The complexity in measurement ramps with the complexity of the channel. In this post, I’ll write a bit about an interpretation of systems thinking, and how I apply it to marketing and marketing modeling.
We all seek to minimize complexity and maximize predictability. We want to minimize risk and maximize empowerment. We want to synthesize a huge amount of information and boil it down to a handful of levers. Levers cause empowerment and they enable people to make really good decisions.
Some organizations already have models in place, and are all fairly standardized. Not every organization has them. Understanding them is pretty important.
This is my approach:
I write a load of variables out onto cards. I talk to a lot of people to get all the variables onto the table.
And then you’re confronted with complexity.
I arrange all the variables in a way that cause and effect makes sense. You want to put the outcome you really want to achieve on your far right, and you want to put the variables you have the most control over on the far left. And then you start piecing things together
It’s very natural to believe you have far more levers at your disposal than you really do. Areas on the left will start to migrate towards the center.
It’s not entirely about your gut. Certain relationships, like the one between age and income, are very well known. Spend and reach is usually well known. Other relationships, like between recall and creativity, are known directionally but not certain.
You’ve now created a model.
Classically trained statisticians are taught to avoid reinforcing variables in their models, as it leads to a very particular error in regressions. I gravitate towards reinforcing variables. I can deal with the regression later on – but it’s truly an artificial barrier. When you’re laying things out, you want to pay special attention to such variables and dynamics. This is where you can get some of the best efficiency and/or effectiveness. A little difference there can go a long way elsewhere.
It’s also where a lot of the most optimistic thinking comes in. It might be attractive to think all reinforcing variables go on reinforcing indefinitely. But in this house, we obey the laws of thermodynamics.
(Obligatory Simpsons Reference)
This is also the root of the ‘virality’ argument. That somehow 2 people will each tell 2 that will each tell 2, and before you know it, the entire planet knows of something. If virality really worked that way, we’d all be aware of everything, ever. But we’re not.
Reinforcing effects typically have a time limit. Even halos dissipate. Even virality dissipates.
Once it’s laid out, you can run a few simulations on it to get a sense of the range and impact of the variables. Then, for the purpose of communicating a model with the fewest number of words, delete the least predictive variables and levers, and communicate a simplified version of the model.
You do the usual ‘what-if’ scenarios and encounter assumptions about the world that don’t quite make sense. You have to aggressively inquire about why people think the way they do. Assume the best in people. Be aware that sometimes people forget that targets are a logical consequence of building a business case. Not everybody thinks ahead, and, in some organizations, there is truly no linkage between targets and business cases.
Remember that the model you generate is one in trillions. Your goal is to generate the most predictively accurate model you can based on what you know. It is all but certain that more accurate models exist, we just literally don’t know what we don’t know at this point. It’s not a question of ‘wrong’, but a question of ‘better’.
Good enough is defined as good enough to make a confident assertion about a set of causes that have likely effects. You’re not aiming for perfect because perfect isn’t possible for another few hundred years (or certainly some other time scale that exceeds your life).
Once you have a model, you have a system. You can use that system to write powerful recommendations that link actions the firm can take with outcomes that are likely. A system ultimately leads to a general understanding, and we move into a process of normal science.
Evidence accumulates that certain relationships are growing weaker, for instance, between TV Spend/GRP and Sales. New channels emerge and fragment customer attention and behavior. In short, systems evolve and much of what was true in 1990 isn’t true in 2011. Much of what is true in 2011 won’t be true in 2020. It’s not that anybody was wrong. It’s only bad if we fail to update our present view of a system with new knowledge.
That’s my take on systems thinking and marketing. Useful?