Taking Note of Music Tech’s VC and Accelerator Market Trends in 2017

As a part of today’s modern music industry there exists a complementary and thriving support system of venture capital firms and music tech startup accelerators who are providing a multitude of innovative services.  A fascinating examination of the current state of this ecosystem appeared in an article entitled Music Pushes to Innovate Beyond Streaming, But Investors Play It Safe: Analysis, by Cherie Hu, posted on Billboard.com on 7/24/17. I highly recommend reading it in its entirety for its insights, assessments and accompanying graphics.

I will summarize this feature here, add some links and annotations and, well, venture a few of my own questions. Also, I believe this is a logical follow to three previous Subway Fold posts about the music biz including:

Tempo

In mid-2017, the music tech market is generating signals as to its direction and viability. For example, Jawbone, the once thriving manufacturer of wearable audio devices is currently being liquidated; Soundcloud  the audio distribution platform let go of 40 percent of its staff recently only days before the firm’s tenth anniversary; and Pandora has experienced high turnover among its executives while seeking a sale.

Nonetheless, the leaders in music streaming are maintaining “the music industry’s growth”. Music tech showcases and music accelerators including SXSW Music Startup Spotlight, the Midemlab Accelerator, and Techstars Music are likewise driving market transformation.   During 2017 thus far, 54 music startups from more than 25 cities across the globe have taken part in these three entities. They have presented a range of submissions including “live music activations and automated messaging to analytics tools for labels and artists”.

While companies such as Live Nation, Balderton Capital and Evolution Media have previously invested in music startups, most investors at this mid-year point have never previously funded a company in this space. This is despite the fact that investments in this market sector have rarely returned the 30% that VCs generally seek. As well, a number of established music industry stars are participating as first-time or veteran investors this year.

Of the almost $900 million funding in music tech for the first half of this year, 75% was allocated for streaming services – – 82% of which went only to the leading four companies. However, there remains a “stark disconnect” involving the types of situations where music accelerators principally “lend their mentorship” in “hardware, virtual reality1, chatbots, label tools”, and the issues that VC concentrate the funding such as “streaming, social media, brands”.  Moreover, this situation has the potential of “stifling innovation” across the industry.

To date, music accelerators have “successfully given a platform and resources” to some sectors of the industry that VCs don’t often consider. For example, automated messaging and AI-generated music2 are both categories that music accelerators avoided until recently, now equal 15% of membership. This expansion into new categories reflects a much deeper “tech investment and hiring trends”. Leading music companies are now optimistic about virtual digital assistants (VDA) including chatbots and voice-activated systems such as Amazon Alexa3. As well, Spotify recently hired away a leading AI expert from Sony.

Rhythm

However, this “egalitarian focus” on significant problems has failed to “translate into the wider investing landscape” insofar as the streaming services have attracted 75% of music tech funding. The data further shows that licensing/rights/catalog management, social music media, and music, brands and advertising finished, in that order, in second at 11.1%, third at 7.1% and fourth at 3.9%.

These percentages closely match those for 2016. Currently, many VCs in this sector view streaming “as the safest model available”. It is also one upon which today’s music industry depends for its survival.

Turning to the number of rounds of music tech funding rather than the dollar amounts raised, by segments within the industry, a “slightly more egalitarian landscape” emerges:

  • Music hardware, AI-generated music, and VR and Immersive media each at 5.0%
  • Live music; music brands and advertising; streaming; and social music media each at 15.0%
  • Licensing, rights, and catalog management at 25% (for such companies as Kobalt Music, Stem and Dubset)

Categories that did relatively well in both their number of rounds of funding and accelerator membership were “catalog management, social music platforms, and live music”.

Those music tech startups that are more “futuristic” like hardware and VR are seen favorably by “accelerators and conference audiences”, but less so among VCs.  Likewise, while corporate giants including Live Nation, Universal Music Group, Citi and Microsoft have announced movement into music VR in the past six months, VC funding for this tech remained “relatively soft”.

Even more pronounced is the situation where musical artists and label services such as Instrumental (a influencer discovery platform) and chart monitors like Soundcharts have not raised any rounds of funding. This is so “despite unmatched attention from accelerators. This might be due to these services not being large enough to draw too “many traditional investors”.

An even more persistent problem here is that not many VCs “are run by people with experience in the music industry” and are familiar with its particular concerns. Once exception is Plus Eight Equity Partners, who are trying to address “this ideological and motivational gap”.

Then there are startups such as 8tracks and Chew who are “experimenting with crowdfunding” in this arena but who were not figured into this analysis.

In conclusion, the tension between a “gap in industry knowledge” and the VCs’ preference for “safety and convenience”, is blurring the line leading from accelerator to investment for many of these imaginative startups.

My Questions

  • Of those music startups who have successfully raised funding, what factors distinguished their winning pitches and presentations that others can learn from and apply?
  • Do VCs and accelerators really need the insights and advice of music industry professionals or are the numbers, projects and ROIs only what really matters in deciding whether or not to provide support?
  • Would the application of Moneyball principles be useful to VCs and accelerators in their decision-making processes?

 


1.  See the category Virtual and Augmented Reality for other Subway Fold posts on a range of applications of these technologies.

2.  For a report on a recent developments, see A New AI Can Write Music as Well as a Human Composer, by Bartu Kaleagasi, posted on Futurism.com on 3/9/17.

3.  Other examples of VDAs include Apple’s Siri, Google’s Assistant and Microsoft’s Cortana.

I Can See for Miles: Using Augmented Reality to Analyze Business Data Sets

matrix-1013612__340, Image from Pixabay

While one of The Who’s first hit singles, I Can See for Miles, was most certainly not about data visualization, it still might – – on a bit of a stretch – – find a fitting a new context in describing one of the latest dazzling new technologies in the opening stanza’s declaration “there’s magic in my eye”.  In determining Who’s who and what’s what about all this, let’s have a look at report on a new tool enabling data scientists to indeed “see for miles and miles” in an exciting new manner.

This innovative approach was recently the subject of a fascinating article by an augmented reality (AR) designer named Benjamin Resnick about his team’s work at IBM on a project called Immersive Insights, entitled Visualizing High Dimensional Data In Augmented Reality, posted on July 3, 2017 on Medium.com. (Also embedded is a very cool video of a demo of this system.) They are applying AR’s rapidly advancing technology1 to display, interpret and leverage insights gained from business data. I highly recommend reading this in its entirety. I will summarize and annotate it here and then pose a few real-world questions of my own.

