Who cares about grandma?

Jeremy Crane at Compete.com reports that

The top 1% of searchers performs a full 13% of all searches in a given month.

If you extend this to the top 20% the number of queries increase to roughly 70%.

John Battelle argues that the focus on making things easy for common users -- make it work for grandma, as I frequently advocate -- could be misguided.

Since power users make the majority of searches, a search engine that targets power users could attract a majority of searches without attracting the majority of visitors.

However, there is some debate in the comments to John's post about whether Jeremy measured the right thing. What matters most is not the number of searchers, but the ad revenue from those searches.

It is unclear whether these power users who are making the majority of searches are actually the most profitable visitors. Some commenters argue, only anecdotally, that power users may be the least likely to click on ads.

It is an interesting question and one that begs for hard data. Does anyone know if 20% of searchers generate 70% of advertising revenue? Is that 20% is the same 20% that does the 70% of searches? Alternatively, is there is a negative relationship between number of searchers performed by a user and the average revenue per search?

Update: At least for banner advertising, AOL EVP Dave Morgan apparently has some data, writing:

Ninety-nine percent of Web users do not click on ads on a monthly basis. Of the 1% that do, most only click once a month. Less than two tenths of one percent click more often. That tiny percentage makes up the vast majority of banner ad clicks.

Who are these “heavy clickers”? They are predominantly female ... older ... [and] Midwesterners ... They look at sweepstakes far more than any other kind of content. Yes, these are the same people that tend to open direct mail and love to talk to telemarketers.

What does all of this mean? It means that while clickers may be valuable audiences, they are by no means representative of the Web at large.

[Found via Danah Boyd via Jeremy Pickens]

Personalizing the newspaper

I do not agree with everything AP CEO Tom Curley said in his Nov. 1 speech, but I am going to shamelessly pick out the parts I agree with in my excerpts below.

First, some excerpts on personalized news:

The perfect paper or newscast is becoming possible -- at least in the reader's or viewer's eyes. What is it you really want to know? We can personalize content now.

We’re not stuck on those 15-ton behemoths that miraculously manufacture a one-size-fits-all package over several hours that gets delivered over even more hours at great cost or captive of a 22-minute time slot engineered to reach a vast range of content tastes.

The economies of scale with mass production of print newspapers or television broadcasts are much smaller on the Web. On the Web, we have the opportunity to print a different newspaper for each reader, giving each reader a personalized front page.

Next, some excerpts on personalized advertising:

The structure for advertising is changing from mass to targeted.

When you drop a cookie on someone in the digital space, the ads you serve that viewer become up to 200 times more valuable ... The future is about serving ads to people, not to pages or programs.

Offline, we have no opportunity to show different advertisements to different people. The newsprint page, the TV broadcast, the billboard, all are static. Online, we can identify each viewer of the ad space and show something that is likely to be relevant (and maybe even helpful) to that viewer.

Just like Amazon shows a different page to each user -- a store for every customer -- newspapers should build a different page for each reader. Newspapers have gone far too long trying to apply the old static offline model to the online world.

Please see also my Nov 2004 post, "It's the content itself", on a much older speech by Tom Curley calling for personalized news.

[Tom Curley speech found via TechDirt]

Show advertising people might want

Tim Lee at TechDirt has a great post about personalization of television advertising:

As the quality of Google ads got better, users started to discover that Google ads were actually useful and relevant, and they got in the habit of looking at them.

Ads are content, and they're a lot more effective if they contain information people actually want.

With a little ingenuity, TV networks could be using devices like TiVo the same way Google uses click-through statistics: as a way to gather data on user attitudes toward different ads.

Display a different set of ads to each viewer, with the ads chosen based on the individual viewer's show-watching and ad-skipping history as well as some basic demographic characteristics. For example, users who frequently skip car ads would be shown fewer car ads. Viewers under 40 would never be shown ads for adult diapers, and all-male households would never be shown ads for feminine hygiene products.

