How to pay the Creator Economy's 'middle-class'

The Pareto Principle at Play

It’s evident by now that you need a lot of hard work and consistency to be ‘successful’ on creator platforms today.

In fact, of 50 million creators, only 4% earn a living from their craft.

1% of Twitch’s streamers earn 60% of the payouts.

1% of Gumroad’s creators earn 60% of the payouts.

The top 1% of podcast creators get 99% of the downloads.

Li Jin’s article, The Creator Economy Needs a Middle Class, discusses the benefits of more inclusive systems where creators with smaller, more focused, or niched audiences, can get in on some of the action.

Also, brands and advertisers are looking for creators with smaller audiences based on the belief that there is more authenticity and more trust - the dichotomy being authenticity dilutes as the audience size grows.

Most of us have experienced the sense of distance that becomes apparent as the creator’s audience grows and an increasing number of seemingly random products intrude into otherwise natural content.

A new model

The use of nano and micro-influencers is an emerging trend - the theory is an advertiser would rather target several authentic creators with smaller audiences than put all their eggs in one basket.

So what would be a suitable model for disbursing a proportionate share of the proceeds to such creators?

Assuming we have a network of creators, I decided we need a way to score creators by various metrics that represent their reach and engagement across relevant platforms.

An advertiser would then be able to filter the creators by category or subject and then weight their scores by how important they are to the advertiser.

This example uses a 0-100, 0 being not important at all and 100 is extremely important.

For example, they could say a large number of YouTube subscribers is very important, or the audience size doesn’t matter as long as the overall watch time is over a certain threshold.

Or, they could specify that a minimal YouTube following is okay as long as there is meaningful engagement on Twitter.

A more balanced distribution

We’d end up with a proportionate distribution across the number of creators, and their respective scores (after weighting) would determine who earned what from the budget (in this example, $25,000).

Creator 6 in the example earns the most as they have the largest TikTok following and a good Like ratio on the platform - which was most important to the advertiser.

One positive is a creator who has a relatively small audience but has doubled down on a specific niche on a particular platform has greater monetization potential instead of the entire budget going to a single creator.

Another positive is that the advertiser can tweak the importance of specific tactics knowing that several creators will be appropriately compensated.

A creator who has spent a long time building a huge audience would probably wonder why they’d be willing to split proceeds with more creators - which is, of course, a valid point - but it would be interesting to know if they would ultimately earn more through a greater number of smaller deals or fewer higher-paying deals.

A counter-argument

John Bardos offers a complete counter to this in his article There Will Never be a Creator Middle Class and Why That’s Good. There is a reason that the world works like this after all; creators have to play by market rules.

But ‘market rules’ do change, and often - and most creators on these platforms are ultimately beholden to the whim of the advertiser.

Considerations

There are a few considerations to this model that could wildly affect its accuracy:

• You would still need a system to ensure the creator promoted the product or service to the advertiser’s satisfaction.
• You would need an ongoing method to measure the metrics for each creator.
• Ideally, you would have some analysis of potential earnings comparing a high number of lower-paying deals over a low number of high-paying deals.