Internet trends: marketing research & predictions

Mobile web will be catching up with text messaging with more teens owning smartphones

November 17th, 2010 by

A recent comScore report (September 2010) found that 58.7 million people in the U.S. owned smartphones (up 15% from the preceding three month period). The large majority of mobile subscribers (67%) used text messaging on their mobile device. Subscribers who used downloaded applications comprised 33.1 percent of the mobile audience (2.5% increase). Accessing of social networking sites or blogs increased 1.8% representing 23.2 percent of mobile subscribers.

Text messaging behavior is currently driven by young mobile users. Once they will own smartphones – TrendsSpotting predicts a  fundamental change in the use of mobile phones: the text messaging phenomena will be replaced by mobile web activities.

We have collected evidences to support our argument:

1. Teens are yet to own smartphone devices:

According to eMarketer only 12% of all consumers under 24 own an iPhone and less than a third own any type of smartphone. Piper Jaffray’s 20th bi-annual teen survey also shows that less than 15% of high school students already own an iPhone with a third planning to buy one within the next 6 months.

2. Teens current text messaging behavior:

Nielsen’s Q2 2010 teen survey (ages 13-17) demonstrates that while text messaging behavior is still growing among all age groups, text messaging among teens, especially teen females is reaching new levels (female teens send and receive an average of 4,050 texts per month versus  an average of 2,539 texts per males).

3. Teens connected to smartphones already embrace mobile web capabilities:

A. According to Nielsen, teens mobile usage has increased substantially versus Q2 last year, from 14 MB to 62 MB. This increase is the largest jump among all age groups. Much of this boost is attributed to males consuming 75 MB of data, versus 17 MB in Q2 last year. Teen females use about 53 MB of data, compared to 11 MB a year ago. Teens are now downloading a wider range of applications: the use of apps (such as Facebook and Youtube) saw a 12 percent increase versus last year, from 26 to 38 percent.

B. Pew Internet’s April survey shows a consistent trend in the use of mobile phones web related activities among teens-

  • 31% exchange instant messages on their phones.
  • 27% go online for general purposes on their phones.
  • 23% of teens access social network sites on their phones
  • 21% use email on their phones
  • 11% purchase things via their phones.

With more teens owning smart phone devices and with decreasing mobile data cost we expect the text messaging trend to be replaced by similar communication behaviors evolving around the mobile web, specifically  instant messages, social networking and MMS (Multimedia Message Services) which we see as the upcoming communication feature, incorporating  images and videos into the messages.

tt twitter Mobile web will be catching up with text messaging with more teens owning smartphones Tweet This Post tt plurk Mobile web will be catching up with text messaging with more teens owning smartphones Plurk This Post tt buzz Mobile web will be catching up with text messaging with more teens owning smartphones Buzz This Post tt delicious Mobile web will be catching up with text messaging with more teens owning smartphones Delicious tt digg Mobile web will be catching up with text messaging with more teens owning smartphones Digg This Post tt ping Mobile web will be catching up with text messaging with more teens owning smartphones Ping This Post tt reddit Mobile web will be catching up with text messaging with more teens owning smartphones Reddit tt su Mobile web will be catching up with text messaging with more teens owning smartphones Stumble This Post

Twitter can predict earthquakes, typhoons and rainbows too..

October 8th, 2010 by

We have already discussed Google’s ability  to predict the spread of epidemics as the flu. Simply by measuring the surge in search by different locations – Google can trace and predict future occurrences and their spread.
As Twitter platform evolves fully on real-time and enjoys a high frequency of reporting, when sudden events as earthquakes occur, people use Twitter to spread news related to the earthquake. That can enable detection of earthquake occurrence promptly, simply by observing the tweets.

A recent academic paper introduced by Takeshi Sakaki, Makoto Okazaki and Yutaka Matsuo from the University of Tokyo investigates the real-time interaction of events such as earthquakes in Twitter and proposes an algorithm to monitor tweets and to detect a target event.
To detect a target event, the researchers have devised a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, the have produced a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location.
What they actually did was to consider each Twitter user as a sensor and apply filtering features for location estimation in ubiquitous/pervasive computing. They used both a temporal model (assuming the time of the tweet) and a spatial model (taking into account the location of the tweet). Semantic analyses were applied to tweets to classify them into a positive and a negative class (to distinguish between relevant and irrelevant event descriptions).

