Dead simple, handy tool for adding contextual help to any web page or entire site. It is installed on this blog — so go ahead and select something with your mouse.
Then you can choose to look up the word(s) on the dictionary, thesaurus, wikipedia, or amazon. Or you can translate it, clip 2 send it, or Google it.
Install on any webpage or blog by way of 1 line of code:
Select any text, contextual bubble appears, click Wikipedia to get more information about the selected text
When more than 5 words are selected, other options are grayed out and clip2send is the link to click to send the selected part of the page via email. Type in the email address; the subject line is autofilled, but editable; the source URL is automatically cited.
Select text, contextual bubble appears, click Amazon link to bring up results on Amazon. For example if you select the words Samsung LED HDTV and then use the contextual bubble to choose Amazon, it will bring you to the page and execute the search for you using the words you selected.
digital orchestration means helping clients orchestrate and coordinate the activities of agencies that have particular specialties — search engine optimization (SEO), search engine marketing (SEM), website design and build, analytics, social marketing, etc. Too often, clients are not comfortable asking about digital or don’t know enough to tell if the agency specialists are recommending the correct strategy or tactic.
search consultants typically help individual clients find individual agencies that are good at a particular area — e.g. TV agency, digital agency, SEO agency, etc. Today, this is no longer as effective because the different disciplines and specialities need to work closely together and feed off of each other to work properly.
The wide-angle 28 mm/F1.9 GR Lens is all new, while the high-sensitivity 10-megapixel CCD and the GR Engine III image processor are likely evolutionary steps from the previous generation.
2009 06 16 What Is Web 3.0 – Presentation Transcript
What is Web 3.0? Dr. Augustine Fou June 16, 2009. June 16, 2009.
Evolution of the Internet microprocessor 40 yrs 10 yrs 20 yrs 5 yrs present web internet 2.5 yrs social networks e-commerce 1.5 yrs Web 1.0 Web 2.0 Web 3.0? June 16, 2009.
Evolution of the “Web” content commerce search social networks social content social search social commerce As each stage reaches critical mass, the next stage is tipped into present June 16, 2009.
Key Characteristics present web 1.0 web 2.0 web 3.0
Speedy
more timely information and more efficient tools to find information
Collaborative
actions of users amass, police, and prioritize content
Trust-worthy
users establish trust networks and hone trust radars
Content
content destination sites and personal portals
Search
critical mass of content drives need for search engines
Commerce
commerce goes mainstream; digital goods rise
Ubiquitous
available at any time, anywhere, through any channel or device
Individualized
filtered and shared by friends or trust networks
Efficient
relevant and contextual information findable instantly
June 16, 2009.
Illustrative Examples – retail/shopping present web 1.0 web 2.0 web 3.0
what friends bought or want to buy
drag-to-share items which friends know friends are looking for
item collections
value in the aggregation
overstock.com amazon.com FB app: MyFaveThings
contextual reviews
reviews of reviews
what others bought
individualized recommendations
June 16, 2009.
Illustrative Examples – social networks present web 1.0 web 2.0 web 3.0
aggregates all your online identities
syndicates all your updates to all social networks
social actions visible to friends
trust networks across geography, time, and interests
collection of personal homepages
geocities.com facebook.com peoplebrowsr.com June 16, 2009.
Illustrative Examples – restaurant reviews present web 1.0 web 2.0 web 3.0
Yelp content vetted through a user’s trust network and individual recommendations made based on situation and need, in real-time
user submitted reviews
related items based on similarity of user preferences
infrequent publication
centralized editorial control
zagat‘s yelp need reco for great Italian + GPS + Yelp 5-star Babbo, been there, love it June 16, 2009.
Illustrative Examples – photos present web 1.0 web 2.0 web 3.0
real-time, contextual “do you like this knit shirt?”
friends give immediate feedback
share photos with friends and strangers
enable visitors to tag and comment
individual albums
kodakgallery.com flickr.com ? June 16, 2009.
Illustrative Examples – real estate present web 1.0 web 2.0 web 3.0
information vetted by fellow users, recommended directly an in context
listings plus relevant information like school zones, comparable sales, alerts
listings based on parameters
corcoran.com streeteasy.com trulia iphone app June 16, 2009.
