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Estimating the dynamics of
Viral GrowthSOME INSIGHTS AND CONSIDERATIONS
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Time
Viral Growth
uHow much?uBy when?uWhy?uWhy not?
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Overviewu Aboutu Research Layout:
u Context & Motivationu Approachu The Charter
u Summary of Insightsu The Results Summary and Detailsu Appendix:
u The Dynamics of Growth in this case explained
Note: There is also a complimentary commentary and discussion available. Please check out website:http://wittigonia.net/news-events/research-news/
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About
u Independent research.u Pragmatic advisory.u Actionable insights for business
leaders and policy makers.u System Leadership Focus.
www.wittigonia.com
WITTIGONIA® is an independent business advisor, innovation and development partner. We areworking with high growth companies in Europe and worldwide. A unique combination ofexperience, expertise and systemic know how make WITTIGONIA® a partner of choice forEntrepreneurs, Investors, Leadership Teams and Policy Makers.
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Research Layout
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Context & Motivation
u In July 2015 WITTIGONIA® Insights launched a series of small research projects with «one-minute-surveys» and simulation games.
u The motivation is to surface insights and learning about behavior of complex systems and effective management approaches.
u We are living in an increasingly complex and «exponential» world. Understanding the dynamics of systems is of the essence for sustainable developments and impact.
u The summary results and insights will be shared broadly in social media and during talks and workshop session.
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Approachu We conducted a short mobile-friendly
survey. The purpose is to find out to what degree «viral marketers» understand the dynamics of exponential growth.
u We selected a simplified simulation of viral adoption.
u Participants were given a simplified «charters» to grow a business through word-of-mouth marketing.
u Participants were asked to estimate the customer base and sales volumes at specific times.
u The same exercise was conducted in workshop seminars to check if there is a bias or a challenge with the tool.
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The 3 QuestionsThe questions of interest are: u How effective can people assess the behavior of
a dynamic system such as a growing customer base?u Customer base (the number of customers)
u Sales (the inflow of customers)
• What is the size of the customer base at different times?Customer Base
• When does Peak Sales occur? (The month in which sales reaches a maximum)Sales Timing
• What is the sales volume in that month?Sales Volume
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Recap: The Charter
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A simplified caseu In this highly simplified example case we are
considering only two factors which impact word-of-mouth promotion and exponential growthu Purchase Probabilityu Number of Contacts
u These parameters were kept constant for this example case.
u The market has a fixed size. u No loss of customers.u Of course in the real-world scenario there are many
factors which impact this factors. u In our workshop seminars (e.g. “Shape of Growth”)
we explore and discuss how growth can be optimized.
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High Level InsightsRESULTS FROM SURVEY AND WORKSHOPS
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Summary of Results and Insights
Participants’Assessments and Estimates:
u Short-term expected growth was much too optimistic
u Long-term growth of the customer base were conservative
u The maximum of sales volume was estimated much too high
u The estimated timing of maximum sales showed a wide variation
u Participants had very different “mental models” of the growth challenge. The estimates showed a wide range of variation.
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Results and Insights cont’du It appears to be very challenging
u to assess exponential growth and
u to understand flows (here: Sales) and resource accumulation (here: a growing customer base).
u to deal with dynamic systems, even in a highly simplified scenario
u Implications: u This indicates one factor which leads to the dilemma between
Startups and Investors
u It gives an indication why many companies miss “the point of re-invention and innovaton”
u And it helps explaining the struggle in coping with rapid, exponential growth (e.g. in social media, a virus outbreak)
u Our ability to decide, to act, and to allocate resources (time, people, financial) depends highly on understanding systems in the real world.
u It highlights the need to develop System Leadership skills
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So what…? Why bother?Some Implications in the Real-Worldu Investors need to assess if a business plan is valid and if the
project or the startup business is taking off. Investors often complain about “hockey-stick” forecasts – too optimistic!
u Managers need to plan for resource readiness to meet demand: products, channel partners, service staff etc.
u Marketers need to understand where, when, and how to allocate resources for u Initiating positive, re-enforcing feedbacku Fueling word-of-mouth with the right timing and amountu Overcoming the chicken-and-egg problem
u Product Managers need to understand when is the time to re-invent and renew the product and portfolio. Missing the time can result in a crisis and collapse.
u Policy makers need to understand and assess the impact of a phenomenon with potentially exponential growth and far reaching, severe impact.
