SEIZING BUSINESS OPPORTUNITIES & UNDERSTANDING THE ECONOMIC RISKS OF NEW
PRODUCTS IN A MULTI-FACETED MARKET
A. L. Matthews, J. M. Osborne, M. H. Lyons
Admin2/OP7, BT Laboratories, Martlesham Heath, Ipswich, IP5 3RE, England.
Tel: +44-1473-645372. Fax: +44-1473-647410.
E-mail: ann.matthews@bt-sys.bt.co.uk
ABSTRACT
As we move into the next millenium, exciting leading-edge technologies will offer businesses unparalleled
opportunities to seize rich new markets. However, the prospect of these enormous new revenues must not be allowed
to blinker the vision of strategic business planners. They must understand the risks as well as the opportunities.
Furthermore, the complex interacting requirements of their multi-dimensioned resources and the multi-faceted
customer market must be understood. Frequently, it will be important to mine through the web of opportunities to
target resources at those products for which the customer market will offer the greatest retum on investment and the
lowest risk.
A Systems Dynamics modelling technique, which is used to explore the futuristic market opportunities available to
telecommunications companies, will be discussed. Its prime objective is to identify how to seize market share by
prioritising resources at particular products through understanding the multi-faceted customer base. Customer takeup
of new products (or services), service usage, costs, revenues and profit will be discussed with respect to the business
opportunities available.
The technique for breaking down the complex problem space into small, manageable, testable units, (which can be
modelled as reusable modules), will also be discussed. The way in which these modules are subsequently bolted
together to form a large, seamless, elaborate and powerful model, for the analysis of "What-if" business scenarios,
will be shown. Most importantly, it will be demonstrated that this technique for telecommunications, can be reapplied
to any business market.
INTRODUCTION
In today’s fast moving market-place, all companies need to maintain a strong competitive edge. This is irrespective of
the types of products, (or services), they provide. To achieve this, their products should be keenly priced with a low
cost base, and the highest possible retum on investment. As it is unlikely that all consumers will prove to be equally
profitable, it is of prime importance to identify which sectors of the consumer market are most likely to purchase the
goods or services. This enables effective targeting of marketing and the matching of company resources to customer
demand.
Furthermore, it is important to assess how this consumer market will change with time. This change may be according
to the time-of-day, or seasonal or show long-term trends over months or years, or be a combination of any of these
time factors. By understanding these time-varying, dynamic customer characteristics, strategies can be put in place to
enable the company to evolve and progress, whilst still maintaining its competitive advantage.
A generic Systems Dynamics modelling technique, using the “PowerSim” tool, has been developed by BT
Laboratories. It assesses the dynamic effects of
: customer behaviour
products and services launch
tariffing
costs of provision
revenue
profit.
In the early parts of this paper, the generic technique will be discussed. It will be shown how it is very important to
develop a model which is modular in format, and reusable for a wide variety of situations. Later, a specific example
relating to telecommunications will be given to illustrate the principle involved.
UNDERSTANDING THE PROBLEM SPACE
Obviously, the model developed by BT is applied to the telecommunications arena. However, to develop the
principle, and show that it is generic, let us reflect on what at first sight appears to be a completely different paradigm.
So, let us choose an example which should be familiar to us all. Consider now the questions that must be answered by
planners wishing to build a grocery superstore, (or K-MART), and optimise its financial retum. Consider that this is a
very large store, which stocks a wide variety of fresh and processed food as well as non-perishable household goods.
The key issues, facing the store planners, are illustrated in the flow diagram shown in figure 1a.
Conrioues (Financial enases iting
a Investment) sfanensore
Costs — Profie,
(capita, maintenance pict
veges, marketing.) Reduces —
Profit compare
(so Unt
Revenue
Rove (Competition
Asse re)
chase Good, Customer Type
(open time of day (reat age
pa wy eteing ani)
Figure 1a. Flow Diagram for the Grocery Superstore, (K-MART), Problem Space
The planners will be allocated a certain Financial Investment which will enable them to build a superstore whose
capacity, stock and facilities will depend upon the geographic site of the store, particularly with respect to the
Competition and the Customer-Types within its catchment area.
Firstly, they must assess the effects of existing and planned Competition from other stores within the area. This is of
prime importance as it may affect the decision to abandon the project or to go-ahead and build the store.
The planners must then assess the Customer-Types that could be using the store. These Customer-Types will be
dependent upon a number of factors such as wealth, age, gender, status within the family and the Proximity of the
Customer to the store. This Proximity will be a function of the status of the roads leading from the Customer s home
to the store, and the access that the Customer has to transport, (such as private car, public transport, and so on).
