A concept that comes up pretty often in decision research is the one of aspiration levels. They are meant to reflect some kind of preference levels, meaning levels of attributes that the decision maker would like to have in an ideal situation. The idea behind the concept is that such levels can guide both the decision maker and the analyst to look for portions of the alternative space that’s relevant – better to search close to the optimal levels.
Now that’s nice and all, but for practical purposes I think an inverse concept is perhaps even more useful. By inverse I mean rejection levels. Or, as I like to call them, what-the-hell-I’m-absolutely-not-willing-to-accept-that levels. The idea is simple enough: rejection levels signify the worst attribute levels you’re willing to accept. A value worse than that means you’ll discard it immediately and look elsewhere.
The benefit is that if you have many alternatives, rejection levels can be used to make the search space smaller very fast. Imagine you’re buying a bike, and there are two criteria: cost and quality. You probably have some aspiration levels – the ideal bike. That’s reflected in the upper left corner (low price, terrific quality). But that only tells us the portion of the search space with the best alternative, but unfortunately very likely a non-existing one. Looking at the picture below, it’s clear there’s still a lot of search space remaining.
Now that’s nice and all, but for practical purposes I think an inverse concept is perhaps even more useful. By inverse I mean rejection levels. Or, as I like to call them, what-the-hell-I’m-absolutely-not-willing-to-accept-that levels. The idea is simple enough: rejection levels signify the worst attribute levels you’re willing to accept. A value worse than that means you’ll discard it immediately and look elsewhere.
The benefit is that if you have many alternatives, rejection levels can be used to make the search space smaller very fast. Imagine you’re buying a bike, and there are two criteria: cost and quality. You probably have some aspiration levels – the ideal bike. That’s reflected in the upper left corner (low price, terrific quality). But that only tells us the portion of the search space with the best alternative, but unfortunately very likely a non-existing one. Looking at the picture below, it’s clear there’s still a lot of search space remaining.
On the other hand, the rejection levels immediately close off a large portion of the graph. You’re not willing to pay more than 1500 euros for any bike, nor are you ready to accept a bike with a quality rating of less than five. The picture shows how much effort you can save with the rejection levels – there’s many options that are closed off just by setting the levels.
The trick with rejection levels is that you need to set them before looking at the options. A bike can be bought without issues, but any more complex decision and trouble arises. For example, house buying is of considerable difficulty in itself. And what marketers know is that if the house makes a good first impression, you’re likely to start coming up with reasons for why that house was just so lovely, convenient, and so on. As a result, people tend to exceed their budget after falling in heavy with a single house.
To avoid this, rejection levels are a great technique. If the price goes above the rejection level, you can confidently say thanks, but no thanks and just move on. By making the rejection decisions with a rule that you’ve committed to beforehand is much, much easier than mulling over each and every option you come across.
To avoid this, rejection levels are a great technique. If the price goes above the rejection level, you can confidently say thanks, but no thanks and just move on. By making the rejection decisions with a rule that you’ve committed to beforehand is much, much easier than mulling over each and every option you come across.
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