Location connecting social and marketing

Location-Based Social Marketing

For extreme introverts the great thing about social media is that you never have to see your connections in real life. But for people who like to actually interact with people face-to-face, as well as the businesses that seek to get people in a physical door, that can actually be a shortcoming. Location-aware social media can bridge that gap between the virtual and physical.

Location awareness is what distinguishes the Locye social media platform. It allows users to “observe social activity at real-time hotspots and places of interest worldwide” and to post content that those nearby can see. For those who don't want to give too much away, there is an option to post anonymously. Users can also select whether they want their posts kept up for just a day or for a virtual eternity. 

Locye's Founder and CEO, Sajjad Mustehsan, discussed the platform's potential for marketing with me. He said that it “will soon be offering business-to-consumer marketing capabilities” with three defining characteristics:

Read more in Location-Based Social Marketing

The problem of visibility in today's complex supply chains

Supply chains today are integral to building a competitive advantage. As they grow in complexity, those who manage them acknowledge that they do not have as full visibility as they should. Technological solutions can point the way to more visible, better managed, and more efficient supply chains that deliver better value for companies and their customers.

While there is wide variation in the supply chains of various businesses, there are still some identifiable trends. The 2017 GEODIS Supply Chain Worldwide survey (PDF) delves into some of them. The report draws on the responses of 623 professionals in 17 countries from various functions related to the supply chain, including finance, operations, marketing, strategy, IT, and management.

The majority (67%) of people surveyed in a position of supply chain leadership rank as C-Level or top management. This does appear to play some role in successfully strategies because businesses in which that position is help by “a middle manager seem less profitable.”

The top concerns for those who responded to the survey were, not surprisingly, “containment of their costs (32%).” That fits with their awareness of dealing with “global competition (28%).”  However, more than half (57%) reported that they see “Supply Chain as a competitive advantage, enabling the development of the company and not” merely an area in which to reduce expenses.   

Read more in 

Today’s Complex Supply Chains Demand Visibility Solutions

Data Scientist Interview

Domingos Lopes is about to begin a new job involving machine learning at Google after having completed the NYC Data Science Academy 12-Week Data Science Bootcamp. He came in with a strong math background, having earned a PhD in that field from NYU in 2016. After deciding that he did not want his job prospects to be limited to academic settings, Lopes decided that the best course of action was to acquire data science skills. In addition to the technical skills like Python fluency, he expects to draw on the ability to communicate data insight and work effectively in a team setting as he embarks on his career at Google.

Read more in https://www.switchup.org/blog/alumni-spotlight-domingos-lopes-hired-at-google

Wait, what?

This is not a post on the popular book that bears that title. (I did write about that here:  uncommoncontent.blogspot.com.) This is my reaction to the number a billion that sounds impressive but is really completely meaningless without context.*

When I shared a link today on LI, it offered me three other links to read. Among them was a FastCompany article, "Six Ways YouTube Is Primed For The Future (And Four Areas That Need Work)" Now read what it says for the fifth and see if you have the same reaction I do:

5. YouTube’s rebuilt algorithms have led viewers to watch 1 billion hours of video a day. YouTube is optimized for what it calls “watch time,” which encompasses what users view, how long they tune in, the length of their overall YouTube session, and so forth. Together, these signals help YouTube algorithms decide which videos a user is most likely to watch shortly after they’re posted and which will lead to the longest overall viewing period.

Do you get what's missing here? How many viewers are there? How many hours did they watch before the algorithms were rebuilt?

Without those two pieces of information, we really have no way of knowing how much of an advance one billion hours of video a day represents. Sure, it sounds like a lot, but we don't know if it represents the two billion people watching an average of a half an hour a day or one billion watching an average of an hour, or half a million watching two hours.

 We also don't know if the actual goal was to bring in more viewers or to keep the ones already watching on the channel for longer. That's a pretty important piece of context, as well, if one is to judge if the algorithms are accomplishing what the company intended for them. The article does refer to 800 million YouTube consumers of music but doesn't clarify whether or not that represents the viewers in total and if that number represents an increase over the number before the adjustment to the algorithms.

The bottom line is this: Don't be dazzled by numbers, no matter how large, that are presented without the relevant context.


*Related post http://writewaypro.blogspot.com/2016/10/data-visualization-you-have-to-c-it-to.html
http://uncommoncontent.blogspot.com/2017/09/missingness-at-museum.html

Expanding comments with AI

The problem with human moderators is that they have human limitations that cannot keep up with a

pic from https://d1avok0lzls2w.cloudfront.net/img_uploads/Blog-Comments.jpg

huge influx of comments. That was the problem the New York Times faced in balancing reader comments demand with an editorial standard of civility for all published comments. Its solution was a partnership with , an incubator owned by Google's parent company, Alphabet.

