Synapsis of a System Dynamics Analysis of the German PayTV Market in the 1990s

1. Analysing Corporate
Performance with
Systems Dynamics
Illustrated with a PayTV Case Study
Daniel Jarosch

2. Case: Premiere is struggling to reach its targets in
subscriber numbers
0
5
10
15
20
25
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Premiere World (Digital)
DF-1
Premiere World (Analog)
Quelle: PricewaterhouseCoopers Research
Subscriber
(x100.000)
Pay-TV Subscriber in Germany
Map
Since 1991, Premiere tries to launch Pay TV to the German Market. For the last 5 years the
subscriber growth has been stagnating. Premiere has been struggling since to gain a critical
subscriber mass in Germany.
1998-2000
ØGR = 5%
• Germany has a large number of free to air
channels, making it hard for Pay-TV to
create value through content alone.
• The number one sport, the soccer
Bundesliga, can not be shown on PayTV
exclusively.
• Premiere‘s pricing models for its content and
set-top-box habe not been accepted by the
market.
• PayTV could not yet utilise the benifits of
digital television, such as VoD, interaction
and additional services.
• The required set top box (manufactured by
Premiere) had a low technical standard.
Common hypotheses for the slow pick
up of Pay TV in Germany

3. Past Resource Combinations are determining
current Corporate Performance
• Resource combinations are networks of
internal/external and tangible/intangible
resources
• These resources determine the
corporate performance with a time lag.
• Resource networks are characterised by
interaction and permanent change.
• Already a small number of interacting
resources lead to dynamic complexity
• To understand the impact of past, current
and future resource combinations on
corporate performance, management
decision making needs a tool that can
address dynamic complex problems
Dynamic Complexity of
Resource Combinations
Revenue
time
Customers
Capacity
Staff
Products
Customers
Capacity
Staff
Products
today
Resource Cluster
2 years ago
Current
Resource Cluster

4. System Dynamics models resource systems
System Dynamics models dynamic complex resource systems as a causal network of ‘stocks’ and ‘flows’.
(See illustration below).
Management can only change stocks by managing the in- and outflows.
Price
€1000
Staff
Capacity
‘000/month
Months
Months
0 12 24
50
150
200
100
Customers
0 12 24
50
150
200
100
Months
0 12 24
2
4
6
Sales
‘000/month
2
4
6
8
Revenue
€m/month
4
6
8
Cost of goods
€m/month
2
4
6
Staff costs
€m/month
0.5
1.0
1.5
1.0
Earnings
€m/month
Months
0 12 24
0.5
1.5
0.2
0.4
Marketing
€m/month
Customers
won/month
Change
capacity
Net hiring
per month
+ 1
+ 2
0
5
- 5
10
15
5
10
15
20
Customers
lost/month
5
10
15

5. Simplified System Dynamics Model of the subscriber flows
of a PayTV operator
Potential Subscribers
Premiere Trial
Subscribers
Premiere Subscribers
new premiere trial
subscriber
new premiere
subscriber
churned trial
subscriber
churned subscriber
Lost Subscribers
Potential Subscriber
40 M
35 M
30 M
25 M
20 M
0 12 24 36 48 60 72 84 96 108 120 132 144 156
Time (Month)
Potential Subscriber : Current people
Potential Subscriber : C:Documents and SettingsDaniel JaroschMy Documents+DANIEL# Dissertation# PhDVensim ModelsThesis Master ModelCurrentpeople
Premiere Trial Subscriber
800,000
600,000
400,000
200,000
0
0 12 24 36 48 60 72 84 96 108 120 132 144 156
Time (Month)
Premiere Trial Subscriber : Current people
Premiere Trial Subscriber : C:Documents and SettingsDaniel JaroschMy Documents+DANIEL# Dissertation# PhDVensim ModelsThesis Master ModelCurrentpeople
Premiere Subscriber
4 M
3 M
2 M
1 M
0
0 12 24 36 48 60 72 84 96 108 120 132 144 156
Time (Month)
Premiere Subscriber : Current people
Premiere Subscriber : C:Documents and SettingsDaniel JaroschMy Documents+DANIEL# Dissertation# PhDVensim ModelsThesis Master ModelCurrentpeople
new premiere subscriber
60,000
45,000
30,000
15,000
0
0 12 24 36 48 60 72 84 96 108 120 132 144 156
Time (Month)
new premiere subscriber : Current people/Month
new premiere subscriber : C:Documents and SettingsDaniel JaroschMy Documents+DANIEL# Dissertation# PhDVensim ModelsThesis Master ModelCurrentpeople/Month
new premiere trial subscriber
400,000
300,000
200,000
100,000
0
0 12 24 36 48 60 72 84 96 108 120 132 144 156
Time (Month)
new premiere trial subscriber : Current people/Month
new premiere trial subscriber : C:Documents and SettingsDaniel JaroschMy Documents+DANIEL# Dissertation# PhDVensim ModelsThesis Master ModelCurrentpeople/Month
churned subscriber
60,000
45,000
30,000
15,000
0
0 12 24 36 48 60 72 84 96 108 120 132 144 156
Time (Month)
churned subscriber : Current people/Month
churned subscriber : C:Documents and SettingsDaniel JaroschMy Documents+DANIEL# Dissertation# PhDVensim ModelsThesis Master ModelCurrentpeople/Month
churned trial subscriber
200,000
150,000
100,000
50,000
0
0 12 24 36 48 60 72 84 96 108 120 132 144 156
Time (Month)
churned trial subscriber : Current people/Month
churned trial subscriber : C:Documents and SettingsDaniel JaroschMy Documents+DANIEL# Dissertation# PhDVensim ModelsThesis Master ModelCurrentpeople/Month

6. System Dynamics simulates the behaviour of alternative
resource systems over time.
100
Abonnenten : NSE
Abonnenten : NSE lower churn
Abonnenten : SE
Subscriber
4 M
3 M
2 M
1 M
0
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
Time (Month)
Subscriber
• The following scenarios have been simulated:
• Status Quo(NSE), i.e. Doing nothing
• Investing in Customer Acquisition (SE)
In Period 25 the Pay TV operator invested
in a marketing campain and increased the
‚inflow‘ of new subscriber.
• Investing in Customer Retention (NSE
lower churn)
In Period 25 the Pay TV operator invested
in improving its customer relationship
management processes. The ‚outflow‘ of
current customers was decreased.
• The simulation shows that both alternatives
increase the subscriber numbers.
• SE leads to a higher subscriber number in the
short term. However, in period 52 NSE lower
churn surpasses the strategic alternative SE.
.
Summary of the Simulation
Nur zur Illustration

7. System Dynamics facilitates the analysis of the
simulation results
Subscriptions
4 M
3 M
2 M
1 M
0
new premiere subscriptions
600,000
450,000
300,000
150,000
0
churn
100,000
75,000
50,000
25,000
0
0 25 50 75 100
Time (Month)
• The improvement of the customer
relationship processes leads to a higher
average customer life time of the whole
existing subscriber base.
• While the inflow to premiere subscriptions
stays the same, the outflow decreases
leading to a sustainable higher subscriber
base in all following periods.
• Alternative SE only leads to higher new
subscriptions (the inflow) only temporarily.
But as the subscribers cancel their
subscriptiosn after the original time span,
the churn numbers will increase after a
certain time lag. After 100 periods this
alternative will result in the same
subscriber numbers as the alternative to do
nothing.
• At this stage alternative NSW lower churn
is to prefer. However, further simulations
have to assess if a combination of the two
alternative could lead to better results.
Interpretation
NSE
NSE lower churn
SE
Nur zur Illustration