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Speaker 1: The future may not be clear, but our commitment is

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Speaker 1: so when you sit with an advisor at Merrill Lynch,

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Speaker 1: we'll put your interests first. Visit mL dot com and

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Speaker 1: learn more about Merrill Lynch, an affiliative Bank of America.

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Speaker 1: Mery Lynch makes available products and services offered by Merrill Lynch.

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Speaker 1: Pierce Veteran Smith Incorporated or Register Broker Dealer remember s

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Speaker 1: I PC. This is Masters in Business with Barry Ridholts

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Speaker 1: on Boomberg Radio. This week on the podcast, I have

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Speaker 1: professor a Nindia Ghosts of n y U Stern, where

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Speaker 1: he focuses on mobile technology and information services. We spent

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Speaker 1: a lot of time talking about his new book Tap

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Speaker 1: Unlocking the Mobile Economy, and rather just repeat what we discussed,

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Speaker 1: I want to give you a visual to think about.

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Speaker 1: I'm a big fan of science fiction and um anything

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Speaker 1: that Philip K. Dick wrote that is subsequently turned into

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Speaker 1: a movie, including Minority Report, and everybody focuses on the

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Speaker 1: precog aspect of that movie, the ability to anticipate who's

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Speaker 1: gonna cause a crime. There's a lot of very forward

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Speaker 1: technologies in the movie. But there's a scene in the

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Speaker 1: film where Tom Cruise The main character is running through

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Speaker 1: a shopping district and is bombarded with advertisement, not general ads,

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Speaker 1: not billboards, but three D holographic ads specifically tailored to him.

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Speaker 1: The retailer identifies him as a unique individual. They have

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Speaker 1: access to his prior um shopping preferences, and they're showing

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Speaker 1: him specifically tailored advertisements as he passes through in pursuit

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Speaker 1: of whatever it was. I don't remember the fascinating aspect

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Speaker 1: of this, uh in our My conversation with Professor ghost

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Speaker 1: wasn't that, hey, this is the future. This is a

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Speaker 1: hundred years or fifty years off in the distance. But

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Speaker 1: rather mobile technology are uniquely identified phones, all the data

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Speaker 1: we generate when we shop, when we use apps, when

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Speaker 1: we just move about in the city, means that that

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Speaker 1: sort of minority report technology is only two or three

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Speaker 1: years away. And I found that to be absolutely astonishing.

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Speaker 1: It's a fascinating conversation about technology and the future, and

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Speaker 1: it turns out the future ain't that far off in

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Speaker 1: the future. It's practically here now. So with no further ado,

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Speaker 1: my conversation with professor A Nindia Ghosts. My special guest

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Speaker 1: today is a Ninda Ghosts. He is a professor of

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Speaker 1: Information Operations and Management Science and n y used Stern

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Speaker 1: School of Business. He is the youngest ever recipd being

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Speaker 1: of the prestigious Informs I S S Distinguished Fellow Award.

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Speaker 1: He has been named by Business Week as one of

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Speaker 1: the top forty professors under forty worldwide. Analytics Week called

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Speaker 1: him one of their top two hundred thought leaders in

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Speaker 1: big data and business analytics. He's written more than a

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Speaker 1: hundred peer reviewed papers and is the author of his

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Speaker 1: first book, tap Unlocking the Mobile Economy and Ghosts. Welcome

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Speaker 1: to Bloomberg. Thank you very much for having me. I'm

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Speaker 1: very excited to be here. You cover some sections of

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Speaker 1: the the economy and some sectors of technology that I

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Speaker 1: find absolutely fascinating. In fact, you cover a broad range

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Speaker 1: of topics consumer behavior, internet, digital marketing, big data, social media,

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Speaker 1: and older related business analytics. How did that broad collection

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Speaker 1: of topics come together for you as a as a searcher.

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Speaker 1: So I first started working in this area in two

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Speaker 1: thousand four, and you know, I was basically curious about

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Speaker 1: all things Internet in two thousand four. You know, we

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Speaker 1: were just about seeing the emergence of e commerce. You know,

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Speaker 1: the likes of Amazon cropping up and people buying and

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Speaker 1: selling books on the internet. Um, so that's how the

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Speaker 1: journey started. Um, as the Internet evolved, you know, we

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Speaker 1: saw the emergence of companies trying to use that as

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Speaker 1: a platform to market their products. So that's why we

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Speaker 1: saw the emergence of search engines like Google come in.

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Speaker 1: We saw the emergence of you know, other kinds of

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Speaker 1: digital marketing platforms on the Internet. And so I've always

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Speaker 1: had this, you know, interest in figuring out what is

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Speaker 1: it that consumers are doing on the Internet and how

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Speaker 1: can companies leverage those differences in behavior, you know, to

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Speaker 1: better accurate products and market products and so on. And

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Speaker 1: as the Internet eventually moved into mobile, by two thousand

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Speaker 1: and eight, the first smartphone was already out there, that

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Speaker 1: really sort of got me thinking more and more deeply

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Speaker 1: about consumer behavior and mobile And on the mobile phone,

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Speaker 1: you're doing social media. You know, you're responding to ads.

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Speaker 1: If you're responding, you're creating a lot of data. And

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Speaker 1: that's how all these things come together. So so let's

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Speaker 1: talk about the smartphone essentially the intersection of all those

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Speaker 1: different technology sectors we just mentioned. Is this the single

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Speaker 1: most transformative consumer device ever to be created. I would

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Speaker 1: say so. In fact, in my book I actually mentioned

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Speaker 1: this that you know, by two thousand and eight, when

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Speaker 1: I first started thinking about this topic, it was pretty

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Speaker 1: clear to me that the smartphone would be, in fact

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Speaker 1: the most transformative thing that we've ever seen. And you know,

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Speaker 1: in ten years after that, now we do see, in fact,

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Speaker 1: there's plenty of evidence it is, because it's almost embedded

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Speaker 1: on us. And you know, there's people often ask me,

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Speaker 1: is there's a tipping point. I don't necessarily see that

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Speaker 1: the smartphone is going to become a more remote control button,

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Speaker 1: smart home, smart cars, everything is going to be controlled

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Speaker 1: by the smartphone. I have to tell you when you

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Speaker 1: look at the amount of tech analogy today that exists.

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Speaker 1: So you can control your nest, you can control your car,

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Speaker 1: you can start your car remotely, you can control the

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Speaker 1: temperature of your pool remotely, your security system, your video camera.

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Speaker 1: At what point does the phone just simply stop being

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Speaker 1: a phone and just becomes a mobile computer? Yeah, I

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Speaker 1: mean I think it's already kind of getting there, right.

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Speaker 1: I mean, by now, the phone is much more than

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Speaker 1: a phone. You know, it's a camera, it's a flashlight,

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Speaker 1: you know, it's my television, it's my book as well,

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Speaker 1: it's everything, right, So I think the convergence of various

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Speaker 1: digital phenomena on the smartphone has already happened. In my

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Speaker 1: prediction I mentioned this in my book, is that a

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Speaker 1: lot more is going to happen. And and by by

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Speaker 1: what I mean by that is exactly what you said,

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Speaker 1: which is people are going to start controlling their appliances,

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Speaker 1: their homes, their cars, other sorts of things. Let's let's

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Speaker 1: put let's put a number on some of the venture

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Speaker 1: capital investing in the space. In two thousand and ten,

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Speaker 1: less than four percent of all venture capital funding was

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Speaker 1: going to mobile startups as of fourteen, which is already

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Speaker 1: a good couple of years ago. It's now about eight percent,

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Speaker 1: So we're up a hundred percent or so from Is

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Speaker 1: mobile just going to consume the lion share of all

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Speaker 1: these new tech startups? I think that number is going

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Speaker 1: to only keep increasing, right, And and there's a couple

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Speaker 1: of reasons for that. One is, over the last five years,

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Speaker 1: we have consumers have been spending more and more time

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Speaker 1: on mobile devices. You know, from from two thousand twelve

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Speaker 1: to two sixteen, the last five years We've come from roughly,

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Speaker 1: you know, about ten percent of our time to about

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Speaker 1: time on these devices. And what's as telling companies that

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Speaker 1: there's a lot of opportunity for monetization because right now

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Speaker 1: advertisers are only putting in twelve percent of the ad

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Speaker 1: dollars and mobile phones. So there's a disconnect. You know,

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Speaker 1: consumers sure spending twice that time, companies are spending half

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Speaker 1: the money. So does a disconnect. Let's let's look at

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Speaker 1: the jobs a little bit. The Boston Consulting Group came

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Speaker 1: out and said that mobile technologies are going to be

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Speaker 1: or or just about at a three point three trillion

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Speaker 1: dollar investable sector over the next few years, and they

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Speaker 1: expected to create eleven million new jobs. Is this the

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Speaker 1: next boom sector or or currently the biggest sector that

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Speaker 1: we should expect new jobs from. I think certainly at

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Speaker 1: least one of the top two or top three biggest sectors.

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Speaker 1: You know, those eleven three point three trillion dollars is

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Speaker 1: only four point two percent of the world's GDP and

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Speaker 1: so you can only imagine it has to grow from here.

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Speaker 1: The other thing that's going to propel jobs is, you know,

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Speaker 1: one thing is that the mobile app economy is growing

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Speaker 1: by leaps and bounds, and so every time you've got

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Speaker 1: a new app, you know you need an entire army

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Speaker 1: of employees. But also aside from the fact is the data.

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Speaker 1: So I talk a lot about how you know, consumer

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Speaker 1: tapping on the phone to create stata. So they need

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Speaker 1: data scientists and need business analytics, not not just tapping

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Speaker 1: on the phone, but where they physically are, what else

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Speaker 1: they're running, what else they had previously been doing. There's

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Speaker 1: an enormous stream of data that we will create just

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Speaker 1: carrying the phones around exactly. Let's talk a little bit

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Speaker 1: about mobile you referenced earlier, the ad spends. Some of

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Speaker 1: the numbers are just astonishing. One estimate claims mobile ad

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Speaker 1: spending will top a hundred billion dollars by next year.

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Speaker 1: How effective is this advertising? And what alternatives do marketers

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Speaker 1: have other than mobile? So my studies and projects over

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Speaker 1: the last ten years, in across three different continents, across

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Speaker 1: multiple countries have actually demonstrated very rigorously than mobile advertising

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Speaker 1: is very effective if you do it right, How do

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Speaker 1: you do it right? And what do people who do

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Speaker 1: it wrong? What are they doing wrong right? So one

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Speaker 1: of the things I talked about my book is companies

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Speaker 1: who are doing it right are the ones who are

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Speaker 1: creating in a concierge or a butler in your smartphone.

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Speaker 1: And the companies who do it wrong are the ones

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Speaker 1: who are creating this creepy stalker. Okay, so if it's

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Speaker 1: some like Uber managed to do both, yeah, it's a

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Speaker 1: full concierge. And then we've just found out they were

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Speaker 1: stalking people even after they deleted, uh deleted the app. Yeah,

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Speaker 1: I mean they're going to get into trouble. And you know,

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Speaker 1: but I mean for a number of reasons. They've been

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Speaker 1: in trouble for a long time. No one episode of another.

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Speaker 1: But you know, just from a brand advertiser's perspective, um,

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Speaker 1: there's a thin line dividing the coolness and the creepiness.

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Speaker 1: And you know, in my book, I talked about a

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Speaker 1: lot of the case studies where have you've been actually

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Speaker 1: been very successful in remaining cool and not digressing into

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Speaker 1: the creepy stuff. And the book is tap unlocking the

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Speaker 1: mobile economy. So what can a app provider do to

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Speaker 1: stay on the right side of the cool creepy divide? Right?

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Speaker 1: So two things. One is you want to reduce the

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Speaker 1: frequency of how often your message your consumer through an

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Speaker 1: offer our ad, and you want to increase the relevancy

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Speaker 1: of that messaging. Okay, right now, if you have the opposite,

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Speaker 1: we are getting inundated by ads which are not necessarily

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Speaker 1: very relevant and redundant. Absolutely, So how does the company

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Speaker 1: do that? How do you focus in on what is relevant?

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Speaker 1: And how do you avoid haranguing um? Because you know,

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Speaker 1: I use an a service called limail because I'm loath

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Speaker 1: to give anybody my real email address because you know,

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Speaker 1: if I buy something, it's not an invitation to send

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Speaker 1: me a daily update. In fact, you know what once

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Speaker 1: a month is plenty for most of the retailers or

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Speaker 1: what have you I'm interested in. But it seems to

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Speaker 1: be all right, we have this person's email address, let's

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Speaker 1: let's bang on their head daily and hopefully they'll buy something.