Illustrative Examples – encyclopedia present web 1.0 web 2.0 web 3.0
content is ubiquitous and available through any channel or device
trust network proactively forwards relevant info to user who needs it
created, updated, and edited (policed) by user actions
digitized version of printed encyclopedia
britannica.com wikipedia.com chacha.com June 16, 2009.
Illustrative Examples – online coupons present web 1.0 web 2.0 web 3.0
coupons delivered contextually and proactively when user needs it (without the user even asking for it)
instant feedback
community action makes it more accurate and useful for others
collection of online coupons – value in the aggregation
Facebook click-through rates of 0.01 – 0.05% (Facebook CTRs)
Facebook effective CPMs turned out to be $0.01 – $0.19 (Facebook eCPMs)
Facebook average CPCs ranged from $0.05 – $0.25 (Facebook CPCs)
Other social media benchmarks from my experiments (Adwords, StumbleUpon, PayPerPost / Izea) can be found here.
As a scientist, I like to run experiments. And I like to make stuff. So my team and I made a few Facebook apps that solved needs that we had (a few samples listed below) and shared them publicly on Facebook to see if they were also useful to other people too.
I beta tested some apps with a few friends by inviting them directly. Then to get it out to a larger number of people, we decided to try Facebook advertising, the much-hyped, holy grail of display advertising on one of the largest and most active social networks.
- social commerce – I’ll buy what he bought; things I have, things I want
But what I found was eye-opening to say the least. Despite the potential of social ads where the social actions of your circle of friends could make the ads more targeted, none of the anticipated positive effects were observed. Despite the promise of mass reach, there was not the corresponding attention or clicks. And despite the use of demographics-based targeting, there was no statistically significant difference between different targets nor the control sample, running during the same time period.
What we saw were click-through rates of 0.01 – 0.05% — and the 0.01% often seemed like rounding because they did not report more than 2 decimal places. As a result of these click rates the effective CPMs turned out to be $0.01 – $0.19 and average CPCs ranged from $0.05 – $0.25. I’ve been running these Facebook ads for more than 12 months; and millions of impresisons later, there is no observable improvements to CTRs and thus CPMs and CPCs. But since I set up the campaigns to only pay when there is a click (CPC basis), I can let these run indefinitely because I am getting so few clicks, it’s not even making a dent on my credit card (which I use to pay for the ads).
Ideas for Facebook
In the spirit of openness, as an advertiser who wants to continue using Facebook advertising, perhaps there are a few things they can do to improve the effectiveness of Facebook display ads.
1. reduce the number of ads per page to 1 – displaying multiple ads artificially depresses click-through rates because users can only click on 1 thing at a time, even if they liked more than one of them. Displaying 3 on a page simply increases the denominator while the numerator does not increase — in the click-through rate equation: clicks / impressions.
2. make ads sharable – in the rare instance a user views an ad, it may or may not be relevant to her, but she may know that it is relevant and timely for a friend. By making ads sharable, she can click and send to a friend, who is very likely to find it useful and valuable, especially having been sent by a friend.
3. let users opt-in to ads in specific topic categories - when users are in the market for specific things, they are more likely to subscribe to pertinent news feeds, offers, etc. related to that topic or category. By giving users more power over what they want to see, it will also give advertisers more targeted and engaged prospects to target.
4. expand search-based advertising – when users search they are looking for something and are open to discovering something they didn’t know to ask for. So ads served up in response to a search is usually a lot more effective than ads served up simply when a page is loaded (display advertising). Facebook can serve display ads based on pertinent search queries.
Earth to Facebook… anyone listening?
By Dr. Augustine Fou. Dr. Fou is Group Chief Digital Officer at Healthcare Consultancy Group a group of agencies within the Omnicom family specializing in pharma and healthcare. He helps clients develop digital marketing programs or improve the efficiency and cost-effectiveness existing campaigns via advanced analytics, social marketing, and digital strategy. You can read more of his writing on digital marketing on this blog and follow him on twitter @acfou.
Excerpt from TechCrunch: “Click fraud is serious business on the big search engine advertising networks because the bad guys can make serious money. Sign up for an Adsense account and put those ads on parked domain names or wherever. Then all you have to do is start clicking those ads like crazy, using bots or cheap labor.” On Facebook, “advertisers are clicking on competitor ads to drive up their costs and drive down their ROI.”
