Causal Loop Diagram

The problems startup teams and innovators have to deal with are often complex and interconnected. And although it would be nice to have a clear mental model to use for every situation, in reality it’s simply not possible to disentangle the web of relations and consequences connected to the problem you want to solve. For example, starting to build a new product might require staff to be trained, which will impact hiring and education budgets, and the new product changes staff time allocation, which in the short term influences their business unit’s bottom line. There are myriads of such connections that might be important to your new product or startup. Systems Thinking – the more ‘organized’ brother of Design Thinking and Visual Thinking – deals with such complex questions. It has been described as a ‘language’ that can help describe and communicate the complex, interdependent issues managers and entrepreneurs face on a daily basis.

The Causal Loop Diagram is one of the tools in the Systems Thinking toolbox. It is a visual representation of the complex entangled net of relationships and dependencies, and it can tell you how one part of the network influences other parts. By looking at the problem through the lens of the Causal Loop Diagram, you can become more aware of the structural forces that produce sometimes puzzling behavior. The Causal Loop Diagram can be used very well to help understand the problems a customer is facing, especially in a B2B setting. Try mapping out a diagram together with your customer!

The Causal Loop Diagram consists basically of two types of visual elements: nodes and edges.

The nodes in the diagram are the variables of the system. Think of variables as (potentially) measurable quantities, such as the number of customers, an NPS score, or market penetration.

The edges (the arrows) represent how variables are connected. If one variable’s change is connected to another variable’s change, there will be a connection between them. The arrow indicates the direction of the influence, and the ‘+’ or ‘-’ sign tells you how the variable will change. ‘Customers’ → + ‘Revenue’

This example has a ‘-’ (negative) sign. That means, when the variable ‘customers’ grows, ‘revenue’ will shrink, and when ‘customers’ shrinks, ‘revenue’ will grow.

The direction of the arrow is important. This example tells you nothing about the other direction, ‘revenue’ might grow or shrink with no effect on ‘customers’ Together, the nodes and edges can create complex networks, and, more importantly, loops. Finding these loops is the point of the exercise, as the name Causal Loop Diagram may have given away.

Think of the adoption of a new app. The example graph shows two feedback loops, a reinforcing loop on the right labeled ‘R’, and a balancing one on the left labeled B.

At first, the app is unknown, and has only a few users. Those users talk to their friends, and influence some to also install it. They are enthusiastic, and tell even more people. The number of users starts to grow exponentially. This exponential growth is driven by the ‘word of mouth’ loop on the right.

After some time, the newest group of people adopting the app runs into difficulties trying to enthuse more people through word of mouth: many of the people they talk to already have the app installed. The growth rate starts to slow down.

Finally, an equilibrium is reached between the two loops.

Of course, in the real world, the loops are not always so neat. They might operate over multiple levels of connections and be much more messy. Luckily, there is a trick to finding these loops, and labeling them as ‘balancing’ or ‘reinforcing’.

The first step is tracing the edges and finding the loops in the diagram. Start with an assumption, e.g. ‘variable 1 increases’. When you have traced the loop and arrive back at ‘variable 1’ with a ‘+’ connection, the loop is reinforcing. When you arrive back with a ‘-’ connection, the loop is balancing.

To be more precise: Reinforcing loops have an even number of ‘negative’ links in them (zero counts as even). Balancing loops have an odd number of ‘negative’ links in them.

Reinforcement loops are associated with exponential change. Balancing loops are associated with reaching a plateau.

The connections in the diagram don’t act all at once. There often are delays, where the influence of a variable change takes time to trickle down to other parts of the system. These delays may influence the strength with which balancing and reinforcing loops work and cause fluctuations to happen.

An example: An entrepreneur of a small company makes a cashflow prognosis, and notices that the next few months not enough projects have been sold. This causes part of his workforce to idle. He kicks off a big effort to turn the situation around, and through this ‘all hands on deck’ situation the team manages to net a substantial amount of new orders. The team subsequently is swamped with the amount of work, and has no time to do sales. The work dries up, and the system is back to the starting point.

To illustrate this oscillation, the entrepreneur decides to map the system with a causal loop diagram.

First, he defines the different variables: the cashflow situation, a big sales effort, the number of signed projects, and the number of delivered projects.

Next, he defines the obvious relationship between sales effort and number of projects. The bad cashflow situation drives the sales effort, and the sales effort leads to a change in the number of projects. More signed projects means (eventually) more delivered projects, which influences cashflow.

But, looking at the company situation, he also realizes there is another relationship at play: the delivery effort influences the sales effort.

Next, it’s time to see how these variables influence each other. Going through the diagram, he asks himself the question: if this variable’s value increases, what happens to the variable it influences? Does it also increase? Or does it decrease? If the increase leads to an increase, or a decrease leads to a decrease, the arrow is ‘positive’ (blue). Otherwise it is negative (red).

Finally, it is time to investigate the loops in the diagram. There are two loops:

The arrow from ‘cashflow’ to ‘sales’ is negative: the reason is that the effect is reversed. Lower cashflow means more sales effort.

Counting the number of negative connections on each of these, it is clear that the first loop (‘cashflow’-’sales’-’signed projects’-’deliver projects’) has an odd number of negative links. This means it is a balancing loop by itself. Cashflow going down drives more sales, which drives more signed projects, which (after a while) drives more delivered projects, which increases cashflow.

The fact that this loop balances itself out is that the sales effort is apparently connected to the cashflow. The direct evidence is the ‘all hands on deck’ effort by the entrepreneur.

The second loop, ’sales’-’signed projects’-’deliver projects’, is also a balancing loop. More sales effort drives more signed projects, which drives more delivery effort. The delivery effort negatively impacts the sales effort.

Looking at these two balancing loops, it is clear that although this system (sort of) protects against a very bad cashflow situation, it will never turn into a reinforcing system. The situation will improve for a while, and then it will reverse itself to where it started.

The entrepreneur can now approach the problem with a better understanding. First of all, removing the dependency between the delivery teams and the sales people, giving each their own role rather than using an ‘all hands’ approach, will remove the second loop. This will make the system’s oscillation time longer, but it does not solve the problem.

What is needed is a way to counter the negative coupling between cashflow and sales. There needs to be a reinforcement happening there. A way to do this might be (but for simplicity other consequences have been omitted) to invest in more sales people:

In practice, it will be the case for a new situation that another balancing loop would be established. Unchecked growth in essence never happens. In this example, the new loop would probably be that the delivery of products becomes a new bottleneck.

In this way, drawing causal relationships can help give more insight into how different parts of your business influence each other, and help you to find potential options to solve the problem.

Steps