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Speaker 1: Usually I unsubscribe, I delete the app, and limail allows

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Speaker 1: me to whether they actually unsubscribe him or not. It

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Speaker 1: allows me to turn that email off so they can

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Speaker 1: market to me all they want. I never see a

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Speaker 1: single a single thing from them. So so where is

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Speaker 1: that line and how do you stay on the right

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Speaker 1: side of it? So basically, right now, what's happening is,

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Speaker 1: you know, companies are sending like hitting dots in the

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Speaker 1: air and hoping one will stick. Right. And the reason

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Speaker 1: it's happening is even though this might seem very surprising,

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Speaker 1: they actually don't have enough information on consumers on a

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Speaker 1: given consumer. And this might seem astonishing given this world

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Speaker 1: of data, right. The reason is because and I talked

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Speaker 1: about this in the book a Lot, is the data

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Speaker 1: is actually fragmented. It's all in silos, so somebody has

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Speaker 1: to come and stitch it together. And absent that, what's

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Speaker 1: going to keep happening is companies will keep in undating

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Speaker 1: us with these offers. So you would think that Apple

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Speaker 1: in iOS and Google with Android are the ones who

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Speaker 1: can stitch all that data together. They have access to

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Speaker 1: all the data that every app generates. Are they moving

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Speaker 1: in that direction? And are we creating a monopoly or

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Speaker 1: all a goopoly amongst the big players who have access

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Speaker 1: to all our data? Right? So what's really fascinating is that,

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Speaker 1: you know, Google and Facebook are sort of the two

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Speaker 1: lead players who are essentially putting this infrastructors together. Meaning

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Speaker 1: what meaning that between the publisher and advertiser, they have

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Speaker 1: created these intermediaries called add exchanges and dsp s and

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Speaker 1: SSPs and a lot of the technical jargon, but the

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Speaker 1: basic job for these companies stitched the data together. And

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Speaker 1: what's also fascinating is the next big challenge to these

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Speaker 1: companies is going to come from telecom providers. So WIDD

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Speaker 1: Verizon buy out, a O L wided Verizon buy out Yahoo.

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Speaker 1: Verizon does not see itself as a telecom provider anymore,

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Speaker 1: if they're not just the pipe there just say actually,

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Speaker 1: if you ask their top management, they will say, well,

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Speaker 1: you know, we are actually the next big media tech company.

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Speaker 1: We want to take on Google and Facebook and they

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Speaker 1: can like us. They have so much data right that

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Speaker 1: that's that's quite fascinating, you know, when we look at

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Speaker 1: some of the data points that mobile is generated, I'm

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Speaker 1: fascinated by this. After the launch of the iPhone, voice

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Speaker 1: minutes amongst eighteen to thirty four year olds fell from

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Speaker 1: twelve hundred to nine hundred minutes per month within two years,

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Speaker 1: so that's a twenty drop over the same time period.

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Speaker 1: Texting more than doubled from six hundred to four hundred

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Speaker 1: messages per month over that same period. The impact of

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Speaker 1: this is really quite enormous it is. I mean, look,

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Speaker 1: first of all, there is a very you know, non

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Speaker 1: trivial consumer behavioral chain. Right, if you're not talking on

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Speaker 1: the phone anymore, you're tapping and texting, right, every tapping,

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Speaker 1: every texting is creating data about our behavior. But more importantly,

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Speaker 1: you know, coming back to the study comp providers, because

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Speaker 1: that's how they get the services. They are not making

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Speaker 1: that much money anymore on those products that they used

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Speaker 1: to make money on, which is Internet advice and and

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Speaker 1: text messaging. That's why, you know, whether it's Verizon or

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Speaker 1: Telephonic and Germany or China Mobile Estelecom, they realize the

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Speaker 1: next big bed is monetizing the user data, the user

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Speaker 1: location data, our behavioral data, you know, all of the

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Speaker 1: contextual data. And that's what they're moving into. Here's another

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Speaker 1: fascinating data point from the book of text messages are

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Speaker 1: read within ninety seconds of delivery. Are we at risk

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Speaker 1: of never being disconnected? And what are the ramifications of that? UM? So,

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Speaker 1: you know, I, I guess I belong to the group

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Speaker 1: who does not think there's a problem in that. UM.

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Speaker 1: I do not mind the fact that we are immersing

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Speaker 1: our devices. UM And the reason is because you know,

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Speaker 1: everything that I've seen is that there's actually a positive

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Speaker 1: site of this as well, which is which is going

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Speaker 1: back to my argument before that the reason company is

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Speaker 1: sending us all these offers is they don't have enough information.

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Speaker 1: But the more of immersals from the device, the more

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Speaker 1: data generates, and the more information not companies will have,

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Speaker 1: which will then let them reduce a frequency of the

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Speaker 1: messaging increase the relevancy. Let's let's talk a little bit

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Speaker 1: about mobile retail and what the impact is. A recent

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Speaker 1: report from Credit Swiss estimated that there could be undred

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Speaker 1: store closings this year, which would be higher than the

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Speaker 1: peak in two thousand and eight and significantly higher than

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Speaker 1: last year. What is the state of retailing in America? Okay,

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Speaker 1: So this is a great question and one of my favorites,

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Speaker 1: because mobile as a studio over the years is a

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Speaker 1: double a sword for retailing. Okay, And here's why. On

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Speaker 1: one side, you know, offline retailers have been petrified by

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Speaker 1: the fact that consumers have been mobile showrooming, meaning that

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Speaker 1: while they're inside a physical brick and model store and

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Speaker 1: they found something they like, and yet they're actually looking

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Speaker 1: for the same product online on their phone looking for

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Speaker 1: a cheaper price. Let me interrupt you and ask you

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Speaker 1: a question about that. I used to have an app

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Speaker 1: on my phone called Amazon price Guard. I'm gonna actually

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Speaker 1: have to take a quick look at it, and yet

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Speaker 1: Amazon ended up. I don't want to say price check

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Speaker 1: is the name of it. Amazon doesn't seem to be

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Speaker 1: supporting that app anymore. Um, is showrooming still the problem

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Speaker 1: that was a few years ago? Okay, so yes or

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Speaker 1: no in the following sense. So showrooming was a massive

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Speaker 1: problem up until recently. The likes of these physical brick

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Speaker 1: and motor retailers were losing fifty of the traffic the

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Speaker 1: likes of Amazon, and someone would go into Best Buy,

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Speaker 1: look at the television or whatever, find it was fifty

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Speaker 1: bucks treeper on Amazon, and walk out, it's right and

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Speaker 1: buy it on Amazon. So you just lost one customer there,

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Speaker 1: So one in two people were doing that. Now. The

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Speaker 1: reason I say it's a double that sort is today

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Speaker 1: the offline retailers have a tremendous resource in the same

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Speaker 1: phone which was once a threat to them, because now,

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Speaker 1: using these location based tagging technologies, they can send you

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Speaker 1: a coupon in real time when you're invest buy to

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Speaker 1: prevent you from walking out without buying the TV. And

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Speaker 1: in my book, actually I talk a lot about these

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Speaker 1: case studies where based on you know, whether it's the

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Speaker 1: WiFi in the store or the beacon in the store

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Speaker 1: or the GPS, these retailers are able to sense where

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Speaker 1: you are and send you an offer and delight you

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Speaker 1: and impression surprise you. So couldn't they simply have just

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Speaker 1: matched the Amazon's price to begin with. And and you know,

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Speaker 1: if Amazon is providing a lower price, or any online

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Speaker 1: retailer is providing a lower price, Uh, what is the

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Speaker 1: advantage of the coupon? Or is it that some people

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Speaker 1: come in and pay the full price anyway and the

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Speaker 1: price insor of people get the coupon and they go

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Speaker 1: that way. That's exactly what it is. Basically. You know,

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Speaker 1: any sort of location based mobile coupon strategy has to

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Speaker 1: be intrementally useful to the company. You don't want to

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Speaker 1: send a blanket price matching policy for everybody, right, because

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Speaker 1: many people will just be happy to paying the higher

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Speaker 1: price when they buy, they're out with they don't want

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Speaker 1: to be bothered, right, It's the stragglers that really price

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Speaker 1: as they folks the bargain hunters whom you don't want

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Speaker 1: to lose. That's where the location based technology is, folks.

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Speaker 1: So I'm gonna I'm gonna move to advertising because it

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Speaker 1: relates back to this. In the old days, we used

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Speaker 1: to take ink and spray it on mashed up trees

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Speaker 1: and and it was called the newspaper or magazine. Back

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Speaker 1: in the nineteen fifties, there was something like fifty there

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Speaker 1: was something like fifty four million paid subscriptions to newspapers.

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Speaker 1: That number is under forty three million as of two

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Speaker 1: thousand ten, but relative to population, it's a seventy decrease.

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Speaker 1: So when I think of all these retailers, I remember

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Speaker 1: it was always the best bicircular in the Sunday paper.

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Speaker 1: There was always a target circular. I assume that still

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Speaker 1: goes on. I just hardly read a print paper the

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Speaker 1: way I used to. What does the the diminishing of

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Speaker 1: print as a communication channel, how how does that impact

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Speaker 1: retailers and what are they doing to overcome the loss

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Speaker 1: of that channel? Right? So it means two things. One is,

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Speaker 1: and if you're a retailer, you're still spending money on

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Speaker 1: physical newspapers for advertising, You're wasting it. Now, people only

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Speaker 1: spend six percent of the entire time reading a physical

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Speaker 1: newspaper or magazine, right, Unfortunately, that's what it is. So

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Speaker 1: what they should be doing is actually putting that money

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Speaker 1: on that magic device, which is the smartphone. Right, So

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Speaker 1: instead of spending money in print newspapers, you should actually

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Speaker 1: spend money investing in these location based advertisement and locations

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Speaker 1: cool pons, all of these technologies that enable companies to

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Speaker 1: do so in real time. Let's talk a little bit

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Speaker 1: about travel and what we see uh here in the

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Speaker 1: United States there are about a hundred and fifty million smartphones.

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Speaker 1: Of these are use location services. Is how is this

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Speaker 1: data being shared and who has access to that data? So? Um,

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Speaker 1: A lot depends on how the app is actually owned. Right.

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Speaker 1: So if the company, the travel company or the travel

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Speaker 1: site owns the app, then clearly you know the app

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Speaker 1: Delapa belongs to the company and they own it. If

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Speaker 1: it's a third body, a developper, then the abdulopper will

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Speaker 1: have it and highly likely they will be sharing it

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Speaker 1: with the company because that's how the arrangement is. Um.

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Speaker 1: So those that happens for sure, and we were talking

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Speaker 1: uh off air before, but let's let's bring this up.

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Speaker 1: There seems to be a sort of ambiguous nous about

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Speaker 1: what people say their concerns with privacy actually are and

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Speaker 1: then what they actually do. So I'm pretty concerned about privacy.

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Speaker 1: I don't like sharing my email address, or my location

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Speaker 1: or a whole bunch of other data points with companies.

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Speaker 1: But yet the generation that's younger than me, they seem

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Speaker 1: to they could not possibly care less about these privacy issues.

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Speaker 1: They share the data free, freely. What is the standard?

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Speaker 1: What's going on with that? What should an app company

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Speaker 1: reasonably expect from their users? So, you know, various studies

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Speaker 1: we have done and others have shown that millennials are

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Speaker 1: very willing to share their data as a currency to

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Speaker 1: get something in value, right, Um of Generation Z and

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Speaker 1: Generation Y a willing to do the same less so

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Speaker 1: it baby boomers. Um. But you know, if you are

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Speaker 1: a marketer today and you're thinking about your target segment,

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Speaker 1: target population, right for the lot for the most part,

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Speaker 1: it's actually Gen ZS, gen X, gener wise and millennials, right,

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Speaker 1: So you want to stay with the trend and keep

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Speaker 1: targeting them, and if they are actually responding to this

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Speaker 1: data exchange data boutter economy. So what you really want

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Speaker 1: to do is be their butler and concierge by sending

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Speaker 1: a curated, relevant message. That's what you want to be doing.

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Speaker 1: So supposedly this was just reported the Times not too

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Speaker 1: long ago when Tim Cook discovered that Uber was capturing

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Speaker 1: the unique identify er the fingerprints of each phone and

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Speaker 1: using it to track Uber users, even those who have

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Speaker 1: removed the app. Um, he threatened them with taking kicking

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Speaker 1: them out of the app store. So raises a couple

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Speaker 1: of questions. First, how bad is the behavior of Uber?

