The Dangers of Optimization

“We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil” -Donald Knuth

In the field of Computer Science (CS) optimization is the process of changing the algorithm (the logic) of a program in order to improve its efficiency (i.e. make it run faster or consume fewer resources.) In order to do this, certain assumptions need to be put in place and my argument is that these assumptions make optimization dangerous and not just in the context of programming.

First of all in order to optimize a process you need to understand it very well. You need to know how it behaves in different circumstances or under various boundary conditions. If we were to look at an organically (necessity) grown business process that we want to optimize, it’s usually necessary to see where the inefficiencies or bottlenecks are in the process and remove them.

Second, when you optimize you may need to restrict boundaries, make certain assumptions, use special cases, tricks and complex trade-offs which will achieve the required result but at the expense of potentially over-complicating a process or over-specializing it. This causes a loss of agility in the long run or over-adaptation (as may be the case in over-optimized diets).

Let’s use a simple example from manufacturing to explain this. Let’s assume that you have a retool-able machine that produces widgets at 95% defect rate. This means that 5 out of every 100 widgets are defective. When you’re looking to optimize the defect rate, you’re looking to produce fewer defective widgets without slowing down the machine or the manufacturing process. Suppose also that you know the process of making this widget very well, since you make millions of them every year.

Since you know the process well you know that there are just a few ways to optimize the defect rate, you can for example utilize the machine better by redesigning the overall process or you can install much more specialized machines that make just this one widget. Now you have an optimized defect rate but it has come at the expense of over-specialization and a huge loss of flexibility or agility. Imagine what can happen after you’ve replaced your retoolable machines with specialized machines and the market changes to where it no longer needs those widgets. You’re practically out of business.

The danger is that not all processes are fully known. Even in a precise field such as computer science there’s always some level of unknown, certain unexpected conditions or special circumstances where the program will fail. If you were to optimize the process without knowing all these cases you run the risk of having an incorrect program that no longer solves the original problem.

Nowhere is this more prevalent than in digital marketing. First let’s get one obvious thing out of the way. SEO or search engine optimization is not really optimization per se. You’re not optimizing your website to work better or faster, you’re adapting it to fit the search engine’s constraints.

The whole idea behind the concept of targeting is really about optimization. When you’re trying to target a certain segment of your audience, you’re trying to optimize the marketing dollars so you increase the efficiency. After all why would you want to spend money on leads that will not respond to your offer?

There is nothing wrong with targeting (although Roy H. Williams aka The Wizard of Ads would beg to differ) The problems arise when you start to over-optimize while assuming that you really understand your audience and what offers they actually respond to. The truth is you really don’t, so all this optimization will really hurt sales. There are many other things in a business you can optimize and improve, such as your close ratio.

A good example of this is a recent story I read in Roy H. William’s Monday Morning Memo (a highly recommended business newsletter by the way) The story is about two lawyers who take different approaches to marketing. One believes in targeting, and converts about 10% of inquiries while the other believes in casting a wider net via radio advertising and ends up converting 60% of inquiries proving once again that marketing is primarily about the messaging and the offer.