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.

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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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Digital Early Adopters: What has changed?

March 25th, 2010 by

diigital early adopters adage Digital Early Adopters: What has changed?

I was recently interviewed by Laura Rich, a digital media reporter, for an Advertising Age paper: Shiny New Things: What Digital Adopters Want, How to Reach Them and Why Every Marketer Should Pay Attention.

I recommend that you read the article as it presents many new angles and insights provided by professional researchers, marketers and early adopters (as Bill Tancer from Hitwise, Steve Rubel from Edelman, Robert Scoble, and many more).

I wish to further develop some of the points I suggested in the article:

Digital Adopters: what has changed?

Following the adoption of technologies in many consumer domains, a shift can be observed in the last few years in the segmentation and characteristics of early adopters. I tend to attribute this change to the wide and global adoption of the internet.
Technology is no longer the domain of a small minority of young male experimenters (previously known as “geeks”). It is now one of the main communication and business channels available. Consumers are no longer passive to new technology, but are fast learning – active producers.

Here are some evidences to the shift in the concept of early adopters:

  • Gender differences are weaker than ever: Women are embracing new technologies.
  • Adoption rates have shortened: from decades to years, from years to months (Facebook, iPhone).
  • Social behavior and technology advancementare well combined: The first smartphone users are first to adopt social networks (Facebook, Twitter), to experiment with apps, to view TV via internet / mobile. (see PEW survey: 39% of internet users with 4+  internet-connected devices use Twitter) and next to use location based solutions.

The power of early adopters:

In the last five years, early adopters have received a stage to influence others. Social Media gave them the screen power.
Early social media users have grown to be the main influencers, and their influence is far beyond technology. They have become the new celebrities. As part of their positioning, they are expected to act as early adopters, much the same as celebrities are needed to keep updated with fashion.

What should marketers consider when marketing to early adopters?

Early adopters are physically easier to reach but now much harder to “buy”. Most of the brands (and Apple is one big exception) have lost their attractiveness. Brands that can provide early adopters a good reason why – will have a chance to influence. It’s all about proven value.

If early users will find your product handy – they will be willing to spread it. Otherwise – they will not hesitate to share their real thoughts.
I suggest you will follow one of the first Coca Cola initiatives in the social media domain. They choose Brazil as their beta site and sent bloggers a free gift. We named it “Rent a blog strategy” which obviously failed…

Marketers definitely need to learn and understand the new social norms shared by early adopters.

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2010 Mobile Influencers: Trend Predictions in 140 characters (6th report)

January 18th, 2010 by

“2010 Mobile Influencers” is the sixth and last report from the series “2010 Influencers Series: Trend Predictions in 140 Characters“.

Findings: Major trends in 2010 Mobile:

Across many of these predictions, we have identified the following trends suggested to influence Mobile in 2010:

# Payment      # Commerce      #Metrics

# Advertising:  networks,  SMS, display, search, premium

# Smartphones: Apple, Apps, iPhone, Google, Android

# GPS      # Location      # Augmented Reality

# Gaming      # Music      # Video

2010 Mobile Influencers: Trend Predictions in 140 Characters, By TrendsSpotting

View more documents from TrendsSpotting.

Already released from the Trend Prediction Influencers Series:

@2010 Social Media (published also at NYTimes / RWW, Mashable, Examiner)

@2010 Consumer Trends.

@2010 Tech and IT.

@2010 Online Marketing.

@2010 Online Video.

@2010 Mobile

I wish to thank all experts who participated in the 2010 Influencer Series, and submitted their insightful prediction tweets.

Many thanks to the TrendsSpotting team, and most to Apurba Sen, Nizan Malkit, Aviv Sher, and Yotam Shochat who made it all possible in such a short time.

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2010 Tech and IT Influencers: Trend Predictions in 140 Characters (3rd Report by TrendsSpotting)

December 31st, 2009 by

“2010 Tech and IT Influencers” is the third report from the series “2010 Influencers Series: Trend Predictions in 140 Characters“.

Findings: Major trends in 2010 Tech and IT:

Across many of these predictions, we have identified the following trends suggested to influence Tech in 2010:
@Clouds – free, private, public, green, data, identity
@Mobile – apps, store, iPhone, Android, Location (AR)
@Netbooks – cheap notebooks / smartphones, Google
@eBooks – Amazon, apps
@Social – enterprise, computing

TrendsSpotting Market Research is now running its third annual prediction reports following major trends in six categories. We will be featuring the predictions of digital and marketing experts on the big changes awaiting us in the coming year.
This year we are adopting a new “tweet style” format, easier for you to focus on, comprehend and forward.