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Speaker 1: And second, it seems like Apple has an awful lot

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Speaker 1: of power because have they kicked Uber out? That pretty

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Speaker 1: much would have been the end of Uber. Sure, I mean,

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Speaker 1: first of all, it's terrible behavior on part of Ober.

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Speaker 1: They violated the two simple mantras of data privacy, which

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Speaker 1: is notice and consent. You need to notify consumers what

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Speaker 1: are you doing with the data? And then you need

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Speaker 1: to ask for consent. They wanted at them both, right,

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Speaker 1: They didn't tell them they were tracking them, and they

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Speaker 1: obviously never got consent exactly. Um. And of course, you know,

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Speaker 1: if you at the end of the day, it's an app,

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Speaker 1: it is a big eighty billion dollar company, but it

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Speaker 1: is an app. The way people access app is through

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Speaker 1: an app store. If an Apple or Samsung decided to

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Speaker 1: kick them out, that's it for them. I mean, how

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Speaker 1: are they gonna It's fatal? Another way, you would think

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Speaker 1: no one would want to mess around do anything that

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Speaker 1: would even remotely put them at risk. I'm surprised Apple

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Speaker 1: didn't just freeze them out for a week to flex

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Speaker 1: some muscles and say, hey, anybody else thinking about doing this,

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Speaker 1: It's gonna cost you a lot of money. But it

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Speaker 1: raises the question does Apple and Android have too much

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Speaker 1: power over the the app economy and modern technological life.

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Speaker 1: I mean, I don't see that way. I don't think

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Speaker 1: they have too much power. I think they have appropriate power.

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Speaker 1: It is their own store at the store, and you know,

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Speaker 1: consumers have a choice. You know, they can go to

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Speaker 1: a Google Player store or an Apple app store, and

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Speaker 1: some of the other third party companies also have their

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Speaker 1: Amazon as an app store. Tour. Um, I think, you know,

430
00:25:03,040 --> 00:25:06,640
Speaker 1: their power is a reflection of consumer preferences and so right,

431
00:25:06,680 --> 00:25:11,760
Speaker 1: so um, that's that's quite that's quite fascinating. Let's let's

432
00:25:11,800 --> 00:25:16,240
Speaker 1: talk about the location based advertising numbers you you were

433
00:25:16,400 --> 00:25:21,119
Speaker 1: referencing it earlier. It's expected to grow to fifteen billion

434
00:25:21,160 --> 00:25:26,240
Speaker 1: dollars in eighteen. This was under two billion just three

435
00:25:26,359 --> 00:25:32,760
Speaker 1: years ago. That's a compound and annual growth rate. How

436
00:25:33,119 --> 00:25:38,600
Speaker 1: long can this continue? Global real time location based advertising?

437
00:25:38,840 --> 00:25:42,199
Speaker 1: How long can this grow at that astonishing rate for

438
00:25:42,400 --> 00:25:45,320
Speaker 1: many years? In the forceable the future? Right now, we're

439
00:25:45,320 --> 00:25:48,359
Speaker 1: only scratching the type of the iceberg here, right And

440
00:25:48,400 --> 00:25:50,880
Speaker 1: it's because a small fraction of people are the ones

441
00:25:50,920 --> 00:25:54,399
Speaker 1: who are opening up their GPS data and saying, Okay,

442
00:25:54,440 --> 00:25:56,879
Speaker 1: here's where I am send me an offer. That number

443
00:25:56,920 --> 00:25:59,720
Speaker 1: is only gonna grow. And in everything that I've seen

444
00:26:00,119 --> 00:26:02,159
Speaker 1: last ten years in different parts of the world is

445
00:26:02,200 --> 00:26:05,080
Speaker 1: telling me that, um, you know, this is a very

446
00:26:05,119 --> 00:26:07,960
Speaker 1: tiny portional the actual potential of how much can be

447
00:26:08,080 --> 00:26:12,359
Speaker 1: monetize in this space. So there's a fascinating um data

448
00:26:12,400 --> 00:26:15,399
Speaker 1: point in the book where you talk about the psychological

449
00:26:15,400 --> 00:26:19,680
Speaker 1: component of how people behave in groups and they're using

450
00:26:19,680 --> 00:26:23,240
Speaker 1: apps and when they're UM shopping, and and the data

451
00:26:23,280 --> 00:26:28,480
Speaker 1: point that jumped out in there were about five and

452
00:26:28,520 --> 00:26:31,600
Speaker 1: a half million people riding the subway in New York

453
00:26:31,640 --> 00:26:36,720
Speaker 1: City every day. That puts it about seventh worldwide. You

454
00:26:36,800 --> 00:26:41,120
Speaker 1: did a study that found on crowded subways, people were

455
00:26:41,240 --> 00:26:45,160
Speaker 1: twice as likely to respond to a mobile offer than

456
00:26:45,280 --> 00:26:50,159
Speaker 1: commuters on non crowded train What is the thinking behind this?

457
00:26:50,320 --> 00:26:54,879
Speaker 1: Why would crowdedness affect mobile purchases? So this is actually

458
00:26:54,880 --> 00:26:57,560
Speaker 1: one of my favorite studies in the book. Basically, you

459
00:26:57,560 --> 00:27:01,359
Speaker 1: know what we did is we exploited leverage the variation

460
00:27:01,440 --> 00:27:04,440
Speaker 1: in crowdedness and different parts of the time, different times

461
00:27:04,440 --> 00:27:08,760
Speaker 1: of the day, in different cities, and send people offers

462
00:27:09,200 --> 00:27:12,400
Speaker 1: and effectively measured how people responded to these offers when

463
00:27:12,400 --> 00:27:15,080
Speaker 1: they're in this crowded train station or subway stations, right,

464
00:27:15,200 --> 00:27:18,680
Speaker 1: and these are real time, real DEO sinc. So it's

465
00:27:18,760 --> 00:27:21,720
Speaker 1: just this one station, even this one, so we know

466
00:27:21,800 --> 00:27:24,600
Speaker 1: exactly we are working away a telecom provider which has

467
00:27:24,600 --> 00:27:28,160
Speaker 1: sending person of the market share. We know where you're

468
00:27:28,240 --> 00:27:30,080
Speaker 1: which train you are in. In five we also know

469
00:27:30,119 --> 00:27:33,960
Speaker 1: which compartment in the train you are in. So that's

470
00:27:33,960 --> 00:27:36,480
Speaker 1: a level of because you know, the definition of location

471
00:27:36,520 --> 00:27:39,720
Speaker 1: has changed completely in our companies can identify you within

472
00:27:39,760 --> 00:27:42,960
Speaker 1: three feet. So what we saw is that as the

473
00:27:43,080 --> 00:27:46,440
Speaker 1: level of crowdedness increased from one person persquire meter to

474
00:27:46,560 --> 00:27:50,000
Speaker 1: up to five percent persquire meter. The propensity for people

475
00:27:50,040 --> 00:27:54,320
Speaker 1: to positively respond to these marketing offers continue to kept increasing,

476
00:27:54,880 --> 00:27:57,359
Speaker 1: and to the point where at five m pers cwire

477
00:27:57,359 --> 00:28:02,560
Speaker 1: meter was fairly crowded, they were percent more likely than

478
00:28:02,720 --> 00:28:06,840
Speaker 1: to respond to the offers uh than in the previous metric.

479
00:28:07,200 --> 00:28:10,400
Speaker 1: And what was happening was people basically said, look, when

480
00:28:10,440 --> 00:28:13,440
Speaker 1: I'm surrounded by all these strangers, I don't really know them.

481
00:28:13,480 --> 00:28:15,640
Speaker 1: The first thing I do is I take my phone out.

482
00:28:15,680 --> 00:28:18,439
Speaker 1: I immerse myself in this phone. This is my escape.

483
00:28:18,480 --> 00:28:21,240
Speaker 1: This and at that time, in those twenty minutes of commute,

484
00:28:21,280 --> 00:28:23,640
Speaker 1: if you can send me a relevant offer, you've got

485
00:28:23,680 --> 00:28:26,119
Speaker 1: me there, you know. I love There's this old photo

486
00:28:26,200 --> 00:28:29,640
Speaker 1: of everybody standing waiting for a commuter train, and it's

487
00:28:29,640 --> 00:28:32,439
Speaker 1: from the fifties, and the men have hats and the

488
00:28:32,480 --> 00:28:36,119
Speaker 1: women are wearing dresses, and everybody has newspaper open. And

489
00:28:36,160 --> 00:28:40,280
Speaker 1: then they the same train station you see taken recently.

490
00:28:41,080 --> 00:28:43,880
Speaker 1: It's the same run of people, only everybody standing looking

491
00:28:43,920 --> 00:28:48,120
Speaker 1: at a cell phone. So what is it about crowding

492
00:28:48,640 --> 00:28:52,680
Speaker 1: on a train or anywhere else that generates those negative emotions?

493
00:28:53,520 --> 00:28:56,800
Speaker 1: Is it simply it's a relief to to think about

494
00:28:56,840 --> 00:29:00,160
Speaker 1: something else, And is that why people respond to ads? Say,

495
00:29:00,240 --> 00:29:03,080
Speaker 1: in those circumstances, it's an escape device, right, So when

496
00:29:03,120 --> 00:29:05,720
Speaker 1: you're surrounded by people that you don't know, it creates

497
00:29:05,720 --> 00:29:10,480
Speaker 1: and conjures many negative emotions, you know, some sort of adversity, hostility, stress,

498
00:29:10,520 --> 00:29:13,600
Speaker 1: and so on. So shopping is actually a big stress reliever.

499
00:29:13,720 --> 00:29:16,800
Speaker 1: You know, psychologists have talk even for millennials because we

500
00:29:16,880 --> 00:29:19,840
Speaker 1: keep reading, well, these millennials are all about experiences and

501
00:29:19,880 --> 00:29:23,640
Speaker 1: not consumerism. Is that true? I don't Actually, we didn't

502
00:29:23,680 --> 00:29:26,120
Speaker 1: see any study evidence so far. I mean, I think

503
00:29:26,160 --> 00:29:30,120
Speaker 1: we've been consistently looking at different demographic splits and we

504
00:29:30,160 --> 00:29:33,120
Speaker 1: continue to find certainly, you know, with like gen X

505
00:29:33,160 --> 00:29:35,480
Speaker 1: and gen Y, it's a lot more, but it's still

506
00:29:35,520 --> 00:29:39,120
Speaker 1: the same qualitative result with millennials. And we've been talking

507
00:29:39,120 --> 00:29:43,440
Speaker 1: about mobile phones, but mobile tablets are also a significant

508
00:29:43,480 --> 00:29:47,080
Speaker 1: part of the market. Or these devices in the midst

509
00:29:47,080 --> 00:29:51,200
Speaker 1: of essentially killing the desktop PC, is that a dying

510
00:29:51,960 --> 00:29:55,960
Speaker 1: um product um? I don't necessarily think so, okay, I

511
00:29:55,960 --> 00:29:58,760
Speaker 1: actually think that, you know, desktops or laptops are actually

512
00:29:58,800 --> 00:30:01,360
Speaker 1: here to stay. Part of the reason is there's still

513
00:30:01,400 --> 00:30:04,920
Speaker 1: a tremendous amount of productivity and work that you can

514
00:30:04,960 --> 00:30:07,960
Speaker 1: get done on these larger devices that it's just hard

515
00:30:08,000 --> 00:30:09,760
Speaker 1: to get done on a tablet or a smartphone. I

516
00:30:10,120 --> 00:30:13,200
Speaker 1: find I generate contents on the desktop and I consume

517
00:30:13,280 --> 00:30:15,920
Speaker 1: contents on the mobile device. And you know what, people

518
00:30:16,000 --> 00:30:20,800
Speaker 1: still buy products a lot more on desktops than on tablets. Okay,

519
00:30:21,760 --> 00:30:24,440
Speaker 1: but that being said, you know, one of the biggest

520
00:30:24,560 --> 00:30:27,640
Speaker 1: myths that unwrapped in the book is that, you know,

521
00:30:27,720 --> 00:30:30,720
Speaker 1: the smartphone or the tablet is not very effective in marketing.

