
measurement errors of N(t) is reduced observed data
Growth models – Determination of optimal parametersOrdinary least squares method with fixed value of the last data point (tf , N(tf))
Ordinary least squares method with fixed value of a parameter ak
80
100
80
100
80
100
606060
20
40
20
40
20
40
00 20 40 60 80 100
00 20 40 60 80 100
00 20 40 60 80 100
[ ]∑≠=
− −−
n
fiiiffkiaa tNtNtaatf
k,1
211}...{ )())(,;,...,;(
11
min [ ]∑=
−−
n
iikiaa tNaatf
k1
21}...{ )(),...,;(
11
min
14
Models for the First Segment of SLCModels for the First Segment of SLC- The logistic model
The Bass model- The Bass model
Literature: http://www.telenor.com/no/resources/images/144-154_GrowthModels-ver1_tcm26-36191.pdf
The logistic model
Describes growth of the number customers observed over time in a closed market, without the impact of any other serviceDifferential form:
L(t)
M
⎟⎠⎞
⎜ ⎝⎛ −⋅ =
MtLt aL
dttdL )(1 )( )(
Analytical form: GR
( )
eaΔt-1
L'(t) = dL(t)/dt
M/2 I
⎠⎝ MdtExponential
growth Negativefeedback
M - market capacity (eaΔt-1)/2
)(1)();( btae
MtLba,M,tL −−+==
M market capacitya - growth parameter (for a<0 decline)b - time shift parameter
b Time
aM/4
0
Inflexion for t =b, when L(b) = M / 2 (maximum of sales)S-curve is centro-symmetric regarding inflexion point I (b, M/2): "Hardly starts to grow up" problem i e t for which L(t) = 0 does not exist!
16
Hardly starts to grow up problem. i.e. t for which L(t) 0 does not exist!
The logistic model
Effect of logistic model parameter change on the form of S-curve100% 100%
50%
75% a = Aa = -A
50%
75%
a = Aa = 0.5·A
0%
25%
-15
-10
B-5 B
B+5
+10
+15
0%
25%
-15
-10
B-5 B
B+5
+10
+15
B- B- B B B+ B+
B- B- B B B+ B+
75%
100%b = Bb = B-5
75%
100%M = MCM = 0.7·MC
25%
50%
75%
25%
50%
75%
0%
B-15
B-10 B-
5 B
B+5
B+10
B+15
0%B-
15
B-10 B-
5 B
B+5
B+10
B+15
17
The logistic model - examples
Applications:Bacterial growth in Petri Dish
50Microwave ovens in USA (mil.)
100
• Biological growth
• Adoption of 20
30
40
40
60
80
• Adoption of consumer durables
• Subscription 0
10
0 1 2 3 4 50
20
1970 1975 1980 1985 1990Subscription services
• Diffusion of innovation and
Mobile customers in Croatia Number of .hr internet domains80 000
new technology• Allocations of
restricted 4 000 000
6 000 000
40 000
60 000
80 000
resources0
2 000 000
1990 1995 2000 2005 2010
0
20 000
1990 1995 2000 2005 2010
18
The logistic model through two fixed points
Embedded values of two (known) data points: (ts , u·M) and (te , v·M) : Mv·M ⎤⎡
⎟⎞
⎜⎛
⎟⎞
⎜⎛ 111
Δt
L(t) v M
⎥⎦
⎤⎢⎣
⎡⎟⎠⎞
⎜⎝⎛ −−⎟
⎠⎞
⎜⎝⎛ −
Δ= 11ln11ln1
vuta
⎟⎞
⎜⎛ 1
u·M⎟⎠⎞
⎜⎝⎛ −−⎟
⎠⎞
⎜⎝⎛ −
⎟⎠⎞
⎜⎝⎛ −
Δ+=11ln11ln
11lns
vu
uttb
Condition: 0 < u < v < 1; Δt = time to saturationts Time te
Case of symmetrical values for u and v = 1 - u :
⎟⎠⎞
⎜⎝⎛ −
Δ= 11ln2
uta
2sttb Δ
+=
model has form:ttt
u
MuttMtL Δ−−
⎟⎠⎞
⎜⎝⎛ −+
=Δ /)(21 s
111),,,;( s
⎠⎝
19
u ⎠⎝
The logistic model through two fixed points
Framework for forecasting of new services adoption prior to launch:
u = 5 %, v = 95 % u = 10 %, v = 90 %
Δt = 2 years )1(9442 s1)( -tt.e
MtN −−+= )1(1972 s1
)( −−−+= tt.e
MtN
Δt = 5 years )52(1781 s1)( .tt.e
MtN −−−+= )52(8790 s1
)( .tt.eMtN −−−+
=
Δt = 10 years )( =MtN )( =
MtNΔt = 10 years )5(5890 s1)( −−−+= tt.e