“So the bad guys just create thousands of fake Facebook accounts with a wide variety of demographic information. This sounds like a lot of work, but it’s highly automated. the going rate was just $10 per 100 accounts if you supply the unique email accounts. Once the accounts are created, they use software to fill out the varied demographic information, and that software also manages all these accounts. The fraudster then logs in to Facebook via these accounts and views the ads that are displayed. The right competitive ads come up and Bingo, the software then clicks them. Facebook rules allow an account to click any advertisement up to six times in a 24 hour period, and all those clicks are charged. All you need is a few accounts to view the ads and then click to the max.”
Despite click fraud, the click through rates are still incredibly low. So if you subtract all the click fraud, is ANY advertiser making ANY money from facebook advertising?
Others have found similarly dismal click through rates from Facebook advertising
Facebook Ad Click-Through Rates Are Really Pitiful
April 7, 2008 – 5:03 pm
Quite by coincidence, I’ve encountered a few statistics on Facebook’s advertising platform. I thought I’d post links to the results I’ve uncovered, in case anybody is wondering about average CTR rates for Facebook.
First up, Rod Boothby got a click-through rate of 0.01%:
This week, I ran $105 worth of Facebook Fliers. That bought me 52,500 impressions. It looks like the flier bought me about an extra 500 site visits. That’s about $0.21 per hit.
Michael Ferguson ran a bunch of Facebook ads for Kinzin:
Click-through rates are abysmal. I was running the identical ad in about 15 different regions (you need to run them as separate ads to get the stats broken out), getting just over 10M views. Our average clickthrough rate was 0.06% (that’s 1 in 1513, for those counting at home). The best we did anywhere was 0.14%.
He later reports that the conversion rate was “at a pretty reasonable clip” at about 5%. By ‘conversion’, I think he’s meaning people who actually signed up for Kinzin’s free service. All of this stuff is contextual, but if visitors had to lay down money, the conversion rate would be considerably lower.
The folks at Valleywag report similarly dismal numbers:
Media buyers — the agency people who book campaigns — report that the college social network is a truly terrible target. They’re mainly students, with low disposable income, of course; but, beyond that, the users appear to be too busy leaving messages for eachother to show much interest in advertising. Facebook’s members appear indifferent even to movie advertising aimed at their demographic. Clickthrough rates, the percentage of time users click on an ad, average 0.04% — just 400 clicks in every 1m views — according to one report seen by Valleywag.
From AllFacebook:
Fred Wilson has been updating the world about his venture in Facebook advertising over the past week. Today, Fred posted and updated screenshot of his ad campaign’s performance and it doesn’t appear to be too stellar. For one of his campaigns, out of 10,080 impressions there were only 8 clicks. The average cost-per-click for Fred was $0.08 and the average CPM was $0.06. This is a less than stellar performance. This is nothing new though.
And lastly, from a digital student marketing blog in the UK. This would seem like a natural fit for Facebook’s audience:
Our most recent campaign saw 1.4 million page impressions delivered at specific universities – and only a 0.04% clickthrough rate. Ouch.
Click-through rates seem to sit around 0.04%, which is profoundly lame if you ask me. I’m no online advertising expert–it’s not really our thing–but I’ve run a bunch of Google AdWords and other contextual advertising campaigns. We regularly get click-through rates of 3%, and I gather that’s nothing special.
Here’s my theory on Facebook: it’s a silo. People visit the Fun House of Facebook, and conceptually treat it slightly different than the rest of the web. They’re in Facebook, interacting with friends, playing games, sending messages and now chatting on IM. As such, they’re really unmotivated to leave. Who wants to leave the Fun House?
We’ve seen similar results across Facebook. It’s really difficult to drive visitors out of the app and to your own website.
Bed Bath and Beyond is blocking RetailMeNot — possibly because their blended average margin is relatively low and it cannot afford giving “expensive” coupons to everyone.
Macy’s is advertising on RetailMeNot — see “Featured Discounts”
OldNavy is advertising AND shutting off user-submitted coupons
Retailmenot.com started later than Dealcatcher, but quickly overtook them and continues to increase — it is simply more useful because of real time consumer feedback on whether the coupons/codes worked or not.
1. post your “viral” video, banner ad, etc.