522
00:30:31,080 --> 00:30:33,440
Speaker 1: Actually that's not true. It may lead to four or

523
00:30:33,440 --> 00:30:36,000
Speaker 1: five percent of your sales, but it has had an

524
00:30:36,000 --> 00:30:39,960
Speaker 1: influence up to your all of your sales because people

525
00:30:40,080 --> 00:30:43,200
Speaker 1: first got exposed to your product on the smartphone, so

526
00:30:43,240 --> 00:30:45,480
Speaker 1: they see it on the smartphone and then we'll either

527
00:30:45,560 --> 00:30:48,680
Speaker 1: book market or email themselves or what have you um

528
00:30:48,760 --> 00:30:51,400
Speaker 1: in order to make the purchase on the desk exactly.

529
00:30:52,120 --> 00:30:55,360
Speaker 1: Is that a temporary phenomenon as we just get used

530
00:30:55,400 --> 00:31:00,600
Speaker 1: to mobile technology tablets or is this just an ingrained

531
00:31:00,640 --> 00:31:03,000
Speaker 1: behavior that's not going to change. It is changing. It

532
00:31:03,120 --> 00:31:05,360
Speaker 1: is changing. In fact, you know, when I first started

533
00:31:05,360 --> 00:31:07,720
Speaker 1: doing this studies into tos and ten or nine, just

534
00:31:07,800 --> 00:31:10,160
Speaker 1: one the tablets were being introduced. You know, we saw

535
00:31:10,160 --> 00:31:12,640
Speaker 1: these numbers were one one and a half percent. Today

536
00:31:12,680 --> 00:31:15,719
Speaker 1: they are up close to five percent, so it's increasing. Uh,

537
00:31:16,080 --> 00:31:18,120
Speaker 1: it takes some time, but there's a ton ahead room.

538
00:31:18,160 --> 00:31:21,040
Speaker 1: There a lot of room. We have been speaking with

539
00:31:21,120 --> 00:31:26,000
Speaker 1: Professor An India Ghosts, author of the book Tap Unlocking

540
00:31:26,240 --> 00:31:30,280
Speaker 1: the Mobile Economy. If you enjoy this conversation about all

541
00:31:30,320 --> 00:31:33,440
Speaker 1: things mobile technology, will be sure and check out our

542
00:31:33,480 --> 00:31:37,440
Speaker 1: podcast extras. You can find that on Bloomberg dot com,

543
00:31:37,600 --> 00:31:40,960
Speaker 1: SoundCloud and Apple iTunes. Be sure to check out my

544
00:31:41,080 --> 00:31:44,360
Speaker 1: daily column. It's on Bloomberg View dot com. You can

545
00:31:44,440 --> 00:31:48,520
Speaker 1: follow me on Twitter at rit Halts. I'm Barry Hults.

546
00:31:48,760 --> 00:31:56,200
Speaker 1: You're listening to Masters and Business on Bloomberg Radio. What

547
00:31:56,280 --> 00:31:58,719
Speaker 1: could your future hold more than you think because at

548
00:31:58,760 --> 00:32:00,720
Speaker 1: Merrill Lynch we work with you to create a strategy

549
00:32:00,760 --> 00:32:03,600
Speaker 1: built around your priorities. Visit mL dot com and learn

550
00:32:03,640 --> 00:32:06,080
Speaker 1: more about Meryll Lynch. An affiliated Bank of America, Mary

551
00:32:06,120 --> 00:32:08,360
Speaker 1: Lynch makes available products and services offered by Merrill Lynch.

552
00:32:08,400 --> 00:32:10,760
Speaker 1: Pierce Federan Smith Incorporated, a registered broker dealer. Remember s

553
00:32:10,760 --> 00:32:18,680
Speaker 1: I PC. Welcome to the podcast, um and India, thank

554
00:32:18,720 --> 00:32:20,640
Speaker 1: you so much for doing this this is. This is

555
00:32:20,680 --> 00:32:25,600
Speaker 1: an area I find absolutely fascinating. I I consider it

556
00:32:25,720 --> 00:32:29,160
Speaker 1: a privilege to have been born in a period where

557
00:32:29,360 --> 00:32:34,240
Speaker 1: I got to watch the arc of technology changed so dramatically.

558
00:32:35,600 --> 00:32:38,280
Speaker 1: The Internet came out, I was still young enough to

559
00:32:38,360 --> 00:32:42,480
Speaker 1: a stop to it, but it wasn't always there. It's

560
00:32:42,480 --> 00:32:45,360
Speaker 1: something I had to learn at a point in my career.

561
00:32:45,880 --> 00:32:49,440
Speaker 1: As opposed to you know, I'm fascinated by my nephews

562
00:32:49,440 --> 00:32:53,400
Speaker 1: and nieces who they grew up. There has always been smartphones,

563
00:32:53,440 --> 00:32:56,760
Speaker 1: has always been tablets, has always been um Internet, and

564
00:32:56,800 --> 00:33:00,200
Speaker 1: to them it's just second nature, as opposed is to

565
00:33:00,400 --> 00:33:06,960
Speaker 1: a And I'm fascinated to watch how that effects um,

566
00:33:07,040 --> 00:33:10,320
Speaker 1: the way they interact with it. Tell me, UM, some

567
00:33:10,360 --> 00:33:12,480
Speaker 1: things about the book that we didn't get to what

568
00:33:12,480 --> 00:33:16,000
Speaker 1: what what do you think is significant that we simply

569
00:33:16,280 --> 00:33:21,120
Speaker 1: didn't touch in any of our questions previously? Well, I

570
00:33:21,160 --> 00:33:23,120
Speaker 1: guess you know, Um, we go out a lot um.

571
00:33:23,160 --> 00:33:25,440
Speaker 1: One of the things that we haven't talked about are

572
00:33:25,600 --> 00:33:28,880
Speaker 1: you know these forces that are actually going to help

573
00:33:28,920 --> 00:33:31,480
Speaker 1: companies unlocked this economy? Right? So I actually in my

574
00:33:31,480 --> 00:33:35,200
Speaker 1: book of Forces Shaping the Mobile Economy, and you list

575
00:33:35,280 --> 00:33:37,320
Speaker 1: about ten of them, and there are nine forces. There

576
00:33:37,240 --> 00:33:45,560
Speaker 1: are nine context, location, time, saliency, crowdedness, trajectory, social dynamics, weather,

577
00:33:45,680 --> 00:33:49,080
Speaker 1: and text mix. We didn't talk about whether I have

578
00:33:49,200 --> 00:33:52,840
Speaker 1: to ask about that because that's fascinating. Um, how does

579
00:33:52,920 --> 00:33:58,320
Speaker 1: weather affect people using mobile technology? Okay? So, um in

580
00:33:58,440 --> 00:34:02,280
Speaker 1: various space? You know, it's if I have to, if

581
00:34:02,280 --> 00:34:04,720
Speaker 1: I had to ask somebody. Um, if it's a nice,

582
00:34:04,760 --> 00:34:08,480
Speaker 1: sunny and bright day, are people more likely to be

583
00:34:08,560 --> 00:34:10,800
Speaker 1: on their phones? Are less likely to be on their phones?

584
00:34:11,440 --> 00:34:13,640
Speaker 1: My instinct is to say less likely they're out and

585
00:34:13,680 --> 00:34:18,880
Speaker 1: about enjoying the weather sturn start. Actually it's actually the opposite. Okay, Um,

586
00:34:19,080 --> 00:34:21,319
Speaker 1: they may be up and about, but they're still on

587
00:34:21,400 --> 00:34:24,120
Speaker 1: their phones. Um. So I might be taking a stroll

588
00:34:24,160 --> 00:34:26,440
Speaker 1: in Center Park and I'm there for three hours, but

589
00:34:26,719 --> 00:34:28,799
Speaker 1: in at least half of that time, I've been on

590
00:34:28,840 --> 00:34:31,800
Speaker 1: my phone. And so you're taking pictures, you're doing Facebook

591
00:34:31,880 --> 00:34:35,400
Speaker 1: or Instagram, you're using the mapping software. As opposed to

592
00:34:35,440 --> 00:34:38,240
Speaker 1: if it's a really nasty, rainy day, you're inside watching

593
00:34:38,280 --> 00:34:41,040
Speaker 1: TV or on your computer. Right, if it's a rainy

594
00:34:41,080 --> 00:34:43,000
Speaker 1: day and nasty day, you are watching TV, but then

595
00:34:43,040 --> 00:34:45,319
Speaker 1: you also have your tablet in front of you. So

596
00:34:45,360 --> 00:34:49,680
Speaker 1: people are doing prow processing and multiple devices. So, um,

597
00:34:49,719 --> 00:34:53,120
Speaker 1: you know, one of my colleagues are quotas on a

598
00:34:53,120 --> 00:34:56,399
Speaker 1: different paper. He actually had a study. Um I wasn't

599
00:34:56,440 --> 00:34:58,000
Speaker 1: part of it, but he esteemed did it, and they

600
00:34:58,040 --> 00:35:01,080
Speaker 1: actually showed that on a sunnier day, people are more

601
00:35:01,120 --> 00:35:03,799
Speaker 1: likely to spend time on their smartphones and make more

602
00:35:03,840 --> 00:35:07,120
Speaker 1: purchases in the online space done in the offline space.

603
00:35:07,239 --> 00:35:11,919
Speaker 1: Really that's quite fascinating, But what's the psychology behind that?

604
00:35:12,200 --> 00:35:15,799
Speaker 1: Is it they're just out enduring the day, or you

605
00:35:15,840 --> 00:35:20,120
Speaker 1: would think presence is such a big movement these days. Hey,

606
00:35:20,160 --> 00:35:22,160
Speaker 1: it's a nice day, we're out and about must your

607
00:35:22,200 --> 00:35:25,600
Speaker 1: face being the phone is a constant harangue. I'm sure

608
00:35:25,719 --> 00:35:30,359
Speaker 1: lots of people have heard from their significant others. Are

609
00:35:30,480 --> 00:35:34,480
Speaker 1: we always engaged with our phones? Even on a on

610
00:35:34,520 --> 00:35:37,279
Speaker 1: a beautiful day. If you're on the high Line or

611
00:35:37,320 --> 00:35:40,239
Speaker 1: in Central Park or wherever you happen to be, you're

612
00:35:40,320 --> 00:35:43,760
Speaker 1: more likely to purchase online than offline. So they actually

613
00:35:43,840 --> 00:35:48,520
Speaker 1: related back to one simple thing, which is mood transformations. Basically,

614
00:35:48,640 --> 00:35:51,640
Speaker 1: when the day is sunnier and nicer, you feel much better.

615
00:35:51,680 --> 00:35:54,040
Speaker 1: I mean you're mooding, You're in a better mood overall. Right,

616
00:35:54,400 --> 00:35:56,840
Speaker 1: when you're in a better mood overall, you're more likely

617
00:35:56,920 --> 00:36:00,000
Speaker 1: to actually go down to that device and start looking

618
00:36:00,080 --> 00:36:04,719
Speaker 1: for things to do, Like shopping is another mood and answer. So,

619
00:36:04,800 --> 00:36:07,200
Speaker 1: now you've got a brighter, sunny day, You've got you know,

620
00:36:07,360 --> 00:36:11,319
Speaker 1: online shopping that is very easily accessible. Would you rather

621
00:36:11,440 --> 00:36:13,600
Speaker 1: step out and walk a few blocks to get to

622
00:36:13,680 --> 00:36:15,799
Speaker 1: the store if you can get that done online on

623
00:36:15,840 --> 00:36:19,240
Speaker 1: your phone. Turn sut that most people prefer to continue

624
00:36:19,280 --> 00:36:21,480
Speaker 1: to be wherever they are, at work, at home, get

625
00:36:21,480 --> 00:36:23,880
Speaker 1: on their phones and buy that product. So controlling, and

626
00:36:23,880 --> 00:36:26,120
Speaker 1: this was a nice control fee experiment, you know, the

627
00:36:26,120 --> 00:36:28,480
Speaker 1: test group and the control group, and they continue to

628
00:36:28,520 --> 00:36:31,880
Speaker 1: find that we are more likely to actually buy things

629
00:36:32,160 --> 00:36:36,840
Speaker 1: on our smartphones, even on anice or sunnier, brighter actually extend,

630
00:36:37,200 --> 00:36:39,160
Speaker 1: extend the mood, elevate or why do I want to

631
00:36:39,160 --> 00:36:44,080
Speaker 1: go into a fluorescent lit whatever shopping supermarket, whatever when

632
00:36:44,080 --> 00:36:47,560
Speaker 1: I could be outside? And I recall there have been studies.