Back in September, the Times announced the partnership in an article that set out the challenge faced by its 14 moderators tasked with reviewing the comments on the 10% of articles that do allow them. That alone amounted to 11,000 comments a day. As the article invited readers to try their hand at moderating, it was entitled Approve or Reject: Can You Moderate Five New York Times Comments?

I took the test. The official summary of my results were: "You moderated 4 out of 5 comments as the Community desk would have, and it took you 81 seconds. Moderating the 11,000 comments posted to nytimes.com each day would take you 49.5 hours."

That summation was followed by this: "Don't feel too bad; reviewing all of these comments takes us a long time, too." According to my calculations, however, the Times actually allows more time for their moderators. Given 14 people working 8 hours a day, the number of working hours each day would be 112, or more than twice the number of hours they said would be required for my rate of moderation.

That investment of so many hours is not something they regret, as they regard it as a requisite part of building up "one of the best communities on the web." However, they recognize that needs must dictate a new approach. That's where the machine assistance enters into the picture, enabling the same number of humans to effectively moderate a much larger number of comments and reduce the delay for reviewing time.

Flash forward to June 13, 2017, and the Gray Lady herself announces: The Times Sharply Increases Articles Open for Comments, Using Google's Technolog

Read more in How AI Can Moderate Comments, Eliminate Trolls

Personalized video marketing

“What's in a name?” Possibly quite a lot when it comes to personalized marketing.

Several months back I received a marketing email from Sprinklr that was rather ironic given its message about effective use of data. It opened like this: “ Hi {lead.First Name:default=}.”  Obviously, something went wrong, and I saw the code rather than my name.

Of course, personalized emails are not all that unusual any more, so some marketers have upped their game with personalized video. After receiving the “A Dog's Purpose” video (above) — in a correctly personalized email — I contacted Adgreetz's co-founder and CEO Eric Frankel about what data is used for this kind of personalized video marketing, and how the video was delivered to people who are 1) dog owners, and 2) have a dog of that particular name? 

read more in A Marketer's Best Friend

Define "good"

I don't mean that in an abstract way or with the kind of depths of thought about what we mean by "qulity" that drove the author of Zen and the Art of Motorcycle Maintenance toward madness. I just mean it in the context of "good pay."  The question arises from this job posting:

We pay really well because we want really good content.
“Really good content” means well-constructed, well-reasoned arguments that are also extremely engaging.
Articles need to be a minimum of 500 words, though analysis pieces need to be a minimum of 1,000 words
We provide you with the headlines and an initial source
We’re looking for both weekday AND weekend writers. There’s a bonus for weekend writers.
We often promote writers to management positions

Ah, I think, they say they have high standards but compensate accordingly, so I was picturing a minimum of three figures and not just with a one or two at the beginning of the number. But my illusions about some shared view on what constitutes "good" pay was shattered an instant later as I read on:

What We’ll PayPay will start at $15/article during the test period. Afterward, we’ll bump it to $20/article for weekday news articles, and $30/article for weekday analysis articles. Weekend news articles are $25/article and weekend analysis pieces are $35/article. 

So, basically, they'll pay you what teen babysitters get per hour for an article that should take you several hours to write.  Certainly, any beginning writer -- and that's the only kind who likely would apply for this job -- would likely need more than hour to crank out an article of that length that is not merely recycled platitudes.

Related: http://writewaypro.blogspot.com/2017/05/on-working-almost-for-free.html
http://writewaypro.blogspot.com/2016/05/writing-for-free-is-not-deductible.html
http://writewaypro.blogspot.com/2016/05/an-idiots-guide-for-writers.html

AI gets a boost from curiosity

As data analytics become increasingly driven by artificial intelligence (AI),

photo credit: https://c1.staticflickr.com/3/2332/2083892100_3e015d810a_b.jpg

researchers search for a way to drive machine learning. The key ingredient its future development may be a dash of curiosity.

There are all kinds of AI systems currently used by various businesses with different names like Alexa and Albert to personalize then. Perhaps it's time for an AI system named George after the monkey whose curiosity propels him into various adventures.

That would be an apt choice for the Intrinsic Curiosity Module (ICM) developed by a group of four researchers at University of California, Berkeley. The attempt to inject curiosity to achieve self-motivated advances in machine learning was the subject of their paper, Curiosity-driven Exploration by Self-supervised Prediction, that was just submitted to the 34th International Conference on Machine Learning (ICML 2017).

Their premise is that external rewards for learning are of necessity limited and actually rather rare in real life. That doesn't mean that people stop exploring or seeking out answers even when there are no prizes for doing. They are motivated by their own human curiosity. Infusing that kind of motivation in a virtual agent gets it to test things out for itself even when not directed to do so. The test of the effect was done in monitoring how far it would proceed in two video games, VizDoom and Super Mario Bros. as you see in the demo video here:

Read more in Machine Learning Taps Power of Curiosity