adjusting forecasting methodsto the needs of the ... · durbin-watson 2.369308 n an example of a...

Examining Forecasting Methodologies To Assist And Support Operators In The Transition From

A Regulated Environment To Deregulated Markets

Mohsen [email protected]

ITU - International Telecommunication UnionMarket, Economics and Finance Unit

in cooperation withIIF - International Institute of Forecasters

Adjusting Forecasting Methods To the NeedsOf The Telecommunication Sector

Expert Dialogues

Geneva, Switzerland

25-26 October 2004

2

Adjusting Forecasting Methods To the Needs Of The Telecommunication Sector

Table of Contents

n Introduction

n A Changing Environment : From a Monopoly Situation to Liberalized Markets

n Limitations of “Traditional” Forecasting Methods and Models

n Case Study from an Important Operator experience in International Traffic : outlining the forecasting methods undertaken and how they held up to the challenges of Telecom Deregulation in 1998

n Conclusion

A Changing Environment

From a Monopoly Situation to Liberalized Markets

4


The Changing Telecom Environment (1/10)

In Europe, 1998 was a very important event because the European Telecom Industry has been entirely deregulated. The main effects and consequences were :

l Increasing number of operators, carriers and Internet providers in the deregulated marketsl Increasing (and aggressive) competition and new

AllianceslExplosion of new products and serviceslEvolution of the Customer Choices and usages : From

Telecommunication to Communicationl Falling Prices and Falling RevenuelPressure on Settlement Rates on Bilateral Routes

5



n The Telecom Deregulation level

From 1 – Regulated markets to 5 – Fully Liberalized markets

6



n Increasing and aggressive competition

Int'l Traffic by Carrier Type

0

20

40

60

80

100

120

140

1990 1992 1994 1996 1998 1999 2000

Out

goin

g In

t'l in

Min

utes

(Bill

ions

) New Carriers in CompetitiveMarketsExisting Carriers in CompetitiveMarketsMonopolies

7



n The International Carrier Boom

Number of Carriers

367586

1760

2805

0

500

1000

1500

2000

2500

3000

1995 1997 1999 2000

8



Fixed Telephony Data Services Mobile Internet

200620021998

448

203

6762

780 bEuros

+10% / year + 5% / year

1162 bEuros

529

427

12383

1460 bEuros

573

581

161145

Source : Idate

20051,2 billion of fixed lines

1,5 billion of mobiles900 millions of Internet Access

n A general overview of Worldwide Telecom Industry

9



Monthly Number of communication by FT’s Customer

1997 2002

fixedvoice32,6

fixedvoice20,2

mobilevoice

19,6

SMS5,2

Mail 1,8

mobilevoice1,0

Voice+ 18%

Text

51%

49%

voice

Structure of communications

97%

43%

3%

42%

11%

4%

fixedvoice

fixedvoice

mobilevoice

SMSInternet

1997 2002

Source : FT/DPS 2003

n P2P Communications : + 40 % n Fixed Voice Calls : only + 18 % n Fixed Voice Share in the Global Call volumes falled

n Explosion of new products and services

10



France : Monthly Number of communication by Customer

0

20

40

60

80

100

120

140

160

180

200

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Fixed voice Mobile voice SMS and mobile data Minitel Internet mail & IM Internet WEB &t P-to-P

Fixed voice

Mobile voice

SMS and mobile data

Internet


Co

mm

un

ication

synchroneC

om

mu

nicatio

nasyn

chro

ne

n Evolution of the Customer usages and Choices

Customer wants Communication rather Telecommunication

11



1,92

3,84

1,49

0,08

0

1

2

3

4

5

6

7

8

1998 1999 2000 2001 2002 2003 2004 2005 2006

SMS

Mobile

Fixed Traffic

eMails


n Evolution of the Customer usages in Enterprises

France : Number of communications by employee and by day

12



n Falling Prices and Falling Revenue

Lowest Available Retail Price for Calls from Germany

0 0,5 1 1,5 2

USA

UK

Turkey

Australia

DM/minute

2000

1997Austria

France

Poland

13



0

10

20

30

40

50

60

70

80

90

100

1994 1996 1998 2000 2002

Line rental (€/month)

