What Can You Learn From Virtual Mirrors?

A virtual mirror allows someone to use a camera and have that image displayed on a large LED screen. Better yet, with the right software, it can change the image. With that ability, virtual mirrors have been used to see what new glasses look like or to try on dresses – a virtual, flexible fitting room.

image

Virtual mirrors and their equivalent as smart phone apps have been around for the last couple of years. There are examples from all over the world. Here are just a couple:

image
image

Marketers have already thought of extending this to social media, as one newspaper reported with a story titled “Every woman’s new best friend? Hyper-realistic new virtual mirror lets you to try on clothes at the flick of the wrist and instantly share the images online”.

This all provides a nice experience for customers and may even help sell a particular item to them. But that’s only the beginning.

Virtual mirrors are a tremendous source of data about consumer behavior. Consider that the system can record every item the consumer looked at and then what she or he bought. Add to that the information about the person that can be detected – hair color, height, etc. With the application of the right analytics, a company can develop insights about how and why some products are successful – for example a particular kind of dress may be what short or tall women are really looking for.

With eye tracking devices, such as those from Tobii, connected to the virtual mirror, even more data can be collected on exactly what the consumer is looking at – for example, the last part of a dress that she looked at before deciding to buy or not to buy.

Going beyond that, an analysis can be done of facial (and body) expressions. I’ve written before about affective computing which is the technology is developing to do and to respond to this kind of measurement.  

[For some additional background on affective computing, see Wikipedia and MIT Media Lab’s website.]

By fully gathering all the data surrounding a consumer’s use of the virtual mirror, its value becomes much more than merely improving the immediate customer experience. In a world of what many consider big data, this adds much more data for the analytics experts on the marketing and product teams to investigate.

Alas, I haven’t seen widespread adoption and merger of these technologies. But the first retailer to move forward this way will have a great competitive advantage. This is especially true for brick-and-mortar retailers who can observe and measure a wider range of consumer behavior than can their purely e-commerce competitors.

16.00

Normal
0

false
false
false

EN-US
X-NONE
X-NONE

/* Style Definitions */
table.MsoNormalTable
{mso-style-name:”Table Normal”;
mso-tstyle-rowband-size:0;
mso-tstyle-colband-size:0;
mso-style-noshow:yes;
mso-style-priority:99;
mso-style-parent:””;
mso-padding-alt:0in 5.4pt 0in 5.4pt;
mso-para-margin:0in;
mso-para-margin-bottom:.0001pt;
mso-pagination:widow-orphan;
font-size:10.0pt;
font-family:”Calibri”,sans-serif;
mso-bidi-font-family:”Times New Roman”;}

© 2017 Norman Jacknis, All Rights
Reserved

Libraries And The Story Of Apple

[Note: I’m President of the board of the Metropolitan New York Library Council, but this post is only my own view.]

For some time now, the library world and its supporters have worried about the rise of the Google search engine. Here’s just a sample of articles from the last ten years that express this concern and, of course, push back against the Google tide:

And there was also John Palfrey’s 2015 book, “BiblioTech: Why Libraries Matter More Than Ever in the Age of Google”, which shares some themes of this post.

This concern has had such a profound effect that many libraries have effectively curtailed their reference librarian services as people instead “Google it”.

No doubt Google is formidable. While there have been ups and downs (like 2015) in Google’s share of the search engine market, it is obviously very high. Some estimates put it at 80% or higher.

But the world is changing and perhaps librarians aren’t aware of a nascent opportunity.

In an article about a month ago, the data scientist Vincent Granville took a closer look at the data about the ways people search and get information. He found “The Slow Decline of Google Search”. Here are some of the highlights:

“Google’s influence (as a search engine) is declining. Not that their traffic share or revenue is shrinking, to the contrary, both are probably increasing.”

“The decline (and weakening of monopoly) is taking place in a subtle way. In short, Google is no longer the first source of information, for people to find an article, a document, or anything on the Internet.”

“What has happened over the last few years is that many websites are now getting most of their traffic from sources other than Google.”

“Google has lost its monopoly when it comes to finding interesting information on the Internet.”

“Interestingly, this creates an opportunity for entrepreneurs willing to develop a search engine.”

As the New York Times reported recently about the announcement of the new Pixel phone, Google has noticed all this and is strategically re-positioning itself as an artificial intelligence company.

What has this got to do with the Apple story?

Apple is now the most valuable company in the world. That wasn’t always so. Indeed, it almost was headed for oblivion as the chart shows. Even now, its earlier business of selling personal computers hasn’t grown that much. It was able to add to its mix of products and services in a compelling way. It is one of the great turnaround stories in business history.