Immersive Insights into Where the Data-Points Point

As Resnick foresees such a system in several years, a user will start his or her workday by donning their AR glasses and viewing a “sea of gently glowing, colored orbs”, each of which visually displays their business’s big data sets2. The user will be able to “reach out select that data” which, in turn, will generate additional details on a nearby monitor. Thus, the user can efficiently track their data in an “aesthetically pleasing” and practical display.

The project team’s key objective is to provide a means to visualize and sum up the key “relationships in the data”. In the short-term, the team is aiming Immersive Insights towards data scientists who are facile coders, enabling them to visualize, using AR’s capabilities upon time series, geographical and networked data. For their long-term goals, they are planning to expand the range of Immersive Insight’s applicability to the work of business analysts.

For example, Instacart, a same-day food delivery service, maintains an open source data set on food purchases (accessible here). Every consumer represents a data-point wherein they can be expressed as a “list of purchased products” from among 50,000 possible items.

How can this sizable pool of data be better understood and the deeper relationships within it be extracted and understood? Traditionally, data scientists create a “matrix of 2D scatter plots” in their efforts to intuit connections in the information’s attributes. However, for those sets with many attributes, this methodology does not scale well.

Consequently, Resnick’s team has been using their own new approach to:

  • Lower complex data to just three dimensions in order to sum up key relationships
  • Visualize the data by applying their Immersive Insights application, and
  • Iteratively label and color-code the data” in conjunction with an “evolving understanding” of its inner workings

Their results have enable them to “validate hypotheses more quickly” and establish a sense about the relationships within the data sets. As well, their system was built to permit users to employ a number of versatile data analysis programming languages.

The types of data sets being used here are likewise deployed in training machine learning systems3. As a result, the potential exists for these three technologies to become complementary and mutually supportive in identifying and understanding relationships within the data as well as deriving any “black box predictive models”.

Analyzing the Instacart Data Set: Food for Thought

Passing over the more technical details provided on the creation of team’s demo in the video (linked above), and next turning to the results of the visualizations, their findings included:

  • A great deal of the variance in Instacart’s customers’ “purchasing patterns” was between those who bought “premium items” and those who chose less expensive “versions of similar items”. In turn, this difference has “meaningful implications” in the company’s “marketing, promotion and recommendation strategies”.
  • Among all food categories, produce was clearly the leader. Nearly all customers buy it.
  • When the users were categorized by the “most common department” they patronized, they were “not linearly separable”. This is, in terms of purchasing patterns, this “categorization” missed most of the variance in the system’s three main components (described above).

Resnick concludes that the three cornerstone technologies of Immersive Insights – – big data, augmented reality and machine learning – – are individually and in complementary combinations “disruptive” and, as such, will affect the “future of business and society”.

Questions

  • Can this system be used on a real-time basis? Can it be configured to handle changing data sets in volatile business markets where there are significant changes within short time periods that may affect time-sensitive decisions?
  • Would web metrics be a worthwhile application, perhaps as an add-on module to a service such as Google Analytics?
  • Is Immersive Insights limited only to business data or can it be adapted to less commercial or non-profit ventures to gain insights into processes that might affect high-level decision-making?
  • Is this system extensible enough so that it will likely end up finding unintended and productive uses that its designers and engineers never could have anticipated? For example, might it be helpful to juries in cases involving technically or financially complex matters such as intellectual property or antitrust?

 


1.  See the Subway Fold category Virtual and Augmented Reality for other posts on emerging AR and VR applications.

2.  See the Subway Fold category of Big Data and Analytics for other posts covering a range of applications in this field.

3.  See the Subway Fold category of Smart Systems for other posts on developments in artificial intelligence, machine learning and expert systems.

4.  For a highly informative and insightful examination of this phenomenon where data scientists on occasion are not exactly sure about how AI and machine learning systems produce their results, I suggest a click-through and reading of The Dark Secret at the Heart of AI,  by Will Knight, which was published in the May/June 2017 issue of MIT Technology Review.

Digital Smarts Everywhere: The Emergence of Ambient Intelligence

Image from Pixabay

Image from Pixabay

The Troggs were a legendary rock and roll band who were part of the British Invasion in the late 1960’s. They have always been best known for their iconic rocker Wild Thing. This was also the only Top 10 hit that ever had an ocarina solo. How cool is that! The band went on to have two other major hits, With a Girl Like You and Love is All Around.¹

The third of the band’s classic singles can be stretched a bit to be used as a helpful metaphor to describe an emerging form pervasive “all around”-edness, this time in a more technological context. Upon reading a fascinating recent article on TechCrunch.com entitled The Next Stop on the Road to Revolution is Ambient Intelligence, by Gary Grossman, on May 7, 2016, you will find a compelling (but not too rocking) analysis about how the rapidly expanding universe of digital intelligent systems wired into our daily routines is becoming more ubiquitous, unavoidable and ambient each day.

All around indeed. Just as romance can dramatically affect our actions and perspectives, studies now likewise indicate that the relentless global spread of smarter – – and soon thereafter still smarter – – technologies is comparably affecting people’s lives at many different levels.² 

We have followed just a sampling of developments and trends in the related technologies of artificial intelligence, machine learning, expert systems and swarm intelligence in these 15 Subway Fold posts. I believe this new article, adding “ambient intelligence” to the mix, provides a timely opportunity to bring these related domains closer together in terms of their common goals, implementations and benefits. I highly recommend reading Mr. Grossman’s piece it in its entirety.

I will summarize and annotate it, add some additional context, and then pose some of my own Troggs-inspired questions.

Internet of Experiences

Digital this, that and everything is everywhere in today’s world. There is a surging confluence of connected personal and business devices, the Internet, and the Internet of Things (I0T) ³. Woven closely together on a global scale, we have essentially built “a digital intelligence network that transcends all that has gone before”. In some cases, this quantum of advanced technologies gains the “ability to sense, predict and respond to our needs”, and is becoming part of everyone’s “natural behaviors”.

A forth industrial revolution might even manifest itself in the form of machine intelligence whereby we will interact with the “always-on, interconnected world of things”. As a result, the Internet may become characterized more by experiences where users will converse with ambient intelligent systems everywhere. The supporting planks of this new paradigm include:

A prediction of what more fully realized ambient intelligence might look like using travel as an example appeared in an article entitled Gearing Up for Ambient Intelligence, by Lisa Morgan, on InformationWeek.com on March 14, 2016. Upon leaving his or her plane, the traveler will receive a welcoming message and a request to proceed to the curb to retrieve their luggage. Upon reaching curbside, a self-driving car6 will be waiting with information about the hotel booked for the stay.

