Flywheel 2.0 - Accelerating Consumption

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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:

1. Consumption driver: a feature that serves up ready-made content to the user (e.g. a newsfeed or digest email)
2. Verbs: the core actions you want the reader to take when consuming content (e.g. like, upvote, downvote, share, retweet, comment, etc)
3. Personalization: data infrastructure and product features that “push” new content to the user (Youtube, Instagram, etc. with newsfeed personalization and “discovery” features)
4. Notifications: feedback loops to the user based on their consumption and verb interactions (e.g. follow-on answers to Quora questions, “AMA session is now live” on Reddit)

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.

• What consumption drivers are appropriate for my product? Quora was well known for the weekly digest email that summarized the top answers each week. This digest worked fantastically well for Quora. However, if LinkedIn sent me a weekly digest of newsfeed posts, I would likely be annoyed as a user.
• What verbs should your product use and is there an opportunity for verb innovation? Not every online community needs the same set of verbs. Some are canonical, such as “comment”. But how else can you use verbs? Facebook innovated with different emotion-based verbs to allow for a more nuanced array of expressions that the “like” button didn’t support. What will yours be?
• How will you personalize the experience and close the loop with users? Will you give them a “discover” tab that pushes new material to the user? What data can you uniquely capture that will supercharge your personalization efforts? Before sending your user more notifications just for the hell of it, make sure it will be interesting and relevant. The average person is exhausted from notification overload. Make sure you close the loop with them intelligently and emphasize quality over quantity.

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.

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Flywheel 3.0 - Accelerating Creation

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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.

1. Detect: some way of identifying when new, high-quality content has been created (e.g. machine-learning-based signal detection, such as a post on Medium that rapidly receives “claps” within X minutes of being posted)
2. Distribute: a few mechanisms you can use to boost distribution for high-quality content (e.g. highlighted content in Medium’s weekly digest email or homepage)
3. Feedback: some way to notify the creator of high-quality content that their content has received a lot of distribution (e.g. in-app notifications like “Your post has been viewed 10,000 times this week”)
4. Streak: a strategy for encouraging the best content creators to create more content, i.e. put them on a “streak” (e.g. Quora’s Top Writers program, Reddit AMAs, Yelp Elite Squad)

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:

• What tools/systems can I put in place to detect when someone creates amazing content? In the early days at Quora, we built an internal tool into the web app interface that allowed employees to “flag” when they saw what they considered to be an amazing answer. The flagged answers would then end up in a pool of candidates we would consider including in the weekly digest emails, which drove nearly 50% of our weekly active users in the first couple of years of the business.
• How will I quickly distribute great content and give it a boost? Assuming you have some method(s) for identifying what “great” is, you’ll want to run it up the flagpole for everyone to see. Common examples today include ML-based systems that look for the velocity of user interactions (e.g. likes) and depth of engagement (e.g. comment rate), as well as a host of other factors, to increase the prominence of the content within a newsfeed.
• How will I let the user know that they created something special? Once the content has been bumped, it’s time to share the news with the creator. Common examples include verb counts (i.e. # of likes, Retweets on Twitter). In Quora’s case, we also gave users access to a metrics dashboard that showed the data on all of the content they had ever created.
• How do I make it easy for the user to “go on a streak” and continue creating content now that they have the wind in their sails? This is an essential yet difficult milestone. Some products don’t lend themselves to streaking behavior as much as others do. Quora, for example, will show you “questions you might answer” as a way to encourage users to create more content. Other products don’t have explicit product mechanisms for doing so. But this is an area worth thinking through at length, since more content created means more kinetic energy released in the 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.

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Pulling the Levers

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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:

• A = the average number of new pieces of content created per user per month (e.g. 2 videos per user per month on Youtube)
• B = average number of visits generated per piece of content per month (e.g. 2 visits per month to each Youtube video)
• C = signup conversion rate from the visits generated by the new content created by each user (e.g. 0.2% conversion rate to signup from SEO traffic)

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:

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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.

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