Once the store is built, the Customers will be able to Purchase Goods which will create Revenue for the store.
However, the Customers will also have certain Time-Of-Day purchasing characteristics. For instance, elderly people
may shop during normal office hours, whereas young professionals may shop in the evenings, and families may shop
just after the end of the normal school day. Furthermore, there may be seasonal variations. For example, the sale of
ice-cream may be lower in the cold winter months. There may be trends in buying habits over the years, such as an
increase in the purchase of pre-packed micro-waveable meals. All of these factors impact on the Stock that the store
must have on its shelves and in its warehouses. Thus, the Stock will have time-of-day, seasonal and yearly variations
which are a reflection of the Customer buying habits.
Hopefully, over the years, the store will experience a growth in the purchase of its Products. This may lead to a Store
Upgrade, which could mean that the store is enlarged to stock more goods and increase the range of its products.
Both the initial Financial Investment required to build the store and the Store Upgrade will contribute to the Costs
incurred by the company. These Costs can include Whole Life Costing factors such as up-front capital investment,
munning costs, maintenance, power, staff wages, marketing and so on.
Then, Revenue, (which can be associated to types of Goods and Customers), minus Costs will give the Profit to the
store.
Using this analytical approach, the complex problem space has been split into 9 modules. Each of these is relatively
small and can be readily tested and validated. Then, by piecing each of the nine modules together, the large and very
complex problem space can be readily modelled. However, it is important to note that each of these modules could be
subdivided to bring more realism to the model. As an example let us consider just three of the modules in greater
detail. Namely, Competition, Customers and Goods. Figure 2a shows how the Competition will be split into different
Competitor companies, which we shall denote by 1, 2, 3, 4,
This multitude of different Competitors is represented
in the model as an array. Each of the Competitors will
have certain Geographic Locations which will enable
them to provide a range of Goods to a variety of
Customer Segments, with a resulting Revenue and
Market Share. These factors will all exhibit time
varying trends, which will enable the planners of the
new store to identify whether their individual
Competitors are growing or losing Market Share and
what proportions of the competitors’ Customers and
Revenue, they could seize. Summing the effects of all
M Competitors, gives the total effects due to
Competiton.
(i ia ~~
J) \ Arenas)
/ a
TLE ag
Goods 2 S& a
Y /( Regular \
Goods 1 ( Purchase
= \Ctrenis_ )
Figure 3a Understanding Customer Behaviour
Figure 4a, shows the Customers of the Goods. Again,
these are stacked in an array of different Customer
segments from 1 to P; where the different segments
could for instance, be divided according to wealth,
subdivided according to family or non-family and
subdivided again according to travelling time from the
home to the store, and so on. For each of the
Customer segments, there are a number of Potential
Customers, who can become Customers of the New
Store and create a Usage of the Store. This Usage
will result in a number of Total Purchases which can
be apportioned according to the time of day giving the
Time of Day Purchases. Both the Total Purchases
and Time of Day Purchases need to be compared to
the Store Capacity to ensure that sufficient goods of
the correct types, are stocked within the store at the
times they are required.
M, where M is some integer.
ex a }
Market Customer, |
Ga em) =)
Figure 2a Understanding the Competitor Threat
Consider next Figure 3a. Here, the different types of
Goods, (from 1 to N), offered by the store are arrayed.
Each item will have an associated Product Definition,
(such as type of product, when it is first launched on
the market, its price and the quantity stocked). This
item will have an Initial Purchase Trend, when it is
first launched, which will be dependent upon factors
such as marketing. After a time, once the novelty
factor has subsided, the purchasing characteristics will
settle into the Regular Purchasing Trends.
Customers)
/ Tot ~ ||
seer) Purch
Capacity “Time of >
pen >
to Store
o
Customers 1 ("oe Y
\ 2
Figure 4a_ Matching Customer Buying Habits to
Store Capacity
Consider now the complexity of the problem space. In Figure 1a, the problem space was split into 9 categories. Just
3 of these categories have been assessed in Figures 2a-4a, inclusive, with MxNxP as their total number of options to
be modelled. It is easy to see, that for the complete model, the number of different scenarios that could be modelled
could be extremely large. However, by maintaining the modular nature of the model, testing and validation are still
relatively simple. So, errors are minimised as the model grows in power and complexity.
