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

3

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.

4

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

5

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

6

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):

23

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