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All drawings appearing in this Recommendation have been done in Autocad.
Recommendation E.508
FORECASTING NEW INTERNATIONAL SERVICES
1 Introduction
The operation and administration of an international telecommunications
network should include the consideration of subscriber demands for new services
which may have different characteristics than the traditional traffic (i.e. peak
busy hours, bandwidth requirements, and average call durations may be different).
By addressing these new demands, Administrations can be more responsive to
customer requirements for innovative telecommunications services. Based on the
type of service and estimated demand for a service, network facilities and
capacity may have to be augmented. An augmentation of the international network
could require large capital investments and additional administrative functions
and responsibilities. Therefore, it is appropriate that Administrations forecast
new international services within their planning process.
This Recommendation presents methods for forecasting new services. The
definitions of some of the characteristics of these services, together with their
requirements, are covered in S 2, followed by base data requirements in S 3. S 4
discusses research to identify the potential market. Presentation of forecasting
methods are contained in S 5. S 6 concludes with forecast tests and adjustments.
2 New service definitions
2.1 A distinction exists between those services which are enhancements of
existing services carried on the existing network and those services which are
novel.
Many of the services in this latter category will be carried on the
Integrated Services Digital Network (ISDN). It is not the purpose of this section
to provide an exhaustive list of services but rather to establish a framework for
their classification. This framework is required because different base data and
forecasting strategies may be necessary in each case.
2.2 enhanced services offered over the existing network
These are services which are offered over the existing network, and which
offer an enhancement of the original use for which the network was intended.
Services such as the international freephone service, credit card calling and
closed user groups are examples of enhancements of voice services; while
facsimile, telefax and videotex are examples of non-voice services. These
services may be carried over the existing network and, therefore, data will
concern usage or offered load specific to the enhancement. Arrangements can be
established for the measurement of this traffic, such as the use of special
network access codes for non-voice applications or by sampling outgoing circuits
for the proportion of non-voice to voice traffic.
2.3 novel services
Novel services are defined as totally new service offerings many of which
may be carried over the ISDN. In the case of ISDN, Recommendation I.210 divides
telecommunications services into two broad categories: bearer services and
teleservices. Recommendation I.210 further defines supplementary services which
modify or supplement a basic telecommunications service. The definition of bearer
services supported by the ISDN is contained in Recommendations I.210 and I.211,
while that for teleservices is found in Recommendations I.210 and I.212. Bearer
services may include circuit switched services from 64 kbit/s to 2 Mbit/s and
packet services. Circuit switched services above 2 Mbit/s are for further study.
Teleservices may include Group 4 facsimile, mixed mode text and facsimile,
64 kbit/s Teletex and Videotex, videophone, videoconferencing, electronic funds
transfer and point of sale transaction services. These lists are not exhaustive
but indicate the nature and scope of bearer services and teleservices. Examples
of new services are diagrammatically presented in Table 1/E.508.
TABLE 1/E.508
Examples of enhanced and novel services
"Novel" services
Enhancement of
existing services Bearer services Teleservices
Teletex Packet Group 4 facsimile
Facsimile Mixed mode
Videotex Videophone
Message handling systems Circuit switched services Videoconferencing
International freephone - 64 kbit/s Electronic funds transfer
Credit cards - 2 Mbit/s Point of sale transactions
Fascicle II.3 - Rec. E.508 PAGE1
Closed user groups Teletex (64 kbit/s)
Videotex (64 kbit/s)
3 Base data for forecasting
3.1 Measurement of enhanced services
Measurements for existing services are available in terms of calls,
minutes, Erlangs, etc. These procedures are covered in Recommendation E.506, S 2.
In order to measure/identify enhanced service data from other traffic data on the
same network it may be necessary to establish sampling or other procedures to aid
in the estimation of this traffic, as described in S 4 and S 5.
3.2 Novel services
Novel services, as defined in S 2, may be carried on the ISDN. In the case
of the ISDN, circuit switched bearer services and their associated teleservices
will be measured in 64 kbit/s increments. Packet switched bearer services and
associated teleservices will be measured by a unit of throughput, for example,
kilocharacters or kilopackets per second. Other characteristics needed will
reflect service quality measurements such as: noise, echo, post-dialing delay,
clipping, bit-error rate, holding time, set-up time, error-free seconds, etc.
