Is Community-Assisted Everything the Future of Apps?

Asking thousands of strangers if those jeans look good on you might not have seemed possible (or desirable) only a few short years ago, but now it’s only an app download away, thanks to the advent of smartphones and mobile bandwidth. Sstyle decisions are only the tip of the iceberg for community-assisted decision making, the newest trend appearing on a smartphone near you.

What began innocuously enough with restaurant reviews and recommendations through apps like Yelp has spread to virtually every possible aspect of human life and work. I already mentioned fashion, but apps also exist to help you make choices about design projects you’re working on, the fastest route you should take when travelling, which apps you should buy, and even which apps you should create.

Do a simple search for “Recommendations” in the iOS App Store (s aapl), and you’ll find a healthy sampling of the kinds of apps I’m talking about. Some noteworthy examples include Pose, Device and Numote. Pose lets you browse photos of fashion finds, like a new coat or dress, and make recommendations about whether or not a user should buy said item. You can also snap your own photos of items you’re thinking about buying, then post them to Twitter and Facebook. Pose even creates a dedicated page for each photo accessible via any browser that lets anyone comment on your style choices.

Device provides art, design and photography opinions in a similar manner, albeit with a built-in voting system that makes results more empirical and easier to measure. Device is a theme-specific, spin-off application of Opinionaded, which offers the same features without the narrow focus. In both cases, the idea is that you upload a photo, choose whether to poll the opinion of the general public or just your friends, then sit back and watch the opinions roll in.

Numote offers a crowd-sourced recommendation engine for television shows, by creating a social network for TV viewers, and also providing recommendations based on your viewing history, location and social interactions with other users. Instead of mindless channel surfing then, an app like Numote, when combined with the crowd and the cloud, saves time and gets you to content you’d most likely want to watch.

Opinions having to do with taste aren’t the only ones people are sharing and using, either. One project that really seems to be catching on is Waze, which provides free turn-by-turn directions generated by other users. As the database grows, it becomes more and more useful. Some local TV stations are even using Waze to provide local traffic updates during news broadcasts. The app just became more social and smarter too: Today, Waze announced a function for users to add road hazard alerts through voice commands.

Recommendation engines are also teaming up with location services to provide personalized, local suggestions and advice in real-time. I’ve been using the private beta of, a web-based app that uses SMS notifications to provide mobile interaction. Basically, you ask the service a question about a business, restaurant, or something else going on in your area, and nearby Localmind users can see your question and respond. For instance, you could quickly check for an available seat at a popular local dining spot, or find out if the library a couple blocks from you has free public Wi-Fi. It’s different from most other local recommendation apps because it offers the chance to receive answers to specific questions, rather than just a mish-mash of general opinion, and it has the advantage of being pretty much platform-independent.

The community-assisted model presents one obvious challenge: It requires a community to function. None of these apps are any good to users if no one is providing input. But, as the success of Quora proves, people seem eager to answer questions, provided they’re properly presented.

Most of these apps are free, but don’t think that’s because developers are creating them out of the kindness of their hearts. Recommendation apps have amazing potential for collecting information valuable to marketers and advertisers. People using these apps are literally volunteering information about exactly what types of goods they buy, what kind of media they consume, and where they like to go to eat, drink and spend money. It’s like a marketing survey, except much better because A) people are actually willing to participate and B) answers are more likely to be honest, since they’re being offered freely.

As a win-win for both users and app developers alike, I think we’ll see many more apps in the near future that integrate community-sourced recommendations, answers and opinions, location services and social networking integration. Standing out among this crowd will depend on delivering the easiest possible experience, while also keeping things specific enough to make sure users don’t get lost in a sea of irrelevant information. I can’t wait to see the software that emerges in pursuit of that mix.

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