Viewers would find ads more useful and less irritating, while advertisers would be willing to pay higher rates for ads that were precisely targeted at relevant subgroups ... Show users ads they actually find entertaining and useful.

We are all bombarded by advertising in our daily lives. Junk mail, ads in magazines, TV ads, it is all ineffective mass market noise pummeling us with things we don't want. It is a useless waste of time, a missed opportunity to capture a fleeting glimpse of attention.

Things change if we view advertising as content. Advertising can be useful information about products and services we actually want. The advertisements we see should be helpful and interesting, not annoying and irrelevant.

Personalizing advertising -- targeting to advertising to individual interests -- can make advertisements relevant, useful, and helpful. By learning from what each person likes and does not like, personalized advertising can use that fleeting glimpse of our attention to show us something we actually might need.

Please see also my March 2005 post, "Personalized TV advertising", and my Nov 2005 post, "Is personalized advertising evil?"

Microsoft Dryad and Google MapReduce

Michael Isard at Microsoft Research gave a Google Tech Talk, "Dryad: A general-purpose distributed execution platform".

Dryad might be considered Microsoft's answer to Google's MapReduce. Both are distributed programming platforms designed for large scale computation over massive amounts of data. Michael directly points to MapReduce in his abstract, and the talk focuses the "more general computations" Dryad supports.

Do not miss the Q&A at the end of the talk (starting at 43:10) with the Google engineers poking at the scalability and reliability of parts of Dryad.

Rick Dalzell retires

Amazon CIO Rick Dalzell retired after ten years at the company.

It was a great pleasure working with Rick during the early days of Amazon. As Jeff Bezos said, "He has been a coach and mentor to many of us."

Thank you, Rick, and congratulations on your retirement!

UW CS professors to lead Google Fremont office

John Cook at the Seattle PI has the scoop that UW CS professors Brian Bershad and Craig Chambers "are joining Google's new development office in Seattle's Fremont neighborhood" and that "Bershad will lead the new Fremont office."

I have to say, this is quite the coup for Google. Shiva Shivakumar at Google Kirkland is quoted in the article as saying, "These two are spectacular", an opinion that matches my own experience with Brian and Craig during my years at UW CS.

By the way, I first heard rumors of this from Erik Selberg. Very cool to see those rumors confirmed.

Update: John Cook at the Seattle PI posts details of his tour of the Google Fremont office.

Targeted advertising and Facebook

Stefanie Olsen at CNet asks, "Can Facebook feed its ad brains?", in an article that describes some of the challenges Facebook will have as it attempts to drive substantial revenue from its website.

Some excerpts:

Facebook must figure out how to serve the right ads to the right people in real time.

That's no small task. In fact, it's a massive computing problem and one that very few companies apart from Google and Amazon have mastered.

Now Facebook must figure out how to take billions of data points about its members and turn that into an automatic ad machine .... With advertising, it's all about matching the right person to the right ad.

Machine learning in online advertising might involve trying many different techniques on affinity groups to figure out which work best ... No one obvious technique is the silver bullet for social networks -- no one has solved the problem of serving ads in that setting before.

I am quoted a couple times in the article arguing that purchase intent is weak on Facebook. People do not come to Facebook on a mission to buy a product. This makes it particularly challenging to get Facebook users to perceive the advertising not as annoying but as useful, which is almost certainly a necessity for their ad platform to generate the unusually high revenues that appear to be expected in their $15B valuation.

For more on advertising and dealing with weak purchase intent, please see my earlier post, "What to advertise when there is no commercial intent?".

Update Eric Eldon posts details on what Facebook has launched so far, saying:

Users will be able to add advertisers as friends ... [and] click on ... buttons ... [that] share the advertising information with their friends on Facebook ... This sounds far-fetched as a successful advertising strategy.

Facebook ... will also run what it is calling SocialAds, which take social actions from your friends -- such as a movie rental -- and combine that information with an advertiser's message to display the most "relevant" ads to you ... The risk, however, is that users will bristle when they get hit with such ads.