communications » Dave Jenkins

Caveat Scriptor: Twitter is Destroying Fame

Aug 052010

One of Kanye's better tweets, mashed with a New Yorker cartoon

My first brush with fame was shaking an astronaut’s hand when I was fourteen (Robert Crippen). Since then, I’ve spotted movie stars in Park City, seen musicians drinking in bars, and actually discussed golf swings with the Prime Minister of Japan. I mention these not to brag, but to say that my exposure to fame has probably been average.

Online, however, I’ve gotten much closer. Ten years ago I became ICQ friends with Will Wheaton (Ensign Crusher), who had some linux support questions that I answered. We traded geek creds, and that was that. On facebook, I’ve got some famous friends; on Twitter, me and @MCHammer are tight (at least I think so, he never answers my pleading to not hurt ‘em).

The Internet and social media have made flash stars of some people, usually not of their own volition– from the numa numa boy to that idiot crying over a rainbow. The instant connectivity to flash something all over the globe and the proletarian accessability to publishing can make anyone a star for about 15 seconds. No duh.

Social media may be destroying the machine that builds fame, however. If “fame” is the marketable asset that comes from being famous, then twitter is destroying value every day. Here’s my thesis:

Actors used to be poor. They could only impress and perform in front of a few hundred people in a given city, and even then, the theatre was long and drawn out. High quality stuff, no doubt (for acting chops), but weak on the easily-remembered guitar licks. Charlie Chaplin was the first major star, and it was a direct result of the medium of film, which could be recorded and distributed to thousands and millions of people. Actors (successful ones) are rich because of the medium and distribution model which allows for small dollars from many many customers.
As actors and musicians became famous via a remote medium (film is one-way interaction, as is radio, records, tapes, etc.), an entire ‘fame’ industry sprang up to provide that proxy access that fans wanted to make the connection back to their idols. Variety Magazine, Papparazzi, TMZ, et al, are all part of this machine. Performers have a weird relationship with it– they say they despise the machinery of fame, yet they depend on it. The successful navigators seem to balance what information and access they dole out sparingly. Marlon Brando never attended the Academy Awards, there’s no way he’d have a twitter account.
Twitter now gives these performers a direct line to their fans. Wait– no. It’s the other way around: fans now have a direct line to the performers. This circumvents the machine, and some performers are seeing their fame getting eaten away.

While Twitter is still technically a one-way medium (I can follow @JeremyPiven, he doesn’t have to follow me back), the format is stripped down to a degree that allows very little of the fame machine to work its magic: no photoshopping of the photos, no glitzy typeface, no room for a publisher or PR handler in-between the performer and the fan.

Some performers have been able to do this well, mostly comics who are used to the short text of a joke. Some are burning their capital, reduced to endlessly pimping their own book store appearances. Still some others have suffered poorly for it– mostly the good looking ones we suspected were vapid shells all along.

There was a Vanity Fair article I read last week about Richard Burton and Liz Taylor, who seemed to be at the apex of the fame machine in it’s best golden era of the 60s, and when Pacino, Hoffman, and other “ordinary” guys started to fill the roles, ol’ Dick Jenkins (Burton) knew the game was changing, and that fame was past him now.

Known for his poetry, drinking binges, and temper tantrums, @RichardBurton would have been one helluva feed.

Qualitative Feature Polarization

ecommerce 1 Response »

Jul 012010

Nice answer. Do you know the question?

Okay, I’m likely going for triple-word-score here, but I’d like to discuss what I call “Qualitative Feature Polarization” in terms of how to read data. I’ve talked about this before in the specific case of how to rate a sushi restaurant, but I think the principles apply to any situation where people are asked to make a judgement/evaluation.