Local call (€/100)

National call (€/100)

International call (€/100)

Fixed to mobile (€/100)

Mobile to mobile (€/100)

CAGR (1997 – 2002)

+ 5.0%

- 2.8%

-13.3%

-12.4%

- 9.5%

-16.9%

n Price Evolution : France Telecom’s Prices for Fixed Voice Traffic

14


The Changing Telecom Environment : Conclusion

The Modeling and Forecasting Techniques in the Telecom Industry are significantly and highly impacted by this changing environment :

l It is necessary to Understand the real issues facing Operators in a newly Liberalized & deregulated environment

l These changes must lead the historical players (incumbents) to “re-engineer”, adapt and reshape their Forecasting processes and techniques to face and challenge the new environment

l These market changes must drive “new” and more suitable forecasting Techniques

Limitations of “Traditional” Forecasting Methods and Models in the Context of

Competitive Markets

16


Monopoly situation

Competitive MarketTransition

Univariate B&J Univariate B&J Univariate B&J

0

200

400

600

800

1000

1200

1400

1600

janv-9

6avr

-96juil

-96oct

-96

janv-9

7avr

-97 juil-97

oct-9

7

janv-9

8avr

-98juil

-98oct

-98

janv-9

9avr

-99juil

-99oct

-99

Traf_D

Forecast_D

0

200

400

600

800

1000

1200

1400

1600

janv-9

6avr

-96 juil-96

oct-96

janv-9

7avr

-97 juil-97

oct-97

janv-9

8avr

-98 juil-98

oct-98

janv-9

9av

r-99

juil-99

oct-99

Traf_D

Forecast_D

0

200

400

600

800

1000

1200

1400

1600

janv-9

6mai-9

6sep

t-96jan

v-97

mai-97

sept-97

janv-9

8mai-9

8

sept-98

janv-9

9mai-9

9

sept-99

janv-0

0mai-0

0sep

t-00

Traf_D

Forecast_D

Using inadequate Forecasting Model : an example

• Jan 1998 : Deregulation of Telecom Market

• Traffic Forecast for 1998 accurate.

• Forecasting Model =Univariate B&J for 1998

• Until early 1998 : Telecom environment was stable.

• In 1998 : Some New players entered the market

• 1999 : Deregulation of Telecom Market has produced its early effects

• Traffic Forecast for 1999 not accurate.


• Starting 1999 : Telecom environment was instable because full competition

• In 1999 : Increase of thenumber of New players in the market

• 2000 : Deregulation of Telecom Market has produced its full effects

• Traffic Forecast for 2000 not accurate et MAPE has shown an increasing error.

• Forecasting Model = Univariate B&J for 2000

• Very important change in the actual traffic

• In 2000 : Telecom market was widely opened and fully competitive

• In 2000 : Increase of the number of New players in the market

17


Monopoly situation

Competitive Market

Transition

0

200

400

600

800

1000

1200

1400

1600

janv-9

6avr

-96 juil-96

oct-96

janv-9

7avr

-97 juil-97

oct-9

7

janv-9

8avr

-98 juil-98

oct-9

8

janv-9

9avr

-99 juil-99

oct-9

9

Traf_D

Forecast_D

0

200

400

600

800

1000

1200

1400

1600

janv-9

6mai-9

6

sept-96

janv-9

7mai-9

7se

pt-97

janv-9

8mai-9

8sep

t-98jan

v-99

mai-99

sept-

99jan

v-00

mai-00

sept-00

Traf_D

Forecast_D

Univariate B&J Univariate B&J with Multivariate B&J Intervention Function Transfer Function

Hubbing & Refiling Prices, Competition, LCR

0

200

400

600

800

1000

1200

1400

1600

janv-9

6avr

-96 juil-96

oct-9

6

janv-9

7avr

-97 juil-97

oct-9

7

janv-9

8avr

-98 juil-98

oct-9

8

janv-9

9avr

-99 juil-99

oct-99

Traf_D

Forecast_D

Adapting the Forecasting Model to the new context

• Jan 1998 : Deregulation of Telecom Market



• Until early 1998 : Telecom environment was stable.

