Last year, Microsoft’s Office group had such courage when it introduced the Ribbon in Office 2007, a radical shift compared to the decade of menu+toolbar-based design that had informed an entire generation of users.

Last week, the same group showed audacity again when Microsoft Office Labs announced pptPlex, an exciting innovation of the way in which PowerPoint presentations are prepared and presented.

With pptPlex, presentations are no longer a linear. All slides can now be placed on a single screen and presented in detail by zooming in. That makes the entire presentation visible in an overview at first; the presenter can then drill down and zoom out at any point and move in non-linear paths more intuitively. Not all presentations need this kind of format but it’s certainly useful for complex topics and presentations with multiple chapters/groupings of slides that are interdependent. I’ve embedded one of the demo videos below; more can be found on the project’s website.

So what?

I’ve seen a dozen projects that aimed to reinvent PowerPoint. I haven’t seen a zoom-based approach, but I’m sure it’s been done before. All of the alternatives I saw had one thing in common though: a different interface or some new and clever way of making the presentation (mindmaps, hyperlinks, 3D, …). The problem: in addition to learning a new tool, users had to wrap their head around a new model for constructing a presentation. (How do you think about a presentation that happens in 3 dimensions if all you need is a beginning, middle and end?)

That’s why there is a second, more subtle innovation in pptPlex’s approach:
it doesn’t break the user’s mental model of a traditional PowerPoint presentation.

The zoomable sections of pptPlex are created with special divider slides that the user inserts into the presentation. As a result, most users will still build their traditional sequence of slides first before adding these special markers, meaning: nothing changes about the process of constructing a presentation; pptPlex is only an added step at the end. (As you get more advanced, you might organize pptPlex presentation differently from the very start, but as a beginner, the process stays as familiar as before.)

I can’t say whether the decision to use divider slides was by necessity (it’s surely easier to implement) or by design (brilliant). Either way, the team didn’t choose to build a new interface that would have implied a new mental model. How many “file system revolutions” have been touted that used smart tricks to reinvent the folder hierarchy? How many succeeded?

If it ain’t broken, …

When mental models change, it hurts — don’t change them if you don’t need to. pptPlex is a great example that didn’t need to change a model in order to achieve an innovation. I’m waiting to see whether and how soon the numerous competitors pick up on zoomable slide decks … and I can’t wait to see my first pptPlex presentation live in person.

Photo Credit: Me in London

But when you’re running a site that’s built around people and the content they bring to you, there is another set of questions that’s just as important: Is your community working? Are people nice to each other and respect your guidelines? How do you know?

Visitor counts, bounce rates and browser statistics won’t help you much here. A new kind of analytics, with new tools, measurements and methods, is needed — so let’s start talking about community analytics.

Community managers and their tools

Community managers have a rather lose job description, spanning everything from hostess to fireman. But no matter how fuzzy their work might sound, anyone running a people-based site will tell you that their role is absolutely essential. The early success of Flickr is largely ttributed to the strong involvement of the team in setting up the platform and being in touch with its first users.

So today, I want to tho introduce the theme of community analytics, and tools for community management at large:

• How do you get insights about your community?
• How do you know what’s going on?
• And what actions can you take in response?

Those are the questions I’ll explore in more detail.

Keeping track

The larger the site (and the smaller your team), the easier it is to lose track of all the moving parts and all the interactions that take place. It’s not difficult to watch your most active of users, but what about your large “middle class” of users? That’s where community analytics is meant to kick in.

I haven’t seen any tools that are built specifically for managing and analyzing live communities though (forums and email don’t count, and academic projects such as NetScan can’t help you on the job either). Moreover, the art of community management itself is still a rather clouded and shrouded topic, which is why I also look forward to interviewing seasoned community managers to share their experience with you here. Needless to say, I’m quite excited about this series.