Listening

Another article about ambient intelligence entitled Towards a World of Ambient Computing, by Simon Bisson, posted on ZDNet.com on February 14, 2014, is briefly quoted for the line “We will talk, and the world will answer”, to illustrate the point that current technology will be morphing into something in the future that would be nearly unrecognizable today. Grossman’s article proceeds to survey a series of commercial technologies recently brought to market as components of a fuller ambient intelligence that will “understand what we are asking” and provide responsive information.

Starting with Amazon’s Echo, this new device can, among other things:

  • Answer certain types of questions
  • Track shopping lists
  • Place orders on Amazon.com
  • Schedule a ride with Uber
  • Operate a thermostat
  • Provide transit schedules
  • Commence short workouts
  • Review recipes
  • Perform math
  • Request a plumber
  • Provide medical advice

Will it be long before we begin to see similar smart devices everywhere in homes and businesses?

Kevin Kelly, the founding Executive Editor of WIRED and a renowned futurist7, believes that in the near future, digital intelligence will become available in the form of a utility8 and, as he puts it “IQ as a service”. This is already being done by Google, Amazon, IBM and Microsoft who are providing open access to sections of their AI coding.9 He believes that success for the next round of startups will go to those who enhance and transforms something already in existence with the addition of AI. The best example of this is once again self-driving cars.

As well, in a chapter on Ambient Computing from a report by Deloitte UK entitled Tech Trends 2015, it was noted that some products were engineering ambient intelligence into their products as a means to remain competitive.

Recommending

A great deal of AI is founded upon the collection of big data from online searching, the use of apps and the IoT. This universe of information supports neural networks learn from repeated behaviors including people’s responses and interests. In turn, it provides a basis for “deep learning-derived personalized information and services” that can, in turn, derive “increasingly educated guesses with any given content”.

An alternative perspective, that “AI is simply the outsourcing of cognition by machines”, has been expressed by Jason Silva, a technologist, philosopher and video blogger on Shots of Awe. He believes that this process is the “most powerful force in the universe”, that is, of intelligence. Nonetheless, he sees this as an evolutionary process which should not be feared. (See also the December 27, 2014 Subway Fold post entitled  Three New Perspectives on Whether Artificial Intelligence Threatens or Benefits the World.)

Bots are another contemporary manifestation of ambient intelligence. These are a form of software agent, driven by algorithms, that can independently perform a range of sophisticated tasks. Two examples include:

Speaking

Optimally, bots should also be able to listen and “speak” back in return much like a 2-way phone conversation. This would also add much-needed context, more natural interactions and “help to refine understanding” to these human/machine exchanges. Such conversations would “become an intelligent and ambient part” of daily life.

An example of this development path is evident in Google Now. This service combines voice search with predictive analytics to present users with information prior to searching. It is an attempt to create an “omniscient assistant” that can reply to any request for information “including those you haven’t thought of yet”.

Recently, the company created a Bluetooth-enable prototype of lapel pin based on this technology that operates just by tapping it much like the communicators on Star Trek. (For more details, see Google Made a Secret Prototype That Works Like the Star Trek Communicator, by Victor Luckerson, on Time.com, posted on November 22, 2015.)

The configurations and specs of AI-powered devices, be it lapel pins, some form of augmented reality10 headsets or something else altogether, supporting such pervasive and ambient intelligence are not exactly clear yet. Their development and introduction will take time but remain inevitable.

Will ambient intelligence make our lives any better? It remains to be seen, but it is probably a viable means to handle some of more our ordinary daily tasks. It will likely “fade into the fabric of daily life” and be readily accessible everywhere.

Quite possibly then, the world will truly become a better place to live upon the arrival of ambient intelligence-enabled ocarina solos.

My Questions

  • Does the emergence of ambient intelligence, in fact, signal the arrival of a genuine fourth industrial revolution or is this all just a semantic tool to characterize a broader spectrum of smarter technologies?
  • How might this trend affect overall employment in terms of increasing or decreasing jobs on an industry by industry basis and/or the entire workforce? (See also this June 4, 2015 Subway Fold post entitled How Robots and Computer Algorithms Are Challenging Jobs and the Economy.)
  • How might this trend also effect non-commercial spheres such as public interest causes and political movements?
  • As ambient intelligence insinuates itself deeper into our online worlds, will this become a principal driver of new entrepreneurial opportunities for startups? Will ambient intelligence itself provide new tools for startups to launch and thrive?

 


1.   Thanks to Little Steven (@StevieVanZandt) for keeping the band’s music in occasional rotation on The Underground Garage  (#UndergroundGarage.) Also, for an appreciation of this radio show see this August 14, 2014 Subway Fold post entitled The Spirit of Rock and Roll Lives on Little Steven’s Underground Garage.

2.  For a remarkably comprehensive report on the pervasiveness of this phenomenon, see the Pew Research Center report entitled U.S. Smartphone Use in 2015, by Aaron Smith, posted on April 1, 2015.

3These 10 Subway Fold posts touch upon the IoT.

4.  The Subway Fold category Big Data and Analytics contains 50 posts cover this topic in whole or in part.

5.  The Subway Fold category Telecommunications contains 12 posts cover this topic in whole or in part.

6These 5 Subway Fold posts contain references to self-driving cars.

7.   Mr. Kelly is also the author of a forthcoming book entitled The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future, to be published on June 7, 2016 by Viking.

8.  This September 1, 2014 Subway Fold post entitled Possible Futures for Artificial Intelligence in Law Practice, in part summarized an article by Steven Levy in the September 2014 issue of WIRED entitled Siri’s Inventors Are Building a Radical New AI That Does Anything You Ask. This covered a startup called Viv Labs whose objective was to transform AI into a form of utility. Fast forward to the Disrupt NY 2016 conference going on in New York last week. On May 9, 2016, the founder of Viv, Dag Kittlaus, gave his presentation about the Viv platform. This was reported in an article posted on TechCrunch.com entitled Siri-creator Shows Off First Public Demo of Viv, ‘the Intelligent Interface for Everything’, by Romain Dillet, on May 9, 2016. The video of this 28-minute presentation is embedded in this story.