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Thomas WittigCEO, WITTIGONIA
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ResultsSIMULATION AND SURVEY RESULTS
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First we look at the results of the simulation of this caseTHIS IS THE DYNAMIC AND BEHAVIOR OF THE SYSTEM IN THIS CASE EXAMPLE
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The results summary (simulation)
Customer Baseu Development of S-
shaped growth
Sales Volumeu Max. Sales Peak of 3.1 Mio. in
month 15
0
5
10
15
20
25
Mio. Customers
6 12
0,56
18
21,48
24
24,93
5,51
1
0,07
3,111
15
The simulation of the case reveals the following dynamic and results. This is the expected, simulated behavior of the system. A detailed explanation and discussion of the dynamics is in the appendix.
Time (Months) Time (Months)
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Estimates of survey participantsHIGH LEVEL INSIGHTS
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Short-term:93% of participants over-estimate growthBy a factor of 2-8x
93 7
Over-estimating
Under-estimating
2-8x
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Long-term: 87% under-estimate growthby 25-35%
13 87
Over-estimating
Under-estimating
25-35%
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Sales Volume: 70% estimate „Peak Sales“ too lateAnd the amount by a factor of 3.8x toooptimistic
73 27
Over-estimating
Under-estimating
3.8x
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All the same…
Little or now differences between
u Startups and investors / Business Angels
u Organization type and size
u Geographies
u Professions
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“”
There is no short-cut to viral growth
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DetailsDETAILED RESULTS
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25
21
6
10
9
5
19
14
0
5
10
15
20
25
3
121 6
Mio
. Cus
tom
ers
2418
Customer BaseEstimates
Estimated Customer Base (Survey)Participants in the survey provided the following estimates: u Short-term growth was over-estimated by a factor of 2-8xu Longer-term growth was under-estimated by 25-35%
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Estimated Peak Salesu The of peak sales was slightly
overestimated (by 10%) BUTu with a wide range (between Month 4
and Month 24)
u 73% estimated that maximum sales would occur after month 15
u The maximum amount of sales u was over-estimated by a factor of 3.8
u 71% of estimates were above.
3,111
1 15
11,793
17 24
Case, ModelEstimates
Time
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Estimates
Short-term
Long-termO
ver-
estim
atin
g
Under-estima
ting
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Customer Base Estimates: Tendency
-100%-80%-60%-40%-20%
0%20%40%60%80%
100%
1 6 12 18 24
Fra
ctio
n o
f est
ima
tes
Time
Fraction of Estimates over- or under-estimating
Over-estimating Under-estimating
u Short-term: the tendency is to be more optimistic about business growth. The sentiment was that word-of-mouth promotion leads to much faster adoption and growth.
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Customer Base Estimates: Valuesu The estimates showed a wide variation throughout the period of
the model exercise. u In addition the differences of estimates versus model results is
particularly high in early phases.
0.1 0.65.5
21.524.9
2.94.8
8.6
14.0
18.8
0.0
5.0
10.0
15.0
20.0
25.0
1 6 12 18 24
Estimations versus Case SimulationAverage and Variation (Standard Variation)
Case Simulation High Low Average
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Sales Volumeu The estimated maximum sales volume was 3.8 times higher and
showed a variation between 8-16 Mio customers per month.u A dramatic difference!
0
2
4
6
8
10
12
14
16
18
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Sales VolumeCase Simulation versus Estimates
Case Simuation High Low Average
The estimates are shown as average and standard deviation.
Average estimate
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Maximum Sales Timingu On average the estimates were not too far off, BUT: with a high
variation of estimates!u Within this variation range the judgement could make a difference
of 5-6 times the volume and 7 month (about 30% of the schedule).
-
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Mio
. Cus
tom
ers
Month
Peak Sales TimingCase Simulation versus Estimates
Sales (Simulation)
Average Max.Sales Estimatation
Variation of Estimates
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Online survey versus Workshop resultsSAMPLES FORM WORKSHOPS. PARTICIPANTS ESTIMATED THE SAME CASE.
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Is this a problem with thetool?
u In our workshops "Shape ofGrowth" we also ask participantsto make estimates for the same exercise
u But this time we let them DRAW the shape on a sticky note
u Here are some examples....
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Growth assumptionsDifferent shapes and colors
u The same case, very different estimates.
u Participants have very different “mental models” of the same challenge and how growth is going to happen over time.
u Apparently it is challenging to assess what will happen, when, and how much?
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So, what does it tell us…?A SHORT DISCUSSION OF INSIGHTS AND IMPLICATIONS
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What does it indicate?