The problems with most evaluations are as follows:

ranking things in a straight good-to-bad list throws out too much information
all data points are contextual, but we rarely understand the context
the real question– and therefore the real answers– are often not present

That last point is perhaps the most important: many times we are asked to make an evaluation from data that is based on assumptions in the goals or undefined context in the business model. For example: If Zappos were to match its prices against WalMart, it would “lose”, but in terms of service, it would “win”. Conversely, wait times on the customer service line to Zappos are “much better” than when I call WalMart. In an evaluation of customer service level, which one is correct? It depends on the business model, and on the context. In Vietnam, the US Army killed bad guys the enemy at a rate far more than our own casualty rate. Wins used to be defined by simple body count, but we saw how that turned out.

The effficiency expert Pete Abila points out the Toyota quality method of “5 Whys” to try and find the real question (in hopes seting up an alanysis to find the real answer). By simply asking the question “why?” at least 5 times, we can often get to the heart of the real problem, and strip away any assumptions or differences in the models we have put around the data results.

So, how to run an analysis that can provide sufficient context while also attempting to find the real issue? Whenever possible, I try to run all data in a method I call “Qualitative Feature Polarization”. This method runs on some basic rules (which echo the problem points above):

Smash data sets together to see if any patterns arise (but remember your college statistics prof warnings about causality and correlation). This is the serendipity part– the chance for an “a ha!” moment that might lead to a further inquiry or data set
Whenever possible, frame your data in left<–>right, blue<–>red, service<–>cost. Applying such a framework to the Zappos vs. Walmart comparison would then lead you closer to the real issue: the business models are fundamentally different.
Whenever possible, highlight the specific areas that the results and evaluation have NOT answered yet. In other words, show that the evaluation has uncovered some results, but that the reader/listener (your boss) should specifically NOT jump to some conclusions (unless he has a mat) based on the data. This isn’t because the data is incomplete, it’s because the data was investigated to look at a specific issue. Thou shalt not extrapolate your summary.
Structure your analysis that leads you to a contextually-rich, polarized set of numbers. These numbers should then guide the next choice of left or right, not good or bad.

Receptiveness to this analysis is a mixed bag. If your audience is a fairly flat organization and the geeks are on equal footing, then qualitative feature polarization works well. If the organization is fairly vertical, they likely just want “the answer.” A good way to guage for this is the degree to which the org uses PowerPoint as a communications mainstay, and the level of complexity in the metaphors you spin out.

Everyone’s an Expert (part 2)

Social Media, ecommerce 2 Responses »

Jun 242010

Meet your new Marketing VP

A couple of days ago I visited the subjective nature of ‘expertise’, and how online community software and social networks are actually exaserbating the relative nature of percieved expertise in any given field. I’d like to continue with a direction where I think the true expertise is developing: it’s all in the numbers.

IBM just bought CoreMetrics. Adobe bought Omniture. The job boards for quantitave analysts are a mile long. Why is this? My take is that the culture of hyperanalysis and sweating over every small trackable bit of behaviour that began with successful dotcoms is finally seeping into the larger marketing departments of larger companies. “Marketing” is no longer those guys from Mad Men thinking up new creative copy while sloshing martinis, it’s Anthony Edwards from Revenge of the Nerds now telling you the exact percentages of retention you’ll need from exact zip codes using precicely worded tweets (the text of which was likely written by a robot algorithm).

But let’s be clear– this isn’t a race to hoarde data. This isn’t a contest to see who can lumber through the largest spreadsheets. The data is everywhere, many times for free (thank Google). The real expert is the person with enough classical logic training, statistical classes, and– most importantly– the ability to write well enough to convey a coherent story that explains all the minutae into some sort of actionable plan. (there’s hope for all those philosophy majors after all).

Relative Goodness

Forrester Research understands this well. They’ve acknowledged the subjective nature of expertise in their data sets: all software rankings and application analyses are based on executive surveys. They figure that if they ask enough questions of enough executives they can get some relevant (subjective as it is) data points from which to present a decent story. Notice that Forrester rarely draws conclusions– they simply present enough data and a nice set of graphics that you can draw your own subjective conclusion. Forrester even gives you the source spreadsheet so you can monkey with the variables and draw your own story. Their success, I believe, is in the strength of their storytelling abilities and presentation skills.

