How I Increased Lonely Planet’s In-App Purchase Revenue By 30% (Case Study Inside)


Understand: Humans rarely make purchase decisions in absolute terms. Most of the time, we rely on comparison between options to make a purchase decision.

Imagine you walking in to Best Buy. You’re looking to buy a new TV but you don’t really have an idea about the current product landscape. You’re then given the three options by the sales agent (or identify them yourselves):

  • 36-inch Samsung for $690
  • 42-inch Samsung for $850
  • 50-inch Toshiba for $1480

Which one would you choose?

Chances are high that most people would naturally choose the 42-inch Samsung for $850.

The third option falls away quickly. We don’t really know if the 50-inch model is worth $1480. In comparison, it is a lot more expensive.

We also don’t really know whether the 36-inch is worth $690. But accepting that price anchor subconsciously, we do know that the 42-inch then looks like a solid deal relative to the 36-inch.

Humans compare and weigh options available when making a purchase. We rarely choose things in absolute terms. We do not have an internal sensor that tells us how much things are actually worth, much rather we figure out how much things are worth compared to similar options.

Reflect on your last difficult purchase for a second.

We focus on the relative price advantage of one thing over another and estimate value accordingly. We do not know the worth of a six-cylinder car is but we can assume it is more expensive than the four-cylinder model.

More importantly, we prefer to compare between similar and easily comparable options, while we avoid comparing things that cannot be easily compared.

Case Study: Increasing Lonely Planet’s In-App Purchase Revenue by 30%

Armed with the general understanding about how relativity influences our purchase decisions, I wondered whether this would also hold true for In-App Purchase decisions. In order to find out I conducted a small study.

I have picked the Lonely Planet’s “Make My Day” app in order to test my assumption. It is an app that allows you to plan your trip to any of the six available cities in the app. Planing the first day of the trip is free, after that, you have to place a purchase to unlock more content and more days.

The study consists of two different groups of 50 people. Both groups were prompted to make a purchase decision. The control group had two options (A and B). The second group was offered three options (A, -B and B).

-B represents the dummy option (like the 36-inch Samsung TV above) that is clearly inferior compared to option B.

  • Control-group: One city $1.99, all cities $4.99
  • Second-group: One city $1.99, two cities $4.50, all cities $4.99

My assumption: The control-group will have a tough time to make a decision because they can’t easily compare options. People’s risk-averse nature will then revert back to a conservative decision and pick one city.

The second-group should find it easier to go for all cities, given the similarly priced tiers of two and all cities, making it a no brainer. It becomes an easier decision because people can tell that buying all cities for $4.99 is a great decision if you compare it to buying two cities for $4.50.

Note: Do you notice how futile the -B option is? Two cities are slightly over double the price of one city.

Case Study: The Results

I conducted the study as a click-test on Usability Hub with a total amount of 100 people. The results have been reassuring and very clear.

While the control group opted for the cheap $1.99 option most of the time, the second group purchase the more expensive $4.99 option 2.29x more often.

The heat map below shows the two mockups I have created for both test scenarios and how the test groups responded to it:

(Have a laugh at the people that can’t read simple instructions and tap text!)

(n = 50 for both groups — clicks out of target are adjusted.)

The second group purchased the most expensive $4.99 option 43.75% of the time, while the control group only bought it 19.1% of the time.

Breaking this down in to revenue for a total amount of 10’000 In-App purchases, the revenue numbers would compare like this:

Control group: $25'630.00
Second group: $33'025.00

The bottom line shows an increase in revenue by 30% or $7'395.00.

Take-away: Choice Architecture and Introspection

Human brain prefers to make decisions between similar and easily comparable options. It tries to avoid options that are hard to compare. This is powerful. It allows you to craft choices in a way that is easy for people to digest and react upon.

If you want your customer to make a certain choice then introducing an option that is similar and easily comparable but inferior in value will nudge (or manipulate) people in to buying the intended option.

Manipulation always brings up a moral question. On this matter, I could really resonate with Nir Eyal’s way to look at it. In his book Hooked: How to Build Habit-Forming Products, he devoted a full chapter on the topic of manipulation. He states that it is okay to manipulate, as long as:

As long as a product materially improves people’s lives and the creators use the product created themselves, it is okay to push people in to this direction. If that is not the case, it is not.

Furthermore, I decided to share this post for purposes of introspection. We are all exposed to this way of thinking and manipulation on a daily basis. Think grocery stores, real estate, magazine subscriptions etc. — knowing about it will help you prevent it.

Dan Ariely’s book Predictably Irrational was the main source of inspiration and knowledge for this experiment. Make sure to give it a read if you want to learn more about behavioural economics. Also, this article of his is great.


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Published in Startups, Wanderlust, and Life Hacking

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