Now that you’ve warmed up a bit with the 1.0 flywheel, it’s time to tackle the next obstacle, which is to craft another flywheel that’s specific to the consumption piece of the community experience.
Users of your community won’t Kobayashi content without some provocation. You’ll most likely have to coax them into consuming a lot of content in your community through clever product design, a buffet of high-quality content, and other mechanisms that you, the founder, must figure out.
Online communities that have low flywheel momentum are those that begin with very poor information discovery. Take a look at eBay’s community product. I’m not sure it’s changed much over the last 10 years. It looks much like the original forums and message boards dating back to the golden age of Yahoo Groups.
You’ll notice that a modern artifact of today’s online communities is missing—a newsfeed. Every major online community or social network is now oriented around a feed. Why? Because it gets people to engage as a consumer of information at an order of magnitude greater than alternatives. It makes us all Kobayashi’s of content. The momentum in eBay’s flywheel is minuscule compared to the momentum in TikTok’s flywheel because TikTok has a much more enticing consumption flywheel due to a few brilliant product design choices, such as allowing you to consume a feed of highly entertaining videos prior to registering for the product.
Yet there are nuanced decisions that need to be made as to how your product will drive a consumption loop where users come back to consume over and over again. It’s time to pick up your paper and pencil again.
Just like there are a few “atomic units” that make up a 1.0 flywheel, I believe there are a few building blocks to a consumption flywheel as well, which include:
In the below example, I’ve diagramed what a consumption loop might look like for a product like Quora. A newsfeed and weekly digest emails are the primary consumption drivers. The user is then given a selection of product verbs as the core content interaction paradigm, such as upvote, downvote, comment, or share. That data is used to enhance personalization back into the newsfeed, weekly digest emails, and other one-off email notifications.
What’s important is that you map out what the consumption flywheel might look like for your product and that you ponder the following questions while designing it.
Assuming you’ve done a quality job at designing and implementing the consumption flywheel, user engagement should increase. That may reveal itself in an uptick in weekly active users (WAUs) or daily active users (DAUs).
To go back to the flywheel physics, the potential energy within your community increases as a byproduct of enhancing the consumption loop. And with higher potential energy comes another wonderful side effect: high-frequency consumers become content creators. Don’t put the pencil and paper down yet as that’s the next flywheel to design.
The third flywheel is the most important. A thriving online community can’t be built without a healthy consumption flywheel. However, a stellar consumption flywheel can’t be built without a high rate of quality content being created. That’s why it’s the most important flywheel—yet, it is also the most difficult to create as it requires more secret sauce (i.e. innovative thinking) than a consumption flywheel.
Similar to the other flywheels, I believe that this flywheel has a few common building blocks worth understanding.
The below diagram captures what this content creation flywheel might look like. Just as you would with a consumption flywheel, you have to take a step back and ask yourself a few key questions when designing a creation flywheel:
Once the creation flywheel kicks in, you can expect to have momentum as potential energy (consuming users) is converted into kinetic energy (creating users). As lots of new content is generated, acquisition channels accelerate, such as SEO, social sharing, and so on.
With the flywheel designs in place, you can instrument each step in the flywheel to understand where your flywheel might not be performing. In the example below, I may label certain parts of the flywheel with conversion metrics to benchmark how it’s performing. This approach would allow me to diagnose where I perceive there to be weaknesses in my flywheel(s) and come up with a plan of attack for improving each sequence.
The green items would indicate which rates I feel are performing well, whereas the yellow and red items likely require some attention and could be slowing the entire system down.
In the above example, the product has only a 13% open rate for the digest email. I should revisit the content I’m putting in that email and the frequency that I’m sending it. Something is clearly wrong since that’s a very low open rate. Consequently, the digest email isn’t contributing meaningfully to the consumption flywheel, so I may need to find alternatives to doing so.
I would also note the very low conversion rate to becoming a content creator. If only 3% of users that read content also create content, there must be something catastrophically wrong with the user experience or the core product value. Or, maybe that’s okay? Youtube is powered mostly by super-creators. They don’t have a high proportion of users that create videos—most users are consumers. But if I’m Reddit and only 3% of users comment on threads or create new threads, that could be cause for concern. That low of a rate may lead me to believe that most new threads are starting off with a low-quality prompt.
Similarly, I would be concerned with the low rate of visits per piece of content per month. Maybe I haven’t optimized for SEO or social sharing? Maybe I have a huge long-tail of content that isn’t interesting enough to warrant any traffic? That’s certainly the case with Yahoo Answers.
Now that you have the flywheels designed and metrics implemented, you’ll want to convert this into a basic model that captures how your product grows. Translate each conversion rate into a variable in a growth equation. Here’s a very simple example based on the above flywheel and one that we tinkered with at Quora in 2011:
To keep it simple for now, let’s use three variables in the flywheel:
Work with your local friendly data scientist, and they’ll produce a growth equation for you. Here’s a basic example based on the flywheel model:
From a model like this, you can project a rate of growth. It may look something like the graph below, which projects the weekly growth rate of total users:
What’s great about using this flywheel design and measuring approach is that you can “pull levers” in the model and find where the model is most sensitive in the long run or at a given point in time.
For example, if you were to increase the average number of visits per month per piece of content from 2 to 2.5 viaSEO improvements for example, you can project the impact on overall growth. And if you modeled that effect against increasing the conversion rate to signup from 0.2% to 1.%, you may find that one lever implies a greater net effect on growth than another. Or, that optimization in one part of the flywheel may create a larger near-term bump, but have a smaller long-term effect.
That’s how you go about designing a content flywheel, instrumenting measurement, and developing crude growth models to understand what the drivers in your flywheel may be. It’s not a perfect science, but it isn’t meant to be. However, it is a very effective approach to systematically architecting and manipulating your growth flywheel to give your online community the best chance to thrive.
Next, we’ll dive into the classic chicken-and-egg problem that online communities face. How do you get people to signup for—‚and engage in your community— when it currently has little-to-no users and engagement? A flywheel doesn’t start on its own. It needs an initial thrust, which is what the next section is all about.
Solving the Coldstart Problem