9.  For the full details on this story see a recent article entitled The Race Is On to Control Artificial Intelligence, and Tech’s Future by John Markoff and Steve Lohr, published in the March 25, 2016 edition of The New York Times.

10These 10 Subway Fold posts cover some recent trends and development in augmented reality.

“Technographics” – A New Approach for B2B Marketers to Profile Their Customers’ Tech Systems

"Gold Rings - Sphere 1" Image by Linda K

“Gold Rings – Sphere 1” Image by Linda K

Today’s marketing and business development professionals use a wide array of big data collection and analytical tools to create and refine sophisticated profiles of market segments and their customer bases. These are deployed in order to systematically and scientifically target and sell their goods and services in steadily changing marketplaces.

These processes can include, among a multitude of other vast data sets and methodologies, demographics, web user metrics and econometrics. Businesses are always looking for a data-driven edge in highly competitive sectors and such profiling, when done correctly, can be very helpful in detecting and interpreting market trends, and consistently keeping ahead of their rivals. (The Subway Fold category of Big Data and Analytics now contains 50 posts about a variety of trends and applications in this field.)

I will briefly to this add my own long-term yet totally unscientific study of office-mess-ographics. Here I have been looking for any correlation between the relative states of organization – – or entropy – – in people’s offices and their work’s quality and output.  The results still remain inconclusive after years of study.

One of the most brilliant and accomplished people I have ever known had an office that resembled a cave deep in the earth with piles of paper resembling stalagmites all over it. Even more remarkably, he could reach into any one of those piles and pull out exactly the documents he wanted. His work space was so chaotic that there was a long-standing joke that Jimmy Hoffa’s and Judge Crater’s long-lost remains would be found whenever ever he retired and his office was cleaned out.

Speaking of office-focused analytics, an article posted on VentureBeat.com on March 5, 2016, entitled CMOs: ‘Technographics’ is the New Demographics, by Sean Zinsmeister, brought news of a most interesting new trend. I highly recommend reading this in its entirety. I will summarize and add some context to it, and then pose a few question-ographics of my own.

New Analytical Tool for B2B Marketers

Marketers are now using a new methodology call technography to analyze their customers’ “tech stack“, a term of art for the composition of their supporting systems and platforms. The objective of this approach is to deeply understand what this says about them as a company and, moreover, how can this be used in business-to-business (B2B) marketing campaigns. Thus applied, technography can identify “pain points” in products and alleviate them for current and prospective customers.

Using established consumer marketing methods, there is much to be learned and leveraged on how technology is being used by very granular segments of users bases.  For example:

By virtue of this type of technographic data, retailers can target their ads in anticipation of “which customers are most likely to shop in store, online, or via mobile”.

Next, by transposing this form of well-established marketing approach next upon B2B commerce, the objective is to carefully examine the tech stacks of current and future customers in order to gain a marketing advantage. That is, to “inform” a business’s strategy and identify potential new roles and needs to be met. These corporate tech stacks can include systems for:

  • Office productivity
  • Project management
  • Customer relationship management (CRM)
  • Marketing

Gathering and Interpreting Technographic Signals and Nuances

Technographics can provide unique and valuable insights into assessing, for example, whether a customer values scalability or ease-of-use more, and then act upon this.

As well, some of these technographic signals can be indicative of other factors not, per se, directly related to technology. This was the case at Eloqua, a financial technology concern. They noticed their marketing systems have predictive value in determining the company’s best prospects. Furthermore, they determined that companies running their software were inclined “to have a certain level of technological sophistication”, and were often large enough to have the capacity to purchase higher-end systems.

As business systems continually grow in their numbers and complexity, interpreting technographic nuances has also become more of a challenge. Hence, the application of artificial intelligence (AI) can be helpful in detecting additional useful patterns and trends. In a July 2011 TED Talk by Ted Slavin, directly on point here, entitled How Algorithms Shape Our World, he discussed how algorithms and machine learning are needed today to help make sense out of the massive and constantly growing amounts of data. (The Subway Fold category of Smart Systems contains 15 posts covering recent development and applications involving AI and machine learning.)

Technographic Resources and Use Cases

Currently, technographic signals are readily available from various data providers including:

They parse data using such factors as “web hosting, analytics, e-commerce, advertising, or content management platforms”. Another firm called Ghostery has a Chrome browser extension illuminating the technologies upon which any company’s website is built.

The next key considerations are to “define technographic profiles and determine next-best actions” for specific potential customers. For instance, an analytics company called Looker creates “highly targeted campaigns” aimed at businesses who use Amazon Web Services (AWS). The greater the number of marketers who undertake similar pursuits, the more they raise the value of their marketing programs.

Technographics can likewise be applied for competitive leverage in the following use cases:

  • Sales reps prospecting for new leads can be supported with more focused messages for potential new customers. These are shaped by understanding their particular motivations and business challenges.
  • Locating opportunities in new markets can be achieved by assessing the tech stacks of prospective customers. Such analytics can further be used for expanding business development and product development. An example is the online training platform by Mindflash. They detected a potential “demand for a Salesforce training program”. Once it became available, they employed technographic signals to pinpoint customers to whom they could present it.
  • Enterprise wide decision-making benefits can be achieved by adding “value in areas like cultural alignment”. Familiarity with such data for current employees and job seekers can aid businesses with understanding the “technology disposition” of their workers. Thereafter, its alignment with the “customers or partners” can be pursued.  Furthermore, identifying areas where additional training might be needed can help to alleviate productivity issues resulting from “technology disconnects between employees”.

Many businesses are not yet using technographic signals to their full advantage. By increasing such initiatives, businesses can acquire a much deeper understanding of their inherent values. In turn, the resulting insights can have a significant effect on the experiences of their customers and, in turn, elevate their resulting levels of loyalty, retention and revenue, as well as the magnitude of deals done.

My Questions

  • Would professional service industries such as law, medicine and accounting, and the vendors selling within these industries, benefit from integrating technographics into their own business development and marketing efforts?
  • Could there be, now or in the future, an emerging role for dedicated technographics specialists, trainers and consultants? Alternatively, should these new analytics just be treated as another new tool to be learned and implemented by marketers in their existing roles?
  • If a company identifies some of their own employees who might benefit from additional training, how can they be incentivized to participate in it? Could gamification techniques also be applied in creating these training programs?
  • What, if any, privacy concerns might surface in using technographics on potential customer leads and/or a company’s own internal staff?