633
00:36:47,880 --> 00:36:51,000
Speaker 1: I'm going to mangle this, so I apologize in advance.

634
00:36:51,360 --> 00:36:54,319
Speaker 1: There have been studies that look at stock market performance

635
00:36:54,360 --> 00:36:57,560
Speaker 1: based on weather and around the world, not just in

636
00:36:57,600 --> 00:37:01,399
Speaker 1: New York, around the world, rainy day and sunny days

637
00:37:01,520 --> 00:37:05,880
Speaker 1: actually have a skew that correlates with sunny days better

638
00:37:05,960 --> 00:37:09,400
Speaker 1: market absolutely days worse, which just tells you, Now, I

639
00:37:09,400 --> 00:37:14,120
Speaker 1: wonder how that changes as we replace human specialists with algorithms,

640
00:37:14,160 --> 00:37:18,600
Speaker 1: but historically there's been this art correlation. Um, let's talk

641
00:37:18,640 --> 00:37:23,959
Speaker 1: about saliency. What is saliency relative to mobile technology? Okay,

642
00:37:24,000 --> 00:37:25,600
Speaker 1: So this is also one of my favorites. You know,

643
00:37:25,760 --> 00:37:29,560
Speaker 1: we live today in the world of information overload and

644
00:37:29,600 --> 00:37:34,279
Speaker 1: attention deficit economy, too much information, too little attention. Saliency

645
00:37:34,360 --> 00:37:37,000
Speaker 1: is the force which basically says, can I give you

646
00:37:37,040 --> 00:37:38,640
Speaker 1: the best offer? As a company? Can I give you

647
00:37:38,680 --> 00:37:41,920
Speaker 1: the best offer and prop it up right at the

648
00:37:41,960 --> 00:37:44,239
Speaker 1: top where you can see it? Okay? I don't want

649
00:37:44,280 --> 00:37:46,680
Speaker 1: you to have to incur the cognitive cost of having

650
00:37:46,719 --> 00:37:49,880
Speaker 1: to search things or browsings or you know, there's a

651
00:37:49,880 --> 00:37:53,480
Speaker 1: lot of clutter out there, okay. And so what salience

652
00:37:53,520 --> 00:37:56,480
Speaker 1: it refers to is the ranking effect. How do I

653
00:37:56,560 --> 00:37:59,440
Speaker 1: get my ad or my offer to be ranked at

654
00:37:59,440 --> 00:38:01,959
Speaker 1: the very top up so that more people are likely

655
00:38:02,000 --> 00:38:04,239
Speaker 1: to see it, respond to it, and it does not

656
00:38:04,360 --> 00:38:07,880
Speaker 1: get lost in the clutter down the screen. How do

657
00:38:07,920 --> 00:38:10,080
Speaker 1: you do that? Okay? So here's where, you know, the

658
00:38:10,120 --> 00:38:14,280
Speaker 1: combination of the same ranking algorithms used by search engines

659
00:38:14,440 --> 00:38:17,920
Speaker 1: in sort of the online internet space can be deployed

660
00:38:18,040 --> 00:38:21,000
Speaker 1: in the mobile app economy. So when I open up

661
00:38:21,080 --> 00:38:24,600
Speaker 1: Yelp today, for example, just to give an example, UM,

662
00:38:24,800 --> 00:38:28,000
Speaker 1: yelps will Yelp has a standard set of algorithms which

663
00:38:28,080 --> 00:38:31,240
Speaker 1: is not actually necessarily based on the kind of bidding

664
00:38:31,239 --> 00:38:34,600
Speaker 1: our auctions that happened on search engines. Right. So I

665
00:38:34,640 --> 00:38:37,960
Speaker 1: am foreseeing a world where within a given app, when

666
00:38:38,000 --> 00:38:41,239
Speaker 1: you've got multiple competitors wine for your tension, we will

667
00:38:41,280 --> 00:38:44,600
Speaker 1: get back into that bidding an auction space, and companies

668
00:38:44,640 --> 00:38:47,800
Speaker 1: will want to bid more, get a better placement and

669
00:38:47,880 --> 00:38:50,880
Speaker 1: a better rank to get more of attention. Um. And

670
00:38:50,920 --> 00:38:53,279
Speaker 1: we did. There's actually, uh you know, elements of this

671
00:38:53,400 --> 00:38:56,799
Speaker 1: in Germany in a massive study involving three seventy four

672
00:38:56,880 --> 00:39:00,560
Speaker 1: cities and towns of Germany with three thousand companies using

673
00:39:00,600 --> 00:39:03,560
Speaker 1: one single app, and we saw these ranking effects were

674
00:39:03,640 --> 00:39:08,080
Speaker 1: dramatically powerful, quite fascinating. And then the other one I

675
00:39:08,120 --> 00:39:13,239
Speaker 1: wanted to ask from from the nine forces trajectory, what

676
00:39:13,400 --> 00:39:16,000
Speaker 1: is trajectory? Okay, so this I have to say, I

677
00:39:16,000 --> 00:39:17,719
Speaker 1: guess I get excited about all of them, but this

678
00:39:17,800 --> 00:39:21,879
Speaker 1: is the most fascinating just because it's the most futuristic. Now,

679
00:39:21,920 --> 00:39:24,840
Speaker 1: I would tell you know, our listeners brace for impact

680
00:39:24,880 --> 00:39:27,920
Speaker 1: because this might be like, wow, it's really happening. But

681
00:39:28,000 --> 00:39:30,960
Speaker 1: here's what it is. Blooming Days is right next door,

682
00:39:31,480 --> 00:39:34,719
Speaker 1: big store, multiple floors, mass a few blocks away, right,

683
00:39:34,880 --> 00:39:39,960
Speaker 1: multiple floors. What if imagine a world where the retailer

684
00:39:40,080 --> 00:39:43,200
Speaker 1: knows not just where you are, but also where you

685
00:39:43,280 --> 00:39:45,400
Speaker 1: have been in the past. We're not too hard to

686
00:39:45,440 --> 00:39:49,800
Speaker 1: figure out. You get that data pretty regularly. Well now, basically,

687
00:39:49,800 --> 00:39:52,400
Speaker 1: you know, the WiFi in these department stores is a

688
00:39:52,440 --> 00:39:55,880
Speaker 1: great instrument for them to get access to our real

689
00:39:55,960 --> 00:39:59,400
Speaker 1: time horizontal and vertical data. Which floor do you go to,

690
00:39:59,600 --> 00:40:02,000
Speaker 1: which or do you go to, how much department within

691
00:40:02,040 --> 00:40:05,040
Speaker 1: a store, which department within a store, which floor, how

692
00:40:05,120 --> 00:40:08,799
Speaker 1: much time you're spending when that's department store? And we

693
00:40:08,840 --> 00:40:12,240
Speaker 1: actually did that. So my chapter on trajectory talks about

694
00:40:12,320 --> 00:40:15,240
Speaker 1: the studies we have done with chopping malls in different

695
00:40:15,239 --> 00:40:18,319
Speaker 1: parts of the world, where again it's very important for

696
00:40:18,400 --> 00:40:21,960
Speaker 1: us to follow the notice and consent formula. Notified consumers

697
00:40:21,960 --> 00:40:25,480
Speaker 1: when they sign on the WiFi that you will get tracked,

698
00:40:25,520 --> 00:40:27,359
Speaker 1: but in return for that, we are going to send

699
00:40:27,360 --> 00:40:30,359
Speaker 1: you more relevant and less frequent offers. And it works

700
00:40:30,480 --> 00:40:33,640
Speaker 1: like a charm. And what what is the responsive consumers

701
00:40:33,719 --> 00:40:37,480
Speaker 1: who are participating in those sorts of exchanges. Are they

702
00:40:37,600 --> 00:40:40,480
Speaker 1: spending more or they're making more purchases? So we have

703
00:40:40,640 --> 00:40:44,919
Speaker 1: thirty five percent higher response rates, we have an order

704
00:40:44,960 --> 00:40:47,840
Speaker 1: of vanguage like twice two and a half times more spending.

705
00:40:48,239 --> 00:40:51,880
Speaker 1: The individual store is benefiting. The overall mall is benefiting too.

706
00:40:52,440 --> 00:40:55,680
Speaker 1: And another nice thing is with this trajectory best advertising.

707
00:40:55,760 --> 00:40:58,400
Speaker 1: Because of the fact that our recommendation and go itthems

708
00:40:58,480 --> 00:41:01,280
Speaker 1: is so accurate, we can make you spend more money

709
00:41:01,280 --> 00:41:04,640
Speaker 1: and spend less time so as to prevent crowdedness. And

710
00:41:04,719 --> 00:41:07,680
Speaker 1: these stores, So let's talk a little bit about these

711
00:41:07,719 --> 00:41:12,040
Speaker 1: big um suburban malls and other types of shopping areas

712
00:41:12,760 --> 00:41:15,920
Speaker 1: we've seen. We mentioned in a number of stores that

713
00:41:15,960 --> 00:41:21,360
Speaker 1: have been closing, we've generally been become over retailed is

714
00:41:21,400 --> 00:41:25,200
Speaker 1: an expression. I like that in the nineties, all these

715
00:41:25,239 --> 00:41:28,759
Speaker 1: big stores, you don't flick the switch and open them

716
00:41:28,760 --> 00:41:32,799
Speaker 1: all their multi year in some case decade long projects.

717
00:41:33,239 --> 00:41:36,360
Speaker 1: They began building them, really expanding them. In the eighties

718
00:41:36,440 --> 00:41:41,399
Speaker 1: nineties and two thousands and failed to acknowledge or think

719
00:41:41,400 --> 00:41:44,160
Speaker 1: about what the Internet was going to do to those retailers.

720
00:41:44,640 --> 00:41:47,560
Speaker 1: And now we've seen a ton of empty stores, a

721
00:41:47,560 --> 00:41:52,560
Speaker 1: lot of retailers that that over retailing, that excess retail

722
00:41:52,600 --> 00:41:55,760
Speaker 1: footprint is in the process of I hate the phrase

723
00:41:55,880 --> 00:41:58,920
Speaker 1: right sizing, but coming back to what is in a

724
00:41:58,960 --> 00:42:03,759
Speaker 1: more appropriate per capital footage square footage, we're i think

725
00:42:03,800 --> 00:42:08,680
Speaker 1: twice as much retail square footage as in America versus Europe.

726
00:42:10,040 --> 00:42:15,280
Speaker 1: How much can technology bridge that gap for these retailers.

727
00:42:15,880 --> 00:42:22,800
Speaker 1: Can they really make the store experience more relevant, faster, smarter,

728
00:42:23,000 --> 00:42:26,000
Speaker 1: more enjoyable. Um. Yes, So the answer for all of

729
00:42:26,040 --> 00:42:28,719
Speaker 1: this is yes, yes, and yes. And here's why. Right

730
00:42:28,760 --> 00:42:31,160
Speaker 1: so that the WiFi technology that I mentioned, you know,

731
00:42:31,239 --> 00:42:33,680
Speaker 1: that's been used to cure it offers in real time

732
00:42:33,680 --> 00:42:36,320
Speaker 1: to people shoppers in real time. It's not just about

733
00:42:36,480 --> 00:42:39,840
Speaker 1: the commercial aspect of it. In terms of retailing. We

734
00:42:39,960 --> 00:42:42,240
Speaker 1: have now seen evidence where they can use the same

735
00:42:42,320 --> 00:42:46,560
Speaker 1: data to redesign the department store or the mall to

736
00:42:46,680 --> 00:42:50,160
Speaker 1: figure out, you know, where are crowds, you know, sort

737
00:42:50,160 --> 00:42:52,680
Speaker 1: of aggregating and how do you dispose the crowds? How

738
00:42:52,680 --> 00:42:55,960
Speaker 1: do you optimize the workflow so as to have more

739
00:42:56,000 --> 00:42:59,959
Speaker 1: homogeneous distribution of crowdedness. Okay. In addition, you can find

740
00:43:00,000 --> 00:43:02,000
Speaker 1: know where the hot spots are and have a higher

741
00:43:02,000 --> 00:43:05,320
Speaker 1: retail price for those stores to we have their stores

742
00:43:05,360 --> 00:43:08,000
Speaker 1: in the hot spots, right. Um, So there's a very

743
00:43:08,000 --> 00:43:10,120
Speaker 1: interesting you know, and since you mentioned you have interested

744
00:43:10,120 --> 00:43:13,160
Speaker 1: in architecture and design, there's a very interesting architectural element

745
00:43:13,239 --> 00:43:16,200
Speaker 1: to these trajectory data. It's not just about you know,

746
00:43:17,000 --> 00:43:20,120
Speaker 1: commercializing three shoppers. You can actually completely change you know

747
00:43:20,160 --> 00:43:22,839
Speaker 1: what We're talking with airports now in the Middle East,

748
00:43:22,840 --> 00:43:25,200
Speaker 1: for example in Dubai and Abu Dhabi, about how these

749
00:43:25,239 --> 00:43:29,040
Speaker 1: trajectory data can be used to redesign the layout of

750
00:43:29,160 --> 00:43:32,960
Speaker 1: the retail segment of the airport. Um. And you know,

751
00:43:33,000 --> 00:43:36,200
Speaker 1: where should be? Where should the most coveted stores be.