• In 1998 : Some New players entered the market

• 1999 : Deregulation of Telecom Market has produced its early effects


• Forecasting Model = Univariate B&J with Intervention Function for 1999

• Starting 1999 : Telecom environment was instable because full competition

• In 1999 : Increase of the number of New players in the market

• 2000 : Deregulation of Telecom Market has produced its full effects

• Traffic Forecast for 2000 accurate et MAPE has shown a stable error.

• Forecasting Model = MultivariateB&J for 2000

• The new model feets well the changing trend of traffic

• In 2000 : Telecom market was fully competitive

• In 2000 : Increase of the numberof New players in the market

18


Customers

Economy

Demand Forecast

TrafficRevenuesCapacity

UsersCustomers

…….

Main drivers in a Monopoly situation

The main drivers in a Monopoly Telecom market are the Economy and the Customer features :

Economy : GNP, GDP, Income,

Consumption, Price index, Investments, Foreign Trade …

Customer : demand, segmentation, price elasticity by customer segment, ….

The main drivers in a Competitive Telecom market are :Economy Customers needs and their perceived value,detailed segmentation, …

Regulation interconnexion rules, offerings

Pricing Strategy adaptative pricing, yield management

Competition, market shares, players, …Product substitutionTechnology & Innovation

The Selection of the Explanatory Variables

Substitute Products

Customers

Regulation Pricing / Yield Management

Economy

Competitors

Company Goals

Company Developments

Demand Forecast

TrafficRevenuesCapacity

UsersCustomers

…….

Main drvers in a Competitive Environment

Innovation/Technology QoS (Quality of Services)

19


Explanatory Variable Coefficient Stand. Error t-Statistic Prob.LOG(Price) -0.471823 0.013197 -35.75160 0.0000

LOG(Time)*LOG(Price) 0.048264 0.003987 12.10482 0.0000LOG(Foreign Trade) 0.477754 0.001810 263.9157 0.0000

AR(1) 0.373937 0.090575 4.128461 0.0001R2 0.984826Adj. R2 0.984320Durbin-Watson 2.369308

n An Example of a Previous Econometric Model used for the Global Outgoing IDD Traffic

n Before 1998, this type of Econometric model has generally provided accurate forecasts :

l For Global Traffic

l For Main Customer Segments : Business and Residential

l For Main Geographical Segmentation : Europe, Asia, NA, …

n For regulated markets, this type of Econometric model could provide accurate forecasts :

l By country (Algeria, Viet-Nam, Cambodia, China, Saudi Arabia, Tunisia, …)

l By area : Middle East, North Africa, South America, …

The Main Forecasting Methods used Before the 1998 Liberalization

20


The Main Forecasting Methods used Before the 1998 Deregulation

Geographical Segments Models/Methods used Explanatory variables

NAFTA

USA-CANADA-MEXICO

Econometric Multivariate ARIMA

Market model

Imports/Exports index Call prices

communication consumption Competition, Hubbing Refiling

Western Europe

Econometric Uni/Multivar. ARIMA

Market model

Imports/Exports index GNP/GDP - Call prices

communication consumption Competition Hubbing Refiling

Japan/HongKong Taiwan/S.Korea

Econometric Univariate ARIMA

Market model


communication consumption

South America

Econometric/Dynamic Reg. Uni.ARIMA/H&W

Market model


communication consumption

Middle East/Africa

Econometric/Dynamic reg. Uni. ARIMA/H&W

Market model


Global investments in ME Caribbean (ex. Martinique &

Guadeloupe) X11 Regression / H&W

Market model Tourism index

GNP/GDP - Call prices

n An Example of Previous Forecasting Models by Geographical Segments for Global Outgoing IDD Traffic

21


The Forecasts Accuracy

n An Example of a Previous Econometric Model for the Global Outgoing IDD Traffic

22


Enhancing the Modeling Process (1/4)

• To enhance the quality of the forecast (accuracy)

• To have a better comprehension and understanding of the Market Structure : Ø for example, how each explanatory variable drive the demand ?

• To learn the relationship between the dependent variable (to be forecasted) and its environment : evaluation of the Economy, Price, Competition, … impacts onthe dependent variable (contribution)

• To assess and test and simulate some alternative scenarios and hypothesis : Ø For example, what is the impact on mobile voice traffic of 15%

reduction of price ?