Who is the expert? The person that can divine a coherent direction out of a sea of numbers, that’s who. If Edwards can comb his hair and write well, he’s got the job.

Everyone’s an expert (part 1)

Network, United States, ecommerce, wiki 2 Responses »

Jun 172010

Times was, I used to be able to spout off any random factiod I thought I knew, and the Internet took it as read truth. The Wikipedia used to be great for this. Now, my rants are pretty much limited to the blarg you’re reading right now– we’re probably all better off for it. Most university professors scowl very deeply if a student references the Wikipedia in a footnote, which is fair, but not for the reasons most people think: Wikipedia is a bad reference source because it’s a derivative work, not because it may be inaccurate: The student should be citing the original work, not someone’s summarized boilerplate. Wikipedia has largely squashed the ‘inaccurate’ label through a zealous use and requirement of all statements must have footnotes.

But that raises a conundrum for many of us: where is truth? Where is the expert? Is the expert the one with the most experience? Is the expert the one with the most money? The most devotees? Is truth simply the mob’s consensus? Graduate school told me that truth is the logical sum of a tested thesis. I spent 15 years being smug that I knew what that meant, only now to really see that when the Internet gave everyone a soapbox from which to preach, now social networks are giving everyone a Hyde’s Park corner complete with audiences. Companies like bazaarvoice and pluck are setting up these cacophonies wherever possible (good for them). These systems invariably include meta-rating systems to rate the reviews and the reviewers, in the hopes of crowdsourcing the good information from the bad. In general, it usually works. It is still, however, all based on a Kuhn-model of mob truth.

The NYT recently published an article on a new computer named “Watson” designed by IBM to play Jeopardy. Another possible use they summized might be to find counter-factual statements to anyone’s gtiven declaration on the Internet. In short: a bullshit detector. I can imagine they will be able to monetize this thing into millions of dollars: every social network and review thread can now come with a robot that can read plain speech, offer immediate counter-responses to erroneous information, and perhaps even show us a numerical score for ‘trustability’ or ‘truthiness‘ (all the footnotes in that link– irony!). The downside here, of course, is that most reviews for most products will be reduced to little more than the barren subjectivism of American Bandstand: “It’s got a good beat, I can dance to it.”

There is still salvation for quality content in quality reviews: hard numerical data, solid logic, and qualitative feature polarization. I’ll explain myself on those in some upcoming posts.

Google Wave is Somewhere in-between

Network, ecommerce 2 Responses »

Nov 052009

Every few years someone re-invents real time chat. Back in the 70s we had teletypes in the high school computer lab. Internet purists had IRC to keep themselves entertained in the 1980s, while the early 90 gave us AOL chat rooms for the rest of us poseurs. Soon, we all had ICQ numbers (I still have mine memorized), then AIM aliases, which were soon replaced by jabber handles, Google chat IDs, and then came the facebooks. All shared some basics: real-time typing, conversation windows, text-centric, and just below the speed of verbal communication. Still, they’re all just variants on the real-time chat, a communication path that’s been around since The Beginning.

If we were to graph a spectrum of communication forms, spreading them out along the x-axis in terms of speed, and y-axis for quality of information, then email would be somewhere to the left and slightly higher than chat: It’s not real-time (you send something, and an answer comes back whenever the other person feels like it), but it can contain pictures and video, so it’s arguably better quality. Below and to the left, we would have twitter: asynchronus, poor quality (short). To the right of chat we would telephones (real-time verbal), and above that we would Skype: real-time verbal communication with the bonus of your friend’s beautiful face on your screen. Skype’s real-time video conferencing should be superior (above and to the right) of all of them, right?
Why do we still have the other forms around?