4 Market research
Market research is conducted to test consumer response and behaviour. This
research employs the methods of questionnaires, market analysis, focus groups and
interviews. Its purpose is to determine consumers' intentions to purchase a
service, attitudes towards new and existing services, price sensitivity and cross
service elasticities. Market research helps make decisions concerning which new
services should be developed. A combination of the qualitative and quantitative
phases of market research can be used in the initial stages of forecasting the
demand for a new service.
The design of market research considers a sampling frame, customer/market
stratification, the selection of a statistically random sample and the correction
of results for non-response bias. The sample can be drawn from the entire market
or from subsegments of the market. In sampling different market segments, factors
which characterize the segments must be alike with respect to consumer behaviour
(small intragroup variance) and should differ as much as possible from other
segments (large intergroup variance); each segment is homogeneous while different
segments are heterogeneous.
PAGE8 Fascicle II.3 - Rec. E.508
The market research may be useful in forecasting existing services or the
penetration of new services. The research may be used in forecasting novel
services or any service which has no historical series of demand data. It is
important that potential consumers be given a complete description of the new
service, including the terms and conditions which would accompany its
provisioning. It is also important to ask the surveyees whether they would
purchase the new service under a variety of illustrative tariff structures and
levels. This aspect of market research will aid in redimensioning the demand upon
final determination of the tariff structure and determining the customers'
initial price sensitivity.
5 Forecasting procedures
5.1 General
The absence of historical data is the fundamental difference between
forecasting new services and forecasting existing services. The forecast
methodology is dependent on the base data. For example, for a service that is
planned but has not been introduced, market research survey data can be used. If
the service is already in existence in some countries, forecasting procedures for
its introduction to a new country will involve historical data on other
countries, its application to the new country and comparison of characteristics
between countries.
5.2 Sampling and questionnaire design
The forecasting procedure for novel services based on market research is
made up of five consecutive steps. The first of these consists in defining the
scope of the study.
The second step involves the definition and selection of a sample from the
population, where the population includes all potential customers which can be
identified by qualitative market research developed through interviews at focus
groups. The research can use stratified samples which involves grouping the
population into homogeneous segments (or strata) and then sampling within each
strata. Stratification prevents the disproportionate representation of some parts
of the population that can result by chance with simple random sampling. The
sample can be structured to include specified numbers of respondents having
characteristics that are known, or believed, to affect the subject of the
research. Examples of customer characteristics would be socio-economic background
and type of business.
The third step is the questionnaire design. A trade-off exists between
obtaining as much information as practical and limiting the questionnaire to a
reasonable length, as determined by the surveyor. Most questionnaires have three
basic sections:
1) qualifying questions to determine if a knowledgeable person has been
contacted;
2) basic questions including all questions which constitute the body of
the questionnaire;
3) classification questions collecting background on demographic
information.
The fourth step involves the implementation of the research - the actual
surveying portion. Professional interviewers, or firms specializing in market
research should be employed for interviewing.
The fifth and final step is the tabulation and analysis of the survey
data. S 5.3-5.7 describe this process in detail.
5.3 Conversion ratios for the sample
Conversion ratios are used in estimating the proportion of respondents
expressing an interest in the service who will eventually subscribe.
The analysis of the market research data based on a sample survey, where a
stratified sample is drawn across market segments, for a service that is newly
introduced or is planned, is discussed below:
Let
X1i = the proportion of firms in market segment i that are very
interested in the service.
X2i = the proportion of firms in market segment i that are interested in
the service.
Fascicle II.3 - Rec. E.508 PAGE1
X3i = the proportion of firms in market segment i that are not interested
in the service.
X4i = the proportion of firms in market segment i that cannot decide
whether they are interested or not.
The above example has 4 categories of responses. Greater of fewer
categories may be used depending on the design of the questionnaire.
Notice that
eq \i\su(j, , ) Xji = 1,
where j = the index of categories of responses.
Market research firms sometimes determine conversion ratios for selected
product/service types. Conversion ratios depend on the nature of the service, the
type of respondents, and the questionnaire and its implementation. Conversion
ratios applied to the sample will estimate the expected proportion of firms in
the survey that will eventually subscribe, over the planning period. For studies
related to the estimation of conversion ratios, refer to [1], [3] and [5].
