Internet trends: marketing research & predictions

THE NEW NEXT: TrendsSpotting’s Trend Prediction Model

December 21st, 2010 by

2011 Trends Predictions THE NEW NEXT: TrendsSpottings Trend Prediction Model

Within the next few days TrendsSpotting will be publishing the 4th annual report – THE NEW NEXT 2011, following major digital trends. We will be featuring the predictions of digital and marketing experts on the big changes awaiting us in the coming year.

What we have learned from working with different consumer domains is that one consumer behavior can develop into another behavior. People are looking for ways to display these behaviors. Technologies offer them such solutions.

Influenced by consumer preferences as well as by external circumstances, new industries are opening up to answer consumer needs. We are interested in finding out how such future developments may unfold next year.

To predict 2011 TrendsSpotting is using a unique forecasting model based on four fundamental roots:

The CTBI Prediction Model

1. Consumers behaviors and needs

2. Technologies: What platforms / devices will enable such behaviors?

3. Business Strategies Business challenges and opportunities

4. Industry Outlook: Which markets will take advantage of these new behaviors?

Lets review one example:

Retrospect:

As the recession started (external event) we reported on a rise in coupons (consumer behavior). This phenomenon is quite evident in the Internet (platform) with deals promotion skyrocketing, and location based deal offerings penetrating mobile devices.

In line with the increase in social interaction platforms, as more people report trusting their friends opinion and take part in group initiatives – the group purchase phenomenon has increased dominance towards the end of 2010 (new consumer behavior) with companies as Groupon and LivingSocial leading the industry.

Prospect:

Looking at Online Shopping in 2011, we predict that consumers will be willing to get rewarded for recommending deals to their friends. Retailers will communicate their offerings through coupons and integrate user’s personal friends into their sales systems.

2011 predictions:

TrendsSpotting has a record of providing digital trend predictions all year long. At the end of the year, we try to reward our readers by providing them with focused insights and multi-perspective trends to follow, in a nicely designed package.
You can refer to our previous entries in the series: TrendsSpotting’s Influencers 2010, 2009, 2008.
You are invited to suggest your insights in the comments.

Watch for our Trend Prediction Report in the next few days.

Best,

The TrendsSpotting team.

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5 points marketers should remember about Twitter’s effectiveness

December 16th, 2010 by

Recent Twitter statistics presented by Pew Internet came with some surprise in the new media. The survey revealed that only 8 percent of American internet users are using Twitter. Pew’s findings are actually in one hand with other research findings as Edison Research survey (1,753 respondents) which reported that while 87% of the American know about the service, just 7% of Americans actually use Twitter.

Though the low penetration rate may lead to some disappointment – here are five points marketers must not forget about Twitter:

1. Twitter attracts most relevant target groups:

In the same Pew survey (November 2010) usage of Twitter among sought after groups are significantly higher than the average statistics: Twitter is used by 14% of internet users aged 18 to 29 and 11% of urban internet users.

2. Twitter is a strong sales promotional tool

Survey by Chadwick Martin Bailey and iModerate Research Technologies (March 2010) found that 79% of Twitter followers (versus 60% of Facebook fans) are more likely to recommend brands since becoming a fan or follower. Moreover, 67% of Twitter followers (versus 51% of Facebook fans) are more likely to buy the brands they follow or are a fan of. Similarly, in a research conducted by Comscore (Q2 2010) 36% of Twitter users reported that they use the service mainly to find sales and product reviews.

3. Twitter generates more click-throughs and leads

Marketing firm SocialTwist analyzed more than one million links on Facebook and Twiiter  platforms. The researchers found that Facebook’s shared links average only 3 clicks, while Twitter’s embedded tweets generate 19 clicks.

In another study among business-to-consumer small and medium-sized companies, more than one-half of those using Twitter generated double the median monthly leads of non-Twitter users. That result held across company size.

4. Twitter users are most active in spreading information:

According to ExactTarget’s survey, daily Twitter users are about three times as likely as internet users on average to upload photos, four times as likely to blog, three times as likely to post ratings and reviews, and nearly six times as likely to upload articles. If you need effective marketing – Twitter users will help you do your job.

5. Twitter as a crisis management tool:

In a recent report by Cisco, the company (who is not only a service user but has also initiated its own social media monitoring platform) concludes that
“Twitter provides a means of communication between our employees and their peers, clients, customers and potential customers. We have learned in the broader landscape how Twitter is effective in crisis, able to reach many people in a short amount of time. It is also effective for information gathering, polling and for vetting information”

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Driving work forces behind millennial women

November 30th, 2010 by

Euro RSCG Worldwide has recently published findings from The Millennial and Gender study (survey of 3,000 adults in China, France, India, the U.K., and the U.S).

The first findings released in the report presented the perceptions of millennial respondents (aged 18 to 25) in three western countries: U.S., U.K. and France.

While the scope of research lies in gender differences  of general life experiences, I’ve chosen to highlight work related findings. In this context, it is important to note the recession might have influenced respondents differently,  according to their country of origin.

1. Priorities in the choice of the workplace:

 Driving work forces behind millennial women

While western men prioritize rather equally between salary, work atmosphere and work life balance while looking for a new job (with an emphasis on salary in particular among men in the U.K.),  western females point to life-work balance as their top concern (that is even when motherhood is not yet a relevant  concern in the life of these young women)

2.  Work motivations:

 Driving work forces behind millennial women

Demographic differences can be easily captured:

  • Women tend to value self fulfillment more than men do (differences look most pronounced among UK respondents).
  • For US millennial women money is a stronger motivation in work compared to US men. US men on the other hand, are the most socially driven people compared to all age and country groups. Their wish to contribute to the country’s welfare is largely pronounced.
  • The importance of self fulfillment is much larger among French respondents (for both men and women) compared to respondent in the UK and US. This might be due to perceptions influenced by the impact of the recession. In countries more badly hit by the downturn, self fulfillment might be perceived as a luxury.

(Note: these two questions are subjected to biases: not all respondents similarly admit true forces that drive their life. Men and women, as well as those coming from different countries, may confront with the social biases differently, thus masking the real effect).

Lastly, the report quotes finding from the recent US National Economic Council, presenting points of success and barriers that awaits the above millennial women in the work force:

 Driving work forces behind millennial women

Will the strong desire for self fulfillment observed in the survey among young females be powerful enough to change this gloomy image?

Further reading:

The white paper, “Are Women the New Men?”

(watch the video presentation of Marian Salzman, president of Euro RSCG Worldwide PR) .

Follow up on TrendsSpotting’s  review on women in technology  “What It Takes To Be A Digital Woman“.

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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.

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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)

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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.

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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:

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Indian Online Women and Moms: Research Review by TrendsSpotting

July 19th, 2010 by

Indian Online Women and Moms: Research Review by TrendsSpotting from Trendsspotting

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!

View more presentations from TrendsSpotting.

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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…”

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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

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