Already released from the Trend Prediction Influencers Series:

@2010 Social Media (published also at NYTimes / RWW, Mashable, Examiner)

@2010 Consumer Trends.

@2010 Tech and IT.

@2010 Online Marketing.

@2010 Online Video.

@2010 Mobile.

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Halloween through Location Based Applications

November 1st, 2009 by

Google Latitude or other location based applications working through mobile phones can let you track your kids whereabouts during Halloween.   Aside from tracking, many of those applications can be used (note privacy concerns) for sharing the Halloween experience. Locations of retailers selling costumes and decorations can also be found by using applications as Poynt. To improve the Halloween experience, Zillow.com, have created a Trick or Treat Housing Index, which presents the top-five neighborhoods to maximize candy harvesting. The index uses four equally weighted data variables: Zillow Home Value Index, population density, Walk Score, and local crime data. Based on those variables, this Index represents neighborhoods that will provide the most candy, with the least walking, and minimal safety risks. Follow the index for top five neighborhoods in SF, LA, Seattle, Chicago and Boston.

To further improve kids safety,  “Offender Locator” (iTunes Link) which works with Blackberry and iPhone, tracks sex offenders on your trick or treating route. The app provides you with a list of offenders based on their proximity to the location given. You can get information as names, pictures, age, height, weight and the specific sexual crime they were charged with.

LBS (Location Based Services) is a market predicted to grow (according to Gartner – LBS subscribers are forecast to grow from 41.0 million in 2008 to 95.7 million in 2009 while revenue is anticipated to increase from $998.3 million in 2008 to $2.2 billion in 2009). Gartner suggests that North America is the largest market due to mobile carriers’ strong efforts in navigation services and family-safety solutions.

I find Halloween as a good example for the conflict introduced by LBS technology systems: The need to share experiences and learn about friends locations comes in contrast to safety measures. The same system that gives you more control, is the one to danger your privacy.

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On Google’s new forecasting capabilities and their importance to Market Research

September 7th, 2009 by

Google’s power lies in knowledge. The knowledge of what people search for.
Google shares some of this knowledge as it updates their tool – Google
Insights for Search
.
With a posteriori examination of historical trends – it suggests a basic
model for predicting the next search trends
.
We can now improve the understanding of consumer behavior in variety of
markets and geographical locations.

How does that work?

On many search trends queries, conducted on the Google insight for Search
website, you receive along with the historical data, a 12 month forecast. For good example search for  “iPhone“.

What’s behind Google’s forecasting model?

Google has characterized the predictability of a trends’ series based on its
historical performance. To do so they compared the discrepancy of forecasted trends, applied at some point in the past, to the trends’ actual performance. When the discrepancy between the forecasted trends and the actual trends is smaller than a predefined threshold, Google denotes the trends query as predictable.

Google’s research observations:

- Over half of the most popular Google search queries were found predictable in 12 month ahead forecast, with a mean absolute prediction error of approximately 12% on average. That means that nearly half of the most popular queries are not predictable.

- High predictable categories: Health (74%), Food & Drink (67%) and
Travel (65%).

- Low predictable categories: Entertainment (35%) and Social Networks &
Online Communities (27%).

- To earn more predictable indications – best to search for trends of
aggregated queries (“all categories” option): 88% of the aggregated category
is predictable with a mean absolute prediction error of less than 6% on
average. The larger the aggregation set is, the smaller would be the
variability of the aggregated time series

- There is a clear association between the existence of seasonality
patterns and higher predictability, as well as an association between high
levels of outliers and lower predictability.

 On Googles new forecasting capabilities and their importance to Market Research

For the above summary results of the 10 categories, the correlation between
the Predictability and the Seasonality Ratio is r= 0.80 while the Deviation Ratio has a (negative) correlation of r= -0.94 with the Predictability.

While TrendsSpotting is working on a new research on US Recession Trends
(you are welcome to contact us for more information) – we have
observed such trends, where seasonality seems high and predictability can
be inferred. Take the trend search for “gift” as a good example for the seasonality effect:

 On Googles new forecasting capabilities and their importance to Market Research

In many of the consumer industries we have followed, the recession has
definitely distracted seasonality and made forecasting abilities more difficult: Refer to the search for “flight” for example.

 On Googles new forecasting capabilities and their importance to Market Research

Whats missing in Google Insights for Search?

1. As I have pointed out some  years ago – Google Trends Search gives you
relative search volume only (relative to the total number of searches done
on Google over time). It doesn’t represent absolute search volume numbers.
2. While Google does calculate forecast parameters – how about releasing
the data on seasonality and deviation for the specific search?