Is it because we don’t necessarily always want to see the other person? [yes]
Is it because we sometimes don’t want to deal with the other person (your boss or girlfriend or bosses’ girlfriend or boss-you-wish-was-your-girlfriend) in real-time? [yes]
Is it because we want to spew something out of low-quality and not have to burden ourselves with the responsibility that comes with actually having to put forward a cogent logical position? [yes]

So, it seems there is room for something that can land in that flexible in-between the safe distance that asynchronous email gives us, but the conversational flow of chat. If it were an open platform, people could start grafting on the higher-quality content elements like music and videos and pictures of cats eating cheezburgers.

Enter GoogleWave. I’ve had it for a little while now, and I see some promise if people understand the construct. Google is betting that people will want to sometimes be real-time, sometimes not-so-real-time, sometimes lo-fi, sometimes hi-fi. I bet they’re right.

My GoogleWave ID is tokyodave@googlewave.com. Hit me up.

Death of the “Panel of Experts”

Network, St Louis, United States No Responses »

Sep 282009

We’re seeing a pattern, in political town halls, industry conferences, and even award shows: the concept of a “panel of experts” at the head of the grand ballroom dispensing wisdom to the masses’ is dead. I blame mobile phones, but we’ll get to that in a minute.

In August 2009, congressmen and senators scattered out of Washington back to their home districts like so many rats carrying plague. They had to get Health Care Reform passed, and it was time to bring in the proletariat on the deals they had already been cooking. The problem is that the prols didn’t play ball. The quick reaction was to chalk it up to sour grape astro-turfing by the GOP– and once it showed up, I have little doubt they did amplify it wherever possible– but I think that people are just as upset with the Town Hall format as they are with the actual message trying to get preached at them by their “representative”. Thanks to the internet, the masses are much more connected and have their opinions (right or wrong) much more set before they go to the meeting; thanks to social networks, people now have the baseline expectations to participate in a two-way conversation, not get lectured at and told what to think. The worst representatives actually yelled at their own constituencies to “shut up and listen“. Ah, irony.

I saw this same pattern at a recent ecommerce conference in Las Vegas. Each morning had the usualy Big Name Keynote address which was just as much show-n-tell as it was informative, but then the afternoon sessions consisted of smaller breakout sessions with a small (3-4 people) “panel of experts” sitting at the front of a long ballroom pontificating about some facet of ecommerce chellenges (customer usability, mobile commerce, social networks, etc.). Here’s the thing– very few people actually listened, I think. Most people had their heads down checking their email, tweeting out what they were hearing in the meeting, or even tweeting out how they’re not getting anything out of the meeting about how to use Twitter. Ah, irony.

On the flight home, I downshifted with a Newsweek magazine, and saw an article about the Emmy Awards for TV, and how the awards shows seem increasingly out of touch with the will of the people. “That makes sense,” I thought to myself: awards shows depend on panels of experts, and that model is becoming increasingly flawed. Anything that is perceived as a one-way street of information transfer, or has a significant amount of time-lag between the chosen opinion coming down from above and the feedback going back up will lose attention with an increasingly twitchy, real-time community.

So what to do? Here’s some cheap shots:

For political town halls, obviously not everyone can talk, and even then not everyone has a cogent thought, but everyone wants to participate. What if everyone was handed a chit or poker chip as they came into the room, and each person could either ask their friends for their chips because she wants to speak, or she could hand her poker chip to someone she trusts to voice her opinion. The microphone would then be ‘auctioned off’ to those with the most poker chips, and passed around as time allowed.
For conference meetings, the panels must absolutely integrate real-time tweets, polls, and feedback. As topics become more tightly defined, the likelihood that smarter people are sitting out in the audience increases. The poker chips might work here as well.
For awards shows– I have no remedy. They really were just a money-scam from the Big Studio era anyway, it seems, to put butts in seats a second time in November, while allowing actors to negotiate higher salaries because they had won something. With the social networks, rotten tomatoes, and Mr Dynamite, we all have sufficient information to judge they good films, music, and TV from the dreck. Those that cannot discern quality content deserve what they get.

.mobi or not .mobi

ecommerce 1 Response »

Apr 092009

hamlet mobile Okay, that was a pretty lame title, but it’s actually the most clear way to express the question: what criteria or elements need to be in place to justify a mobile version of a website?

