User Inactivity: One Reason and Another

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Last week Fred Wilson posted The Difference Between Total Users and Active Users, which proposed that web services focus on active users rather than obsessing over the many, many inactive user accounts that litter every even slightly successful service. He noted that:

It is not a problem for a service to have a large group of non-active users if they have a large group of active users. It’s the latter group that you need to focus on. Over time, I’ve learned that many non-active users become active for one reason or another. But they don’t become active by focusing on them. They become active because you focus on the successful users and make them even more successful. [Emphasis mine.]

While I don’t by any means believe Fred was suggesting that we shrug our collective shoulders and say “fuck ’em, they’ll turn active if they feel like it. Or not. Whatever…” I’ll hit the point where I think he glossed over some real work before the point where I agree with him.

Once a user has registered with your service, that person needs to do something. If you’re clever or lucky enough to have a registration path that (painlessly) catches people as they’re already using your service to fulfill their heart’s desire, you’re already in good shape; in many cases, however, you’re dealing with people for whom registration is a first step, and you need to (a) make it very, very clear what they do next and (b) make that action useful to them as an individual user.

Proposition One: We’re All Selfish Bastards

With so many sites including—or centered on—”social” components, it’s easy to create an onboarding process that has new users working to benefit the service or community rather than themselves. While I’m terrified to think about some of the product meetings happening post Fred’s “20 seconds tweet,” it’s still important to acknowledge that we’re all selfish to some degree: if a web service isn’t really useful to you until you’ve been working with it for days or weeks, what are the odds you’ll keep with it until you hit that turning point?

To be fair, though, even services that offer both clear post-registration direction and immediate benefit to the individual have users…a lot of users…who fade into inactivity almost immediately. It’s hard to get these people going precisely because you’ve done a pretty good job at showing them what you have to offer them.

Your “hey, we haven’t seen you in a while” emails or text messages will prod a percentage back into activity, but many of them just didn’t click right now with what you’re doing, and there’s no one single reason for it. And this leads us into the area where I very much agree with Fred:

Proposition Two: Even Selfish Bastards Have Friends

If you focus on your inactive users, you’re going to find a dozen different possible reasons for their inactivity for every ten users you analyze; if you ask them why they’re inactive, eight will ignore you…and the other two will give you those same dozen reasons plus a couple of others for good measure.

But, as I said, even selfish bastards have friends, and some of their friends may be among your active users. If you make those friends increasingly happy with what you provide, they will (as Fred noted) become your best advocates. They will, little by little, convince their lapsed brethren to give your service another try.

Twitter, in my experience, is the reference implementation of this phenomenon. Many people (myself included) created an account, didn’t see the point, and only transformed into active users weeks or months later, as others espoused the value of the service. That’s not to say that I necessarily find it useful in the same way as the people who were pitching it to me, but rather that their enthusiasm was sufficient to carry me through to the point where I found my own selfish use for it.

In most situations I think that you learn more from digging into your failures than from examining your successes, but user activity is one curious case where extra attention to what you’re already doing right has a far bigger payoff.

Follower Count: Pageviews 2.0

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On the Hype Machine’s Twitter Music Chart

The Hype Machine crew recently released a Twitter Music Chart. First off, it’s pretty excellent:

We monitor Twitter for links pointing to tracks on the Hype Machine. We then give each of those tweets a number of points based on the number of followers (and the ratio of friends & followers) that person has. Finally, we add up all the points and figure out which tracks are tweeted by either the most influential twitter users, or by the largest group. Simple.

I like it because it’s simple, and because it offers a new view on the relative popularity of the music that appears on hypem.com; maybe all those twittered links to Hype Machine tracks are of no particular significance, but it’s well worth a little digging to see whether there’s anything there in that data. And I particularly like how open Anthony and team are about how the charts are built: there’s no secrecy about how and what they’re measuring.

What I like less is what they’re measuring. It’s a sweet start, but I think that having Twitter follower count and ratio at the center ends up a weakness: friend/follower counts are the pageviews of the social Web.

I will admit that these “pageviews” can be a very useful metric, and—probably more significant—that we haven’t worked out many other metrics for the social aspects of the Web, but nevertheless pageviews for Web sites begat site visits, unique visitors, visit frequency, average time on site, goal conversions, and any number of other metrics precisely because by themselves pageviews didn’t end up signifying much. Who were those pageviews, where were they coming from, what were they doing? Without context, a count of pageviews just isn’t substantial enough.

Since it’s pretty lame to complain without offering some kind of solution, I’ll offer my first thought: keep the follower data as a component of the algorithm, but add some context. The twittered links have a timestamp associated with them, so factor in visits to the linked hypem.com page and plays of the track in the 60 minutes following the tweet. If someone has a high follower count on Twitter but their followers don’t click through on or listen to their Hype Machine tweets, that person’s influence on the charts should decrease over time.

That’s certainly not a perfect answer (it doesn’t work on the first hypem.com tweet, becomes increasingly complicated if multiple people twitter a link around the same time, etc.), but some change along these lines would move the charts in a productive direction. Information is data placed into context, and follower count is data; find the right place for it and then we’re really in business here.