• To catch the dynamic of the market environment (lagged explanatory variables) Ø For example, when (after how many months or weeks) a 10%

reduction of price will increase the demand ? This could allow the operator to adapt the periodicity of some actions through the time (special offers, …)

n The Econometric Modeling

23



• We can integrate in some Time Series Models the effects of :

Ø specific actions/events (dummy variables, intervention variable)

Ø Multivariate Time Series Analysis : including explanatory variables (Transfer Function Modeling, i.e Box & Jenkins with Transfert function or Kalman Filtering)

n Time series Modeling

n Growth Curves/S-Curves

• These models are adequate for new product studies

Ø The Logistic (Local, Extended, …)Ø GompertzØ Exponential

24



n A specific Model for each Customer Segment : by customer, by market, by product, ...

For example :

• By Age

• By Income

• By Occupation

• By overall Spend

• By business sector

The benefit is to learn for example how each Type of Customer reacts to thePrice, Economic, Competition … variables

- For example, the Young Customer has a high Price elasticity . The « High Revenues » Customer has a high Economy elasticity than Priceelasticity.

- This information could help theoperator for its Marketing strategy (Packages for Young Customers, Prepaid cards for…..)

25



n The need of Information and Data in the Competitive markets

ØIt is absolutely important to improve the « quality » of the Data to be integrated in the forecasting models

ØIt is « vital » to track « new » information concerning the competitive market

• Competition • Innovation• Pricing• Regulation• Customer attitudes and choices• QoS• Substitution Products• Marketing• Strategy• Sales

How to collect them :

• Market intellingence• Benchmark studies• Consultancy sources : Ovum, IDC, Giga, Yankee Group, Idate, Frost& Sullivan, ……• Customer surveys / Trade Off studies• Int’l Offices os Statistics : ITU, WB, IMF, EU, • National Offices os Statistics : INSEE, Census, …• Other sources : Wefa, ….

There is a very important need of onformation and data concerning « new » drivers to be considered in a competitive market :

26


Limitations of traditional forecasting methods : Conclusion

The Forecasting Process (Modeling, Estimating, Accuracy) must be adapted continuously to the changing environment :

l The market changes must drive “new” and more adequate forecasting Techniques (for example the Simultaneous Equation Models)

l Telecom Forecasting Models must take into account :

l More adequate and accurate explanatory variables

l The dynamic of demand and supply : lagged variables

l The Necessity of more detailed Demand Segmentation

l The Players behaviour and Strategies (Alliances, …)

l Using Econometric Models solely could lead to inconsistent and not accurate Forecasts :

l It is necessary to integrate in the Forecasting Process other contributions outside the Econometric Model (see Case Study in 3rd Part) : Learning from Customer Surveys, Market Studies, Trade Off studies, … and experience

l Integrating a flexible pricing strategy : stable Price elasticity ?

l Learning from the experience of deregulated and competitive telecom markets : UK, USA, …

l Need for frequent re-forecasting and adaptative forecasts

l Integrating new traffic routing strategies (LCR, hubbing, refiling,…)

A Case Study from an important Operator Experience in the International Traffic

Outlining the Forecasting Methods undertaken and how they held up to the Challenges of Liberalization in 1998

28


Adoption of New Approaches and Forecasting Models

Module Expected Results

F o r e c a s t E c o n o m e t r i c M o d e l To ta l I n c o m i n g T r a f f i c t o F r a n c e

R o u t i n g :F r a g m e n t a t i o n

Tota l T ra f f i c f rom or ig in c o u n t r y t o F r a n c e b y o p e r a t o r / carr ier

R o u t i n g :- C o n c e n t r a t i o n

- Repar t i t ion

Tota l I ncoming T ra f f i c t o F r a n c e - b y R o u t e & b y o p e r a t o r

- b y d e s t i n a t i o n o p e r a t o r/car r ie r

n Building a new approach for Incoming International Traffic (IDD Traffic)

29


Forecast Econometric Model

Total Incoming Traffic to France

Country A FranceEstimation of Global Traffic from Country A to France

n Module 1 : Global Traffic between Origin country and France :

l “Traditionnal” Econometric Modelling & Estimation

l But with More suitable explanatory variables

Transit

« Hubbing »