752
00:43:36,360 --> 00:43:39,840
Speaker 1: Should they be closer to the terminals are further away?

753
00:43:40,320 --> 00:43:43,760
Speaker 1: You know, does and all of those things. That's quite fascinating.

754
00:43:44,360 --> 00:43:47,480
Speaker 1: With the malls, it's not gonna be the Is it

755
00:43:47,560 --> 00:43:49,479
Speaker 1: going to be the individual stores or is it gonna

756
00:43:49,480 --> 00:43:52,319
Speaker 1: be the the mall owner that's going to have to

757
00:43:52,360 --> 00:43:55,360
Speaker 1: implement these technologies. Yeah, so between the department store and

758
00:43:55,400 --> 00:43:57,279
Speaker 1: the mall. There are these differences. You know, some of

759
00:43:57,360 --> 00:44:02,520
Speaker 1: most centralized ammonies centralized um you know stores right right um.

760
00:44:02,520 --> 00:44:05,200
Speaker 1: In fact, in the US, the Mall of American Minneapolis.

761
00:44:05,239 --> 00:44:06,800
Speaker 1: I have a colleague here who was a professor in

762
00:44:06,800 --> 00:44:09,960
Speaker 1: the University of Minnesota at Carlson, and he was telling

763
00:44:09,960 --> 00:44:11,879
Speaker 1: me that, you know, he was beginning to do these

764
00:44:11,920 --> 00:44:14,920
Speaker 1: talks with them all of America. And you know, most

765
00:44:14,920 --> 00:44:17,200
Speaker 1: likely I'll also get involved, because you know, both of

766
00:44:17,200 --> 00:44:18,839
Speaker 1: them are aware of the work that we have done

767
00:44:18,840 --> 00:44:21,719
Speaker 1: in the Far East. But look here in Minneapolis, in

768
00:44:21,760 --> 00:44:24,160
Speaker 1: the U. S small of America wants to use WiFi

769
00:44:24,239 --> 00:44:28,200
Speaker 1: data to Sandy's trajectory based offers to consumers and use

770
00:44:28,239 --> 00:44:30,560
Speaker 1: it for you know, design layouts and all of that.

771
00:44:30,880 --> 00:44:34,719
Speaker 1: So it's not just that they're in this department in

772
00:44:34,800 --> 00:44:38,480
Speaker 1: the store. They could say they're walking towards Macy's. Let's

773
00:44:38,480 --> 00:44:41,640
Speaker 1: get them a Macy's coupon. Yeah. So, in fact, and

774
00:44:41,680 --> 00:44:43,960
Speaker 1: more than that, so even within Macy's, you know, are

775
00:44:44,000 --> 00:44:46,360
Speaker 1: you walking to the two me luggage store or to

776
00:44:46,600 --> 00:44:51,879
Speaker 1: the Armani's men's session. It is getting very granular and

777
00:44:52,239 --> 00:44:55,920
Speaker 1: I and I find this awesome fascinating. That's amazing. It's amazing.

778
00:44:56,080 --> 00:44:59,400
Speaker 1: It's it's reminds me of the scene in Minority Report

779
00:44:59,880 --> 00:45:03,120
Speaker 1: X where Tom Cruise is walking in the store is

780
00:45:03,160 --> 00:45:07,560
Speaker 1: giving a customized pop up add to him. It's not

781
00:45:07,600 --> 00:45:10,719
Speaker 1: on his mobile phone. It's a three D hologram projected

782
00:45:10,760 --> 00:45:14,040
Speaker 1: in space in front of him, and the the expectation

783
00:45:14,160 --> 00:45:16,920
Speaker 1: is they're either tracking him by his mobile device or

784
00:45:17,000 --> 00:45:20,719
Speaker 1: some other piece of technology. We're going to get Minority

785
00:45:20,760 --> 00:45:24,640
Speaker 1: Report on steroids. Really, yes, and that very soon because

786
00:45:25,000 --> 00:45:27,560
Speaker 1: and they have to take years, five years how far

787
00:45:27,600 --> 00:45:29,960
Speaker 1: away is and I would I would reckon two to

788
00:45:30,080 --> 00:45:33,960
Speaker 1: three years really, because they have to do this otherwise

789
00:45:34,440 --> 00:45:35,880
Speaker 1: these guys are going to go out of business. Then

790
00:45:35,920 --> 00:45:38,640
Speaker 1: there's only going to be the Amazons and the jet

791
00:45:38,719 --> 00:45:42,560
Speaker 1: dot coms. And it's just too competitive. If they're not

792
00:45:42,640 --> 00:45:46,879
Speaker 1: grabbing people's attention, they're in a desk place. Wow, that's

793
00:45:46,920 --> 00:45:49,560
Speaker 1: that's amazing. So I know I only have you for

794
00:45:49,760 --> 00:45:53,200
Speaker 1: a limited amount of time. Let's let's jump to some

795
00:45:53,280 --> 00:45:58,399
Speaker 1: of my favorite questions that I ask all my guests. Um,

796
00:45:58,480 --> 00:46:01,799
Speaker 1: let's let's start a little bit out your background. Have

797
00:46:01,960 --> 00:46:05,680
Speaker 1: you always wanted to be in academia? You also do

798
00:46:05,840 --> 00:46:08,799
Speaker 1: a lot of consulting work as well. What were you

799
00:46:08,840 --> 00:46:13,840
Speaker 1: doing before, um, h N while you you were briefly

800
00:46:14,000 --> 00:46:16,600
Speaker 1: Warden if memory serves, UM, I was at Warden for

801
00:46:16,640 --> 00:46:18,640
Speaker 1: a new year between two total eleven and two thousand

802
00:46:18,640 --> 00:46:22,040
Speaker 1: and twelve. UM. But before you know, I decided to

803
00:46:22,120 --> 00:46:25,840
Speaker 1: join academia. UM. Well, I know I did my PSD.

804
00:46:25,920 --> 00:46:28,080
Speaker 1: Everybody has to do it. But before that I worked

805
00:46:28,080 --> 00:46:30,239
Speaker 1: in the industry for a couple of years with IBM

806
00:46:30,320 --> 00:46:34,279
Speaker 1: and with a company called HC. L. Hewlett Packard. Both

807
00:46:34,320 --> 00:46:37,560
Speaker 1: of them were in India. And you you mentioned your

808
00:46:37,719 --> 00:46:40,840
Speaker 1: you get both a master's and a PhD from Carnegie Mellon,

809
00:46:40,960 --> 00:46:44,160
Speaker 1: that's right, and from there to IBM or from there

810
00:46:44,200 --> 00:46:47,239
Speaker 1: to Warden. No, so from there I joined n YU

811
00:46:47,440 --> 00:46:50,319
Speaker 1: after my PSD, and I've basically spent twelve years at

812
00:46:50,400 --> 00:46:53,480
Speaker 1: en VYU and one year at Wharton UM. But before

813
00:46:53,520 --> 00:46:56,040
Speaker 1: my master's and PhD in the US at Carnegie Mellon,

814
00:46:56,120 --> 00:46:59,280
Speaker 1: I did an NBA in India UM and undergraduate and engineering.

815
00:47:00,400 --> 00:47:03,239
Speaker 1: And where does IBM fit into that timeline? So after

816
00:47:03,320 --> 00:47:06,000
Speaker 1: my m b A, UM, you know, I wanted to

817
00:47:06,040 --> 00:47:08,799
Speaker 1: do something in I T consulting, so I got a

818
00:47:08,880 --> 00:47:11,799
Speaker 1: job with x C L Hulett Packard and then I

819
00:47:11,840 --> 00:47:13,920
Speaker 1: spent a year there and then I moved to IBM

820
00:47:13,960 --> 00:47:18,520
Speaker 1: again very similar portfolio, e commerce consulting. That's really when

821
00:47:18,560 --> 00:47:20,719
Speaker 1: I started getting interested about the Internet. It is nine

822
00:47:22,280 --> 00:47:25,640
Speaker 1: song just the Internet was kind of blowing up, blowing up,

823
00:47:25,880 --> 00:47:30,239
Speaker 1: and I guess you know, I didn't find those two

824
00:47:30,400 --> 00:47:34,360
Speaker 1: years very satisfactory. Um. I guess I wasn't. You know.

825
00:47:34,520 --> 00:47:38,080
Speaker 1: You call it intellectual challenge or not enough curiosity, and

826
00:47:38,200 --> 00:47:41,920
Speaker 1: that's what made me think about academia. And you also,

827
00:47:42,120 --> 00:47:44,080
Speaker 1: you still do a lot of consulting for a lot

828
00:47:44,160 --> 00:47:49,000
Speaker 1: of retailers and uh, tech companies, mobile companies. Who do

829
00:47:49,040 --> 00:47:51,920
Speaker 1: you consult for? So I've considered a lot for you know,

830
00:47:52,120 --> 00:47:56,200
Speaker 1: telecom providers, for tech companies Facebook and Apple and Samsung.

831
00:47:56,440 --> 00:47:58,920
Speaker 1: And then you know, I also worked with Esctelecom in

832
00:47:59,000 --> 00:48:03,399
Speaker 1: South Korea with China Mobile. Um I've worked with other

833
00:48:03,480 --> 00:48:06,600
Speaker 1: smaller startups and so on. UM. So I try to

834
00:48:07,000 --> 00:48:09,880
Speaker 1: mix it up and it keeps it always interesting and

835
00:48:10,040 --> 00:48:12,320
Speaker 1: I love it. I love it. So tell us about

836
00:48:12,400 --> 00:48:14,680
Speaker 1: some of your early mentors, who were the people who

837
00:48:15,719 --> 00:48:19,840
Speaker 1: affected how you think about the world about technology. So

838
00:48:20,239 --> 00:48:22,919
Speaker 1: I think I, I, you know, my early PhD days

839
00:48:22,960 --> 00:48:25,279
Speaker 1: at Carni emil and is when I had you know,

840
00:48:25,360 --> 00:48:28,800
Speaker 1: a few amazing individuals who played a role. Um. You know,

841
00:48:28,880 --> 00:48:31,080
Speaker 1: one of them was a professor in economics. His name

842
00:48:31,120 --> 00:48:34,040
Speaker 1: is Odeya Ragin. He's a professor now in the University

843
00:48:34,040 --> 00:48:38,240
Speaker 1: of Michigan and an Harbor, and you know, he became

844
00:48:38,680 --> 00:48:41,560
Speaker 1: my closest mentor. I would basically spend you know, a

845
00:48:41,760 --> 00:48:45,879
Speaker 1: lot chunk of the time with him. Um. In fact,

846
00:48:45,880 --> 00:48:48,920
Speaker 1: he even brought me a laptop, so you know, PSC students,

847
00:48:49,000 --> 00:48:53,040
Speaker 1: you know, poor people every dollar accounts. And the fact

848
00:48:53,120 --> 00:48:54,959
Speaker 1: that he would go out of his way, he didn't

849
00:48:54,960 --> 00:48:58,600
Speaker 1: have to write, um, just buy me a laptop was amazing.

850
00:48:59,600 --> 00:49:02,000
Speaker 1: And it just so happened that we connected very well.

851
00:49:02,120 --> 00:49:05,640
Speaker 1: And you know, he gave me some awesome advice over

852
00:49:05,680 --> 00:49:07,680
Speaker 1: the years. So he had a huge role at the beginning.

853
00:49:08,520 --> 00:49:10,480
Speaker 1: And then when I came to n YU as a

854
00:49:10,600 --> 00:49:13,759
Speaker 1: very young junior assistant professor, there's a professor and why

855
00:49:13,800 --> 00:49:17,960
Speaker 1: you called body of Wisenfeld. She's a professor of management. UM.

