LiveLarge Presentation

A brief survey of the Event Finding and Planning domain:
http://mashable.com/2007/09/12/events-toolbox/
35+ tools for event discovery!

An Analysis of the State of Event Discovery:
http://worcester.typepad.com/pc4media/2005/09/event_search_ca.html

A venture capital firm talks about the future of browsing
http://lsvp.wordpress.com/2007/10/17/discovery-versus-search/

VIDEOS:
http://www.podtech.net/home/2860/lunchmeet-event-discovery-with-attendio
CEO of Attendio discusses online event discovery

http://edcorner.stanford.edu/authorMaterialInfo.html?mid=13
The needs of customers change fast, and the corresponding acceleration of internet browsing rewards an in-browser search experience. Constant window or tab switching for event browsing and discovery is no longer sufficient!

http://edcorner.stanford.edu/authorMaterialInfo.html?mid=348
Appeal to the customer, not the monetization

Trends

Despite the incredible wealth of online services such as iTunes, NetFlix, and Amazon.com that aid in the discovery, organization, and purchasing of creative goods, there is currently no widespread service that allows users to manage and pursue all their interests in conjunction with one another. The current lack of integration between “cultural interest” services such as Pandora and Yelp deny web users the opportunity to explore how their interests in one category might affect their preferences in another. Since a consumer’s interest in one category, such as music, is likely to be influenced by the movies they’ve seen, the places they’ve traveled to, and even the magazines they like to read, we see tremendous value in facilitating this cross-pollination of consumer interests. Ultimately, the vision of this opportunity is to build a user-specific, cross-category recommendation engine that returns relevant results to the web savvy consumer.

Customers

The design of such a recommendation engine must take into consideration several principles of effective online consumer applications. First, we want to target users who use the internet to discover new hobbies and interests of importance to them. In addition, these users must believe in the value of a social network, and will put effort into maintaining it. The combination of these two results in a user that is willing to not only experience new trends and content, but also reach out and experiment with their friends and family, strengthening the predicting power of the Live Large engine.

Revenue Models

The long term revenue model will be based on advertisements and sponsorships from companies whose products and services that strive to have cultural impact and significance. The retail industry fits readily into this criterion. Monetization of similar discovery web sites such as Pandora may also provide insight.

Risks:

Over saturation of the search and social network space – Current investments in “the next big thing” in these two domains reveal a lot of fragmentation across various efforts to tap into the general sphere of user wants. A recommendation engine based on a holistic appraisal of user interests requires a platform on which all types of interests can be managed and usefully interpreted. Yet the lack of market focus of a platform-construction approach limits the opportunity’s investment attractiveness.

Additionally, the flexibility required of an unconstrained interest tracking and recommendation engine may be very difficult to implement technically. Considering the large investment by Pandora in building a music-specific genome classification service, generalizing the recommendation technology to work for all types of interests may require much experimentation and iteration before relevant results are consistently delivered.

Next steps
Where do we go next to validate our observed trends?
Propose primary investigations and surveys?
Key players to talk to (such as Trae) to reduce risk and improve understanding of competitors
More concrete market sizing; would all discovery service users necessarily want to use a service like LiveLarge?

Unlike Pandora.com, Netflix.com, and Yelp.com, our product provides cross category recommendations that simultaneously accounts for our users’ tastes in music, movies, food, and more.