As a pilot application, the researchers constructed an earthquake reporting system in Japan.

earthquaqe tweets Twitter can predict earthquakes, typhoons and rainbows too..

Because of the numerous earthquakes and the large number of Twitter users throughout the country, they were able to detect an earthquake with high probability (96% of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. The system that was developed to detect earthquakes promptly sends e-mails to registered users. Notification is delivered much faster than the announcements that are broadcast by the JMA.

Using the same Twitter based system the researchers can trace typhoons as well and are planned to track other events as rainbows.

Further research on Twitter as a prediction platform:

Twitter can predict the box office performance of several films (HP labs).

Twittter can predict the elections (UK)

Twitter can predict influenza rates (US)

tt twitter Twitter can predict earthquakes, typhoons and rainbows too.. Tweet This Post tt plurk Twitter can predict earthquakes, typhoons and rainbows too.. Plurk This Post tt buzz Twitter can predict earthquakes, typhoons and rainbows too.. Buzz This Post tt delicious Twitter can predict earthquakes, typhoons and rainbows too.. Delicious tt digg Twitter can predict earthquakes, typhoons and rainbows too.. Digg This Post tt ping Twitter can predict earthquakes, typhoons and rainbows too.. Ping This Post tt reddit Twitter can predict earthquakes, typhoons and rainbows too.. Reddit tt su Twitter can predict earthquakes, typhoons and rainbows too.. Stumble This Post

5 predictions for socio-location recommendation behavior

September 10th, 2010 by

Google (and Yahoo) brought opportunities to the online retail. Location Based Services will bring promising opportunities to offline shopping.

Much has been said about recommendation sites and smart engines as Pandora, Netflix, Amazon and Google.
Looking back at the last ten years – recommendation engines started with item comparison. Personalized engines were then developed and offered suggestions (predictions) based on users past behavior, claimed preferences, or computer pre-defined identification systems.

When social parameters were added – users were exposed to other decisions made by anonymous shoppers (or popular search results).

Today, when social interactions are mainstream, and technology (smartphones adoption continue to rise ) enables location based services, we get new dimensions added to the equation.

According to the Social Comparison Theory people are especially prone to compare themselves to people they view as similar to them. Research has also shown a strong link between social comparison and peer communication about consumption.
Given a location system added to the social knowledge – users are exposed to practical and immediate choices.
Having a direct knowledge on friends buying decisions in times relevant to decision making will certainly influence decision making process. Acknowledging that, Facebook has established Places.

What will social networks and location based recommendations add to this eco-system of recommendation sites?

Following the entrance of location based networks as Foursquare and Gowalla, we have prepared a list of predictions and highlights for future research:

Prediction 1.
Multiple based recommendations might bring to consumer confusion:
Issues to be tested:
1. Will people be able to differentiate between location based recommendation (just because you are here) to a different recommendation type (their pass behavior for instance)?
2. Will people want to learn how to differentiate between parameters which influence their decision?
3. Assuming this given choice – will people really put efforts to chose their preferred recommendation parameter in real time?

Prediction 2:
Location will improve personal voting behavior if it will be connected to real benefits.
Issues to be tested:
What benefits will influence personal voting behavior? (checkout discounts, product giveaways etc) and what will be the preferred form of benefits (first to come, coupons, accumulate loyalty ..)

Prediction 3:
Social presence (quantity: amount of friends / people) will count as quality.
To be tested:

1. Assuming many of ones friends visited a place or purchased a product – would this replace reading their reviews?
2. Are all friends come equal? Will people differentiate between friends (work friends. network friends) as the reliable source of influence?

Prediction 4:
Offline offerings will be more dominant than online offerings with LBS entering the decision making process:

Entertainment (restaurants and bars, events) and offerings made by physical stores will lead the local revolution.
Issues to be tested:
What offline industry sectors will better fit the local recommendation behavior (entertainment? fashion? electronics?)

Prediction 5:
With LBS, local cultures will define consumer behavior.

Consumer learning will shift from demographics (traditional behavior) and digital networking (global influence) to local communities.