Forecasting the Int’l Incoming Traffic (1/9)

n Transit & “hubbed” traffics to country A are not included in the Global traffic

l They should be added outside the model

30



n Model Specification & Estimation

n Information & Data Set :

l Annual Data from 1990 to 1998

l Traffic between 54 countries , i.e 9 years x 54 countries x 53 country-pairs

31



n Actual & Predicted data

32


Country A FranceTraffic to France by Route and by

Operator/Carrier

n Module 2 : Fragmentation of the Incoming Traffic from Origin Country to France :

n Transit & “hubbed” traffics to country A by Route and by Carrier/Operator must be added to the global Traffic

Transit

« Hubbing »

Routing :Fragmentation

Total Traffic from origin country to France by operator / carrier

OP1CAR1

OP2CAR2

…..


n Assumptions and hypothesis are defined for actual and future situation of players & market :

l Alliances and Partnership Policy

l Liberalization evolution and Telecom players behaviour in the origin country

33


Traffic to France (excluding hubbing)

Res

elle

rs

Loca

l Acc

ess

OR

I1

OR

I2

C's

C

Detail

Resellers

Local Access

ORI1

ORI2

C's C

Int’l Traffic origination to France

Traffic to be sent from the origin country to France

Int’l Traffic from other countries to A (‘hubbed’)

Total Int’l Traffic to France

Traffic Concentration chain in the Origin country (Retail → wholesale)

From concentration

module in third country

To concentration & Distribution modules

Main categories of operators/carriers considered (couldiffers from a country to another country)

Improbable Case

Could happend but rarely

Traffic from origin Country A to France


n Fragmentation of Traffic in the Origin Country

Methodology

34



n Fragmentation of Traffic in the Origin Country

35



Country ARoute 1

Routing :- Concentration- Distribution

Total Incoming Traffic to France - by Route & by operator

- by destination operator/carrier

Route n

Route 2

OP1 OP2

OP3OP4

France

OP1 OP2

OP3OP4

n Module 3 : Traffic Concentration in the Origin Country & distribution in France

« Hubbing »

Transit

36



interconnection Cost In France

Tariffs of Half-circutsInt’l Leased Lines

Countries andOperators/Carriers

Commutation Cost

Cost of End-to-EndRoute

Cost of « Hubbing »Route

Traffic on the Considered Route

Possible Routes Percentage of TrafficManaged by LCREconomic Criterion

Geostrategic Criterion

n Concentration of Traffic to France by Route and by Carrier/Operator

37



Operator Traffic

Traffic on Operator bilateral route + Traffic on « Third » party bilateral route + End to End Traffic (if Affiliated)

Competitors Traffic

Traffic on the Carrier’s bilateral route +Traffic on « Third » party bilateral route + End to End Traffic (if partnership)

n Distribution of Int’l Incoming Traffic to France by Carrier/Operator

Conclusion

39


Conclusion :

The Forecasting process (modeling, estimating, accuracy checking) must be adapted continuously to the changing environment :

l The market changes must drive “new” and more adequate forecasting Techniques

l “Refresh” the forecasting model & approaches

l Build alternative model & approaches when necessary

l Use alternative scenarios to assess the demand & supply

l Telecom Forecasting Models must take into account :

l More adequate explanatory variables

l The dynamic of demand and supply : lagged variables

l The Necessity of Demand Segmentation

l The Players behaviour and Strategies (Alliances, …)

l The flexibility of pricing strategy

l New traffic routing strategies (LCR, hubbing, refiling,…)

l It is necessary and very useful :

l to integrate in the Forecasting Process other contributions outside the Econometric Model : Learning from Customer Surveys, Market Studies, Trade Off studies.

l To learning from the experience of deregulated and competitive telecom markets : UK, USA, Australia, Canada, …

l To set-up and use a re-forecasting process

40


THANK YOU FOR YOUR ATTENTION

[email protected]

Tel : + 33 1 56 66 34 50Mobile : + 33 6 82 93 15 72Fax : + 33 1 56 66 53 83

adjusting forecasting methodsto the needs of the ... · durbin-watson 2.369308 n an example of a...

Documents