tN )5(4390 s1)( −−−+= tt.e
tN
Δt = 15 years )57(3930 s1)( .tt.e
MtN −−−+= )57(2930 s1
)( .tt.eMtN −−−+
=
According to: T. Modis - Conquering Uncertainty, McGraw-Hill, 1998:Services consist of units sold that have typical life-cycle of 6 to 10 quartersService families consist of related services that have a typical business cycle of 5 years Basic technologies consist of a set of related service families that have a typical cycle of 10 to 15 years
20
The Bass model
Introduces the effect of innovators via coefficient of innovation p, which corrects deficiency of simple logistic growth Differential form:
Effect of imitators
( ))()(1)()( tBMpM
tBtqBdt
tdB−+⎟
⎠⎞
⎜⎝⎛ −=
Effect of
Analytical form:
Effect of imitators(Logistic growth)
Effect of innovators
))(( s1 ttqpe −+−−
M - market capacity
))((s
s1
1)();(ttqpe
pqeMtBtq,p,M,tB
−+−+==
p yp - coefficient of innovation, p > 0q - coefficient of imitation, q ≥ 0t - time when service is introduced B(t )=0ts time when service is introduced, B(ts) 0
4 free parametersshape of S-curve similar to the logistic growth model, but shifted down on y-axis
21
s p S s g s g , s y s
The Bass model - Examples of durables diffusion
100CATV(p=0.001 , q=0.060)Pocket calculators
60
80
Pocket calculators(p=0.143 , q=0.520)Wireless phones(p=0.004 , 0.338)Audio CD players(p=0 055 q=0 378)
40
60 (p 0.055 , q 0.378)
0
20
s-2
s-1 ts +1 +2 +3 +4 +5 +6 +7 +8 +9 10 11 12 13 14 15
-20
ts ts
t
ts+
ts+
ts+
ts+
ts+
ts+
ts+
ts+
ts+
ts+1
ts+1
ts+1
ts+1
ts+1
ts+1
For all product ts is fixed and M is set to 100.
Li B B R h I i h //b b
22
Literature: Bass Basement Research Institute: http://bassbasement.orgData: Predicting the speed of technology introduction http://andorraweb.com/bass
The Bass model - Examples of durables diffusion
Databases and software tools (e.g. GBASS):
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Lilien, G. L., A. Rangaswamy, C. Van den Bulte, Diffusion Models: Managerial Applications and Software, New-Product Diffusion Models pp. 295-336, Kluwer Academic Publishers
The Bass model
Characteristic values and points of the Bass model of growth
I fl i i t i ft i l h I fl i i t i b f i l h
M Mp
qp )( 2+
M
Inflexion point is after service launch Inflexion point is before service launch
0
Mp s ≥ 0.5
0t +10 t +20
qqpM
4)( +
qpqM
2)( −
Is < 0.5
ts+10 ts+20ts
q ≤ p
tI
qpqM
2)( − I
ts ts+10 ts+20tI
q > p
B(t)dB(t)/dt
q ≤ pB(t)dB(t)/dt
Near inflexion point t sales is maximal! Excel
Innovators prevailImitators prevail
24
Near inflexion point tI sales is maximal!example
Example: Prepaid customers of cellular mobile networksin Croatia
4 000 000
5 000 000 Quarter Decimal time Data ModelQ1 2000 1999.25 425 078 416 114Q2 2000 1999.50 585 520 595 057Q3 2000 1999.75 765 009 764 857Q4 2000 2000 00 932 490 925 047
2 000 000
3 000 000Q4 2000 2000.00 932 490 925 047Q1 2001 2000.25 1 069 899 1 075 345Q2 2001 2000.50 1 205 473 1 215 638Q3 2001 2000.75 1 330 777 1 345 965Q4 2001 2001.00 1 469 382 1 466 494Q1 2002 2001.25 1 580 179 1 577 502Q2 2002 2001 50 1 681 662 1 679 355
0
1 000 000
99 00 01 02 03 04 05 06 07 08 09 10
Q2 2002 2001.50 1 681 662 1 679 355Q3 2002 2001.75 1 787 853 1 772 481Q4 2002 2002.00 1 890 128 1 857 356Q1 2003 2002.25 1 950 434 1 934 488Q2 2003 2002.50 2 005 313 2 004 397Q3 2003 2002.75 2 052 803 2 067 608
