3 Consumer Behavior Experiments to Inspire Your Startup's Growth Here we review three experiments conducted on consumer behavior and discuss how leveraging these studies can boost your startup's performance.

Startups are synonymous with innovation. In this fiercely competitive market, they must constantly come up with innovative ideas. However, not all ideas are good enough, so it becomes essential to test the ideas beforehand in order to validate them. A well-known method for doing that is called A/B testing — or more accurately, experimentation.

Experiments, however, fail too often due to inaccurate experiment design. We have all witnessed it. This leads to a prejudice against the process of experimentation altogether. We strongly advocate against this prejudice. Experimentation can be the key to growth for a startup.

Many things can go wrong with experiment design, however. The one that we are particularly going to shed light on in this article is how the understanding of consumer behavior intelligence can lead to successful experiment design. This is something that is not commonly talked about, and I see many in the industry not paying attention to it.

Related: The 5 Key Elements of a Successful Experiment

Consumer behavior intelligence, or behavioral economics, is the field where scientists study human behavior in terms of money and value. Behavioral economics reveals that human decisions, especially in the context of money, can prove to be quite irrational. Hence, we can leverage behavioral economics in designing our experiments.

If you are looking to improve your revenue, your uptake, your trial rates or your retention, then experimentation in alignment with behavioral economics can be a growth hack for you. It can be a very powerful concept in designing your experiments. Given the vastness of behavioral economics, let's break down three concepts and discuss how they can be used in experimentation.

1. The allure of free

It seems logical that charging an insignificantly small fee for a service should not make any difference to customer growth. That's not the case.

Let's study this concept with a much-talked-about case study: Hershey's Kisses vs. Lindt, the gourmet chocolate. An experiment was conducted where Hershey's was priced for 1 cent and Lindt was priced for 15 cents. Consumers were asked to choose between them.

A significantly higher number of people chose Lindt at a much higher cost than Hershey's, which was nearly free. The consumers justified their choice by saying Lindt was Luxury chocolate.

Later, the experiment was repeated with the same group of people. However, this time, the choice was a little different. There was a 1-cent reduction in the price for both cases. Meaning Lindt went down to 14 cents, and Hershey's became free. To the researchers' surprise, the results completely flipped, and a significantly higher number of people picked Hershey's, even though Hershey's was regular chocolate.

This experiment conclusively proves that there is a deep allure for "free" in human minds, and we greatly distinguish between "free" and "nearly free." So, here is a tip: If you are in an app business, know that in a user's mind, "free" and "nearly free" make a big difference. They are two distant options. Keep this in mind while designing your experiment as well.

Related: The Basics of Experimentation and Why It's Key to Your Startup's Growth

2. Priming

Another experiment was carried out at an airport. People at the airport were asked to either pick up yogurt or fruit from a counter. Initially, nearly half of them chose yogurt, and the other half chose fruit.

For the next part, someone on the way to the counter talked to people in the queue. What they found is, that when this person spoke to them about yogurt, people were picking more yogurt. And when this person spoke to them about the fruit, they picked more of the fruit. This is a great example of priming.

What we learn here is: The key is to draw the consumer's attention to a product/service you want to sell. Their decision will likely be influenced automatically. The way to draw attention doesn't even have to be direct.

The person who was talking to the people in the queue, didn't necessarily need to point out the product. What they say can simply revolve around the product. To sell the fruit, we need not ask them to buy the fruit, we can just ask them what fruit they like, and that does the magic.

3. The decoy effect

Let's take the example of a dating app. The app gives three matching profiles in the free account and then has an option for upgrading. The upgrade has two options.:There is a basic upgrade and a premium upgrade. Let's say there are 5% of users who upgrade, and the split is 4% for basic and 1% for premium. Can we introduce a decoy in these choices to switch the ratio around? Well, it's possible!

Suppose the app introduces another upgrade option. It is in line with the premium package and is called standard pricing, but it is inferior to premium pricing. Let's say the basic upgrade has five features, the premium upgrade has five plus seven amazing features, priced at $999, and the standard upgrade has five plus one other feature, priced at $899.

You would be surprised to know that just by introducing this inferior alternative, the app will be able to switch the ratio from 4%:1% to 1%:4%. The reason for this shift is that earlier, the users weren't able to directly compare basic to premium. However, now they have found a comparison between the premium and standard packages. Five plus one is available for $899, instead of 5 plus 7 at $999. It's an easy comparison, and the app might be able to influence people toward the premium. This is another very useful concept that you can use to design your experiments.

Related: 4 Ways to Make the Most of A/B Testing Right Away

Many studies in behavioral economics give us a fair idea of consumer behavior in terms of money and value. Startups can leverage these studies to design and perform successful experiments. We discussed three simple, yet very powerful concepts of behavioral economics above, and implementing them could help you conduct successful experiments yourself.