Then,
c1X1i = the proportion of firms in market segment i that expressed a
strong interest and are expected to subscribe.
c2X2i = the proportion of firms in market segment i that expressed an
interest and are expected to subscribe.
c3X3i = the proportion of firms in market segment i that expressed no
interest but are expected to subscribe.
c4X4i = the proportion of undecided firms in market segment i that are
expected to subscribe.
where cj = conversion ratio for response j.
The proportion of firms in market segment i, Pi, that are expected to
subscribe to the service, equals
eq Pi = \i\su(j=1,4,cj Xji) (5-1)
The conversion ratio is based on the assumption that there is a 100%
market awareness. That is, all surveyees are fully informed of the service
availability, use, tariffs, technical parameters, etc. Pi, therefore, can be
interpreted as the long-run proportion of firms in market segment i that are
expected to subscribe to the service at some future time period, T.
Two issues arise in the estimation of the proportion of customers that
subscribe to the service:
1) while Pi refers to the sample surveyed, the results need to be
extrapolated to represent the population.
2) Pi is the long-run (maximum) proportion of firms expected to subscribe.
We are interested in predicting no just the eventual number of
subscribers but, also, those at intermediate time periods before the
service reaches a saturation point.
5.4 Extrapolation from sample to population
To extrapolate the data from the sample to represent the population, let
Ni = size of market segment i (measured for example, by the number of
firms in market segment i)
Then Si, the expected number of subscribers in the planning horizon,
equals:
Si = PiNi (5-2)
5.5 Market penetration over time
To determine the expected number of subscribers at various points in time
before the service reaches maturity, let
pit = the proportion of firms in market segment i that are expected to
subscribe at time t.
Clearly,
pit < Pi
and pit -> Pi as t -> T
The relation between pit and Pi can be explicitly defined as:
pit = ait . Pi (5-3)
ait is a penetration function, reflecting changing market awareness and
acceptance of the service over time, in market segment i. An appropriate
functional form for ait should be bounded in the interval (0,1).
As an example, let ait be a logistic function:
ait = eq \f( 1, 1 + ebit) (5-4)
bi 0 is the speed with which pit approaches Pi in market segment i, as
illustrated in Figure 1/E.508.
PAGE8 Fascicle II.3 - Rec. E.508
For other examples of non-linear penetration functions, refer to the Annex
A.
Figure 1/E.508 - T0201030-87
The introduction of a new service will usually differ according to the
market segment. The rate of penetration may be expressed as a function of time,
and the speed of adjustment (bi) may vary across segments. Lower absolute values
of bi, for the logistic function will imply faster rates of penetration.
While the form of the penetration function relating the rate of
penetration to time is the same for all segments, the parameter bi varies across
segments, being greater in segments with a later introduction of the new service.
Let t0i = time period of introduction of service in market segment i.
Then, t - t0i = time period elapsed since service was introduced in
market segment i.
In the diagramatic illustration, of Figure 2/E.508, the service has
achieved the same level of market penetration a0, in tC periods after its
introduction in market C as it did in tA periods after its introduction in market
segment A. Later introductions may not necessarily lead to faster rates of
penetration across segments. However, within the same market segment, across
countries with similar characteristics, such an expectation is reasonable.
Figure 2/E.508 - T0201350-88
5.6 Growth of market segment over time
The above discussion has accounted for gradual market penetration of the
new service, by allowing pit to adjust to Pi over time. The same argument can be
extended to the size of market segment i over time.
Let nit = size of market segment i at time t.
Then, the expected number of subscribers at time t in market segment i,
equals:
sit = ait . pit . nit (5-5)
and
eq St = \i\su(i, , Sit ) = expected number of subscribers across all
market segments at time t.
5.7 Quantities forecasted
The above procedure forecasts the expected number of customers for a new
service within a country. Other quantities of interest may include lines,
minutes, messages, revenue, packets, kilobits, etc. The most straightforward
forecasting method for some of these quantities is to assume constant
relationships such as:
expected access lines = (average access lines) x expected number of
subscribers
expected minutes = (average use per line) x expected access lines
expected messages = expected minutes/(average length of conversation)
expected revenue = (average rate per minute) x expected minutes
The constants, appearing in parentheses, above, can be determined through
1) the process of market research, or 2) past trends in similar services.
5.8 Forecasting with historical data: application analysis
After a new service has been introduced, historical data can be analyzed
to forecast demand for expanded availability to other countries. Development of a
new service will follow trends based on applications, such as data transmission,
travel reservations, intracompany communications, and supplier contact.