199
200
200
200
200
200
200
200
200
200
200
201
3 000 000ModelPodaci
Q4 2003 2003.00 2 111 900 2 124 640Q1 2004 2003.25 2 154 800 2 175 995Q2 2004 2003.50 2 181 950 2 222 158Q3 2004 2003.75 2 232 100 2 263 587Q4 2004 2004.00 2 348 900 2 300 716Q1 2005 2004 25 2 357 100 2 333 948
The Bass model - results:2 000 000
Q1 2005 2004.25 2 357 100 2 333 948
M = 2 603 238 v = 95%
0
1 000 000p = 0.30676
q = 0.17825
Δt= 7.08
tI 1997.59
(t t )/Δt = 15 8%
25
1999
2000
2001
2002
2003
2004
2005 ts = 1998.71 (tI-ts)/Δt = -15.8%
Source: WirelessIntelligence on-line business intelligence databasehttps://www.wirelessintelligence.com/index.aspx
The Bass model with explanatory parametersFramework for forecasting of new services adoption prior to launch (assumed: Δt and tI )
(tI-ts)/Δt = -20% -10% 0% 10% 20% 30% 40% 50% 60% 70%
p = 2 2480/Δt 2 0585/Δt 1 8318/Δt 1 5654/Δt 1 2605/Δt 0 9257/Δt 0 5850/Δt 0 2853/Δt 0 0866/Δt 0 0102/Δtv = 95%
p 2.2480/Δt 2.0585/Δt 1.8318/Δt 1.5654/Δt 1.2605/Δt 0.9257/Δt 0.5850/Δt 0.2853/Δt 0.0866/Δt 0.0102/Δt
q = 1.1413/Δt 1.4494/Δt 1.8318/Δt 2.3054/Δt 2.8921/Δt 3.6211/Δt 4.5346/Δt 5.7062/Δt 7.3055/Δt 9.8083/Δt
v = 90%p = 1.7231/Δt 1.6079/Δt 1.4722/Δt 1.3129/Δt 1.1269/Δt 0.9125/Δt 0.6720/Δt 0.4187/Δt 0.1889/Δt 0.0427/Δt
v 90%q = 0.9996/Δt 1.2127/Δt 1.4722/Δt 1.7906/Δt 2.1858/Δt 2.6842/Δt 3.3275/Δt 4.1865/Δt 5.3995/Δt 7.3030/Δt
Example: Growth dynamics of new service:M = 1 000 000 market capacityM = 1 000 000 market capacityΔt = 10 years to the service growth saturationv = 95% at the end of 10th year no. of customers is 950 000 (penetration is 95%)Maximum of sales is assumed at the end of 3rd year form service launch (tI = 3) (tI-ts)/Δt = 30%y ( I ) ( I s)
Find p & q from table: p = 0.9257/10 = 0.09257 p + q = 0.45468q = 3.6211/10 = 0.36211 q / p = 3.91174
600 000
800 000
1 000 000
N(t) no of customersts = 2010 MODEL:
)2010(454680
)2010(45468010000001)(−⋅−−
⋅= t
t.etN 0
200 000
400 000
600 000 N(t) - no. of customersN'(t) - salesLevel of saturation
26
)2010(4546809117431)( −⋅−⋅+ t.e. 2010 2015 2020 2025
Models for whole Service Life CycleModels for whole Service Life-Cycle- Interaction between services
Multi Logistic Model- Multi-Logistic Model
Models for whole Service Life-Cycle
Interaction between services on the marketOnly at the beginning of the service life-cycle there is no interaction with other
i di k t d ti th f it th b i t d ith services regarding market adoption, therefore, its growth may be approximated with simple S-shaped growth models (logistic, Bass, Richards)
In latter phases of SLC, interaction between different services is evident, due to:• New market opportunities for service emerge (economical or technological)• Confrontation with competition: identical service offered by other provider(s), or p y p ( ),
similar, but technologically more advanced service(s)
Interaction between different services can be divided into three types (combination of Interaction between different services can be divided into three types (combination of types are possible!):
• Service competition• Service co evolution• Service co-evolution• Service revolution.