MODELLING THE TELECOMMUNICATIONS ARENA
Let us now consider the actual environment in which the real BT model has been developed. It is known as the
“Integrated Networks & Services Model”, (INSM), and has been created to assess the multi-dimensional problem
space of providing a plethora of new services to a variety of customers via BT’s telecommunications networks. In this
section, it is intended to illustrate that the issues facings BT's network planners are very similar to those discussed in
the previous section, which confront store planners. Indeed, it is hoped that by considering these similarities, for the 2
different types of planners, the reader will appreciate that these issues are the same ones that they must tackle in their
own environment. In this way, the reader should be able to reapply the philosophy and methodology used within the
INSM to their own situation.
To highlight the similarities between the telecommunications and store environments, the descriptions for the INSM
will follow as closely as possible the descriptions for the store model. Consider Figure 1b.
Financial
Investment
Contributes
Enables building
of a new network
‘Network
Capabilities
Costs
(capital, maintenance
wages, marketing...)
Contributes
wo
Competition
Assessment
(number, ype,
Network
Upgrade
Revenue
(from types of services)
& customers)
Increased
Demand
Services
(pes, time of day
seasonal sus)
(wealth, age,
Spending. proximity.)
Characteristes
Figure 1b. Flow Diagram for the Telecommunications Problem Space
BT’s network planners will be allocated a certain Financial Investment which will enable them to build a network
whose capacity, products and functionality will depend upon the geographic distance of the network from the
exchange’, the Competition and the Customer-Types within the network’ s reach.
Firstly, they must assess the effects of existing and planned Competition from other network operators within the area.
This is of prime importance as it may affect the decision to abandon the project or to go-ahead and build the new
network.
BT’s network planners must then assess the Customer-Types that could be using the new network and services. These
Customer-Types Will depend upon a number of factors such as wealth, age, gender, status within the family and the
Proximity of the Customer and their network connection to the exchange. This Proximity will be a function of the
quality of the network connections leading from the Customer s home to the exchange, as well as the physical
distance between the home and the exchange.
* This distance is important as it can affect whether a Nenwork Service can actually be provided to the customer, and the quality of the services.
Once the network is built, the Customers will be able to Purchase Services which will create Revenue for BT.
However, the Customers will also have certain Time-Of-Day usage characteristics, which will lead to an increased
loading on the network. This loading is known as Traffic. For instance, residential customers primarily use the
services early in the moming and in the evening, whereas business customers primarily use the Services during office
hours. Furthermore, there may be seasonal variations. For example, the usage of a “Networked Games” service may
be lower in the warm summer months, when children are playing outside. Consequently, the Traffic generated by the
Networked Games will be lower, at those times. There may be trends in usage patterns over the years, such as an
increase in the usage of video-phone calls. All of these factors impact on the Network Capacity that BT must provide
across its network. The Traffic will have time-of-day, seasonal and yearly variations which are a reflection of the
Customer usage habits, and sufficient network capacity must be made available to cope with these differing Traffic
demands.
Hopefully, over the years, BT will experience a growth in the purchase and usage of its Services. This may lead to a
Network Upgrade so that the network capacity is increased and BT is able to provide services to more customers and
increase the range and quality of the services offered.
Both the initial Financial Investment in the new network, and the Network Upgrade will contribute to the Costs
incurred by BT. These Costs can include Whole Life Costing factors such as up-front capital investment, running
costs, maintenance, power, staff wages, marketing and so on.
Then, Revenue, (which can be associated to types of Network Services and Customers), minus Costs will give the
Profit to BT.
As before, the complex problem space has been split into 9 modules, each of which is relatively small and can be
easily tested and validated. By piecing each of the nine modules together, the large and very complex problem space
can be readily modelled. Once again, as an example let us consider the same three modules in greater detail. Namely,
Competition, Customers and Goods.
Figure 2b shows how the Competition will be split
into different Competitor companies, which we shall
denote by 1, 2, 3, 4, ...... M, where M is some integer.
Each of the Competitors will have certain Geographic
Locations which will enable them to provide a range
of Network Services to a variety of Customer
Segments, with a resulting Revenue and Market Share.
Each of these factors will exhibit time varying trends,
that will enable BT’s network planners to identify
whether their individual Competitors are growing or
losing Market Share and what proportions of the
competitions' Customers and Revenue, BT could
Figure 2b Understanding the Competitor Threat seize. Summing the effects of all M Competitors,
gives the total effects to BT due to Competiton.