Applications of a service vary widely and no single variable may be an adequate
indicator of total demand.
The following procedure links demand to country characteristics for
forecasting expanded availability of a new service to other countries.
Let D = (Di, D2, . . . . . ., Dn)`
represent a vector of country-specific annual demand for the service across n
countries, where the service currently exists. Let C = matrix of m
characteristics relating to each of the n countries that are reasonable
explanatory variables of demand. The components of m would vary depending on the
nature of the service and its application.
Fascicle II.3 - Rec. E.508 PAGE1
Some essential components of m would be the price of the service (or an
index representing its price) and some proxy for market awareness. As discussed
in earlier sections, market awareness is one of the key determinants of the rate
of market penetration of the service. Reasonable proxies would be advertising
expenditures and time (measured as t* = t - t0) where t* would measure time
elapsed since the service was first introduced at time t0. Market awareness can
be characterized as some non-linear function of t*, as presented in S 5.5. Other
components of m may include socio-economic characteristics of the customers,
market size and location of customers.
The model that is estimated is:
D = Cā + u (5-6)
where
C is a (n x m) matrix of country characteristics
D is a (n x 1) vector of demand
ā is a (m x 1) vector of coefficients corresponding to each of the m
characteristics
u = (n x 1) vector of error terms
The estimated regression is:
eq \o(\s\up4(^),D) = C\o(\s\up4(^),ā) (5-7)
Traditional methods of estimating regressions will be applied. Equation
(5-7) can be used for predicting demand for any country where the service is
being newly introduced, as long as elements of the matrix C are available.
5.9 Forecasting with limited information
In the extreme case where no market research data is available (or is
uneconomical given resource constraints), or country characteristics that affect
demand are not easily available or quantifiable, other methods of forecasting
need to be devised.
For example, to forecast the demand for a new international private line
service using digital technology, the following elements should be taken into
account in the development of reasonable estimates of the expected number of
lines:
a) discussions with foreign telephone companies,
b) discussions with very large potential customers regarding their future
needs,
c) service inquiries from customers,
d) customer letters of intent, and
e) any other similar qualitative information.
6 Forecast tests and adjustments
6.1 General
Forecast tests and adjustments are dependent on the methodology applied.
For example, in the case of a market research based forecast, it is important to
track the forecast of market size, awareness and rate of penetration over time
and to adjust forecasts accordingly. However, for an application-based
methodology, traditional tests and adjustments applicable to regression methods
will be employed, as discussed below.
6.2 Market research based analysis
This section discusses adjustments to forecasts based on the methodology
described in SS 5.2 to 5.8. The methodology was based on quantification of
responses from a sample survey.
The forecast was done in two parts:
a) extrapolating the sample to the population, using market size, Ni;
b) allowing for gradual market penetration (awareness), ait of the new
service over time.
The values attributed to nit (which represents the size of market segment
i at time t) and ait can be tracked over time and forecast adjustments made in
the following manner:
a) As an example for nii, the segments could be categorized as travel or
financial services. The size of the segment would be the number of
tourists, and the number of large banks. Historical data, where
available, on these units of measurement can be used to forecast their
sizes at any point of time in the future. Where history is not
available, reasonable growth factors can be developed through subject
matter experts and past experiences. The forecast of nit should be
tracked against actual measured values and adjusted for large
deviations.
PAGE8 Fascicle II.3 - Rec. E.508
b) For ait, testing with only a few observations since the introduction of
the service is more difficult.
Given that,
ait = eq \f( pit,Pi) (6-1)
and Pi is assumed fixed (in the long run), testing ait is equivalent to
testing pit. pit can be tracked by observing the proportion of
respondents that actually subscribe to the service at time t. This
assumes the need to track the same individuals who were originally in
the survey, as is customary in a panel survey. Panel data is collected
through sample surveys of cross-sections of the same individuals, over
time. This method is commonly used for household socio-economic
surveys. Having observed pit for a new period, values of ait can be
plotted against time to study the nature of the penetration function,
ait, and the most appropriate functional form that fits the data should
be chosen. At very early stages of service introduction, traditional
functional forms for market penetration, such as a logistic function
(as illustrated in the example in S 5.5), will be a reasonable form to
assume. Other variations of the functional form depicting market
penetration would be the Gompertz or Gauss growth curves. The
restriction is that the penetration function should be bounded in the
interval (0,1). See Annex A for an algebraic depiction of functional
forms.