856
00:49:18,200 --> 00:49:21,239
Speaker 1: She was appointed as my mentor. And initially I was

857
00:49:21,280 --> 00:49:23,840
Speaker 1: a little surprised because she's from management. I am a

858
00:49:23,960 --> 00:49:26,640
Speaker 1: more of a con person, but it just founded that

859
00:49:26,719 --> 00:49:28,640
Speaker 1: I was the best decision and why you probably took

860
00:49:28,680 --> 00:49:31,319
Speaker 1: for me really because she has and I thank her

861
00:49:31,440 --> 00:49:34,359
Speaker 1: very explicitly, both her and the origin and my acknowledgments

862
00:49:34,480 --> 00:49:36,960
Speaker 1: that there's a couple of lines just for them that

863
00:49:37,239 --> 00:49:39,839
Speaker 1: you know, I'm in depthing grateful to them and why

864
00:49:39,920 --> 00:49:42,320
Speaker 1: you does this. As a matter of course, they have

865
00:49:42,480 --> 00:49:47,839
Speaker 1: a mentoring program between junior and senior professors, Is that right, Yeah,

866
00:49:47,880 --> 00:49:49,520
Speaker 1: So we try to do this because you know, the

867
00:49:49,560 --> 00:49:53,239
Speaker 1: world of academia is very cutthroat, especially in these very

868
00:49:53,320 --> 00:49:57,040
Speaker 1: competitive top tier schools. You know, getting tenure is a

869
00:49:57,200 --> 00:49:59,640
Speaker 1: non trivial process. You know, lots of people come in,

870
00:49:59,719 --> 00:50:02,400
Speaker 1: as in your professors, only a handful of them get tenured.

871
00:50:02,880 --> 00:50:04,640
Speaker 1: You can get lost along the way, you know, you

872
00:50:04,680 --> 00:50:08,560
Speaker 1: can get devastated. You So having a mentor is really useful,

873
00:50:09,200 --> 00:50:13,240
Speaker 1: and I think Stern really does it well by also pairing,

874
00:50:13,520 --> 00:50:18,360
Speaker 1: you know, sort of different back people with different backgrounds.

875
00:50:18,440 --> 00:50:21,319
Speaker 1: So I had a quant background. They could have given

876
00:50:21,360 --> 00:50:23,920
Speaker 1: me somebody from the quant area. They didn't. They decided

877
00:50:23,960 --> 00:50:26,480
Speaker 1: to give me somebody who's more qualitative in terms of

878
00:50:26,640 --> 00:50:30,759
Speaker 1: you know, or more behavioral, but somehow that you know,

879
00:50:30,880 --> 00:50:34,120
Speaker 1: that is magical that that's a great, great idea, quite

880
00:50:34,239 --> 00:50:39,279
Speaker 1: quite fascinating. Tell us about, um other thinkers who influenced

881
00:50:39,400 --> 00:50:44,400
Speaker 1: your approach to looking at and analyzing technology. So this

882
00:50:44,600 --> 00:50:51,160
Speaker 1: might sound very very strange, but my biggest influences in

883
00:50:51,239 --> 00:50:55,320
Speaker 1: this space haven't necessarily come from technology. Um. One of

884
00:50:55,400 --> 00:50:59,040
Speaker 1: my biggest hobbies, which I try to pursue religiously, is mountaineering.

885
00:50:59,080 --> 00:51:03,399
Speaker 1: I'm basically altitude climber. Someone a previous guest was also

886
00:51:03,480 --> 00:51:06,360
Speaker 1: a high altitude climb I'm trying to remember who it was.

887
00:51:06,760 --> 00:51:09,560
Speaker 1: But I was surprised because I didn't expect it was

888
00:51:09,640 --> 00:51:13,080
Speaker 1: like really yeah, yeah, so yeah, I and most people

889
00:51:13,160 --> 00:51:15,040
Speaker 1: think I'm crazy, right, you know, why would you want

890
00:51:15,080 --> 00:51:19,960
Speaker 1: to go and climb these mountains with big fifty sixty backpacks? But? Um,

891
00:51:20,160 --> 00:51:23,000
Speaker 1: what what have you climbed recently? Um? So, I've climbed

892
00:51:23,000 --> 00:51:26,160
Speaker 1: a fair bit in South America and you know, Bolivia, Ecuador.

893
00:51:26,640 --> 00:51:29,879
Speaker 1: I climbed climbing Jarrow in Africa. I have climbed parts

894
00:51:29,920 --> 00:51:33,000
Speaker 1: of the Himalayas. I've climbed a lot in the Cascades,

895
00:51:33,080 --> 00:51:36,400
Speaker 1: Rainier and a fur forty. How did you find your

896
00:51:36,440 --> 00:51:39,279
Speaker 1: way into mountaineering? Like, how did how did that become

897
00:51:39,320 --> 00:51:42,680
Speaker 1: a thing for you? So almost you know, like by luck,

898
00:51:42,800 --> 00:51:45,480
Speaker 1: I guess. So I started doing my undergrad in a

899
00:51:46,480 --> 00:51:48,879
Speaker 1: the northern part of India, in a state called pun Job,

900
00:51:49,200 --> 00:51:52,919
Speaker 1: which is actually very close to the photos of the Himalayas. Now,

901
00:51:53,080 --> 00:51:55,560
Speaker 1: you know, every other weekend or every other month, my

902
00:51:55,640 --> 00:51:58,520
Speaker 1: friends and I would just back our backs and go hiking.

903
00:51:59,440 --> 00:52:02,239
Speaker 1: And I realized that was when I was eighteen, and

904
00:52:02,520 --> 00:52:04,320
Speaker 1: I'd gone these hikes a few times a year. I

905
00:52:04,480 --> 00:52:08,080
Speaker 1: loved it. And then I also realized some are physically

906
00:52:08,080 --> 00:52:09,880
Speaker 1: and mentally it was easy for me to do these

907
00:52:09,960 --> 00:52:12,680
Speaker 1: things compared to some of the others other friends, and

908
00:52:12,719 --> 00:52:14,799
Speaker 1: they started egging me that, hey, you know, if you're

909
00:52:15,360 --> 00:52:17,480
Speaker 1: just good at this hiking, maybe you should take it

910
00:52:17,600 --> 00:52:20,200
Speaker 1: up a notch and think about something more challenging. And

911
00:52:21,080 --> 00:52:22,920
Speaker 1: I said, yeah, you know, I could do that. So

912
00:52:23,000 --> 00:52:26,359
Speaker 1: I joined this Himalin Mountaining Institute. Of course, a one

913
00:52:26,440 --> 00:52:30,360
Speaker 1: month boot camp course, very very tough, you know, like

914
00:52:30,440 --> 00:52:32,960
Speaker 1: forty people got in, only seven came out alive. Kind

915
00:52:33,000 --> 00:52:38,759
Speaker 1: of camp. But um, that really kind of uh you know,

916
00:52:39,480 --> 00:52:43,279
Speaker 1: taught me that outdoors is where I belong. Um, you know,

917
00:52:43,400 --> 00:52:45,799
Speaker 1: if I had my way, my job would have been

918
00:52:45,840 --> 00:52:50,359
Speaker 1: that of like a professional mountaining guide. Um, but then

919
00:52:50,400 --> 00:52:53,200
Speaker 1: I realized, while, uh, you know, it's not the best

920
00:52:53,280 --> 00:52:56,000
Speaker 1: paying jobs, so maybe I should do something else. Why,

921
00:52:56,239 --> 00:52:58,840
Speaker 1: you know, continuing to claimb So before I interrupted you,

922
00:52:58,920 --> 00:53:02,400
Speaker 1: you're gonna say that one of the people who influenced

923
00:53:02,480 --> 00:53:07,440
Speaker 1: your thinking right was ad Vestures. He's a one amazing

924
00:53:07,840 --> 00:53:12,080
Speaker 1: legendary American climber. He's climbed all fourteen eight thousand meter

925
00:53:12,200 --> 00:53:15,480
Speaker 1: mountains without bottle oxygen. And here's why I was most

926
00:53:15,520 --> 00:53:18,080
Speaker 1: I read all his books, in many books, but his

927
00:53:18,280 --> 00:53:21,760
Speaker 1: mantra has been there are no short cuts of the top.

928
00:53:22,440 --> 00:53:26,600
Speaker 1: It's all about diligence, you know, the kind of preparation, preparation,

929
00:53:26,760 --> 00:53:30,919
Speaker 1: planning discipline. You know, in my profession, people often say, wow,

930
00:53:31,120 --> 00:53:34,440
Speaker 1: you know that person is so super intelligent. Okay, I've

931
00:53:34,480 --> 00:53:40,719
Speaker 1: always believed that in academics, intelligence is highly overrated. Success

932
00:53:40,840 --> 00:53:45,239
Speaker 1: to me is diligence and one percent intelligence, And that

933
00:53:45,400 --> 00:53:47,920
Speaker 1: the fact that it's all about diligence and planning actually

934
00:53:48,040 --> 00:53:51,320
Speaker 1: came from reading and sort of experiencing vicariously what d

935
00:53:51,400 --> 00:53:54,719
Speaker 1: Vestures has been doing all his life. Yeah, I referenced

936
00:53:54,760 --> 00:53:59,320
Speaker 1: Michael Morbison's paradox of skill all the time, which is,

937
00:54:00,000 --> 00:54:02,520
Speaker 1: in the market, you have lots of really smart people.

938
00:54:02,719 --> 00:54:07,080
Speaker 1: It's a given that these folks are smart, and paradoxically

939
00:54:07,200 --> 00:54:10,239
Speaker 1: that makes luck all the more significant. If you put

940
00:54:10,280 --> 00:54:13,640
Speaker 1: a whole bunch of really sharp people in a room,

941
00:54:14,160 --> 00:54:16,560
Speaker 1: whoever gets a lucky break and and it turns out

942
00:54:16,600 --> 00:54:18,799
Speaker 1: to be the same in sports, where you have all

943
00:54:18,840 --> 00:54:22,479
Speaker 1: these people who are physically gifted, hardworking, and so they're

944
00:54:22,560 --> 00:54:27,440
Speaker 1: also highly skilled, and then a lucky bounce uh uh

945
00:54:27,640 --> 00:54:33,160
Speaker 1: an officiating mistake, the lucky break often affects the outcome.

946
00:54:34,360 --> 00:54:36,840
Speaker 1: It's just a given everybody is smart or skilled or

947
00:54:36,840 --> 00:54:38,640
Speaker 1: what have you. And I also believe that you know,

948
00:54:38,760 --> 00:54:41,040
Speaker 1: you can create your luck to some extent. You know,

949
00:54:41,160 --> 00:54:46,000
Speaker 1: if you are our discipline, hardworking, planning, you know, driven person,

950
00:54:46,520 --> 00:54:50,960
Speaker 1: you can influence some extent that luck. Um there's an expression,

951
00:54:51,080 --> 00:54:54,080
Speaker 1: and I know I'm mangling this, but it's hard work

952
00:54:54,239 --> 00:54:58,040
Speaker 1: is the mother of of fortune, meaning the you know,

953
00:54:58,160 --> 00:54:59,920
Speaker 1: the harder you work, the lucky you tend to get.

954
00:55:00,280 --> 00:55:02,840
Speaker 1: Although that said, every now and then a little bit

955
00:55:02,880 --> 00:55:05,880
Speaker 1: of luck the right way if you go along a

956
00:55:06,000 --> 00:55:10,000
Speaker 1: long way. So so back to mountaineering, what do you

957
00:55:10,120 --> 00:55:14,399
Speaker 1: do to prep to do? Forget an eight thousand meter

958
00:55:15,320 --> 00:55:18,960
Speaker 1: meter or foot meter eight thousand meter mountain? What do

959
00:55:19,000 --> 00:55:22,200
Speaker 1: you do for a five thousand meter So a lot

960
00:55:22,239 --> 00:55:24,719
Speaker 1: of training and I would train easily four or five

961
00:55:24,760 --> 00:55:27,600
Speaker 1: months continuously in a given year. What are you doing

962
00:55:27,680 --> 00:55:31,080
Speaker 1: when you're training. I'm carrying big backpacks and I'm climbing

963
00:55:31,239 --> 00:55:34,000
Speaker 1: hundreds of floors every single day. And some of the

964
00:55:34,440 --> 00:55:37,040
Speaker 1: security guards and the tall skyscrapers of New York City,

965
00:55:37,080 --> 00:55:39,399
Speaker 1: you know, there's weird you know, Indian American guy who

966
00:55:39,440 --> 00:55:41,200
Speaker 1: comes with the backpack and says, it's kind of use

967
00:55:41,280 --> 00:55:44,080
Speaker 1: the stair of it. So I've had to get permission

968
00:55:44,120 --> 00:55:47,279
Speaker 1: from the condor the CoAP boards to do funny um.