A Market Research perspective:

I welcome Google for sharing more trends search capabilities.
I personally met a few of the Google Trends team and highly appreciate their work.

Web search trends are very important for the understanding of consumer behavior. In the many years I have worked as a market researcher, I have always preferred direct methods to analyze consumer behavior.
Compare that to surveys – those can tell you mostly about intentions (far future), about previous actions (depending on good memory skills that respondents  don’t have..), and perceptions (but not in a natural manner where one freely chooses to express her/his feelings). Surveys can indeed provide some indications and estimations for behaviors but they lack the means to reflect consumers’ real behavior.
In the last years, web metrics as search trends, blog citations
tools, and social media monitoring tools have captured my full attention. As a Social Psychologist – I find them to be valuable tools in examining people’s natural behavior. TrendsSpotting (company and blog) is all about making sense of it all.

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Twitter’s shopping list: most common “must buy” tweets

August 15th, 2009 by

Many of us perceive Twitter as a tool to monitor consumer behavior. As Twitter reflects the live streaming thoughts and needs of consumers – what can be more insightful for marketers than following the “must buy” products Twitter users tweet about.
We have generated a tag cloud for 24 hours cycle of tweets containing the actionable words “must have”. We have worked hard to clean irrelevant words and came up with the following shopping list:

 Twitters shopping list: most common must buy  tweets

You can see Twitter users tweet about their urge to buy tickets, books and games.  On the brands list we see:  iPhone, Ikea, Dell, Wii and iTunes. Most common products discussed are black and pink color.

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Apple is Risking its Brand Image

July 29th, 2009 by

 Apple is Risking its Brand Image

Just as Apple succeeded to light consumers’ enthusiasm once again with Apple’s tablet (Twitter tweets:  trend volume, mostly positive sentiments), Apple seems to be risking its well known reputation.

Since the launch of the iPhone, the Apple brand was influenced by negative attitudes towards AT&T, mostly for issues of connectivity and costs. Only thanks to its brands strength (high involvement among Apple’s fans, high emotional engagement, design and innovation credits) Apple’s brand image was protected.

It seems today this status quo might change as Google Voice service is getting pulled from Apple’s App Store. Reading the extremely negative posts and tweets (see some of the sentiments analysis here) I wonder if Apple is not risking too much.

Will social pressure work on Apple as it did for Facebook? or Digg?

Blocking Google seems a risky game even for Apple’s fans. Don’t you think?

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Android’s Hype Cycle: Will Netbooks Revive It?

July 2nd, 2009 by

It’s almost the time to review Android . We reported earlier that Google’s announcement on Android raised lots of doubts & ambiguity over the web. Moreover this much touted “iPhone 2.0″ competitor demonstrated relatively low effect on web.

Last September T-Mobile launched the world’s first Android-powered mobile phone in partnership with Google & recently the software giant expected that around 18-20 phones from 8-9 manufacturers on the market worldwide will be based on the Android operating system.

Riding on the hype Android’s US market share on mobile OS surpassed 6%, its application store hosted around 5,000 applications (in comparison to Apple’s 50,000-plus) until this happened at the Computex Taipei electronics show in May 2009 :

Gartner announced Android running on devices was “snappy” & stopped endorsing the platform saying that Android is a work-in-progress. However, backed by the strong Google brand Android may be an alternative to Windows in Netbooks.

We have attempted to put in place the evidences for the hype cycle curve for Android & in the absence of a perfect measure we have relied on proxies to derive the Y axis of the cycle as ‘Expectations’ -more precisely the red trend line describes the summation of “shared interest indicator” & “market share”. We used Google Trends (US search volume) as an indicator of shared interest & Android’s OS share in US market as an indicator of market penetration & real progress.
Androids Hype cycle Trendsspotting Androids Hype Cycle: Will Netbooks Revive It?

So what can be learned from this Android graph and its “Expectation” measure ? Android appears to have had a plateaued growth to the peak of inflated expectations – followed by a quick fall since May end when its mobile OS market share nosedived to less than 1%. Possibly that’s an indication of Android approaching the trough of disillusionment.

Research firm Strategy Analytics have previously suggested that Android will be running on about 12% of global smartphones by 2012. Another estimates from IDC suggests that netbook shipments will grow from 11.4 million in 2008 to 22 million in 2009, a potentially massive growth area for Android.

Will Android find its slope of enlightenment & subsequently its plateau of productivity via netbooks growth (or smartphones) that remains to be seen, possibly when we revisit this trend again in future.

More on Gartner Hype cycle here.

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