Optimization work and testing challenges everything you think you know about marketing and your users. This can lead to some introspection about why you think you know things and what lead you to hold those erroneous beliefs in the first place.
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Enter the world of cognitive psychology and cognitive biases
The number of ways that people can and do misuse information or confirm their already held beliefs is simply mind boggling. A quick journey down the hundreds of known biases and fallacies can lead one to question how anyone ever gets anything right.
While these biases do impact just about everything we do there are ways to help mitigate their impact and to leverage their existence to improve your program. The first key however is to identify what ones you will be dealing with the most and to help put systems in place to exploit their existence. With that in mind here are the top 5 biases that you will face in your time running an optimization program, what they are, and what to do to help mitigate their impact.
Keep in mind that these biases impact everything part of every day in your life, work and otherwise, and you should keep an eye out for this behavior in all cases, not just optimization.
Bias #1: Congruence Bias
We love to create false comparisons and then pretend they answer our primary question.
What it is: Congruence bias is the name that is given when we create false dichotomies of choices and then think we have answered a question when we have really just chosen between a limited set of options.
I know that sounds wordy but what this really means is that you create a false choice such as one banner versus another or one headline versus another, and then test to see which is better. In reality there are hundreds of different ways that you can go but we become blinded by our myopic view of an answer. You create a false choice of what is there versus what you want to see happen.
Even worse with this bias is that you will get an answer, it just won’t mean much. Just because the new headline you wanted to try is better, it doesn’t mean that it was good choice. It also means that you can’t apply too much meaning in limited comparisons because you are in reality just validating a flawed hypothesis. It is the job of the researcher or tester to compare all valid alternative hypothesis, yet we get stuck on the things we want to see win and as such we get trapped in this bias on an almost hourly basis.
Example: You feel you need to improve your landing page so you choose to test out a new call to action. In reality you need to figure out what matters most and try out many different options.
What you should do: One of the key disciplines of a successful program is to always be looking to maximize resources and to challenge what people think is right. This means avoiding congruence bias by making sure you are comparing a large range of possible answers and by challenging the current mindset. Always make sure that you avoid only testing what you think will win and also ensure that you are designing tests around what is feasible, not just the most popular opinion about how to solve the current problem.
Bias #2: Dunning-Kruger Effect
The more you don’t know, the more sure you are that you know everything.
What it is: We all know that person we work with that always thinks they know all the answers and is always sure they have it all figured out, yet we all know they have no clue what they are talking about. That is Dunning-Kruger effect in action, where people who are incompetent don’t know that they are incompetent, and more importantly they are more likely to super confident in their actions because they don’t know what they don’t know. This is why you can have people talk about how amazing their experience is and how great their results are when in fact they are a negative influence on results for everything they touch.
All jokes about industry “experts” aside, this is a big deal because there is also a direct correlation between sociopathic tendencies and positions of power in most businesses, and this is one of if not the primary cause of this. The corollary effect of people who know a lot about a subject being less confident leads to cases of persuasion winning over functional knowledge. If you ever want to know why people still have “I think/I believe/I feel” conversations this is why.
Example: Obviously the call to action is the most important, why would you ever test the layout of the page? The more sure you of something, the more important it is that you challenge that assumption.
What you should do: This is where having a rational decisioning system in place prior to starting a test is so important. You have to be able to put every idea through a system that maximizes impact to the bottom line of the business and not just their egos. It doesn’t matter what someone thinks will happen or what they were trying to accomplish, it only matters if the change impacted the bottom line of the business.
By enforcing such a stringent discipline for measurement and by making sure that you avoid only validating ideas you can ensure that the limits of someones own understanding does not also limit the possible outcome of your optimization efforts.
Bias #3: Narrative Fallacy
We love to answer why even though it is impossible to know why something happened from any form of available information.
What it is: People love a good story, and there is no more requested story then why. In all honesty 90% of the time wasted in our industry is people professing some deep understanding of why something happened or creating elaborate stories or in depth presentations persuading you that they have a deep insight into the persona of your users.
Why did a certain headline win? Obviously because it was persuasive. Why didn’t people go to your site? Because it wasn’t relevant…
Anytime you hear someone explain why something happened or anytime you try to take too much meaning out of a test result you are facing the Narrative Fallacy in the face. People feel empty when they get a result that goes against their beliefs or that goes against conventional wisdom so they inherently starting searching for why. In some organizations executives don’t care about the results, only the why. Beware anytime you or anyone else ever starts explaining why something happened.
The problem is that it is impossible to tell why. Even if you ask someone to their face you are only going to get their rationalized version of what happened, not the actual series of things that lead to that outcome. You can not say that an event is related to another or is the cause of another when you get only 1 data point from a test, and yet that is the maximum you can get from a single result in a test, be it that both metrics went up, or down, or inverse of each other, the maximum amount of data is a single data point.