What have you come across? How do you gain insight into what’s going on with the sites you run or worked with?

photo credit: pruned.blogspot.com

Designing for this sense of Audience is a powerful tool to create cohesion and sense of utility among users of a service.

Twitter is a prime example for this kind of attachment: it survives countless outages and a slew of alternatives that could all pull users away… but don’t.

So how do you design for Audience?

Feedback Loops: The main ingredient

A system that wants to tap the power of Audience like Twitter needs 3 important feedback loops:

• Users building a model of their audience
• Audiences giving feedback to the people who post content
• Users giving feedback to the system about the people and content that are important to them.

Surprisingly few of today’s services actually close these loops effectively — many of them, including Facebook, have a number of lose ends.

#1 • Users building a model of their audience

A list of friends isn’t enough to create a sense of audience, it’s merely a pre-requisite. What users need the most is feedback (re-assurance) that their audience is 1) present and 2) listening to them.

Follow Me, Follow You. The loop starts with the notification that another user “is now following you” (on Twitter or Flickr or FriendFeed, which are all based on the same passive follower pattern). Note that Faceook’s model of a relationship is symmetric such that both users have to agree on being connected. (What are the implications?)

• Let users know who’s following them – so obvious that it shouldn’t need mentioning
• You’re already getting those countless emails. How could your system send fewer and still be effective? GetSatisfaction sends one update per day, Dopplr send one a week.
• Bonus points: Make a distinction between reciprocal followers, like Flickr: “Yay! grex has marked you as a contact too.” vs. “If you don’t know grex, grex is probably a fan of your photos …”

Being present: In Twitter, that’s is communicated by the other status updates. It signals who else used Twitter recently and who is presumably checking tweets, too.

• A system should clearly highlight the recent activity of other users.
• What’s important is the quick glance perception of quantity and frequency; the full content only matters on second glance when users are actually in “catch-up mode.”

Who’s listening: Retweets, @replies and references in future conversations all reinforce the user’s model of who’s listening on Twitter.

More generally speaking: Feedback that users receive from others is the primary source for building a model of Audience. It’s not the only source, but in this context, it’s the most relevant to consider.

For a blog or marketing campaign, visitor demographics and site analytics would be another relevant source for understanding who’s listening (and how frequently), but that’s beyond the scope here.

• The more, the better: Feedback between users. That’s the whole next section.

#2 • Audiences giving feedback to the people who post content

One of the most powerful ways to make your users feel good is to highlight the feedback and recognition they receive from their peers. It also turns out that this feedback is the best way to build and support a sense of Audience.

Providing different modalities of feedback is key here: Counting views and clicks, getting favorites and comments, etc. They all have different levels of effort and send different signals. Imagine a concert, in which you could only leave written notes at the door — no clapping, no booing.

In Twitter, it’s the re-tweets and @replies that reward the user with social recognition, especially when the @reply is not a “reply” but just a reference to the other person. Tweets that say congrats are often an example.

This is a loop, so it must be closed to work effectively: It’s not enough to provide the ability of giving feedback — the system must also provide the means to easily discover new feedback that users receive.

Which service understood this from the beginning? Flickr.

see this screenshot from 2005

Flickr’s Recent Activity tab was part of the interface since the early days and it answers a crucial question with just one click: What new feedback have I received?

Facebook, FriendFeed and other services all have feedback mechanisms, but none provide the range and easy access that Flickr supports:

• counting # of views
• favorites
• comments
• photo notes
• contributing tags
• inviting a photo/user to a group
• adding the user as a contact
• Your Stats
• and probably more

Facebook’s feedback mechanisms focus mostly on comments and gives no insight into the other activity that takes place around content: who and how many viewed my photos? who re-shared my link? who also become a fan of X? or joined my group Y? Facebook answers none of those rewarding questions. Bummer.

FriendFeed goes further and provides two modalities: comments and likes/favorites, but it doesn’t facilitate the discovery of new feedback. There’s no “You’ve got Feedback” link. FF now sends an email about new feedback (if you haven’t checked FF recently). But what if I “checked” FF and didn’t the see the feedback? Bummer.

