Causal Loop Diagramming (Part 1)
This post is the start of a series of posts on causal loop diagramming. It introduces the notation, begins to investigate the dynamics of customer acquisition and provides a small interactive prototype to experiment with.
In this post, I would like to introduce you to causal loop diagramming. It is the first in a small series of posts that will develop a simple, but complete, model of customer acquisition.
We live in a world full of dynamic complexity: within society, in companies, in our personal life. Little changes here may have unexpected consequences somewhere else; we are full of energy today, yet exhausted tomorrow. One day you are making steady progress towards a deadline with time to spare; the next day you find a serious bug in your product and you are suddenly struggling to ship on time.
All these are example of what we call dynamic systems.
Causal loop diagrams are a great tool for analyzing the behavior of such systems and for presenting this behavior in a very compact way. They are very easy to understand and thus are very useful in communicating about complex systems.
I first learned about these diagrams when I read Peter Senge’s wonderful and widely influential book “The Fifth Discipline”, shortly after it was published in the 90’s. I can still remember my fascination at these elegant diagrams that conveyed so much meaning in so little space.
We at transentis make heavy use of causal loop diagrams, especially when prototyping business models and market strategies.
A Simple, But Powerful Notation
Let us take a look at this little diagram that shows how new customers are acquired through advertising.
This is a very simple example of a dynamical feedback system.
What do we mean by these terms?
A system is a set of elements that interact with each other. With this definition, pretty much anything is a system: society and companies are examples of socio-economic systems; we humans are biological systems and we all depend heavily on ecological and technical systems.
A dynamical system is a system whose state changes over time and a feedback system is a system that influences itself.
We will discuss this diagram in detail throughout this chapter, but it is actually straight forward to see why this is a feedback system: The more customers you acquire through advertising, the more your market will become saturated. Thus your adverts will reach fewer people who have not already got your product. In consequence, the more customers you already have, the less new customers you can acquire and you end up with what is called a negative, or balancing, feedback loop.
The notation used in causal loop diagrams is very simple but it is essential that you understand it well. On causal loop diagrams you have only two kinds of symbols. You have text labels, which denotes the important elements or factors of your system and you have arrows that connect those labels.
When thinking about systems using causal loop diagrams, we always ensure that the elements of the system that we identify are quantifiable at least in principle, even if determining the concrete quantity may be difficult. In our diagram, we can count the number of customers and estimate the size of the market and the number of people we reach through our advertisements.
The arrows can carry a plus or minus sign:
- The plus sign means that the influence is in the same direction so in this case, if the value of A goes up, so does the value of B. And if the value of A goes down, so does the value of B.
- The minus sign means that the influence is in the opposite direction. If the value of A goes up, then the value of B goes down. If the value of A goes down, then the value of B goes up.
It is important to note that the diagram does not say anything about which factors are really changing – for instance, if you don’t invest time or money into advertising, your product is not likely to sell.
Our First Causal Loop Diagram: Customer Acquisition
In this series of posts, I would like to do to develop this little model of customer acquisition – after all, this is one of the most important processes any business has and understanding it well is important.
Actually this is only a small model, but I find it is already quite overwhelming. In practice, we would rarely present a model like this initially – we would walk our clients through it step-by-step.
Let’s do that now.
We’ll start with this little snippet of the model. What this part of diagram tells us is that the more people you reach for advertising, the more potential customers you are going to reach. And the more potential customers you reach, the more will actually purchase your product or service.
When causal loop diagramming, I find it is important to think through the numbers right from the beginning. Otherwise there is a real danger of ending up with a model that looks neat but has little do with reality.
To make it easier for you I’m providing you with a little cockpit here so that you can play with the numbers, but you should try to make these calculations for yourself – all the models presented in this chapter can easily be built using a spreadsheet.
If you would like, you can open the cockpit in a separate tab.
My initial assumption here is that we reach 800,000 potential customers per month, and that our advertising success rate is 0.1%.
That means we will be acquiring around about 800 new customers a month and we expect a steady, linear increase in our customer base.
Of course, if we assume our advertising success rate is at 1%, then we will acquire 8,000 customers a month and our customer base will grow much faster.
Learnings From Our Experiments
I’ve plotted some different scenarios here that show results for low, medium, and high advertising success.
This illustrates nicely why being quantitative is important – the same model leads to quite different results, depending on our assumptions on how successful our marketing will be.
We can also learn something else from this – there must be something wrong with our model, because the way it is now, we just gain more and more customers, no end in sight.
What are we missing?
We know that our customer base cannot grow indefinitely, simply because the number of potential customers is finite. Every market must saturate eventually.
Obviously we need to include market saturation in the model, I’ll show you how to do that in my next post.
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