Example: You know that the CTA that you tested won because it obviously was more relevant to the context of what your users were looking for. In reality you only know that it won and anything else you say past that can only lower the value of future actions.
What you should do: Never, and I can not stress this enough, never explain why something happened. You may feel 100% certain (see Dunning Kruger above) that you know why, but avoid going down that road at all times. Not only are you guaranteed to be basing your conclusion on no real information, but by you opening this door you are allowing others to also start getting into story telling. Stop these conversations in their track as they only distract from making the correct decisions on how to act on the data and what to do next.
In many cases this is the act that will cause the most cognitive dissonance with your group as this goes against just about all human nature. This is why it is also important that you establish rules up front and spent your time educating people on how to act on data before you ever get to the point of actual action. Success and failure is determined before you launch a test, not by the results after.
Bias #4: Graveyard of Knowledge
Winners tell us nothing yet they are the ones we always turn to for information.
What it is: Go to any bookstore and look at the business section and all you will see is books written by people from majorly successful companies. We love to hear from entrepreneurs and leaders who have gone the extra distance and have really achieved real results.
What you won’t see is books by people who didn’t succeed, who didn’t get to be a rockstar. The funny part is that those people we don’t talk to or that didn’t succeed offer so much more information than those who did. Not only are they a much larger source of information, but those that do succeed often downplay luck and random success and also over import small things that may or most likely do not play a part in their success.
Also known as Survivorship Bias, the graveyard of knowledge is a bias that makes us only look at those that are there at the end and try to apply all knowledge from that group. We look at those that did purchase for common traits, not those that did not. We create personas of the people who buy our products, but don’t really give much consideration for the people who don’t buy, despite the fact that they represent just about every person on this planet. We get so caught up in looking at what was there at the end that we lose massive amounts of information and lose all the value from that information.
Example: You really want to target your CTA based on if the user is coming from google or not. In reality the message could work for anyone and you might need to change the experience based on a completely different factor, like browser or time of day.
What you should do: Never presume that you have a read on your users at the exclusion of other possibilities. Be it in the content you make or the way you think you are creating a page, always look at the various possibilities and allow the data to dictate the direction. This is especially true for personalization and for segmentation. You have to always serve all versions to every user and make sure that you look at all feasible segments, even if you really want to target a message based on a behavior or source. You have to make sure that you are maximizing outcomes, not maximizing your own opinions.
Bias #5: The Halo Effect
The more we like one trait the more we like all traits of that person or object.
What it is: We all love the good looking pages that really resonate with us. We just know that the better looking page is the best performer, despite the fact that the most profitable page on the internet is a big white page with a search box in the middle. We also trust the experts that talk the best or who connect with us in a way that makes the information really stick. All of these are examples of the Halo Effect, where you apply positive or negative feelings to other traits based on a single trait of the object or person.
We really do trust good looking people and good looking pages more, despite the fact there is no correlation to actual outcomes. We assume that because some expert can speak well that this must mean that their information is better than those that aren’t as eloquent. We really do choose the tallest presidential candidate in almost all elections even though that should have no correlation with their ability to lead.
Example: Every page analysis that has ever taken place. You don’t like the look of the page so everything, especially that CTA needs to go. In reality your impression of a page or how much you like any part of the experience has no bearing on the value of that item to performance.
What you should do: Let people vote beforehand for what they think will do best and second best out of all the options you test. Do this even a couple of times and it will become apparent that no one, be it the most seasoned marketer or the new intern, can tell the performance of anything by just looking at it.
While some people are slightly better than others in choosing an option that is slightly better, very few if any will even come close to a 10% success rate in choosing the best option. Confronting this bias head on will also force you to test out things that people consider “ugly” or that go against their vision of the site, and when they win it allows you to really open up what the real best user experience should be.
It is easy to list all of these biases that shape our view of the world and the impact of the work we do. This look didn’t even get into actual experiment design biases like observer-expectancy or selection bias, because at the end of the day those don’t play as big a role as the ones that we experience every moment of every day. People as a whole are actually a very passionate and capable group, but they limit themselves so much by what they shut out from their consciousness and how they choose to see the world. By creating discipline and not allowing yourself to get trapped by these biases you allow them to really achieve results their other natural talents should allow them to do.
Most people don’t think that their decisions are flawed. They think they are being rational in the moment, though they will observe irrational behavior in others or in reflection of past decisions. In reality nothing we think about or do fails to be influenced by a multitude of shortcuts that our brain makes to allow us to get through the day. We have to be aware of these and create systems that mitigate the damage, else we will never come close to achieve what we can and should.
One last thing. Think that this is all about other people and that you are better than these silly biases? Keep one simple fact in mind. The smarter you are, the more likely you are to to fall victim to biases. So which is it, are you incompetent or should you start really putting effort in stopping biases from damaging your organization?