The document discusses various web metrics and tools for analyzing consumer sentiment towards cell phone carriers like Sprint, Verizon, Cingular, and T-Mobile through blogs, photos, and videos. It highlights the importance of user-generated content in shaping brand perception and the limitations in obtaining demographic data. Key metrics from platforms like Digg and the overall tonal analysis of blog and video content reveal trends in how consumers engage with these brands online.

1. what the web has to say about: Sprint, Verizon, Cingular, and T-Mobile Web Metrics for cell phone carriers lextant

2. Measuring blog postings What is it? Blog search/traffic tracking engine Why it matters? Frequency counter for blog traffic specific to keywords. How’s it work? Works like a search engine!

3. What the blogs say…verizon What is it? Blog context +/- summary tool Why it matters? Gives you the voice of what people say in blogs regarding specific keywords. Also provides at a glance measurement. How’s it work? Works like a search engine! Where’s it track? MySpace Xanga Blogger etc What’s it give us? Tone of voice Customer voice Context of pain/pleasure Timeline

4. What the blogs say…sprint What is it? Blog context +/- summary tool Why it matters? Gives you the voice of what people say in blogs regarding specific keywords. Also provides at a glance measurement. How’s it work? Works like a search engine!

5. What the blogs say…t-mobile What is it? Blog context +/- summary tool Why it matters? Gives you the voice of what people say in blogs regarding specific keywords. Also provides at a glance measurement. How’s it work? Works like a search engine!

6. What the blogs say…cingular What is it? Blog context +/- summary tool Why it matters? Gives you the voice of what people say in blogs regarding specific keywords. Also provides at a glance measurement. How’s it work? Works like a search engine!

7. Exploring flickr clusters …cingular What is it? Photo tagging, social network. Why it matters? How do users tag and organize images. How’s it work? Works like a search engine!

8. Exploring flickr clusters …verizon What is it? Photo tagging, social network. Why it matters? How do users tag and organize images. How’s it work? Works like a search engine!

9. Exploring flickr clusters …sprint What is it? Photo tagging, social network. Why it matters? How do users tag and organize images. How’s it work? Works like a search engine!

10. Exploring flickr clusters …t-mobile What is it? Photo tagging, social network. Why it matters? How do users tag and organize images. How’s it work? Works like a search engine!

11. Viral video effects… What is it? Video sharing networks. Why it matters? Ample collection of published, and user created content- taggable, searchable. You can track video buzz by most voted for, most discussed, date, relevance, or like keyword assoications. How’s it work? Works like a search engine!

12. What is it? DIGG is a user generated buzz network. Why it matters? Often called the pulse of the web. Digg generates traffic that rivals the WallStreetJournal, NewYorkTimes etc How’s it work? Users submit a story to digg and then the community can vote on it, giving it more diggs. Voting on story is casting a vote and saying “ya this matters, keep it” The more digs a story has the higher it is ranked and reigns supreme to the collective net suggesting that this story is really “hot” right now. Basically its community favor, if a story gets a lot of votes, it goes to the front page and sticks. Summary of the top ten digg stories for each brand in the past 365 days. political - stories that get caught up in outside factors (gov, other companies, scandals, issues, problems etc) Bs - stories that are twists on the experience, “that’s bs!”, usually customer service orientated, or complaints Hardware - stories that talk about the carriers phone or technology offering Hack - stories that expose an unintended feature, usually generates lots of buzz, it’s a freebie, it’s a cool thing Promise - stories that say “we’re getting better, we’re going places, our stuff is gonna rock soon..” Positioning - stories that like the promise but more on fact, “we have x amount of stuff, think statements Bigger the font the stronger the message overall (past 365 days) # of diggs = # of individuals that voted New phone buzz, lots of promises Somewhat positive hardware , promise , positioning, hack 6,293 tmobile Many hacks, apple deal, minor bs issues positive Hack , promise, bs 14,631 cingular Too political, messy, big brother Somewhat negative political , promise, hack 15,004 sprint Trouble, customer service issues, fiscal responsibility negative political , bs, hack 24,821 verizon feels +/- Story types # of diggs

13. delicious profiling.. (on hold) What is it? Delicious is a social book marking site. Why it matters? How do people tag and organize meaning and value on the web? What can we learn from how a user tags content and organizes it to be saved and recalled later? How’s it work? Instead of book marking in your browser, you do it in delicious. You submit a link to your profile, call it something, and then tag it with meaning. Delicious shows you related tags and related people that share similar tags with you.

14. Conclusions? Still new frontier on tagging… Tapping into how people tag and categorize a brand is huge I think. Especially since I can see like tags and like users of that brand. Harder to analyze cause there’s few tools in this area. For example with our own company brand, I can tell you who tagged it, who wanted to remember it, how they tagged it, what word they used, when they tagged it, make assumptions based on why that time and then (did we do an event or show, or article on the net), and tell you the associated tagged links around that time- exposing the behavior or like sites that were part of that browse and tag session. I can also tell you what other people used that same word to tag us. Course it doesn’t really tell me anything voice wise, its all in the tag. Stuff I didn’t do but could… One thing I didn’t look at was phone manufacturer associations with these tools. How does nokia vs motorola stack up, and the problem with that is that its not USA specific any more cause phone brands are global, the net is global, so we’ll get that data vs t-mobile, a rather USA flavor. Could be harder to make connections from the data back to USA specific carrier brands. … .. There’s lots of tools on the web to monitor, observe, track and explore blog, photo, and video content. We can see how users categorize and organize tagged content, explore tone of voice, get the “skinny” on whats buzz worthy and see how brands spike and fall daily via their own buzz. What we don’t know is demographic information, we don’t know who people are other than sex, sometimes age, sometimes geographically. User generated content pushes out brand specific content. Meaning there’s more videos/blogs out there filled with customer voice than with the brand voice, brand generated, brand sponsored. My thoughts? Its interesting to see the DIGG and OpinMind results line up regarding the brands. They are generally in agreement. So do blogs take their cues for postings on general web buzz via portals like digg, youtube etc?? I’d say yes to an extent. On the video side it’d be interesting to see what the summary of video and audio user generated content there is specific to the brands. IE, who rants and praises the brand the most in a video fashion. Videos are more viral than words, they are click and watch me kind of things, and re-postable on many different web sites.