Teach Your Monster are the non-profit company behind Teach Your Monster to Read – a free, BAFTA-nominated videogame that is helping over three million children every year learn to read

I worked with the team as a Product Management Consultant over a period of two weeks to help them work out what pricing strategy might work best for a new game they’re working on.

The team had a list of different ways they thought they could charge for the game, but were unsure which would work best for them. They had extensive models predicting potential revenue from each of the options, but without having the game live and trying each pricing model they were unsure how to decide which option(s) to launch with.

To complicate their decision-making, as a non-profit company they need to balance the requirement of having revenues that cover the costs of developing and maintaining the new game, while also not wanting to to make too much from the proceeds.

To help them make their decision I developed a demand and conversion experiment designed to give the team more reliable data about the likely income each pricing model would generate.

I designed and built 5 variants of a landing page promoting the game, each with a different pricing model and associated messaging. We randomly selected ~8,000 people from the existing game’s user base, split them into 5 groups, and sent emails to them explaining that the new game was coming soon and inviting them to join a waiting list to be amongst the first to play it.

Each group was assigned to one of the 5 different landing page variants (all of which allowed visitors to sign up for the waiting list).

We tracked the open rates of the email, the clickthrough rates to the landing pages and then the conversion numbers for the sign ups.

This data gave us an indication of the likely conversion numbers of each of the different pricing strategies and, coupled with the team’s existing modelling, the ability to predict potential revenue with a higher degree of confidence.

The experiment’s results confirmed some of the team’s original assumptions about how each model would work for them and challenged some of their preconceptions about how people would react to different price points.