LinkNYC Rollout Brings Speedy Free WiFi and New Opportunities for Marketers to New York

Link.NYC WiFi Kiosk 5, Image by Alan Rothman

Link.NYC WiFi Kiosk 5, Image by Alan Rothman

Back in the halcyon days of yore before the advent of smartphones and WiFi, there were payphones and phone booths all over of the streets in New York. Most have disappeared, but a few scattered survivors have still managed to hang on. An article entitled And Then There Were Four: Phone Booths Saved on Upper West Side Sidewalks, by Corey Kilgannon, posted on NYTimes.com on February 10, 2016, recounts the stories of some of the last lonely public phones.

Taking their place comes a highly innovative new program called LinkNYC (also @LinkNYC and #LinkNYC). This initiative has just begun to roll out across all five boroughs with a network of what will become thousands of WiFi kiosks providing free and way fast free web access and phone calling, plus a host of other online NYC support services. The kiosks occupy the same physical spaces as the previous payphones.

The first batch of them has started to appear along Third Avenue in Manhattan. I took the photos accompanying this post of one kiosk at the corner of 14th Street and Third Avenue. While standing there, I was able to connect to the web on my phone and try out some of the LinkNYC functions. My reaction: This is very cool beans!

LinkNYC also presents some potentially great new opportunities for marketers. The launch of the program and the companies getting into it on the ground floor were covered in a terrific new article on AdWeek.com on February 15, 2015 entitled What It Means for Consumers and Brands That New York Is Becoming a ‘Smart City’, by Janet Stilson. I recommend reading it in its entirety. I will summarize and annotate it to add some additional context, and pose some of my own ad-free questions.

LinkNYC Set to Proliferate Across NYC

Link.NYC WiFi Kiosk 2, Image by Alan Rothman

Link.NYC WiFi Kiosk 2, Image by Alan Rothman

When completed, LinkNYC will give New York a highly advanced mobile network spanning the entire city. Moreover, it will help to transform it into a very well-wired “smart city“.¹ That is, an urban area comprehensively collecting, analyzing and optimizing vast quantities of data generated by a wide array of sensors and other technologies. It is a network and a host of network effects where a city learns about itself and leverages this knowledge for multiple benefits for it citizenry.²

Beyond mobile devices and advertising, smart cities can potentially facilitate many other services. The consulting firm Frost & Sullivan predicts that there will be 26 smart cities across the globe during by 2025. Currently, everyone is looking to NYC to see how the implementation of LinkNYC works out.

According to Mike Gamaroff, the head of innovation in the New York office of Kinetic Active a global media and marketing firm, LinkNYC is primarily a “utility” for New Yorkers as well as “an advertising network”. Its throughput rates are at gigabit speeds thereby making it the fastest web access available when compared to large commercial ISP’s average rates of merely 20 to 30 megabits.

Nick Cardillicchio, a strategic account manager at Civiq Smartscapes, the designer and manufacturer of the LinkNYC kiosks, said that LinkNYC is the only place where consumers can access the Net at such speeds. For the AdWeek.com article, he took the writer, Janet Stilson, on a tour of the kiosks include the one at Third Avenue and 14th Street, where one of the first ones is in place. (Coincidentally, this is the same kiosk I photographed for this post.)

There are a total of 16 currently operational for the initial testing. The WiFi web access is accessible with 150 feet of the kiosk and can range up to 400 feet. Perhaps those New Yorkers actually living within this range will soon no longer need their commercial ISPs.

Link.NYC WiFi Kiosk 4, Image by Alan Rothman

Link.NYC WiFi Kiosk 4, Image by Alan Rothman

The initial advertisers appearing in rotation on the large digital screen include Poland Spring (see the photo at the right), MillerCoors, Pager and Citibank. Eventually “smaller tablet screens” will be added to enable users to make free domestic voice or video calls. As well, they will present maps, local activities and emergency information in and about NYC. Users will also be able to charge up their mobile devices.

However, it is still too soon to assess and quantify the actual impact on such providers. According to David Krupp, CEO, North America, for Kinetic, neither Poland Spring nor MillerCoors has produced an adequate amount of data to yet analyze their respective LinkNYC ad campaigns. (Kinetic is involved in supporting marketing activities.)

Commercializing the Kiosks

The organization managing LinkNYC, the CityBridge consortium (consisting of Qualcomm, Intersection, and Civiq Smartscapes) , is not yet indicating when the new network will progress into a more “commercial stage”. However, once the network is fully implemented with the next few years, the number of kiosks might end up being somewhere between 75,000 and 10,000. That would make it the largest such network in the world.

CityBridge is also in charge of all the network’s advertising sales. These revenues will be split with the city. Under the 12-year contract now in place, this arrangement is predicted to produce $500M for NYC, with positive cash flow anticipated within 5 years. Brad Gleeson, the chief commercial officer at Civiq, said this project depends upon the degree to which LinkNYC is “embraced by Madison Avenue” and the time need for the network to reach “critical mass”.

Because of the breadth and complexity of this project, achieving this inflection point will be quite challenging according to David Etherington, the chief strategy officer at Intersection. He expressed his firm’s “dreams and aspirations” for LinkNYC, including providing advertisers with “greater strategic and creative flexibility”, offering such capabilities as:

  • Dayparting  – dividing a day’s advertising into several segments dependent on a range of factors about the intended audience, and
  • Hypertargeting – delivering advertising to very highly defined segments of an audience

Barry Frey, the president and CEO of the Digital Place-based Advertising Association, was also along for the tour of the new kiosks on Third Avenue. He was “impressed” by the capability it will offer advertisers to “co-locate their signs and fund services to the public” for such services as free WiFi and long-distance calling.

As to the brand marketers:

  • MillerCoors is using information at each kiosk location from Shazam, for the company’s “Sounds of the Street” ad campaign which presents “lists of the most-Shazammed tunes in the area”. (For more about Shazam, see the December 10, 2014 Subway Fold post entitled Is Big Data Calling and Calculating the Tune in Today’s Global Music Market?)
  • Poland Spring is now running a 5-week campaign featuring a digital ad (as seen in the third photo above). It relies upon “the brand’s popularity in New York”.

