On designing the social user experience

One of the themes that came up a lot was the idea of the growth team finding a leading indicator of a user who would turn into an engaged user later on. The growth team would then focus on optimizing for that metric. […]

Characteristics of leading indicator metrics

The various leading indicators fit into three categories:
• Network density: friend or following connections made in a time frame
• Content added: files added to a Dropbox folder
• Visit frequency: D1 retention

[…]

Chamath Palihapitiya, who used to run Facebook’s growth team, spoke about how his growth team discovered the “7 friends in 10 days” leading indicator. He said that they looked at cohorts of users that became engaged, and cohorts of users that did not become engaged, and the pattern that emerged was that the engaged cohorts had hit at least 7 friends within 10 days of signing up.

— Growth hacking: leading indicators of engaged users

distribution is much harder on mobile than web and we see a lot of mobile first startups getting stuck in the transition from successful product to large user base. strong product market fit is no longer enough to get to a large user base. you need to master the “download app, use app, keep using app, put it on your home screen” flow and that is a hard one to master.

— What Has Changed

One fairly common “law of web/mobile physics” is the ratio of registered users/downloads to monthly actives, daily actives, and max concurrent users (for services that have a real time component to them).

I call this ratio 30/10/10 and so many services that we see exhibit it within a few percentage points here and there. Here’s how it works:

30% of the registered users or number of downloads (if its a mobile app) will use the service each month

10% of the registered users or number of downloads (if its a mobile app) will use the service each day

the max number of concurrent users of a real-time service will be 10% of the number of daily users

We see these ratios across social web apps, social mobile apps, games, music services, and many other consumer web and mobile services.

— 30/10/10

To keep our attention, products must have a degree of novelty. Without variability, users figure out the patterns and tire of the experience.

[…]

Some products are built to be infinitely variable. These products involve rewards users find novel for long periods of time. For example, few things are more fascinating to people than other people; we always want to know more. Whether communicating with loved-ones or keeping up with celebrity gossip, we love the infinite possibilities endemic to the human experience.

— Where Have The Users Gone?

At the heart of the desire engine is a powerful cognitive quirk described by B.F. Skinner in the 1950s, called a variable schedule of rewards. Skinner observed that lab mice responded most voraciously to random rewards. The mice would press a lever and sometimes they’d get a small treat, other times a large treat, and other times nothing at all. Unlike the mice that received the same treat every time, the mice that received variable rewards seemed to press the lever compulsively.

Humans, like the mice in Skinner’s box, crave predictability and struggle to find patterns, even when none exist. Variability is the brain’s cognitive nemesis and our minds make deduction of cause and effect a priority over other functions like self-control and moderation.

If you’ve ever asked someone a question while he or she was engrossed in a video game, only to receive a mumbled “sure, ok, whatever,” you’ve seen this mental state. Players will agree to almost anything to get rid of distraction and keep playing. Variable rewards seem to keep the brain occupied, removing its defenses and providing an opportunity to plant the seeds of new habits.

Bizarrely, we perceive this trance-like state as fun. This is because our brains are wired to search endlessly for the next reward, never satisfied. Recent neuroscience has revealed that our dopamine system works not to provide us with rewards for our efforts, but to keep us searching by inducing a semi-stressful response we call desire.

— Want To Hook Your Users? Drive Them Crazy.

The infinite scroll is interaction design’s answer to our penchant for endlessly searching for novelty.

[…]

Nothing holds our attention better than the unknown. The things that captivate, engross, and entertain us, all have an element of surprise. Our brains can’t get enough of trying to predict what’s next and our dopamine system kicks into high-gear when we’re waiting to know if our team will make the field goal, how the dice will land, or how the movie plot ends. Like a loose slot machine, the infinite scroll gives users fast access to variable rewards.

Interestingly, our brain isn’t wired to seek pleasure alone. In fact, much of our motivation comes from alleviating the pain of desire. Dopamine levels spike when we’re just about to find reward and plummet after we receive it. To get us to do just about anything, evolution uses this chemical cascade to induce anticipation, motivation, and finally pain alleviation. Somehow we call this endless merry-go-round “fun.”