28
Interaction between services - Service competition
Both services are competing in market with unchanged total market capacity:capacity:
100
[%] M
75
100 p 1 (t)+p 2 (t)p 1 (t)p 2 (t)
50
75
25
50
00 5 10 15 20 25 30Time
29
0 5 10 15 20 25 30Time
Interaction between services - Service co-evolution
Complementary services change the total market capacity. As a result there is no decrease of existing service penetration: there is no decrease of existing service penetration:
[%] M 1
100
125p tot (t)p 1 (t)p 2 (t)
75
100
50
0
25
30
00 5 10 15 20 25 30 35 40 45Time
Interaction between services - Service revolution
New attractive service almost completely eliminates the existing one, total market capacity is noticeably increased:
100
[%] M
total market capacity is noticeably increased:
75
100 p tot (t)p 1 (t)p 2 (t)
50
75
25
50
00 5 10 15 20 25 30Time
31
0 5 10 15 20 25 30Time
Forecasting of existing services growth
Multi-Logistic Model
ntntttttttt
u
MM
u
MM
u
MMMtMLM nnΔ−−Δ−−Δ−−
⎟⎠⎞
⎜⎝⎛ −+
−++
⎟⎠⎞
⎜⎝⎛ −+
−+
⎟⎠⎞
⎜⎝⎛ −+
−+= −
/)(212
/)2(211
/)1(21 SSS
111...
111111)( 11201
0
Model for the current SLC segment
Model for the first successive SLC segment
Example: Decomposition of a growth dynamics presented on slide 7 into 6 simple logistic growth model:
N(t) N(t)
0 Time
M1 M2 - M1
M4 - M3 M1
M2
M3
M4
0
T1 T2 T3 T4 T5 T6
Time
M3 - M2
M5 - M4
M6 - M5
0
M5
M6 T T T T T T Ti
32
T1 T2 T3 T4 T5 T6 Time
Loglet Lab tool
Decomposition of growth into 3 components:
Loglet Lab by Perrin S. Meyer, Jason Yung and Jesse H. AusubelURL htt // h k f ll d /L l tL b/
M = Saturationb = Midpoint (time shift)a = 4 3944/ Growth Time 10% δ 1),-
δ1ln(
Δt2a ==
33
URL: http://phe.rockefeller.edu/LogletLab/ a = 4.3944/ Growth Time δΔt
Revenue forecastingRevenue forecasting- Bottom-up revenue forecasting flow chart
Market share modeling and forecasting- Market share modeling and forecasting
Bottom-up Revenue forecasting flow chart
Market segments (1..i )
Top-down ARPU
N tot i (t) ms i (t) Volume i (Δt) Price (t) ARPU i (Δt)
Environment: CompetitionΣ
Revenue (Δt)
Environment: Competitioncause-and-effect of similar services (analogy & impact)TechnologyMacroeconomics
Blocks:Growth dynamics forecasting
Ntot i (t) = Number of customers in market segment i at time t; for all operators on the market (not only for the observed one)
Regulation
Growth dynamics forecastingper market segmentsARPU dynamics forecasting
operators on the market (not only for the observed one)msi (t) = Market share of chosen operator in market segment i at time t;Volumei(Δt) = Standard service usage (traffic) in segment i in ΔtPrice (t) = Price at time t of service volume unit
35
ARPU i(Δt) = average revenue per user/customer in Δt
Market Share Modeling – Markov chains
Example: Two operators
1 1
t = t
p (t )(t )
Provider1
Provider2
Nonusers
n0( t) n1(t) n2( t)
t = t
p (t )(t )
Provider1
Provider2
Nonusers
n0( t) n1(t) n2( t)
0.5
n0 n1 n2
0.5
n0 n1 n2
t = t+Dt
p00 ( t) p 22(
p 11(
Nonusers
Provider1
Provider2t = t+Δt
p00 ( t) p 22(
p 11(
Nonusers
Provider1
Provider2
0 Time
0 Time