To learn more on experimental marketing activities of brands using socio-location  incentives- follow the reviews made by Click, Read Write Web and ABI Research.

tt twitter 5 predictions for socio location recommendation behavior Tweet This Post tt plurk 5 predictions for socio location recommendation behavior Plurk This Post tt buzz 5 predictions for socio location recommendation behavior Buzz This Post tt delicious 5 predictions for socio location recommendation behavior Delicious tt digg 5 predictions for socio location recommendation behavior Digg This Post tt ping 5 predictions for socio location recommendation behavior Ping This Post tt reddit 5 predictions for socio location recommendation behavior Reddit tt su 5 predictions for socio location recommendation behavior Stumble This Post

Visualization of Real Time Moods

July 26th, 2010 by

We have previously discussed visualization techniques of moods and emotions. We have used “We Feel Fine” data mining engine to uncover what the year 2007 holds for people as they write about their feelings in blogs. Moodgrapher helped us to follow emotions during symbolic dates as Valentine or tragic events as the one of Hurricane Katrina. We have also analyzed the development of “emoticons” and what they reflect.

With the ability to track feelings on real time we can further extend our knowledge:

Computer scientist Alan Mislove at Northeastern University in Boston and colleagues followed emotions expressed in  Twitter’s tweets.

They have found that the west coast is happier than the east coast, and across the country happiness peaks during early morning time. Unsurprisingly, people are happier during weekends.

 Visualization of Real Time Moods

Method:

1. The researchers analyzed 300 million public tweets of claimed US Twitter users, posted between September 2006 and August 2009.

2. They ranked words according to a psychological word-rating system (Affective Norms for English Words) which can distinguish between positive and negative words.

3. The researchers calculated the average mood score of all the users living in a state hour by hour and so created a timed series of mood maps. They morphed the maps so that the size of each county reflected the number of Twitter users living there.

Have a look at the map (video) to learn how different emotions (green represents happier emotions) are washing the nation east to west throughout the day – and notice the three hours time delay effect.

Read past research on Twitter:

tt twitter Visualization of Real Time Moods Tweet This Post tt plurk Visualization of Real Time Moods Plurk This Post tt buzz Visualization of Real Time Moods Buzz This Post tt delicious Visualization of Real Time Moods Delicious tt digg Visualization of Real Time Moods Digg This Post tt ping Visualization of Real Time Moods Ping This Post tt reddit Visualization of Real Time Moods Reddit tt su Visualization of Real Time Moods Stumble This Post

Indian Online Women and Moms: Research Review by TrendsSpotting

July 19th, 2010 by

In this presentation we review online Indian women as they  become a major player in the Indian Online arena.

Recent research indicate that a third of young online women in India are active users. Moreover, Indian mothers can be considered a worthy target online:
1- Indian Online moms see the internet as a vital communication and information tool.
2- They spend more time on the web compared to all other media
3- They are highly engaged in all internet related behaviors (search, read newspapers, listen to music, watch TV)
4- Many of them share experiences on brands and purchases online.

Previous report on Online India: TrendsSpotting’s Handbook of Online India.

Previous presentation on Digital Women:”What it takes to be a digital women

Enjoy!

Indian Online Women and Moms: Research Review by TrendsSpotting
View more presentations from TrendsSpotting.

tt twitter Indian Online Women and Moms: Research Review by TrendsSpotting Tweet This Post tt plurk Indian Online Women and Moms: Research Review by TrendsSpotting Plurk This Post tt buzz Indian Online Women and Moms: Research Review by TrendsSpotting Buzz This Post tt delicious Indian Online Women and Moms: Research Review by TrendsSpotting Delicious tt digg Indian Online Women and Moms: Research Review by TrendsSpotting Digg This Post tt ping Indian Online Women and Moms: Research Review by TrendsSpotting Ping This Post tt reddit Indian Online Women and Moms: Research Review by TrendsSpotting Reddit tt su Indian Online Women and Moms: Research Review by TrendsSpotting Stumble This Post

What Goes Viral? Digital Storytelling

July 19th, 2010 by

 What Goes Viral? Digital Storytelling

I happened to be involved in an interesting case study on “Digital Storytelling”.

Guy Kawasaki is preparing the last draft on his new exciting book “Enchantment : The Art of Getting People to Do What You Want”. Last week, I was discussing storytelling with Guy for one of the chapters in his book. I suggested him to read a good article I’ve found on the subject, written by Rick Braddy. Guy has tweeted the article in Alltop and renamed it “How to use storytelling for a product intro”. Unsurprisingly, the article went viral in no time.

Rick Braddy has dedicated a post on that. He says that “after several years of investing in my blog, it’s great to see I’ve finally struck a chord and people are excited about incorporating storytelling into their marketing”.

We can learn much on Digital Storytelling from this case study.