Competitor M
Marker. pe
ant (Revo
Consider next Figure 3b. Here, the different types of
Network Services, (from 1 to N), provided by BT are
arrayed. Each item will have an associated Service
Definition, (such as type of service, when it is first
launched on the network, its tariff and the quality of
the service). This Network Service will have an Initial
Usage Trend, when it is first launched, which will be wae |
dependent upon factors such as marketing. After a Services 2
time, once the novelty factor has subsided, the usage ewwaek
characteristics will settle into the Regular Purchasing Services 1 ce
Trends. are
Figure 3b Understanding Customer Behaviour
Figure 4b, shows the Customers of the Network
Services. Again, these are stacked in an array of
different Customer segments from 1 to P; where the
different segments could, for instance, be divided
according to wealth, subdivided according to family
or non-family and subdivided again according to
distance of the connection to the exchange, and so on.
For each of the Customer segments, there are a
number of Potential Customers, who can become
Customers of the New Services and create a Usage of
the Service. This Usage will result in the Total Traffic
demand on the network which can be apportioned
according to the time of day, giving the Time of Day
Traffic. Both the Total Traffic and Time of Day
Traffic need to be compared to the Network Capacity
to ensure that sufficient capacity is provided within
Figure 4b Matching Customer Buying Habits to the network at those times of the day when it is
Network Capacity required.
ae
-_ ff é
* Services.
, =
Potential \~~
/ Compa
. fore ia
(Customers? | to Network \*
(Customers I
So, we see that the problem space facing BT’s network planners is very similar indeed to the problem space facing the
store planners. The challenge for the reader is to reapply this technique to their own area.
EXAMPLES OF THE OUTPUTS FROM THE INTEGRATED NETWORKS & SERVICES MODEL
As an example, consider now that BT wishes to provide 3 new networked services: Internet, Movies-on-Demand and
Networked Games. These services will be provided to three different types of customer, which will be denoted as
Customer Type 1 or 2 or 3. In competition with BT are 3 Intemet Service Providers and 5 Cable TV companies. All
of the competitors already have market share, in either internet provision or broadcast services, at the start of the
modelling simulation, (Year 0). At this time, their market share is growing. In this particular scenario, only BT’s
Intemet service has already been launched on to the market place, and has an existing customer base. BT's
Networked Games service is launched at the start of Y ear 0, and BT’s Movies-on-Demand service is launched at the
start of Year 2. Figures 5a and 5b show that the Total Traffic generated by the summed total of all three Customer
Types at the end of Y ears 1 and 5, respectively.
Notice that there is a very substantial
x19.3 overall growth from Y ear 1 to Y ear
5. A considerable amount of this growth
in Traffic is due to the services becoming
available to a much larger number of
customers. However, there is also an
Figure 5a Total Traffic in Year 1
increase of x3.2 for the average traffic nae
generated per customer. This is a very g
significant change per customer. It is due é
to the higher bandwidth transmission of 3
a Intemet
the Internet and Games services, and the
launch of a new service, Movies-on-
Demand, (in Year 2). In addition, there is
an increased average usage of the services AR AaRAAR
by individual customers. Hour of Day
Note particularly, that the substantial x19 growth in Traffic has occurred even in the presence of significant
Competition. This is indicative of the whole market growing not only for BT, but also its competitors. That is, the
graphs show a very healthy and growing market for BT, (and its competitors), to compete within.
Figure 5b Total Traffic in Year 5
+ Netw orked
Games
a Internet
Traffic x 19
—a—Movies on
Demand
Hour of Day
Notice too, that in Y ear 5, the dominant generator
of BT's Traffic is the new Movies-on-Demand
service. This shows than it is important for
network planners to "future-proof" their networks
by ensuring that sufficient network capacity is
made available to cater for high traffic generating
services which are likely to be launched in the
future. Otherwise they risk being unable to
support the new services or they may suffer
degradations in their Quality of Service. These
could potentially result in a loss of customers to
the Competition. That is, the scenario analysis
indicates that expensive over-provisioning of the
network in the early years, can increase the profits
in subsequent years if it more than offsets the even
higher costs of later "just-in-time" network
upgrades.
Consider now the three graphs, 6a-c, which show the traffic generation in Year 5 allocated to each of the three
individual Customer Types. All the graphs have heen plotted to the same scale on the axes. So, the higher sized plots
in Figure 6b do actually represent a higher traffic generation from Type 2 customers. The customer population can be
considered to be apportioned as 27% are Type 1, 39% are Type 2, and 34% are Type 3.