[ ]
( ) ( ) ( )⎤⎡
=Δ+Δ+Δ+
ttt
ttnttnttn kL10 )()()(
users 1 2n0( t+ Dt) n1(t+ Dt) n2(t+ Dt)
users 1 2n0( t+ Δt) n1(t+ Δt) n2(t+ Δt)
Churn rate for 1st operator
TimeTime[ ]
( ) ( ) ( )( ) ( ) ( )
( ) ( ) ( )⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
×=
tptptp
tptptptptptp
tntntn
kkkk
k
k
k
KMOMM
LL
L
10
11110
00100
10 )()()(
)()()( tntnMtN
Descriptive features:
1
75%
100%n 1 ChurnRate1)(1
)(
)(
)(
)(
)()(
0
11
tntn
tnM
tnM
tN
tNtms i
k
ii
ik
ii
ii −
=⋅
⋅==
∑∑==
0
0.5
0%
25%
50%( ) ,...,ki,p(t)ChurnRate iii 11 =−=
,...,kj,...,k;i,p(t)N(t)GrossAddij
jiji 01 ==⋅= ∑≠
36
0 0%Time,...,kitChurntGrossAddtNetAdd iii 1),()()( =−=
Market Share Modeling and forecasting – MCDG Model
Markov chains based on diffusion growth (MCDG model):
Example:(MCDG model):
Fixed Broadband access technology diffusion
in Norway 1 0
Fixed Broadband access technology diffusion
in Norway 1 0
[ ] [ ][ ] Q
P
×+
+×=Δ+Δ+
)()(
)()()()(22
0
00
tntn
tntnttnttn
k
kk
L
LL
Matrices P and Q have the following
1.0 Non fixed BB customers
1.0 Non fixed BB customers
elements: 0.5
Cable modemFTTx
xDSL
0.5
Cable modemFTTx
xDSL
⎥⎥⎤
⎢⎢⎡
k
kpppppp
LL
11110
00100
P0.0
2000 2001 2002 2003 2004 2005 2006 2007 Q3 2008
0.0 2000 2001 2002 2003 2004 2005 2006 2007 Q3
2008⎥⎥
⎦⎢⎢
⎣
=
kkkk
k
ppp
ppp
LMOMM
10
11110P
⎤⎡ −−− ppp1 LQuality of modeling by MCDG measured via RMSEindicator is around 20 times higher than modeling by Markov chains!⎥
⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
−−−
−−−−−−
=
kkkk
k
k
ppp
pppppp
1
11
10
11110
00100
LMOMM
LL
Q
37
Pricing ModelsPricing Models- Principles
Fair test- Fair-test
Pricing modelsTheir main purpose is to adjust operator's offer to the market laws of demand. As a key for success in customer acquisition retention and success in customer acquisition, retention and business in general, pricing model must encompass the following attributes:
ProfitableCosts
- Profitable,- Billable,- Flexible,
Competition
Customers
Pricingd l- Ensure large customer base,
- Easy to understand,- Exploit willingness-to-pay,
p
Similarservices
Model
- Consistent with regulation,- Ensure competitiveness,- Consistent with other services /pricing
Regulation
C s s s s /p gmodels in portfolio.
It must be fair in )()()( VolumeChargeVolumeChargeVolumeVolumeCharge +≤+
39
sense of usage: )()()( 2121 VolumeChargeVolumeChargeVolumeVolumeCharge +≤+
Pricing model Fair test
ChargeCharge
T1
40
VolumeKnown: T1
Pricing model Fair test
ChargeCharge
T1T2
41VolumeKnown : T1 and T2
Pricing model Fair test
ChargeCharge
Excel example
T3
T1T2
42VolumeKnown : T1 , T2 and T3
Price elasticity of volume
dpVdV
EV =E 25R0
R (Charge)
pp
p – unit price [€/min, €/GB, €/SMS]V – realized volume of service [min, GB, #SMS]
Ev = -2
Ev = -1.5
Ev = -1
Ev = -0.5
5R0
4R0[ , , ]R – revenue [€]
vEVpp
1
⎟⎟⎞
⎜⎜⎛
=
VEpVV ⎟⎟⎞
⎜⎜⎛
= 0
Ev = 0
3R0
oVpp 0 ⎟⎟
⎠⎜⎜⎝
=op
VV ⎟⎟⎠
⎜⎜⎝
0
1
0
+
⎟⎟⎠
⎞⎜⎜⎝
⎛=⋅=
VE
ppRVpR
2R0
What should operator do to
⎠⎝ 0pR0
increase revenue?For Ev = -0.5
For E = 1 55p04p03p02p0p0
p00- increase unit price
decrease unit price
43
For Ev = -1.5 - decrease unit price