There are various statistical forms that may be chosen as representations
for the penetration function. The appropriate functional form should be based on
some theoretical based information such as the expected nature of penetration of
the specific service over time.
Continuous tracking of nit, pit and ait over time will enable adjustments
to these values whenever necessary and enable greater confidence in the
forecasts.
6.3 Application based analysis
The application based analysis is a regression based approach and
traditional forecast tests for a regression model will apply. For instance,
hypothesis tests on each of the explanatory variables included in the model will
be
Fascicle II.3 - Rec. E.508 PAGE1
necessary. Corrections may be needed for hetero-elasticity, serial correlation
and multicollinearity, when suspect. The methodology for performing such tests
are described in most econometrics text books. In particular, references [2] and
[4] can be used as guidelines. Recommendation E.507 also discusses these
corrections.
Adjustments need to be made for variables that should be included in the
regression model but are not easily quantifiable. For example, market awareness
that results from advertising and promotional campaigns plays an important role
in the growth of a new service, but data on such expenditures or the associated
awareness may not be readily available. Some international services are targeted
towards international travelers, and fluctuations in exchange rates will be a
determining factor. Such variables, while not impossible to measure, may be
expensive to acquire. However, expectations of future trends in such variables
can enable the forecaster to arrive at some reasonable estimates of their impact
on demand. Unexpected occurrences such as political turmoil and natural disasters
in particular countries will also necessitate post forecast adjustments based
upon managerial judgement.
Another important adjustment that may be necessary is the expected
competition from other carriers offering similar or substitutable services.
Competitor prices, if available, may be used as explanatory variables within the
model and allow the measurement of a cross-price impact. In most situations, it
is difficult to obtain competitor prices. In such cases, other methods of
calculating competitor market shares need to be developed.
Regardless of forecasting methodology, the final forecasts will have to be
reviewed by management responsible for planning the service as well as by network
engineers in order to assess the feasibility both from a planning implementation
and from a technical point of view.
ANNEX A
(to Recommendation E.508)
Penetration functions (growth curves)
Some examples of non-linear penetration functions are illustrated below:
A.1 Logistic curve
ait = a / {1 + e-bt} (A-1)
For a = 1, the curve is bounded in the interval (0,1). Changing b will
alter the steepness of the curve. The higher the value of b, the faster the rate
of penetration. This curve is S-shaped and is symmetrical about its point of
inflection, the latter being where;
eq \f(d2ait,dt2) = 0 (A-2)
A.2 Gompertz curve
eq ait = a exp eq \b\bc\{(-be-kt) (A-3)
As t -> ait -> a, the limiting growth.
Holding k = 1 and a = 1, higher values of b will imply slower rates of
penetration. This curve is also S-shaped like the logistic curve, but is not
symmetrical about its inflection point.
When t = 0, then ait = ae-b, which is the initial rate of penetration.
PAGE8 Fascicle II.3 - Rec. E.508
A.3 Gauss curve
ait = a eq \b(1 - e-bt2) (A-4)
As t -> , then ait -> a
As t -> 0, then ait -> 0.
Choosing a = 1, the curve is bounded in the interval (0,1).
References
[1] AXELROD (J. N.): Attitude measures that predict purchase, Journal of
Advertising Research, Vol. 8, No. 1, pp. 3-17, New York, March 1968.
[2] JOHNSTON (J.): Econometric methods, Second Edition, McGraw-Hill, New York,
1972.
[3] KALWANI (M. U.), SILK, (A. J.): On the reliability and predictive validity
of purchase intention measures, Marketing Science, Vol. 1, No. 3, pp.
243-286, Providence, RI, Summer 1982.
[4] KMENTA (J.): Elements of econometrics, Macmillan Publishing Co., New York,
1971.
[5] MORRISON (D. G.): Purchase intentions and purchase behavior, Journal of
Marketing, Vol. 43, pp. 65-74, Chicago, Ill., Spring 1979.
Bibliography
BEN-AKIVA (M.) and LERMAN (S. R.): Discrete choice analysis.
DRAPER (N.) and SMITH (H.): Applied regression analysis, Second Edition, John
Wiley & Sons, New York, 1981.
Fascicle II.3 - Rec. E.508 PAGE1