I was talking with a friend of mine, who recently got the .mobi version of his company’s domain, but hadn’t done anything with it. He asked for advice, and I offered up the following crude thumbnail that Internet (not web) information can be broken into three main types:

Transactional snippets of information: flight times, restaurant addresses, bank account info, sports scores, short text email, answers to salient questions. Mobile phones do very well at this– iphone or not– just about everyone with a phone participates in this type of internet transaction. If you see it that way, the iphone app store really becomes a collection ot targetted info queries all eye-candied up.
Search-centric information: Any business model that centers around the shear number of stuff for sale/rent/download/sharing is really a search-centric model and not a browsing model. Amazon, zappos, walmart, piratebay, and wikipedia are all search-centric. So is Yelp. Notice how all of these operations are either exploiting a mobile strategy fairly well (half-way), or are likely close to one.
Browsing ecommerce information: anything that is a context-rich window-shopping experience doesn’t do well on a mobile phone. The screen isn’t big enough, and we don’t have the patience to make the Solomon’s decision of either viewing a stripped down version of the site or spend the time endlessly scrolling around to see the website in it’s original layout. Etsy, Borders magic shelf, Dell, or other ecommerce sites that are inherently dependent on the web-surfing serendipity of the site won’t do well on a mobile phone– at least not without some major rethinking on what works and what doesn’t.

My friend is in an information-sharing business. He handles sets up relationships between business partners, and brokers deals where he sees possible matches. His real strength is the depth of information, but he certainly has plenty of small transaction-level updates that would be more valuable if offered in near real-time. My advice to him was to look through all the activities he does, find those that are in the first bucket, and go with the .mobi version.

The Rise of the Network Biologist

Network, ecommerce, wiki No Responses »

Mar 212009

So, the Internet is everywhere. Times was (back in the day), that we used to surf around to websites just to see the design or some cool functionality, but we are no longer enamored with the technology (well, almost). Futurists no longer spend their time pontificating about capacity, bandwidth, or the extent of data that could be recorded in their great computers– all of that is assumed to be in place. Rather, these seers spend their time in two activities:

a) Blowing their own horn on twitter — not worth watching

b) Showing insights on the social interaction of the great online hive that has now come into being — these are what I’ll call “Network Biologists”, and are worth your time.

The network biologist will spend his/her time researching the strange interactions between people, and the even stranger medium that is created as a result. They are not sociologists, because it is more than the interactions of the humans; there are robots, scripts, and crude AI influencing the mix. The environment itself is ever changing– and the actors change as a result– but the center of focus has shifted to the behaviour of the fish, not the mechanical workings of the reef: hence the term ‘biologist’.

The usability managers in ecommerce companies were an early manifestation. Now, everyone in the online marketing department, merchandising, and even finance is trying to ascertain how the huge mass of people will react to the online environment. This is different from standard “retail science” or “catalog management” because of the constant arms race in online functionality as well as the multiple-variable equation where customers will influence each other in real time, as well as try to get in on the deal with some sort of affiliate, coupon, or recommendation in exchange for a slice of the profits.

The best results so far have been to segment and clasify online users into their various behavioural patterns. Oddly enough, people don’t mind surrendering them willingly. The current spate of “what [blank] are you?” viruses circulating on facebook are a segmentation maker’s dream: people are happy to tell us exactly what drives their brightest fears and darkest hopes. The most successful websites out there have tapped into the hive behaviour that humans portray when given just the right mix of anonymity and self-aggrandizement: Google’s page rankings are a canopy of dominant players and ground-dwellers in their shadow; Amazon’s entire merchandising catalog for millions of products is an expansion of fecundity like salmon spawning; Facebook is basic tribalism that proves Dunbar’s number, De.licio.us is our own pollen-finding wiggle dance; twitter is a sea of iridescent jellyfish desperate for attention; there is a flavour of pr0n out there for every strange perversion you could imagine (and a few you don’t want to).