Capturing and Interpreting the Network’s Data

Link.NYC WiFi Kiosk 1, Image by Alan Rothman

Link.NYC WiFi Kiosk 1, Image by Alan Rothman

Thus far, LinkNYC has been “a little vague” about its methods for capturing the network’s data, but has said that it will maintain the privacy of all consumers’ information. One source has indicated that LinkNYC will collect, among other points “age, gender and behavioral data”. As well, the kiosks can track mobile devices within its variably 150 to 400 WiFi foot radius to ascertain the length of time a user stops by.  Third-party data is also being added to “round out the information”.³

Some industry experts’ expectations of the value and applications of this data include:

  • Helma Larkin, the CEO of Posterscope, a New York based firm specializing in “out-of- home communications (OOH)“, believes that LinkNYC is an entirely “new out-of-home medium”. This is because the data it will generate “will enhance the media itself”. The LinkNYC initiative presents an opportunity to build this network “from the ground up”. It will also create an opportunity to develop data about its own audience.
  • David Krupp of Kinetic thinks that data that will be generated will be quite meaningful insofar as producing a “more hypertargeted connection to consumers”.

Other US and International Smart City Initiatives

Currently in the US, there is nothing else yet approaching the scale of LinkNYC. Nonetheless, Kansas City is now developing a “smaller advertiser-supported  network of kiosks” with wireless support from Sprint. Other cities are also working on smart city projects. Civiq is now in discussions with about 20 of them.

Internationally, Rio de Janeiro is working on a smart city program in conjunction with the 2016 Olympics. This project is being supported by Renato Lucio de Castro, a consultant on smart city projects. (Here is a brief video of him describing this undertaking.)

A key challenge facing all smart city projects is finding officials in local governments who likewise have the enthusiasm for efforts like LinkNYC. Michael Lake, the CEO of Leading Cities, a firm that help cities with smart city projects, believes that programs such as LinkNYC will “continue to catch on” because of the additional security benefits they provide and the revenues they can generate.

My Questions

  • Should domestic and international smart cities to cooperate to share their resources, know-how and experience for each other’s mutual benefit? Might this in some small way help to promote urban growth and development on a more cooperative global scale?
  • Should LinkNYC also consider offering civic support services such as voter registration or transportation scheduling apps as well as charitable functions where pedestrians can donate to local causes?
  • Should LinkNYC add some augmented reality capabilities to enhance the data capabilities and displays of the kiosks? (See these 10 Subway Fold posts covering a range of news and trends on this technology.)

February 19, 2017 Update:  For the latest status report on LinkNYC nearly a year after this post was first uploaded, please see After Controversy, LinkNYC Finds Its Niche, by Gerald Schifman, on CrainsNewYork.com, dated February 15, 2017.


1.   While Googling “smart cities” might nearly cause the Earth to shift off its axis with its resulting 70 million hits, I suggest reading a very informative and timely feature from the December 11, 2015 edition of The Wall Street Journal entitled As World Crowds In, Cities Become Digital Laboratories, by Robert Lee Hotz.

2.   Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia (W. W. Norton & Company, 2013), by Anthony M. Townsend, is a deep and wide book-length exploration of how big data and analytics are being deployed in large urban areas by local governments and independent citizens. I very highly recommend reading this fascinating exploration of the nearly limitless possibilities for smart cities.

3.   See, for example, How Publishers Utilize Big Data for Audience Segmentation, by Arvid Tchivzhel, posted on Datasciencecentral.com on November 17, 2015


These items just in from the Pop Culture Department: It would seem nearly impossible to film an entire movie thriller about a series of events centered around a public phone, but a movie called – – not so surprisingly – – Phone Booth managed to do this quite effectively in 2002. It stared Colin Farrell, Kiefer Sutherland and Forest Whitaker. Imho, it is still worth seeing.

Furthermore, speaking of Kiefer Sutherland, Fox announced on January 15, 2016 that it will be making 24: Legacy, a complete reboot of the 24 franchise, this time without him playing Jack Bauer. Rather, they have cast Corey Hawkins in the lead role. Hawkins can now be seen doing an excellent job playing Heath on season 6 of The Walking Dead. Watch out Grimes Gang, here comes Negan!!


The Mediachain Project: Developing a Global Creative Rights Database Using Blockchain Technology

Image from Pixabay

Image from Pixabay

When people are dating it is often said that they are looking for “Mr. Right” or “Ms. Right”. That is, finding someone who is just the right romantic match for them.

In the case of today’s rapid development, experimentation and implementation of blockchain technology, if a startup’s new technology takes hold, it might soon find a highly productive (but maybe not so romantic) match in finding Mr. or Ms. [literal] Right by deploying the blockchain as a form of global registry of creative works ownership.

These 5 Subway Fold posts have followed just a few of the voluminous developments in bitcoin and blockchain technologies. Among them, the August 21, 2015 post entitled Two Startups’ Note-Worthy Efforts to Adapt Blockchain Technology for the Music Industry has drawn the most number of clicks. A new report on Coindesk.com on February 23, 2016 entitled Mediachain is Using Blockchain to Create a Global Rights Database by Pete Rizzo provides a most interesting and worthwhile follow on related to this topic. I recommend reading it in its entirety. I will summarize and annotate it to provide some additional context, and then pose several of my own questions.

Producing a New Protocol for Ownership, Protection and Monetization

Applications of blockchain technology for the potential management of economic and distribution benefits of “creative professions”, including writers, musicians and others, that have been significantly affected by prolific online file copying still remains relatively unexplored. As a result, they do not yet have the means to “prove and protect ownership” of their work. Moreover, they do have an adequate system to monetize their digital works. But the blockchain, by virtue of its structural and operational nature, can supply these creators with “provenance, identity and micropayments“. (See also the October 27, 2015 Subway Fold post entitled Summary of the Bitcoin Seminar Held at Kaye Scholer in New York on October 15, 2015 for some background on these three elements.)

Now on to the efforts of a startup called Mine ( @mine_labs ), co-founded by Jesse Walden and Denis Nazarov¹. They are preparing to launch a new metadata protocol called Mediachain that enables creators working in digital media to write data describing their work along with a timestamp directly onto the blockchain. (Yet another opportunity to go out on a sort of, well, date.)  This system is based upon the InterPlanetary File System (IPFS). Mine believes that IPSF is a “more readable format” than others presently available.

Walden thinks that Mediachain’s “decentralized nature”, rather than a more centralized model, is critical to its objectives. Previously, a very “high-profile” somewhat similar initiative to establish a similarly global “database of musical rights and works” called the Global Repertoire Database (GDR) had failed.

