How we signed up 10,000 paying subscribers in just over a month

For about a year, I’ve been working with the “Catalyst” team at Envato to design, develop and launch a compelling new subscription for graphic designers, marketers & creatives. Our new product, Envato Elements, beta launched on August 1 and attracted around 10,000 paying subscribers in just over 1 month. That’s a very fast start for a new subscription!

Reflecting on that experience, we identified five (5) factors that were critical to this success. See the article on Medium to find out what they are and why they matter.

Read the full article “How we signed up 10,000 paying subscribers in just over a month”

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Pioneering Subscriber Share for Contributor Payouts

Introduction

Most subscription services that use third-party content – such as music streaming services – use the so-called “big pool” method to work out how much money to pay individual contributors. That method has come under fire for being unfair to indie artists and for being vulnerable to click fraud. But it’s far and away the simplest and most common method:  i.e. put all the money to be shared into a big pool, and divide it based on the number of uses.

For contributor payouts on Envato’s new subscription for digital assets – Envato Elements – we chose not to use big pool, but instead a novel method called ‘subscriber share‘. We think it’s much fairer to contributors, and should also lead to a more diverse and interesting content library for subscribers.

Exploring subscriber share

I first became aware of this approach in a great article by Sharky Laguana called How to Make Streaming Royalties Fair(er).  He kindly responded to my email, answering various questions and explaining that there was considerable interest in his proposed method. However, there did not seem to be any large-scale implementation by a major subscription service, so for many it remained simply a good idea but not really a practical option.

An Associate Professor, Arnt Maasø, also wrote about the method (calling it ‘user centric’ instead) in his article A Better Way to Cut a Cake and Eat It Too. Interestingly, he published a detailed paper that analyzed data from two months of data for a Norwegian music streaming service (WiMP). As far as I can tell, that’s the only analysis based on real-world data looking at the impact of big pool versus subscriber share on contributor earnings (for now!)

After learning as much as we could about the proposed approach, we explored some scenarios of our own. It quickly became clear that the subscriber share method had significant advantages over big pool. It was significantly fairer, more resistant to fraud, and aligned everyone’s incentives. So we decided that we wanted to use it for Envato Elements, if it was feasible in practice.

A proof of concept

Before committing to using subscriber share, we had to be confident we could implement it at scale. After all, our new unlimited download subscription was expected to rapidly attract tens of thousands of subscribers. After a few half-joking suggestions that “we’ll just work it out in a spreadsheet”, I eventually set about building a payout calculator using the statistical programming language R (often used by data scientists with large datasets)

My first step though was to map out an algorithm to implement it, based on data we’d have about subscribers, contributors and usage events. This was harder than it sounds, and it quickly became clear it was a good plan to use a sophisticated tool like R, given the nature of the computations required.

With the algorithm in hand, we moved on to a proof of concept using sample data generated for this purpose (since we didn’t have a live subscription service yet!). After reassuring myself, my colleagues, and then finally our finance department, we finally committed to using subscriber share for Envato Elements contributor payouts.

Implementing subscriber share

The payout calculator has evolved significantly since that initial proof of concept, with several major re-factoring efforts, as well as hooking it up to use production data. We enlisted the support of our finance development team for unit testing of the functions involved, and also conducted integration tests by manually calculating (painfully!) the expected output from a given set of input data.  Finally, we wrapped it all up into an interactive application using the Shiny library for R, so that a non-technical user can select the time frame for analysis, the data inputs, and a few other relevant parameters.

Coding aside, our implementation also involved a lot of effort around communicating what subscriber share is, why it matters, and how it works.

This is vital for our contributors, since it plays such a large role in determining their earnings from participating in our subscription service. But it’s also important for subscribers, helping them understand how their behaviour influences contributor earnings and encouraging them to behave responsibly with their unlimited download access.

This educational effort for subscriber share includes the following resources:

We’re also making subscriber share a big part of our PR efforts in launching the new subscription. We think it’s an important part of our offering, as well as a newsworthy innovation in its own right.

What’s next?

Our new subscription – Envato Elements – had its beta launch on August 1st, and official public launch on August 29th. At the time of writing, it’s still early days so we haven’t yet done a payout run. But in the coming months we’ll look very carefully at the actual data on subscriber usage and contributor payouts.

Ultimately, I’d love to prepare a paper that systematically examines the impact of our choice to use subscriber share, and compares the distribution of payouts for subscriber share versus big pool with real-world data. I feel that this would be a useful contribution to the world’s understanding of the subscriber share method, and perhaps encourage other subscriptions (such as those in the music streaming industry) to consider adopting the method.

In the meantime, I encourage you to go read the article on Medium


 

Overcoming the ROI roadblock: how to invest in innovative projects despite the uncertainty

“It’s an interesting project but do we have a strong enough business case? Let’s run some ROI calculations and see how it stacks up..”

This sort of eminently reasonable comment often sounds the death-knell for many a worthwhile innovation, despite the best intentions of all involved. Let’s have a closer look at why this is the case, why it’s a problem, and what you can do about it.

The siren song of ROI: why is it so compelling?

Every organization has a limited set of resources, and so it is only natural and prudent to seek a good return on any investment that you make. The heart of the issue is how we define what a “good” return on investment (ROI) actually is.

Commonly ROI is measured using well-established financial tools by looking at the present-value of the expected returns and comparing them to the costs.