I would imagine that colleges will soon have some sort of degree in Network Biology: it will be a combination of sociology, crowd biology, and basic network mechanics, to show how it is all wired together.

Free iPhones in Japan

Japan, Network No Responses »

Feb 252009

I am not sure if it is a show of weakness, or just another table-upsetting play by our old friend Son Masayoshi, but Softbank is now offering a Free 8GB iPhone as long as you sign up for the two-year data plan. We’ve seen this model before: Japan pioneered the ‘free crack pipe’ model almost 10 years ago with game consoles and cell phones. But as functionality, swiveling screens, and other doohickeys made their way into the small devices, prices started to creep up. Hardware prices took a real hike as the portable chips made jail breaking the phones an assumption, and as all signal carriers standardized. (In fact, most electronics stores will transfer your chip into your new phone right there when you buy it.)

But Softbank has two things going for it: 1. jailbreaking the iPhone is possible but not easy, 2. the 3G network is still somewhat proprietary. With these, Softbank can go back to the market-share giveaways that made them famous. Earlier, I didn’t see the iPhone taking off so strongly inside the Empire. Now– maybe we’ve got a real race. In response, competitors could go either way:

a) Use Android to lower the cost of the hardware (also offer for free), and then use VoIP wherever possible to lower radio costs. However, this doesn’t work because– believe it or not– open wifi networks are not that common in Tokyo

b) Use Android or another OS platform to out-app the iphone (weak strategy)

If the iPhone can get sufficient marketshare, it will be fascinating to see what unexpected apps the Japanese developer community comes up with.

Crowdsourcing your next job

Network, St Louis, ecommerce 4 Responses »

Feb 232009

My good friend has decided to look for a new job. Today, she brought in some good Mexican food for the crew as a thank you. It was, however, not a free lunch. In return for the tacos, we were supposed to go to the white board in the conference room and suggest where she might work next. For the price of 2 dozen lunches, my friend tried to crowdsource her next job.

Soon enough (if not already), everyone will be connected to everyone else in their immediate market segment. We’ll all have a Kevin Bacon number of 3 or lower. Linkedin, which originally provided value as the “inside connection” to a given company or executive, now has become the ubiquitous contact folder for everyone. Where recruiters used to thrive on Linkedin because it complimented and extended their most valuable asset: their rolodex of contacts, it now threatens to replace that rolodex completely. The Recruiter still has value, as someone who knows how to interview a candidate and get at the soft chewy center of a person to see if they are a good match for the company with an open position, but not as a simple nexus of resumes in one hand and job openings in another.

Given that Linkedin has given us all that magic rolodex, why not try to crowdsource positions? How could one simultaneously incent the armchair recruiter in all of us, yet invoke enough friction to keep out the spammers and robots?

Here is my idea:

Vigorously pursue companies to list their open positions on the network
Invite people to recommend people in their network for the open positions, with a standing bounty of 10% of first-year salary (still leaving room for the recruiter doing the actual interviews to make 10-15%)
If Andy is going to recommend Betty to C Corporation, then Andy needs to pay $5 to Betty (she’s the one looking for a job, and probably needs the $5 anyway)
C Company would see that Betty is recommended by 7 of her friends (all willing to stake $5 on it), and therefore she is probably worth a look. If Betty is hired, the 10% is split amongst the 7 people who recommended her.
Andy just profited $1423 for his work (assuming 10% of $100,000 job, spilt 7 ways, minus the $5)

Hmmm. This might work. I should ping Harry or Alex or my old friends at Daijob.

UPDATE: 27 May 2010: Looks like I called it. http://www.notchup.com/ is almost a perfect match for this business model.

Older Entries

Qualitative Feature Polarization

Everyone’s an expert (part 1)

Google Wave is Somewhere in-between

Death of the “Panel of Experts”

.mobi or not .mobi

The Rise of the Network Biologist

Free iPhones in Japan

Crowdsourcing your next job

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