Storytelling needs not only a well written memorable tale, but also a catchy title which is instructive “How to use” and is directly connected to needs (“a product intro”), an effective distribution channel (Alltop is one good example) and of course, a well respected teller (or ambassador in this case, which can bridge between different cultures- business, marketing and digital).

That’s all on “How you do storytelling on storytelling…”

tt twitter What Goes Viral? Digital Storytelling Tweet This Post tt plurk What Goes Viral? Digital Storytelling Plurk This Post tt buzz What Goes Viral? Digital Storytelling Buzz This Post tt delicious What Goes Viral? Digital Storytelling Delicious tt digg What Goes Viral? Digital Storytelling Digg This Post tt ping What Goes Viral? Digital Storytelling Ping This Post tt reddit What Goes Viral? Digital Storytelling Reddit tt su What Goes Viral? Digital Storytelling Stumble This Post

Eight Insightful Twitter Search Queries

June 29th, 2010 by

Social media conversations can reflect peoples’ attitudes, needs, desires and intentions. The challenge is to listen wisely and use market research skills to “ask” the right questions.

When you wish to follow perceptions and intents keep in mind that you will need to follow day to day jargon, and that the conversations traced are typical to the specific social network you are searching at.

Currently, I find that there is no tool as Twitter to extract such knowledge and map real time reflection of peoples minds.

While conducting many social media research projects, I have collected some insightful search queries, useful for marketers and to those who wish to keep track on perceptions and shared interests.

I have divided eight social media queries to the following three categories:

1. Needs and desires

2. Attitudes toward brands

3. Buying intentions

Most of the examples presented here are global, but as you can now search Twitter conversations within specific locations (you can simply choose distance from a desired location and trace locations by coordinates), some of the conversations were generated in the New York area and by that reflect a specific demographic community.

 Eight Insightful Twitter Search Queries

Continue reading Eight Insightful Twitter Search Queries

tt twitter Eight Insightful Twitter Search Queries Tweet This Post tt plurk Eight Insightful Twitter Search Queries Plurk This Post tt buzz Eight Insightful Twitter Search Queries Buzz This Post tt delicious Eight Insightful Twitter Search Queries Delicious tt digg Eight Insightful Twitter Search Queries Digg This Post tt ping Eight Insightful Twitter Search Queries Ping This Post tt reddit Eight Insightful Twitter Search Queries Reddit tt su Eight Insightful Twitter Search Queries Stumble This Post

Boomers Feel Increasingly Comfortable Online: Research Indications

June 15th, 2010 by

AARP has published its recent survey on Boomers (conducted by GFK). According to this survey only 40% of adults age 50 and over consider themselves extremely (17%) or very (23%) comfortable using the Internet. That implies that about 60% of them feel uncomfortable for some degree and that they are still trying to figure out the web.

Is that really so?

According to Pew Internet 7o percent of those aged 50 to 64 use the Internet.  The drop in internet use is found for the 65+ group (38%). Therefore, the skewed internet perception was probably made by the merging of the two age groups.

{AARP, GFK: If you wish to track technological trends among the older generation – you must be aware of the differences between its inner groups. I believe that the two age groups (46-64 age group commonly defined as Boomers and the 65-75 defined as Matured) are largely distinctive by their online behavior}

What can be learned on Boomers?

Recent Pew Internet surveys reveal that about two-thirds (66%) of those 50-65 positively perceive the internet as a change “for the better” (only 18% say it has been a change for the worse). Moreover, Among adults 50-65: 56% have home broadband connection, 46% have accessed the internet wirelessly, and 9% are already using Twitter or another status update system.

eMarketer (January 2010) has dedicated a report on Online Boomers.   According to the findings they present – Boomers feel very comfortable online:

  • Deloitte 2009 survey brings evidence to the large increase in Social Networking among Boomers and Matures:

46 percent of Boomers (up from 31% in year 2008), and 14 percent of Matures (up from 36% in year 2008) maintain a profile on a social networking site like Facebook, MySpace or LinkedIn (compared to 77 percent of Millennials, 61 percent of Gen Xers)

  • Boomers are testing other social media as well, including Twitter, blogs and review sites, but have yet to adopt them (Only 10% of online boomers were Twittering in September 2009)
  • eMarketer also points to the fact that 49% of Boomers claim that their purchase decision was influenced by an online review or recommendation on a retailer’s site.