However, this approach makes a number of critical assumptions:

  • We can accurately predict the costs that will be incurred
  • We can accurately predict revenue, asset sales, and other such returns
  • We can accurately predict the timing of all costs and returns
  • We understand our alternative opportunities and can apply an appropriate interest rate to discount future costs & returns back into what they are worth today.

In the case of a well-established business considering projects that it understands well from past experiences, these assumptions often hold true. There’ll still be some surprises but the actual ROI probably won’t differ too much from what is expected.

In such circumstances, using ROI assessments to choose which projects to fund and which to avoid is highly effective, which is why ROI has become a widely used approach.

Innovation killer: when is ROI the wrong tool?

The danger of having an effective and proven tool is that we tend to apply it to every situation, regardless of suitability. As a result, ROI is often applied where the underlying assumptions are clearly invalid, such as in entrepreneurial activities & innovative projects where costs, returns, timing and alternatives cannot be predicted with any meaningful level of accuracy.

(Note: another scenario where discounted cash flow and ROI analyses tend to give faulty outcomes is when failing to act has serious consequences – i.e. where “do nothing” is equivalent to “get left behind” – because the core business is declining or is vulnerable to erosion by competitors. For more on this, I strongly recommend reading Christensen, Kaufman & Shih’s HBR article “Innovation killers: How financial tools destroy your capacity to do new things“. )

Think of a project with potentially very high returns but also considerable uncertainty. Maybe you’re introducing a new product, expanding into an unfamiliar region, investing in a new technology, significantly changing your value proposition, or setting up a new distribution channel. You have good reasons for thinking that the return is worth the risk, but the outcome is far from guaranteed and the past is not a good predictor of the future.

When this sort of project comes up against the ROI hurdle, it struggles to win support because the future returns are uncertain, they probably won’t contribute to the next budget year, and there is a lack of robust “evidence” from past experience. So despite all its promise, the innovative project ends up being inherently hard for people to justify on traditional ROI metrics.

“Established enterprises struggle whenever they are required to reallocate resources away from established lines of business in order to fund the growth of new lines of business. The period of greatest pain is when the new business is big enough to demand a material amount of resource but not yet big enough to create a material return.” – Geoffrey Moore

As a result, innovative projects consistently lose out to incremental improvements and scaling opportunities.  The traditional ROI approach has a bias towards lower but more certain outcomes, and – over time – this kills the capacity of an organization to innovate and achieve above-market growth, despite the best intentions of all involved.

A better way to assess & manage innovative projects

When the future is uncertain, it makes good sense to focus on learning. No amount of planning & analysis can make up for a genuine lack of information & insight. So rather than building precise-looking spreadsheets that ultimately disappoint, why not focus instead on identifying and testing (in the real world) the key assumptions that will make or break your project.

As the saying goes, it’s better to be roughly right than precisely wrong.

One of my favourite approaches for managing investments in uncertain environments is Discovery Driven Growth (DDG), a powerful framework by Rita McGrath and Ian MacMillan. They suggest shifting from the conventional management focus of “making your numbers & hitting projections” to instead emphasize “learning as much as you can for the least possible cost”.

By focusing on key assumptions and reducing the cost of failure (rather than the frequency), you can systematically shrink uncertainty and limit your risk to the funds needed to reach the next learning milestone. All going well, you’ll eventually know enough to invest confidently to scale up the project. On the other hand, if key assumptions turn out to be false and the opportunity is flawed, you can simply disengage from the project, capture & share the insights for future use, pat everyone on the back, and redeploy people to the next opportunity.

Here are a few practical suggestions for helping your organization pursue innovative projects:

  • Frame the challenge: what growth is big enough to matter? Where is your strategic focus?
  • Set up small, focused projects, with funding contingent on validating key assumptions.
  • Reward the right behaviours: learning at least cost, adapting based on insights, etc.
  • Eliminate fear: growth initiatives should be career-enhancing, even if discontinued.
  • Create time to think about opportunities, free from the pressures of business-as-usual
  • …and of course, don’t apply conventional ROI tools to projects with high uncertainty!

Recommended reading:

  • Innovation Killers: How Financial Tools Destroy Your Capacity to Do New Things – a great HBR article from 2008 by Clayton Christensen, Stephen Kaufman, and Willy Shih that explores in depth how three specific issues (NPV/DCF analysis;  how fixed & sunk costs are handled; and a focus on earnings per share) conspire to derail successful innovation.
  • Learning to Escape the ROI Trap  – A very practical perspective with examples from Chuck Hollis, chief strategist at VMWare and previously at EMC for many years.  He argues that “because we don’t teach people how to evaluate imprecise opportunities, they fall back on tools that expect precision. And we end up with poor outcomes because we chose the wrong tools.” Chuck provides useful tips about reframing ROI in terms of the “risk of ignoring”.
  • Discovery Driven Growth – a powerful approach from Rita McGrath  & Ian McMillan based on the idea that capitalizing on uncertain opportunities requires a different mindset than pursuing business-as-usual growth in your core business. Some of the concepts here – e.g. investing small amounts of money to get useful information and make ‘roughly right’ decisions – have been widely embraced in the lean startup movement but are still not nearly common enough in more established businesses seeking innovation.
  • Escape Velocity – a fascinating book by Geoffrey Moore about how to escape the gravitational pull of business as usual and invest in breakthrough innovation.

Your perspective?

How does your organization support & fund innovative growth projects? What advice would you offer people with innovative projects that are stumbling at the ROI hurdle due to uncertainty?