(Mine maintains this page of a dozen recent posts on Medium.com about their technology that provides some interesting perspectives and details about the Mediachain project.)

Mediachain’s Objectives

Walden and Nazarov have tried to innovate by means of changing how media businesses interact with the Internet, as opposed to trying to get them to work within its established standards. Thus, the Mediachain project has emerged with its focal point being the inclusion of descriptive data and attribution for image files by combining blockchain technology and machine learning². As well, it can accommodate reverse queries to identify the creators of images.

Nazarov views Mediachain “as a global rights database for images”. When used in conjunction with, among others, Instagram, he and Walden foresee a time when users of this technology can retrieve “historic information” about a file. By doing so, they intend to assist in “preserving identity”, given the present challenges of enforcing creator rights and “monetizing content”. In the future, they hope that Mediachain inspires the development of new platforms for music and movies that would permit ready access to “identifying information for creative works”. According to Walden, their objective is to “unbundle identity and distribution” and provide the means to build new and more modern platforms to distribute creative works.

Potential Applications for Public Institutions

Mine’s co-founders believe that there is further meaningful potential for Mediachain to be used by public organizations who provide “open data sets for images used in galleries, libraries and archives”. For example:

  • The Metropolitan Museum of Art (“The Met” as it is referred to on their website and by all of my fellow New York City residents), has a mandate to license the metadata about the contents of their collections. The museum might have a “metadata platform” of its own to host many such projects.
  • The New York Public Library has used their own historical images, that are available to the public to, among other things, create maps.³ Nazarov and Walden believe they could “bootstrap the effort” by promoting Mediachain’s expanded apps in “consumer-facing projects”.

Maintaining the Platform Security, Integrity and Extensibility

Prior to Mediachain’s pending launch, Walden and Nazarov are highly interested in protecting the platform’s legitimate users from “bad actors” who might wrongfully claim ownership of others’ rightfully owned works. As a result, to ensure the “trust of its users”, their strategy is to engage public institutions as a model upon which to base this. Specifically, Mine’s developers are adding key functionality to Mediachain that enables the annotation of images.

The new platform will also include a “reputation system” so that subsequent users will start to “trust the information on its platform”. In effect, their methodology empowers users “to vouch for a metadata’s correctness”. The co-founders also believe that the “Mediachain community” will increase or decrease trust in the long-term depending on how it operates as an “open access resource”. Nazarov pointed to the success of Wikipedia to characterize this.

Following the launch of Mediachain, the startup’s team believes this technology could be integrated into other existing social media sites such as the blogging platform Tumblr. Here they think it would enable users to search images including those that may have been subsequently altered for various purposes. As a result, Tumblr would then be able to improve its monetization efforts through the application of better web usage analytics.

The same level of potential, by virtue of using Mediachain, may likewise be found waiting on still other established social media platforms. Nazarov and Walden mentioned seeing Apple and Facebook as prospects for exploration. Nazarov said that, for instance, Coindesk.com could set its own terms for its usage and consumption on Facebook Instant Articles (a platform used by publishers to distribute their multimedia content on FB). Thereafter, Mediachain could possibly facilitate the emergence of entirely new innovative media services.

Nazarov and Walden temper their optimism because the underlying IPFS basis is so new and acceptance and adoption of it may take time. As well, they anticipate “subsequent issues” concerning the platform’s durability and the creation of “standards for metadata”. Overall though, they remain sanguine about Mediachain’s prospects and are presently seeking developers to embrace these challenges.

My Questions

  • How would new platforms and apps using Mediachain and IPSF be affected by the copyright and patent laws and procedures of the US and other nations?
  • How would applications built upon Mediachain affect or integrate with digital creative works distributed by means of a Creative Commons license?
  • What new entrepreneurial opportunities for startup services might arise if this technology eventually gains web-wide adoption and trust among creative communities?  For example, would lawyers and accountants, among many others, with clients in the arts need to develop and offer new forms of guidance and services to navigate a Mediachain-enabled marketplace?
  • How and by whom should standards for using Mediachain and other potential development path splits (also known as “forks“), be established and managed with a high level of transparency for all interested parties?
  • Does analogizing what Bitcoin is to the blockchain also hold equally true for what Mediachain is to the blockchain, or should alternative analogies and perspectives be developed to assist in the explanation, acceptance and usage of this new platform?

June 1, 2016 Update:  For an informative new report on Mediachain’s activities since this post was uploaded in March, I recommend clicking through and reading Mediachain Enivisions a Blockchain-based Tool for Identifying Artists’ Work Across the Internet, by Jonathan Shieber, posted today on TechCrunch.com.


1.   This link from Mine’s website is to an article entitled Introducing Mediachain by Denis Nazarov, originally published on Medium.com on January 2, 2016. He mentions in his text an earlier startup called Diaspora that ultimately failed in its attempt at creating something akin to the Mediachain project. This December 4, 2014 Subway Fold post entitled Book Review of “More Awesome Than Money” concerned a book that expertly explored the fascinating and ultimately tragic inside story of Diaspora.

2.   Many of the more than two dozen Subway Fold posts in the category of Smart Systems cover some of the recent news, trends and applications in machine learning.

3.  For details, see the January 5, 2016 posting on the NY Public Library’s website entitled Free for All: NYPL Enhances Public Domain Collections for Sharing and Reuse, by Shana Kimball and Steven A. Schwarzman.

The Predictive Benefits of Analyzing Employees’ Communications Networks

Image from Pixabay

Image from Pixabay

In the wake of the destruction left by the Enron scandal and subsequent bankruptcy in the early 2000s, one of the more revelatory and instructive artifacts left behind was the massive trove of approximately 1,600,000 of the company’s corporate emails. Researchers from a variety of fields have performed all manner of extensive analyses on this “corpus” of emails as it known. Of particular interest was the structure and operations of this failed company’s communications network. That is, simply stated, extracting and examining who’s who and what’s what in this failed organization.

No other database of this type, size and depth had ever been previously available for such purposes. What the researchers have learned from this and its subsequent and significant influences in many public and private sectors was the subject of a fascinating article in MIT Technology Review posted on July 2, 2013 entitled The Immortal Life of the Enron E-mails by Jessica Lander. I highly recommend reading this.

[July 18, 2017 Update:  For a new deep and wide analysis of the Enron email database, see What the Enron E-Mails Say About Us, by Nathan Heller, in the 7/24/17 edition of The New Yorker.]