#3 • Giving feedback to the system: Who’s interesting to me

The third important loop has users giving feedback to the system about the people and content that are interesting to them. Most of this data can be collected passively over time, by keeping track of page views, click-throughs and other interactions between two users.

To the dismay of some, this concept isn’t supported by Twitter… except for the Device Updates setting. Do you really want to receive SMS & IM alerts from everyone? No. Even on Twitter, some people are more important than others.

Leaving comments and favorites are other pro-active signals that users can give, but because they require extra effort, there will be fewer of them (far fewer than passive signals), which makes them less useful.

Once you have it, use this data to personalize and prioritize the content presented to users. It sounds obvious on paper, but in reality, personalization is often hard and computationally expensive. But you could start with simple steps, such as merely re-grouping and re-ordering items based on personal preferences. It would already be a strong improvement over the linear, time-sorted lists that prevail in many places today.

What closes the loop in this case?
Signaling to the user when personalization actually kicked in and what information was added, removed or re-arranged.

Not surprisingly, Amazon is a master of this feedback by letting customers know why certain recommendations are being made and giving them the ability to tweak those filters for the future:

Your Recommendations on Amazon

One more: The Ego Loop

There is actually a fourth loop that’s less apparent and it has even fewer real-world examples compared to the previous three: the system giving feedback to users about their consumption. Prime examples: Google Reader Trends and FriendFeed stats:

taken from entries on the Google Reader blog and FriendFeed blog

These statistics are often seen as gratuitous, a pastime for only the self-obsessed. But in reality, no data is more interesting to us than the data about ourselves.

Google Reader illustrates how this type of feedback can also be turned into actionable information: by combining a list of least read feeds with a conveniently located “unsubscribe” button, users can easily remove noise and adjust their stream. Users may also decide to change their habits in response to this statistical feedback — for example, someone reading 90% of articles in technology-related feeds may feel compelled to catch up on other things (or go outside).

Important: Guilt-free data only. The bathroom scale presents a daily opportunity for instant data about ourselves. You get the point. Don’t build bathroom scales, build achievement meters and guilt-free indicators.

Focus on rewarding information and on what users actually did, instead of what they missed or failed to do. Don’t say: “You read 2% of your messages”. Instead you could say, “Based on the messages you read, people find interesting are: Mike, Anna and Carl.”

3 + 1 + You

Feedback loops contribute greatly to a user’s sense of Audience within a service. I talked about four such loops here. What other loops exist? What else builds and supports a sense of Audience?

Facebook is designed for person-to-person and group communication. But is it designed for Audience?

Profile Pages, Audiences and the News Feed

Profile Pages on Facebook can have audiences of course, but this requires that users continually roam Facebook to look for news in their network. Facebook realized this limitation and introduced the News Feed. Its intent was to move a user’s “acts and performances” from the stage of the profile page to a single and central stage, a single place for Audience.

Sharing with the News Feed: Did it ever reach my friends?

Facebook was the first major social network to introduce the News Feed concept, which has since become a standard sauce for stickiness in many places (not StudiVZ surprisingly).

But Facebook’s implementation of the News Feed doesn’t capture the full power of designing for Audience: While Twitter distributes every message consistently, Facebook decides algorithmically which update is shown to whom in the News Feed. Algorithmic filtering is nice in theory, but such black-box behavior is simply unpredictable for the user.

• “When I post new things, will my friends actually see them?”, one might wonder.
• And conversely:
  “Have my friends posted something that I’m not seeing? The news feed is cluttered right now with people I don’t care about.”

Anything that’s unpredictable produces a feeling of uncertainty — and that’s never a comfortable feeling. Even with Facebook’s recent attempts to introduce smarter filters, users only have relative means to customize their feed (more of this, less of that).