That history offers a lesson for librarians. The battle against what Google originally offered has been a tough one and libraries have suffered in the eyes of many people, especially the public officials and other leaders who provide their funding.

But looking forward, libraries should consider the opportunities arising from the fact that Google’s impact on Internet users is lessening, that the shine of Google’s “do no evil” slogan has worn off in the face of greater public skepticism and that artificial intelligence – really augmented human intelligence – is now a viable, disruptive technology.

As many once great and now defunct companies, other than Apple, show, there aren’t many second chances. Libraries should take advantage of its second chance to play the role that they should
play

in a knowledge and innovation economy.

© 2017 Norman Jacknis, All Rights Reserved

The Rebirth Of The Learning Organization

Among the more ambitious and expansive CEOs, there’s a special kind of holy grail – transforming their organizations into learning organizations.   Jack Welch, former and famous CEO of GE, put it this way in the 1990s:

“an organization’s ability to learn, and translate that learning into action rapidly, is the ultimate competitive business advantage.”

The Business Dictionary defines a learning organization as an

“Organization that acquires knowledge and innovates fast enough to survive and thrive in a rapidly changing environment. Learning organizations (1) create a culture that encourages and supports continuous employee learning, critical thinking, and risk taking with new ideas, (2) allow mistakes, and value employee contributions, (3) learn from experience and experiment, and (4) disseminate the new knowledge throughout the organization for incorporation into day-to-day activities.”

Or as Peter Senge, one of the founders of the learning organization movement, has famously said in 1990:

“A learning organization is an organization that is continually expanding its capacity to create its future.”

As you can see, this dream started more than 25 years ago.

By the first decade of this century, however, Mark Smith wrote:

“[W]hile there has been a lot of talk about learning organizations it is very difficult to identify real-life examples.”

The companion in thought of the learning organization movement was the knowledge management movement. Its goal was to capture, organize and distribute knowledge among the members of an organization.

But, in his 2014 paper, “A Synthesis of Knowledge Management Failure Factors”, Alan Frost was already conducting autopsies for the failure of knowledge management initiatives in many organizations.

In some ways, this reminds me of the first great wave of Artificial Intelligence in the 1980s when a lot of effort went into trying to codify the knowledge of experts into expert systems by extensively questioning them about their decision processes. It turns out that it is hard to do that.

Often experts – the knowledgeable ones – can’t really articulate their decision rules and to make matters worse those rules are at times probabilistic. Like other humans, experts often seek to develop rubrics to simplify a problem, which unfortunately can limit their ability to continue to observe what’s happening. Human perception, in general, in an imperfect instrument.

Thus, even if an organization is successful in widely distributing the knowledge developed by its staff, it may be just propagating ideas that are, at best, only partly true.

All of these factors, and many others, has slowed down the march to the dream of learning organizations.

But now we are possibly at the beginning of a rebirth of the learning organization.

What’s different today? Analytics and big data make the process of organizational learning much easier and better. Indeed, it is perhaps time to add analytics as a sixth discipline to Senge’s Five Disciplines of a learning organization.

After all, it’s not that people don’t know things that are important to an organization’s success – it’s just that they don’t know everything that is important and they can’t or won’t keep up with the torrent of new data vying for their attention.

The traditional gathering of the human knowledge combined with the continuously improving analytics models can achieve the dream so nicely stated by the executives and visionaries of twenty-five years ago.

For example, instead of trying to interview experts at length to capture their knowledge, today, someone in analytics would prefer to review the thousands of cases where the characteristics of the case, the decision by an expert and outcome was known. Then some kind of machine learning would search for the underlying patterns. In that way, the expert’s tacit understanding of the world would arise out of the analytics.

Nor does this cut out experts in the knowledge acquisition process. It just changes their role from being a memoirist. Instead, the experts can help kick off the building of the model and even assist in interpreting the results of the analytics.

Once the learning has begun, there is still much to learn (no pun intended) from the pioneers of this field. While they had great difficulty obtaining the knowledge – feeding the learning organization – they knew the rules of distributing that knowledge and making it useful. That is a lesson that today’s folks specializing in analytics could learn. Among these:

  • The importance of organizational culture
  • Leadership interest and support – especially for open discussion and experiment
  • Measurement (which would certainly provide grist for the analytics mill)
  • Systems thinking

For an illustration, see “Evidence in the learning organization” from the National Institutes of Health, in which these issues are focused on the medical profession.

If a marriage of the learning organization and knowledge management movements with the newer focus on analytics takes place, then all of those fields will improve and benefit.

© 2017 Norman Jacknis, All Rights Reserved