When you search for a potential market growth – I would definitely check out the Boomers. They are feeling more and more comfortable online!

tt twitter Boomers Feel Increasingly Comfortable Online: Research Indications Tweet This Post tt plurk Boomers Feel Increasingly Comfortable Online: Research Indications Plurk This Post tt buzz Boomers Feel Increasingly Comfortable Online: Research Indications Buzz This Post tt delicious Boomers Feel Increasingly Comfortable Online: Research Indications Delicious tt digg Boomers Feel Increasingly Comfortable Online: Research Indications Digg This Post tt ping Boomers Feel Increasingly Comfortable Online: Research Indications Ping This Post tt reddit Boomers Feel Increasingly Comfortable Online: Research Indications Reddit tt su Boomers Feel Increasingly Comfortable Online: Research Indications Stumble This Post

For Google TV to Succeed – social features must be included. Social TV Research Indications

May 27th, 2010 by

 For Google TV to Succeed   social features must be included. Social TV Research Indications

Image based on “Living Room and TV Digital Art” by Carlos Cunha.

Google has teamed with Intel, Sony and Logitech to bring Google TV to televisions, Blu-ray players and companion boxes. Google TV uses search to give you an easy and fast way to navigate to television channels, websites, apps, shows and movies. As the company is opening up Google TV to developers, and considering  the dominance Google and its partners have on both web and traditional TV  – it has a good potential to be a success, in the same place many other players failed with web TV (AppleTV, Vudu, Roku, TiVo, Sony Bravia Internet Link, Logitech, Jadoo, Microsoft).

But, while competing on our living room, Google in its recent release left out the social aspect of the promised integration between web and TV.

Trendsspotting has previously discussed the social nature of television versus the internet, based on users perceptions. Today, more than ever – it seems that these two media channels are not to be separated. There are many indications  for the social need to combine TV watching with the ability to exchange opinions with friends about the content they are watching.

In this review we will examine the current role of TV, learn on preferences concerning the use of TV online, and examine the needs surrounding TV social sharing.

1. The importance of TV:

According to a 2009 survey conducted by Pew Research Center- 52% of Americans (down from 64%) think of their TV as a necessity. Cable or satellite TV loses importance (23% in 2009, down from 33% in 2006). 24% of respondents indicating that they have reduced or cancelled cable or satellite TV subscription. A recent national survey from Arbitron and Edison Research (April 2010) shows a similar trend: more Americans said the Internet was “most essential” to their lives when given a choice along with television, radio, and newspapers. A full 42% chose the Internet as “most essential,” followed by 37% who selected television, 14% who chose radio, and 5% who cited newspapers.

In contrast – Deloitte’s 2009 “State of the Media Democracy survey reveals a 26 percent increase in the number of Americans choosing the TV as their favorite type of media as compared to the previous year. Nielsen reports that almost 99% of video content watched in America is still done on traditional television.

2. Watching TV online:

Deloitte’s 2009: When watching their favorite TV programming, 86 percent of survey respondents prefer watching on their television set, enjoying the programming either live, via their DVR/TiVo, or using an “On Demand” feature. While less than 10 percent of Americans say they prefer watching the same content online, a growing number of consumers are using online platforms to watch their favorite TV shows.

Nielsen 2010 study suggests that online video is a replacement of DVR use, or used by those who do not have immediate access to TV. Nielsen claims that  TV network content online is used to catch up with programming, and not typically as a replacement for TV viewing.

3. The needs for Social TV:

A 2009 Parks and Associates survey indicates that over one-fourth of users ages 18-24 are interested in having more social media features integrated into their TV.

ABI research (February 2009) shows that among Social Media users 36%
report they`d like to access their networks on the TV screen. Younger consumers were more interested in engaging with their friends through chat and messaging, while middle-aged respondents were more likely to be interested in more passive social networking behavior such as checking status updates. The most popular potential application for those over 50 who expressed interest in TV social networking was being able to see what their friends were watching on TV.

Sharing video content with friends: Media experiments

Social viewing is certainly a trend spotted by many players. Although Youtube’s RealTime and Hulu’s social sharing features were not much of a success, companies are still trying to find the right formula. Only recently BBC annonunced its ’s social video on demand service -  iPlayer, and U.S. cable company Comcast is bringing a new site Tunerfish, that will enable users to share their favorite shows, letting people know what they are watching on internet and TV.