Insightsu Growth expectations
u Short-term over-rated
u Long-term optimistic
u Sales u Volumes over-rated
u Timing judgement is challenging
Implicationsu Explains the “hockey-stick”
forecasts
u Missing the inflection point for innovation readiness (max sales)
u High “burn-rates” in early stages of startup projects
u Risk of managing growth: being overwhelmed and unprepared
u Risk of unsustainable growth
u Root-cause of systemic crisis
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Further Information
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Insight CommentaryPlease check also out website for the complimentary material:u “The Shape of Growth” – A commentary by Thomas
Wittig, CEO, WITTIGONIAu Real-world implications, Examplesu Practical guidance for project leaders, managers, start-ups
and investorsu Sensitivity Analysisu Dynamic Simulation
u Learning opportunities:u Online Course, Webinaru Workshop Seminars
u Tools form our Lab (including the simulator for this case)
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Online SimulationAvailable soon on www.wittigonia.com
Focusu Discover and explore
different assumptions
u Advance your awareness of dynamics in different settings
u It is not about accuracy. It is about the dynamic behavior of the system
The Tool
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WITTIGONIA Insights Mailing List
u Complete Research Reportsu Upcoming Online Simulationsu Joining the research panelu Research and Analysis commentaryu Learning opportunitiesu Tools from our “System Management Lab”
Go To: http://wittigonia.net/news-events/research-news/
Also join our event newsletter for live talks.
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Available talks, briefings and workshop sessions
Shape oft Growth•Viral Marketing•SaaS•Social reach•Quality Issue
Platform Wars•Multi-Player Simulation
Innovation•Multi-Player
Entre-preneurship•Multi-Player
Epidemic and Crisis Response•Multi-Player
Contact us for details.
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Orientation:The Shape of Growth Series
Basics•Awareness•System Mindset•Concepts•Cases, Hacks•Simulation Tool
In Action•Applications:
SaaS, Service, Subscription
•Growth, Churn, Service Renewal
Performance•Metrics, KPI: LTV,
ARPU, MRR, CAC
•Optimization
academy.wittigonia.comGo to
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Appendix
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Why?A BRIEF EXPLANATION OF THE DYNAMIC SYSTEMBEHAVIOR IN THIS EXAMPLE CASE
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First, let’s look at the Structure of this case
u There are two «buckets»:u Potential Customers
u Customers in your installed base
Customer BasePotential Customers
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The flow
u Potential Customers become Customers in your customer base
Customer BasePotential Customers
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Potential Customers
The Feedbacku There is a feedback loop: «word of mouth»
u Customers will talk to potential customers
u The more customers you have the more «word-of-mouth» will happen.
u This causes theu virtuous cycle of re-enforcing exponential growth, as well as the
u depletion of your addressable market (the potential customers)
Customer Base
«word of mouth»
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Potential Customers
The Conversion Flowu In this case the conversion flow is influenced by
u Number of contacts each customer has (e.g. followers, friends, colleagues)
u Probability of purchase (i.e. the likelihood that a contact will also purchase)
Customer Base
Conversion
Number of contacts
Purchase Probability
«word of mouth»
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Keep in mind…
u Nothing grows forever!
u «…All models are wrong…»
u This is a very simplified case
u And still it seems challenging to estimate the impact and order of magnitude
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Customer BaseThe Shape of Growthu The start is slow. Only few
customers generate word-of-mouth.
u The more customers join, the stronger the word-of-mouth becomes.
u When a “critical mass” of customers is reached, the growth accelerates.
u Positive, compounding feedback sets in.
Growing Customer BaseCustomer Base
30 M
22.5 M
15 M
7.5 M
00 2 4 6 8 10 12 14 16 18 20 22 24
Time (Month)
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Addressable MarketBalancing Feedbacku As more and more
customers join the party, the pool of potential customers becomes small.
u At a certain point the balancing feedback dominates the growth.
u Growth rates are flattening.
u The pool of potential customers is finally depleted.
Depleting Addressable MarketPotential Customers
30 M
22.5 M
15 M
7.5 M
00 2 4 6 8 10 12 14 16 18 20 22 24
Time (Month)
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Conversion & Sales VolumeSales peaku With positive feedback of
word-of-mouth sales is growing exponentially. Times are great!
u Sales is growing until a certain inflection point is reached in month 15.
u Then a rapid decline sets in, until the pool of potential customers is running empty.
u A new product or a new addressable market would need to be introduced in Month 14, otherwise crisis mode will set in soon.
Sales Volume
Sales4 M
3 M
2 M
1 M
00 2 4 6 8 10 12 14 16 18 20 22 24
Time (Month)
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NoteIn our Insight Commentary and video we talk about Risk & Sensitivity Analysis and Performance Scenarios:u The same system can product different results,
depending on the key parameters AND their changes over time.
u A sensitivity and risk analysis can show the range of potential growth, change and outcomes.
u Therefore it is prudent to u Assume ranges and probabilities for the parameters
u Test and validate the parameters through real-world experiments (surveys, Minimum Viable product)
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Learn more at
u WITTIGONIA Online Academy:
http://academy.wittigonia.com
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Thank You!@WITTIGONIA