Short-term vs. Long-term Thinking

Long time no blog!

The last couple of months I’ve actually been producing blog-worthy thoughts on a weekly basis, so why not generalize them a bit and just put them out there. Part of my approach this time is to not aim to create the perfect post, rather aim for publishing whatever I have on my mind.

Here we go:

If your vision takes 30+ min to explain and includes PowerPoint slides, it’s a bad vision. Especially if there is no explanation on where you want to go or what you want to achieve. A vision is something that is very unlikely going to be reached. For me this doesn’t make sense. There was a mix up in terms of vision, mission and strategy.

A strategy is something you execute in order to fulfill a mission that will get you closer to your vision. This vision should align with your overall purpose.

If the purpose of your department is to maximize revenue based on certain constraints and market parameters. Then the vision of having a fully automated system that can maximize that without human interaction could be desirable. The mission along the way could be to minimize the losses while maximizing the learning potential of the algorithms being designed to do this. For this to be successful you can have a number of different strategies supporting it, but they should all align towards the most important thing. This includes short-term, mid-term as well as long-term strategies. If a short-/mid-term strategy is to maintain an acceptable level of algorithmic learning while minimizing the potential loss of revenue you might do a couple of things. While if you are aiming to maximize the algorithmic learning while trying to minimize losses you will have a different focus. Or if you simply are trying to maximize the short-term profit, your algorithmic learning will suffer. If I draw a simple graph comparing short-term vs. long-term thinking and its potential effect on revenue it can look something like this:


The interesting areas to note here are alpha, beta, gamma, delta, epsilon and zeta. As a business you are interested in maximizing the area under the curve and these areas represent the differences based on the approach.


If we look at the picture above we have two of the curves and the potential revenue in these two cases are either alpha+beta or beta+delta. Looking at it from this perspective it becomes a no-brainer that the blue curve is the much better option. Not in the short-term however, but the loss in revenue through investment in the early days will very soon be surpassed once the blue line crosses the yellow. How come there is a discussion at all in the first place?

My thoughts on this is because of the X-axis, the time aspect. We are not able to think that far in the future to see the error in our thinking.


If we change the perspective here and take a look at a smaller section I think you’ll see what I mean.

As we can see here is that if we look into the foreseeable future our forecasts show us a difference 2.5x the revenue. This is resulting in not making conscious decisions about the potential impact we can have on the business. So based on the chosen time horizon the choice seems yet again obvious.

If we throw a number in to the image above and say it overlooks the coming year it means that the first image corresponds to the coming 3-4 years. Now 4 years in the current market in any industry is a very long time, especially looking at the tech start-up scene.


Here I’m trying to illustrate the potential impact short-term vs. long-term thinking could have on revenue. Here we are also assuming that the 3 different strategies illustrated are mutually exclusive, i.e. you cannot jump between them. Once you’ve chosen one path you cannot change your mind without starting over from scratch, effectively jumping back to 0,0 on both axis.

But what if we could jump between strategies, adapt to whatever works best for us right now? Well, then we would only be interested in the intersections of the curves.


This would mean that we could effectively adapt to market conditions and change strategy accordingly to maximize the revenue. Always being able to utilize our potential to the max. BUT, this capability doesn’t come for free. This is something you have to invest in heavily. Not only on the technical side but also in the people, how you structure, organize and collaborate within the organization.


Another reason why you should want to invest in this kind of adaptability is what I’m trying to illustrate above, there is always a bigger fish. When operating in a complex environment you never know what might happen tomorrow, or a year from now, or ten for that matter. The capability to adapt and quickly validate our assumptions will ultimately be what makes or breaks any player in any market going forward. What made us get to this point will not take us to the next.

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