Figure 6a Year 5 Total Traffic from Customer Type 1
+ Netw orked
Games
8 Interet
Traffic
{ |—e—Movies on
Demand
Hour of Day
Figure 6b Year 5 Total Traffic from Customer Type 2
—o— Netw orked
Games
2 Intemet
Traffic
—e—Movies on
Demand
Hour of Day
Notice that customers of 7ype /, have much lower
usage levels of the services that either Type 2 or 3.
However, they do still provide valuable custom,
and there is a rapid growth in their usage of the
network, (Traffic), with the corresponding Revenue
opportunities. Although customers of Zype /
account for 27% of the population, they only create
20% of the Traffic.
Notice too, that the overall growth in Traffic for
Customer Type 2, is greater than that for either of
the other Customer Types. This is for two main
reasons. Firstly, more customers of 7ype 2 have
subscribed to the services than from the other two
Customer Types. Secondly, individual customers
of Type 2, typically have a high usage of the new
services. This means that in areas of the country
predominantly occupied by customers of Type 2,
BT should plan its network investment and
implementation to take into account large numbers
of customers subscribing with typically a high
usage of the services. In this way, BT should
avoid the risks of having too little network
capacity which would lead to either low levels of
Quality of Service, or having to refuse to connect some customers who wish to subscribe to the new services. A
substantial 45% of the total Traffic is generated from the 39% of the population in Class 2.
Customers of Type 3, have a generally lower
level of Traffic than Type J. However, notice
that Customer Type 3 has a particularly high
usage of the Internet compared to the
Customers of Type 1 or 2. This means, that
when dimensioning its network, BT must take
into account that Intemet users tend to keep
—e—Movies on on-line for hours rather than minutes. So, for
Demand areas predominated by Customer Type 3, BT
needs to have sufficient capacity within the
network to allow for other network users to
still have good Quality of Service, at times of
the day when there is heavy Internet usage.
Customers of Type 3, account for 34% of the
population, and generate 35% of the Traffic.
Figure 6c Year 5 Total Traffic from Customer Type 3
—o— Netw orked
Games
—a~ Internet
Traffic
Hour of Day
If it can be assumed that Profit to the network operator is directly proportional to the loading, (or Traffic), on its
network, then we might suggest that it is of importance to target initial network deployment to customers of Type 2.
The secondary target market would be Customer Type 3, with provision to Customer Type | occurring last. However,
although not illustrated in this report, the model has the ability to assess real revenue levels for given tariffing
structures for the different services. Such an analysis may suggest that priority for service provision to the different
Customer Types might be changed.
In the example given above, the provision of the network will be different for each of the Customer Types, as each has
differing Traffic generation characteristics. Customer Type 2, have a generally high generation of Traffic, and it will
be important that sufficient capacity is made available. That is, a higher level of network capacity, and hence higher
Investment, must be allowed for Customer Type 2. However, this should be compensated by a higher Profit. The
network requirement for Customer Type 3 will be average, whereas a lower capacity and so lower cost network can be
installed for Customer Type 1.
SUMMARY
The "Powersim" tool has been used to create a powerful Systems Dynamics model, known as the Integrated Networks
& Services Model, (INSM). It analyses an extremely complex problem space, but maintains its simplicity and
accuracy by splitting this space into a number of small modules. Each one of these modules analyses a small part of
the problem such as Customer Numbers, or Revenue generation, or Costs, or Service Usage, and so on. The
individual modules can be easily tested and validated. Then the complete model is formed by piecing together the
modules, rather like a jigsaw puzzle.
It is important to note that the technique is generic, and can be applied to a range of telecommunications scenarios.
Moreover, it has been shown that the technique can be applied to totally different paradigms, which lie outside the
telecommunications arena. This was illustrated by the supermarket example.
ACKNOWLEDGEMENTS
I wish to thank Michael Lyons and Alan Steventon for reviewing this paper and authorising its publication, and co-
developer Jo Osbome for her contributions to the development of the Integrated Networks & Services Model.
REFERENCES
(1 Matthews, A. L., “Interacting Demands for Multimedia Services”. Paper to the IBTE/FITCE Congress,
October 1997, London. Published in British Telecommunications Engineering Journal, January 1998.
[2] Matthews, A. L., “Scenario Planning: Seizing Opportunities & Minimising Risk”. Paper to the IEE Seminar,
“How to Make Money from Manufacturing,” February 1998. To be published in a seminar edition of a
joumal by the IEE.
CC BY-NC-SA 4.0