2. tweet about it
3. see if any one of your followers re-tweets it
4. check twitt(url)y to see “twitter intensity” around you asset
this is a quick way to tell if what you think is viral is viral. If even your own circle of followers don’t retweet it, it probably isn’t viral. What you think is cool may actually not be that cool. And sticking it on YouTube and supporting it with a lot of paid media, doesn’t make it viral!
Every year around SXSW, there’s a surge in interest about twitter. This time around people have even gone as far as to proclaim twitter to be “the next google” or “the future of search” etc. Bullocks!
Here’s why:
1) distant from other social networks – While we are seeing a massive surge in interest and usage of twitter, it is still a long way off from the number of users of other social networks; it will take a long time to get to critical mass; and this is a prerequisite for twitter to assail the established habit of the majority of consumers to “google it.” — Google’s already a verb.
2) no business model – It remains to be seen whether Twitter can come up with a business model to survive for the long haul. Ads with search are proven. Ads on social networks are not. And given the 140-character limit, there’s hardly any space to add ads.
3) lead adopters’ perspective is skewed – Twitter is still mostly lead adopters and techies so far; so the perspectives on its potential may be skewed too positively. As more mainstream users start to use it, we’re likely to see more tweets about nose picking, waking up, making coffee, being bored, etc…. This will quickly make the collective mass of content far less specialized and useful (as it is now).
4) too few friends to matter – Most people have too few friends. Not everyone is a Scott Monty ( @scottmonty ) with nearly 15,000 followers. So while a user’s own circle of friends would be useful for real-time searches like “what restaurant should I go to right now?” the circle is too small to know everything about everything they want to search on. And even if you take it out to a few concentric circles from the original user who asked, that depends on people retweeting your question to their followers and ultimately someone notifying you when the network has arrived at an answer — not likely to happen.
5) topics only interesting to small circle of followers – Most topics tweeted are interesting to only a very small circle of followers, most likely not even to all the followers of a particular person. A great way to see this phenomenon is with twitt(url)y. It measures twitter intensity of a particular story and lists the most tweeted and retweeted stories. Out of the millions of users and billions of tweets, the top most tweeted stories range in the 100 – 500 tweet range and recently these included March 18 – Apple’s iPhone OS 3.0 preview event; #skittles; and the shutdown of Denver’s Rocky Mountain News. Most other tweets are simply not important enough to enough people for them to retweet.
6) single purpose apps or social networks go away when other sites come along with more functionality or when big players simply add their functionality to their suite of services.
Am I missing something here, people? Agree with me or tell me I’m stupid @acfou
Lift in search is a great indicator of interest. Modern consumers may be inspired by TV ads, but they usually go online to do more research for themselves, to inform their own purchase decision. The following examples show the lift in search after Superbowl commercials or for launch of products like Subway Footlongs. The use of unique, made-up words makes it easier to detect lift in search (see related post: made up words are great for tracking buzz and search volume ). There is now a correlation between offline paid advertising and online behaviors of modern consumers that can be tracked and ultimately related to sales.
What is harder to do is track lift in search from smaller TV media buys or from terms which are generic — e.g. American Express OPEN, Proctor & Gamble’s TAG (men’s deoorant), etc. And furthermore, people may or may not remember the brand name itself and may type in a more general search query — e.g. “talking baby” instead of” e-Trade” or “dancing lizards” instead of “SoBe LifeWater.” And most people usually forget to type in special URLs specified in the ads. So the opportunity is to 1) use made-up words which can be used to detect lift in search and 2) search-optimize around other more generic terms that people may search for if they remembered the ad, but did not remember the brand name itself.
key learnings include:
1. only the superbowl TV ads generates enough awareness to drive lift in search volume detectable above the noise or normal levels
2. made up words are useful in correlating paid advertising and subsequent online actions (e.g. search) because most users forget or are too lazy to type special URLs
3. is is always better to have real analytics from the site to see when paid campaigns hit; site analytics will also reveal more information about users including demographic information, what they are looking for, and even whether they “convert” to a sale or a desired action — like print off a coupon, etc.
Notice the January spikes for several of the examples below — these are their Superbowl ads in action. But also notice how sharp the spikes are — most of them go back to prior levels within 1 – 3 days (see related post: the ephemerality of the Superbowl halo )
Recent Articles by Dr. Augustine Fou