Few other methods for displaying information produce the curiosity to see what’s next like the infinite scroll.

— Infinite Scroll: The Web’s Slot Machine

Once someone posted in a group, others would feel comfortable enough to reply and start using it as a space for sharing and conversation. But trying to get people to make that first post was tricky.

Groups for Schools: How Facebook Solved 3 Big Challenges Through Design

We’re moving into an age where habits matter more than virality… If you can’t retain users, if you can’t engage users, growth is not enough.

Even without the viral potential you can still get big slow … through engagement and retention and habits.

Nir Eyal: Behavior By Design talk at Innovation Endeavors

When you talk about data-driven decision making it’s not necessarily about the everyday metrics. It’s about trying to figure out behavior in the data. And that can come through looking at actual data in your databases, or it can come through interviewing users and trying to figure out, “This person’s a really engaged user of my product…. Why?”

Product Design - Dave Morin // Startup Elements

you could say that growth is broken down into a few fundamental questions:

1. How do I increase the rate of acquisition i.e. get more signups?
2. What can I do to activate as many users as quickly as possible in their first ‘N’ days?
3. What are the levers for engagement and retention and how can I pull them?
4. How do I bring churned users back into the system to “resurrect” them from the dead?

What are some decisions taken by the “Growth team” at Facebook that helped Facebook reach 500 million users?

Traditional methods of usability lab testing won’t cut it anymore–we need to learn more about our users, their relationships, and even relinquish design control to them.

 […]

Real Users Don’t Do Tasks:
Rethinking User Research for the Social Web 

Usability testing isn’t telling us what we need to know about the social web

• Tasks aren’t what you think
• Satisfaction is correlated with control, engagement; not on task completion

Interaction 12 Sketchnotes: Day 3
(Presentation by @danachis, Sketchnotes by @kryshiggins)

Engagement & Retention

Biggest Challenges:

• Although testing and iterating is how social devs have found success on Facebook, rapid iteration is not so easy or fluid on mobile platforms like iOS. It can take from three days to two weeks to receive approval for each new version from the App Store, making it difficult to fine-tune to optimize on the fly.

• Many users don’t update their apps regularly, resulting in outdated, or “dirty,” data from the combination of “old” and “new” app version users. This makes it challenging to iterate and improve your app based on user data without being misled, unless your data tools easily filter users by app version.

Biggest Opportunities:

• Design the app to use data-driven back-end systems to control user experience in-app when a connection is available on the device. This way, developers can dynamically tweak play balance, economy balance, etc., and then perform split-testing to further optimize customer economics.

• Use an analytics solution that allows filtering data by app version, location and device type (for Android). This is very important in addressing the dirty data issue, and in determining which platforms and devices in which to commit time, effort and resources.

— Applying Lessons Learned on Facebook to Mobile App Development

All of this is shored up by the tremendous analytics [Twitter] has. You have ways of deeply understanding the varieties of engagement and the levels of engagement that people have … And those folks are embedded with each of the teams, so each of the teams can really understand whats going on.

— Mark Trammell & Jesse James Garrett | Creating Engagement on Twitter

The growth team was created in 2007 and was the first example of data driven design at Facebook … A simple visual design change on registration page resulted in 9M more people per year joining. 3% increase. For growth this is a big deal.
…

A few months ago (2011), Facebook created a team called engagement – modeled after the growth team. Thought it would operate the same way. First tried quantifying success by looking at number of read/writes. One of the early ideas was comment liking. Saw an increase of writes in the system 7% up when this feature was rolled out.

Though writes went up 7% it was the same people creating writes. 85% of reads/writes of Facebooks are generated by 20% of users. Same people were doing the activity. Had to go back and reset metrics for the engagement team.

Current metric for engagement at Facebook: L6/7 number of users that come back to Facebook 6 out of 7 days a week.

— Web App Masters: Data & Design at Facebook