In the first two parts of the “Where is my Travel 2.0?” series I talked about the long tail aspect and data-drivenness of online travel applications. In my today’s post I’ll write up some thoughts on Tim O’Reilly’s notion of:

Users Add Value

So this means that we need to get users to contribute, right? That obviously is an old hat but many of the mashed-up and meta-search sites clearly lack the ability to efficiently harness user contributions – which doesn’t have to be a bad thing per se, especially if we look at all the community 2.0 (trademark someone?) stuff being implemented lately in an reactive act of adoption.

Social Networks

It’s not clear at all how websites that implement relationship-centered friendship systems (which means they are projecting and extending the offline social networks of their users onto their database servers) will automatically be enabled to harness these relationships by means other than just enhancing the stickiness of the application. The basis of this doubt is the predominance of object-centered sociality, which has been depicted by Jyri Engeström (see his blog post and presentation) the founder of Jaiku.

Self-Interested Users

Another assumption that plays into the realm of object-centered sociality is that the majority of users (still) is self-interested. The main difference between services like delicious or flickr and travel communities is that the former were not primarily designed as a communication/collaboration tool. Reviewing hotels, trips or the like does not help me getting my things done, but is meant to help others in the first place. I don’t get an immediate value from reviewing like I get when tagging my bookmarks online at delicious. Participation is not intrinsic to the application.

Cognition Problems and the Wisdom of Crowds

Most problems addressed by communities that feature rating, ranking and review mechanisms are essentially cognition problems: What are the options? These almost always translate into decision problems: Where should I go? Which hotel should I pick? – and so on. In his book “The Wisdom of Crowds“, James Surowieki emphasizes the difference between such problems that people have to deal with within social settings. He suggest classifying them into following categories: Cognition or decision problems, coordination problems and cooperation problems. (David Pollard summarized these three types of problems nicely in a blog post back in 2004)
Surowieki notes that

[...] collective solutions to coordination and cooperation problems are not like the solution to cognition problems. They are fuzzier and less definitive. These solution tend to emerge over time, rather than beeing the product of a single collective decision. (emphasis mine)

Basic examples for coordination and cooperation problems of travelers include, for instance, the manifold side effects of group travel or situations where there is a high excess demand for a certain resource (e.g. hotel or flight).

Conclusions

Today’s travel communities do a great job at utilizing “their members’ need to get something off their chests” for helping users solve cognition problems. But they don’t address problems that affect groups as a whole and for which combined solution are needed. They rely on the user’s unselfishness but don’t aim at cooperation problems where a collective interest of such a kind is a requirement anyway. I am not aware of any travel website where a self-interested user gains immediate value by contributing to the collective wisdom. This is why I fancy some kind of project management tool for travelers where capturing and valuing past experiences directly and transparently translates into additional value for future travel planning – how this should work out in detail? Sly old dog…

PS: Did you have a look at VibeAgent lately? What do you think?

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Today Jordan Chark published a list with 75+ US travel sites at Mashable. He recently wrote a similar post called Travel Hacking: Essential Sites for Summer Travelers. I link to these instead of updating and enriching my own List of Travel 2.0 and Social Travel Site.

Note: Thanks to Jordan’ s diligence and his post SHOPPING SPREE: 18 Sites for Social Shopping & Deals the same cost-efficient updating mechanism could be applied to my List of Shopping 2.0 and Social Shopping Sites, but that would be too much of resting on laurels of others.

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In my last post I maundered about potential properties of the eTourism’s Long Tail. Today, as promised, I will extend the discussion (actually it’s a monologue still) to another aspect of Web 2.0 Design Patterns, more precisely to what Tim O’reilly refers to as Data is the Next Intel Inside, meaning that most web 2.0 applications are essentially data-base driven. O’reilly emphasizes that the challenge here is to create a unique, hard-to-recreate source of data.

Mashups and Meta-Searches

Price comparison and booking sites have a hard time to accomplish this since their USP generally is their ease of use (e.g. a fast and fancy AJAX-interface) and the sheer amount of data that can be compared. However, that data is being pulled from external sources most of the time and a state-of-the-art interface doesn’t really pose a insuperable entry barrier these days. This applies to mashed-up interfaces as well. If there is no additional data added in the process of mashing and therefore no other sustainable value created than the way the emerging results are presented you should better do some cool searching stuff.

Recommender Systems

On the other hand, web usage data can be tremendously valuable for marketing research. But this layer of information doesn’t easily translate into immediate value for the users – at least as long as there is no recommender system implemented that can harness these usage patterns and relay it to the users (or even social interaction patterns, if the site implements some kind of friendship model). However, recommender systems become truly powerful first when combined with data that reflects users’ unbiased preferences acting as fixed points to tie the fuzzy data mining algorithms to. Another thing about those systems is that they help driving the demand down the tail by enabling people to find their niches more easily. Moreover, like Chris Anderson points out, in combination with an increasing variety on the supply side the demand curve flattens out.

Bring Sexy Back!

The classic approach for travel and shopping sites looking to enhance their data is to drive people into reviewing, ranking or rating – or even creating new assets, which on the other hand bears the danger of introducing inconsistencies into the data-base.

If I think of unique, hard-to-recreate source of data the word that crosses my mind first is the word “unsexy” (a germanized Anglicism meaning “not sexy”). In a perfect 2.0 world the uniqueness of a data-source should not be based on assets in certain data-bases but on the way the data is humanized. As I mentioned, recommender systems are most powerful when harnessing direct user contributions (voluntarily revealed preferences) and web usage profiles – but what makes them smart is the way they combine these two realms and the way they draw conclusions.

Now, open those/source your data silos! Give me some APML! And then we’ll see who’s really smart.

Read more in my next post in which I will address item 3 on the List of O’reilly’s Web 2.0 Design Patterns: “Users Add Value”. That post will also feature some nice and shallow background knowledge on buzzwords like social networks, the wisdom of crowds and collective cognition. So if you’re looking for something to impress the ladies (or gents), stay tuned.

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