I immediately recalled this piece recently while reading a column posted on the Harvard Business Review blog on February 10, 2016 entitled What Work Email Can Reveal About Performance and Potential by Chantrelle Nielsen. This analytical processes and consulting projects it describes could be of highly practical value to all manners and sizes of organizations. I also suggest reading this in its entirety. I will summarize, annotate and pose some emoji-free questions of my own.

I believe this post will also provide a logical follow-on to the February 15, 2016 Subway Fold post entitled Establishing a Persuasive Digital Footprint for Competing in Today’s Job Market. That post covered the importance a job candidate’s digital presence before being hired while this post covers the predictive potential of an employee’s digital presence after they have become an employee and integrated themselves into an organization.

Data Generation

The author begins by focusing her attention upon the modern tools and platforms used in the workplace for people to communicate and collaborate such as Skype and Slack. More traditionally, there is email. While these modes are important, they can also be a “mixed blessing”. Careful management of these technologies can assist is determining which forms of “digital communications are productive” for both employers and their employees.

Most importantly, these systems produce huge volumes of data. As a result, some firms are developing “next generation products” containing analytical capabilities to deeply dive into these databases and the networks they support.¹,²

The author mentions that her former company, VoloMetrix, is engaged in this field and has been acquired by Microsoft. The examples and her article concern work done for the firm’s clients before it become part of MS. During this time, VoloMetrix worked for years “with executives in large enterprises” to enable them to discern patterns within employees’ digital communications.

Predicting Employee Performance

A “strong network” can be a predictive factor of an employee’s performance. For example, a software company looked at a year’s worth of anonymized employee email data across all job categories. The findings showed that:

  • The best performers were characterized by 36% larger in-house networks, when compared to average performers, where they connected “at least biweekly in small group messages”. (This criterion was used to determine “strong ties”).
  • The lower performers exhibited “6% smaller networks” when compared to average performers.

On an annual basis, the “size and strength” of employees’ networks proved to a better predictor of their performances than managers’ more traditional assessments. Thus, being “intensely engaged” in collaborating with their peers was a driver of their work performance.

This effect was likewise seen at other business-to-business sales concerns. For instance, at a software company the top 10 workers in sales were, on average, connected to 10 or more of their colleagues. Their internal networks proved to be 25% larger than the networks of low performers. When social graph data (used to visualize the structures of networks), was examined it frequently indicated that connections within a company were even more important than those outside of it.

Predicting Employee Potential

Some businesses use “engagement programs” to assist the careers of employees are seen as having high potential to become future leaders. For example, a utility company studied the networks of a few hundreds of these people. They discovered that:

  • Those people who “were often the most connected” were shown to have networks “52% larger than average”.
  • Nonetheless, there were still others within this same group having networks of “below average” dimensions.

Managers surveyed reported that the less connected workers also had “great skills or ideas”, but displayed “potentially less” extroversion³ or emotional intelligence 4 needed to become influential. Still, opportunities are available to assist these people to “gain a broader audience” with better connected “agents” who, in turn, can promote their ideas.

Furthermore, growing a large network only for its own sake is not always the optimal approach. Rather, some networks are “more effective” because of who they include. That is, if they include people who have higher degrees of influence.

Another client, a hardware company, advanced their analysis to examine the “composition and quality” of the networks assembled by their sales reps. Their findings indicated that:

  • The “involvement of certain sales roles” corresponded to a 10X increase in the size of deals with customers.
  • Some sales roles were characterized as “middlemen” and, as such, did not “clearly demonstrate” anyone’s personal leadership potential.

Synthesizing Two Approaches

As described above, two analytical approaches have emerged for examining and leveraging the insights gained from communications networks. Both can work well in conjunction with the other. First is awareness whereby business leaders:

  • Communicate the importance of building networks
  • Provide the network analytical tools
  • Maintain the “faith” that their employees will understand this message and act upon it

The second is the prediction of outcomes, most often by sales organization to determine “which deals will close”. While this currently is applied less often than the awareness approach, this situation is now changing.

The insights gained from studying communications networks which are then applied to help build better working relationships and performance, must be “used thoughtfully” while balancing human and technological factors. Moreover, for these to work properly and “make connections more meaningful and efficient”, effectively gathering sufficient data on how employees do their jobs and communicate with their peers is essential.

My Questions

  • What standards should be established to assess communication and collaboration networks? Should they be the same for all businesses and job types or varied from field to field? Should they be differentiated further from employer to employer within a field and then perhaps for every department and job title within the same firm? (For some excellent new reading on how professional networks compare in their breadth and effectiveness in different professions, I highly recommend reading another new article on The Harvard Business Review blog posted on February 19, 2016 entitled How Having an MBA vs. a Law Degree Shapes Your Network by Adina Sterling.)
  • How should “influential” members of a network be defined in a business environment? Is influencer marketing, where individuals with a significant online presence appear to have more influence upon others in their social networks and are thus given special attention by marketers, the correct model to consider?  If so, should businesses consider developing and applying the equivalent of a Klout score to their employees? (This is an online service that rates one’s relative influence across much of social media.)
  • Would it be helpful to a company’s workforce to make this data and analytics readily available to everyone on their internal network and, if so, what would be the benefits and/or drawbacks of doing so? Would access to one’s network’s shape and reach result in some unintended consequences such as pressuring workers to increase the size of their internal and external contacts?
  • Should rewards systems be piloted to see whether they can positively incentivize employees to nurture their networks? For example, for X amount of new contacts added that support a company’s goals, Y additional days off might be awarded.
  • Can network analytics be used to fairly or unfairly restrict workers with non-competition and non-disclosure clauses when they change jobs?

 


1.   Many of these 26 Subway Fold posts under the Category of Social Media also involve metrics and analytical systems for interpreting the voluminous data generated by a wide range of social media services.

2.  A thriving market exists today in enterprise search products that can index, search and unlock the valuable knowledge embedded deep within corporate email and other data platforms. Here is a list of vendors on Wikipedia.

3.  For a completely different and highly engaging analysis of the virtues of being an introvert in social and business environments, I highly recommend reading a recent bestseller entitled Quiet: The Power of Introverts in a World That Can’t Stop Talking (Broadway Books, 2013), by Susan Cain.

4.  The authoritative and highly regarded work on this subject is Emotional Intelligence: Why It Can Matter More Than IQ (Bantam Books, 2005), by Daniel Goleman.