Conclusions:

1. People seek to bring the web into their TV: more content (free, on demand) together with the social experience.

2. TV online viewing is growing, but that reflects people’s best current option.

3. People prefer the convenience of their TV for television watching.

4. For Google TV to succeed – it must include social features.

tt twitter For Google TV to Succeed   social features must be included. Social TV Research Indications Tweet This Post tt plurk For Google TV to Succeed   social features must be included. Social TV Research Indications Plurk This Post tt buzz For Google TV to Succeed   social features must be included. Social TV Research Indications Buzz This Post tt delicious For Google TV to Succeed   social features must be included. Social TV Research Indications Delicious tt digg For Google TV to Succeed   social features must be included. Social TV Research Indications Digg This Post tt ping For Google TV to Succeed   social features must be included. Social TV Research Indications Ping This Post tt reddit For Google TV to Succeed   social features must be included. Social TV Research Indications Reddit tt su For Google TV to Succeed   social features must be included. Social TV Research Indications Stumble This Post

Mobile Trends and Augmented Reality: RWW Unconference

May 8th, 2010 by

ta 300x264 Mobile Trends and Augmented Reality: RWW Unconference

I was honored to participate in the RWW Mobile Summit which took place today in Mountain View, California. The event was structured in a unique unconference style, great for encouraging brainstorming and insights sharing.
Richard MacManus, Marshall Kirkpatrick and others from the incredible RWW team gathered a mixed group of start-ups, entrepreneurs, developers, journalists, marketers and researchers, who are not only experimenting with mobile innovations, but are taking an active part in this revolution.

While the topics of interest were freely suggested, it came by no surprise that Augmented Reality enjoyed most of the attention.
Personally, I see the excitement around AR somewhat similar to the experience of getting satisfying search results (can you imagine that?). Add to that a great UI experience and you get Augmented Reality!
Augmented Reality from my perspective is all about users’ interaction with new and relevant information embedded in a unique UI display. That can hold for any QR codes (or other markers), location based solutions (take Foursquare and add into it one more display layer) and of course more advanced sensory system which can also monitor and provide feedback.

When we praise AR today – we actually enjoy the visualization aspect of it, as the information we currently receive is still in its infancy. All the rest is our imagination, what we believe can be applied soon, very soon.

I would like to share some of the discussions I’ve enjoyed participating in:

Marshall  Kirkpatrick managed the “Augmented Reality and Beyond” discussion.
The discussion group suggested that AR is most likely to be developed in the following directions:
-  more interfaces: extended screens, accessories and moving objects, human body interface.
-  more tools to gather information (EveryBlock)
- more utilities and practical implementations
- standardization.

In another group managed by Gigi Wang (VLAB) we’ve discussed the monetization of Augmented Reality.
While the common model will go towards relevancy (context and location) consumer ads sponsored by advertisers, the more exciting models will be those which users will find valuable enough to pay directly:
- Traveling (museums, historical monuments) and entertainment (games) can offer users unique experiences (Layar is experimenting already in this direction with its new apps store).
- Real-estate can provide users with unique content that can be cost effective.
- Health monitoring systems can be valuable for its life saving potential.

Lastly, I’ve enjoyed meeting with Jeffrey Pierce – from IBM Research who is researching mobile experiences. We have discussed mobile unique behavioral patterns. Pierce had introduced me with an IBM study which suggested that mobile search in compare with web search (in the enterprise environment) is done mostly during morning time and late night (next to the TV) and is known to be oriented at very specific needs. I’ve suggested that we might learn from other human dynamics with communication devices (landline calls versus mobile call behavior). People are likely to use different system to interact with friends / colleges at different times. They will SMS friends when they have a specific need, call from their mobile when their need is more obscure, or choose to call from a landline when they have a more general interest in learning whats new. Can that have anything to do with our search behavior patterns (web versus mobile?)

Hats off to Read Write Web for one of the most inspiring conferences!

tt twitter Mobile Trends and Augmented Reality: RWW Unconference Tweet This Post tt plurk Mobile Trends and Augmented Reality: RWW Unconference Plurk This Post tt buzz Mobile Trends and Augmented Reality: RWW Unconference Buzz This Post tt delicious Mobile Trends and Augmented Reality: RWW Unconference Delicious tt digg Mobile Trends and Augmented Reality: RWW Unconference Digg This Post tt ping Mobile Trends and Augmented Reality: RWW Unconference Ping This Post tt reddit Mobile Trends and Augmented Reality: RWW Unconference Reddit tt su Mobile Trends and Augmented Reality: RWW Unconference Stumble This Post

« Previous Entries Next Entries »

Real Time Web Analytics