Blended Teaching: Introducing our Revenue Attribution Model

Blended Teaching was born from a simple idea: paper textbooks are based on technology from the 15th century, and are long overdue for an upgrade. Since we launched last August, we’ve uploaded over 69 hours of videos to our digital Classbooks, which have been viewed for over half a million minutes. 

But the content was not the only thing that needed upgrading.

If you wanted to write your own textbook, you would have to: find a publisher, spend years writing it, spend more time waiting for it to be published, and then receive barely anything for each copy that was sold. (And that’s assuming that there wasn’t already an established textbook that everybody was already using.) 

We wanted to create a better way for leading educators to share their knowledge, that’s not only professional, engaging and loved by students, but also fairly compensated.  

And so today, we’re excited to take the next steps to making this a reality, by releasing the first version of our revenue attribution model. 

The Ideal Model

So let’s start with the model we’re working towards. 

A student pays for access to the platform, either with a personal subscription or via their university. This revenue is then distributed to the content creators (i.e. probably you, if you are reading this) in proportion to the content the student has watched, pro-rata every month. 

(It’s kinda like how the Spotify model works, but with numbers that don’t typically round to zero.) 

So let’s say a learner has a typical semester (four-month) subscription and pay $103. (They may not really pay $103, but it’s a nice number for this example.) Of that $103, Blended Teaching can collect $100 (as $3 is taken by the 3rd-party payment tool used to collect it). This $100 is what we count as the revenue into the business, and is then split equally into four parts ($25 for each month), and then divided according to the content they watch: 

• Month one: they watch 10 hours of Module A. In this case, 100% of the $25 is allocated to the content creator of Module A. The content creator will then receive their revenue share percentage of that $25. For this example, let’s say that the content creator gets 10% of any allocated revenues. That would be 10% of $25, amounting to $2.50. This is only from one student for one month, so the numbers get bigger as we add more students!
• Month two: they watch 5 hours of Module A (10%), and 45 hours of Module B (90%). The $25 of income from that student for that month is allocated 10% (or $2.50) to the Module A content creator and 90% ($22.50) to the Module B content creator. If both content creators have a 10% revenue share percentage, then the content creator of Module A receives $0.25 and the content creator of Module B receives $2.25 for the month from that one student.
• Month three: they watch 5 hours of Module B (100%). The $25 of income from that student is allocated 100% to the content creator of Module B. With a 10% revenue share, they would receive $2.50.
• Month four: they’re finished, so they watch nothing (0%). In this case, no revenue is allocated to a content creator as no content was consumed.

Obviously, the pro-rata approach means that the proportions are different than if we wait until the end of their subscription, and sum over what they watched over all four months. (In the example above, Educator A would get less, while Educator B would get more.) There will also be some averaging for subscriptions that do not align perfectly with the calendar months. However, everyone will be affected equally by these effects – and we’d much rather pay you as frequently as possible. 

(In the case of university agreements, we pay you when we’re paid.) 

Version 1.0

We’ve laid out the end goal for our revenue attribution model, but we can’t fully implement this vision today, due to limitations in the tools that we’re using. So instead of counting what’s watched on a learner-by-learner basis, we use the total minutes watched per module, across all learners, as reported by our third-party provider LearnWorlds. This has some caveats: 

• It does not include ‘flat’ module pages, which are not hosted in LearnWorlds, so we are counting these as 0 for now. (If you’re wondering if you have any flat pages, don't worry: you would already know as we would have discussed it together at length.) We are setting up a way to measure the time spent per flat page module within Q2 / Q3.
• We don’t know how long students spend on quizzes, so we are counting these as 0 for now. We are building our own quizzing platform and will be able to measure that accurately when it is delivered in Q4 2023.
• The content usage calculation includes ‘free’ learners, who have not paid to use the platform.

These imperfections in the calculation mechanism impact all content creators in a similar way, so the net result is that we will have small skews, where some educators will receive a little more or a little less than the ideal system in any given month, but over time it should even out for all relatively fairly. We think this is a good solution until we have built our own system in Q4 2023.

In true community fashion, we’d much rather start getting money out to our content creators now knowing that there are minor imperfections in the attribution calculations, than wait a year until we can get it ‘perfect’. We learned that in “Time Value of Money” 🙂

(If you’re a nerd like me and don’t mind maths* equations, you can see how it all works in this document.)

Working Towards the Ideal Model

We’re working on improving the accuracy over time. Here are the steps on our roadmap: 

• Add tracking to ‘flat’ module pages. 

• In general, we’re going to improve progress tracking across Classbooks. At the moment, students can see what chapters they’ve completed – but we’re going to extend this to include modules and Classbooks too.) 

• Develop our own quiz system, starting with multiple-choice quizzes. This will come with many benefits, but will also allow us to track time spent more accurately.

• Finally, start counting time spent on a learner-by-learner basis.