Fewer Feedback mechanisms

Facebook mainly offers only one kind of feedback in the News Feed: commenting. Imagine a concert, in which you could only leave written notes as you left — no clapping, no booing.

For the person who shared something, there is no insight about the other activity that takes place around it: who and how many viewed my photos? who re-shared my link? who also become a fan of X? or joined my group Y? Facebook answers none of those rewarding questions.

Users could be posting & sharing more, but …

Because users don’t get a good sense of who’s listening and who isn’t, Facebook hasn’t been embraced as a place to publish pro-actively. Sending inbox messages, events or photos is mostly push-driven (and generates an email — “you are invited to an event” or “tagged in a photo”). But for everything else you share on your profile (links, status updates, joining a group, etc), do you know if it ever reached your friends?

Who capitalized on this gap? FriendFeed.

FriendFeed is the same setup as Twitter, but with more content: You know who’s listening and you choose the people you listen to. A useful premise but it also has a catch: the word “more”. Too much content, too many people — which is exactly the problem that Facebook is trying to address with its algorithmic feed. But what’s a solution then? It’s not the “middle ground” and it has nothing to do with smarter filters.

The answer is feedback loops, which is a perfect segue into the next post:
The 3 Loops of Designing for Audience.

No matter how long the outage du jour, Twitter users continue to stay attached to the service despite an ever-changing backdrop of alternatives.

Blogging isn’t for everyone either. But unlike blogging, Twitter enjoys a far a greater variety of users — they include people, many people, who would never think of starting a blog and people who would never touch an RSS reader. The 140 character limit is a plus for Twitter, but it isn’t all.

What explains the Twitter phenomenon then? What produces the positive feeling and the strong attachment among those who tweet? And moreover: How can other systems learn from this?

The answer lies in understanding Audience.

Twitter has a simple premise: You tweet & the message is pushed to your friends. The actual mechanics are slightly different (messages go to everyone who follows you, whether they’re your “friends” or not, assuming your stream is public) — but from a user’s perspective, the circle of receivers consists only of the people they know. Everyone else is part of a faceless crowd that’s hidden behind the follower count.

This simple premise holds the key to Twitter’s success: messages go to a well-defined audience. In the moment you release a tweet, you know who’s on the line and you have an idea of who can catch a glimpse of your message. @replies are the best illustration for this sense of audience: Even though Twitter is not a point-to-point message delivery system (let alone a reliable one), @replies are sent with the understanding that they will be read by the intended people because they are known to be in the audience. (Imagine a newspaper article that suddenly greeted a specific reader.)

Blogging on the other hand has no such clearly defined audience. An aspiring blogger who hasn’t crossed the chasm speaks into the void. Direct feedback can only come in the form of written comments (a relatively high barrier of effort) and it’s diminished by spam and vocal trolls these days.

FeedBurner’s subscriber count only provides the equivalent of Twitter’s opaque follower count and MyBlogLog didn’t solve this problem either.

So it’s not surprising that the majority of blogs are abandoned — the most-cited reason being “No one was reading it.” No one might be following your Twitter stream either, but Twitter is designed for network effects to take hold and given the natural reciprocity among groups of friends, it’s likely that most people have at least a handful of followers they know.

Back to Twitter: Why Audience works
Twitter works and enjoys such strong attachment because it provides real-time access to a well-defined audience. The backlog of all previous tweets is a guarantee of permanence (you can even search it) and you can catch up on it anytime. As a result, people use Twitter because they have an idea of who will see their lightweight messages and this sense of audience is reinforced by @replies, re-tweets and references in future conversations (online and offline).

Designing for the sense of Audience is a powerful tool to create cohesion and a sense of utility among users of a service. This lesson from Twitter can apply to many other services too and I dedicated a separate post to the principles of designing for Audience.

In closing, let me also refer you to a follow-on post, which looks one service that has missed the full power of Audience so far: Facebook. Designed for Audience? Not so much.

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This post was also published on TechCrunch.