969
00:55:47,800 --> 00:55:50,480
Speaker 1: But I do a lot of that um and then

970
00:55:50,800 --> 00:55:53,480
Speaker 1: our stairs roughly equivalent to going up the side of

971
00:55:53,520 --> 00:55:56,920
Speaker 1: a mountain or is it just the vertical exercise? Yeah,

972
00:55:56,960 --> 00:56:00,239
Speaker 1: you know, it's not nearly as great as it lambing

973
00:56:00,280 --> 00:56:02,759
Speaker 1: a real mountain, not even the same. But it's about

974
00:56:02,800 --> 00:56:04,440
Speaker 1: the closest thing you can do in a city like

975
00:56:04,560 --> 00:56:07,759
Speaker 1: ours where we don't have any mountains. Right that said,

976
00:56:07,800 --> 00:56:09,600
Speaker 1: every now and then, you know, when I'm getting in

977
00:56:09,600 --> 00:56:11,799
Speaker 1: the middle of my training, I do take a drive

978
00:56:11,880 --> 00:56:15,040
Speaker 1: out to you know, New Jersey or Upsett, New York,

979
00:56:15,560 --> 00:56:19,200
Speaker 1: Bear Mountain and so on, and do multiple rounds with

980
00:56:19,360 --> 00:56:22,520
Speaker 1: heavy backpacks, run up to the top down Yeah, yeah,

981
00:56:22,560 --> 00:56:24,759
Speaker 1: as fast as possible, go up and down, up and down.

982
00:56:24,880 --> 00:56:27,160
Speaker 1: You know, it's about feet or sort of the top

983
00:56:27,640 --> 00:56:30,920
Speaker 1: two or three rounds and that's pretty exhausting. What can

984
00:56:31,000 --> 00:56:35,400
Speaker 1: you do to prep for altitudes where the oxygen is thinner?

985
00:56:35,800 --> 00:56:38,320
Speaker 1: Is there is there something you could literally because the

986
00:56:38,440 --> 00:56:42,920
Speaker 1: last trip I had to Colorado where we were in um,

987
00:56:45,600 --> 00:56:50,160
Speaker 1: I'm drawing a blank on which of the ski resorts veil.

988
00:56:50,440 --> 00:56:55,440
Speaker 1: I was shocked at at how much the lack of

989
00:56:55,520 --> 00:56:59,479
Speaker 1: oxygen impacts you. It's not even like running a mile.

990
00:56:59,640 --> 00:57:03,960
Speaker 1: You're walking a hundred yards across a space and you're like, wow,

991
00:57:04,040 --> 00:57:07,080
Speaker 1: I am genuinely out of breath and out of shape. So,

992
00:57:07,239 --> 00:57:10,560
Speaker 1: you know, one can be the most well trained athlete

993
00:57:10,600 --> 00:57:14,000
Speaker 1: on sea level and yet suffer the worst altitude mounted

994
00:57:14,040 --> 00:57:17,439
Speaker 1: sickness MS. Right, It's just one of those things where

995
00:57:17,600 --> 00:57:20,280
Speaker 1: it's very difficult to plan and predict how it's going

996
00:57:20,360 --> 00:57:23,200
Speaker 1: to be for me myself, you know, for the same

997
00:57:23,360 --> 00:57:26,720
Speaker 1: level of training for similar altitude I've experienced different symptoms.

998
00:57:26,840 --> 00:57:30,080
Speaker 1: Sometimes it's been fantastic, sometimes not so good. What can

999
00:57:30,160 --> 00:57:32,320
Speaker 1: I do? Not a whole lot. You know, there's something

1000
00:57:32,400 --> 00:57:34,840
Speaker 1: called a training mask. You may have seen those pictures

1001
00:57:34,880 --> 00:57:37,800
Speaker 1: of you know. Sometimes it has three different nozzles. You know,

1002
00:57:37,920 --> 00:57:41,080
Speaker 1: each nozzle represents a certain percentage of air that can

1003
00:57:41,160 --> 00:57:44,200
Speaker 1: come in. You can try to simulate the high altity thing.

1004
00:57:44,280 --> 00:57:46,800
Speaker 1: It's not nearly as great as the real thing. Some

1005
00:57:46,920 --> 00:57:49,480
Speaker 1: of the real professional climbers, they use a tent, a

1006
00:57:49,640 --> 00:57:52,880
Speaker 1: tent that is able to actually suck out the oxygen um.

1007
00:57:52,960 --> 00:57:54,640
Speaker 1: I forget the name of the tent, but it's a

1008
00:57:54,680 --> 00:57:57,000
Speaker 1: new product I'll just launched by some of the big

1009
00:57:57,040 --> 00:58:01,000
Speaker 1: sporting companies who encourage climbers to spend twenty four hours

1010
00:58:01,160 --> 00:58:03,800
Speaker 1: before they fly out of the big So it's the

1011
00:58:03,920 --> 00:58:08,000
Speaker 1: opposite of a hyperbaric chamber where you're pumping in that's

1012
00:58:08,080 --> 00:58:10,440
Speaker 1: right here, you have less oxs exactly. Yeah, you just

1013
00:58:10,480 --> 00:58:14,120
Speaker 1: suck it out. Yeah, that's that's amazing. So let's shift

1014
00:58:14,160 --> 00:58:17,120
Speaker 1: gears a little bit. Let's talk a little bit about books.

1015
00:58:17,240 --> 00:58:20,240
Speaker 1: Tell me about some of your favorite books. What do

1016
00:58:20,280 --> 00:58:23,320
Speaker 1: you like to read so well, you know, like I said,

1017
00:58:23,400 --> 00:58:27,120
Speaker 1: I love all books in mountaineering. I've I've basically read

1018
00:58:27,160 --> 00:58:29,360
Speaker 1: a ton of them and not just give us a

1019
00:58:29,400 --> 00:58:31,880
Speaker 1: few names. Okay, well, no short coust of the top

1020
00:58:31,920 --> 00:58:34,720
Speaker 1: and my favorite, no shortcuts to the top by my

1021
00:58:34,840 --> 00:58:40,080
Speaker 1: advers yours. It taught me a lot about life lessons. Um.

1022
00:58:40,560 --> 00:58:43,400
Speaker 1: Then you know there are there are books written by

1023
00:58:43,960 --> 00:58:47,200
Speaker 1: written on K two. The Savage Mountain, you know, is

1024
00:58:47,240 --> 00:58:50,720
Speaker 1: one of them. Why is K two considered so challenging

1025
00:58:50,920 --> 00:58:54,960
Speaker 1: versus others? So? Is it just the sheer assent or um? So?

1026
00:58:55,120 --> 00:58:58,640
Speaker 1: K two is basically built like almost a perfect pyramid.

1027
00:58:58,760 --> 00:59:02,360
Speaker 1: It's extremely steep, and you know it is um So

1028
00:59:02,440 --> 00:59:05,160
Speaker 1: even though Everest is a dead but higher it's considered

1029
00:59:05,280 --> 00:59:09,560
Speaker 1: less technical from a climbing perspective than K twys Um.

1030
00:59:10,080 --> 00:59:14,240
Speaker 1: There's also you know, it's more treacherous because of the

1031
00:59:14,280 --> 00:59:17,240
Speaker 1: steepness and the terrain. The weather comes in very quickly,

1032
00:59:17,400 --> 00:59:21,560
Speaker 1: quickly and very unpredictable. And if you look at the stats,

1033
00:59:21,640 --> 00:59:24,200
Speaker 1: you know, one in seven climbers of climb Everest have

1034
00:59:24,360 --> 00:59:28,560
Speaker 1: died really one in seven, right historically, um, And that

1035
00:59:28,720 --> 00:59:32,040
Speaker 1: number is you know, going down all the years because

1036
00:59:32,480 --> 00:59:34,920
Speaker 1: technology has improved so much, so we have fewer debts.

1037
00:59:35,400 --> 00:59:37,160
Speaker 1: But up until you know, two or three years back,

1038
00:59:37,280 --> 00:59:40,520
Speaker 1: that was a number. With K two, it's one in three.

1039
00:59:40,920 --> 00:59:43,320
Speaker 1: That's even more instant, it's even more insane. And that's

1040
00:59:43,360 --> 00:59:46,560
Speaker 1: not the worlds. The world's actually statistically is Annapurna, which

1041
00:59:46,600 --> 00:59:48,920
Speaker 1: is also one of the telemeta mountains, and it's one

1042
00:59:48,960 --> 00:59:53,200
Speaker 1: in two. So, um, flipper coin, maybe you make it?

1043
00:59:53,320 --> 00:59:56,200
Speaker 1: Maybe you know something like that. Yeah, yeah, I, UM,

1044
00:59:57,520 --> 01:00:01,320
Speaker 1: I don't watch TV this weekend. I'm not climbing anything. Um,

1045
01:00:01,600 --> 01:00:03,600
Speaker 1: give us some other books. But what else do you

1046
01:00:03,680 --> 01:00:08,120
Speaker 1: like along the lines of K two? Have you ever

1047
01:00:08,200 --> 01:00:14,760
Speaker 1: read uh, the Shackleton Story Endurance. Um, it's a fascinating book.

1048
01:00:14,840 --> 01:00:17,400
Speaker 1: It's very familiar. I may have read it. Um. This

1049
01:00:17,600 --> 01:00:20,280
Speaker 1: is Antarctica now, okay, Okay, I know the name because

1050
01:00:20,320 --> 01:00:25,040
Speaker 1: of the store. There's an amazing narrative about the book.

1051
01:00:25,120 --> 01:00:27,600
Speaker 1: The book came out, I want to say, in the

1052
01:00:27,720 --> 01:00:30,640
Speaker 1: thirties or forties. Maybe it was right before the war

1053
01:00:31,360 --> 01:00:35,160
Speaker 1: or right after the war, fifties, and it did okay,

1054
01:00:35,280 --> 01:00:38,439
Speaker 1: but you know, no great shakes. And thirty years later

1055
01:00:38,640 --> 01:00:41,080
Speaker 1: somebody both the rights to it reissued it and it

1056
01:00:41,320 --> 01:00:44,320
Speaker 1: just explodes. It was the right time and it's one

1057
01:00:44,360 --> 01:00:47,920
Speaker 1: of these stories that you the same thing about the

1058
01:00:48,640 --> 01:00:55,520
Speaker 1: no shortcuts. Everybody should have died, and I don't. I

1059
01:00:55,560 --> 01:00:58,280
Speaker 1: won't spoil the ending, even though everybody knows it. But

1060
01:00:58,480 --> 01:01:00,680
Speaker 1: it's just that the ship gets caught in the ice.

1061
01:01:00,800 --> 01:01:04,960
Speaker 1: The ice crushes it. You're in Antarctica with essentially you

1062
01:01:05,000 --> 01:01:06,480
Speaker 1: don't have a cell phone, you don't have any way

1063
01:01:06,520 --> 01:01:09,880
Speaker 1: to communicate with people. There's a hope that you're supposed

1064
01:01:09,880 --> 01:01:12,160
Speaker 1: to meet people six months from now. How do I

1065
01:01:12,240 --> 01:01:15,160
Speaker 1: get from this side of the world to there with

1066
01:01:15,400 --> 01:01:18,640
Speaker 1: essentially whatever supplies we can scrape up from the ship

1067
01:01:18,760 --> 01:01:21,640
Speaker 1: as it's you know, being crushed in the ice. It's

1068
01:01:21,680 --> 01:01:26,080
Speaker 1: in It's insane, I'm sure. Um. Some of the tales

1069
01:01:26,160 --> 01:01:30,480
Speaker 1: of K two are are similar. Let's let's move away

1070
01:01:30,560 --> 01:01:36,400
Speaker 1: from extreme um activities. What other books, technology, economics? What

1071
01:01:36,520 --> 01:01:41,720
Speaker 1: else have you enjoyed over time? So um, I mean

1072
01:01:42,280 --> 01:01:45,000
Speaker 1: this is not necessarily technology. But again, you know, one

1073
01:01:45,080 --> 01:01:49,160
Speaker 1: of the other breed of folks. I greatly respected by

1074
01:01:49,360 --> 01:01:53,920
Speaker 1: our special forces. Um, so I have. I'm fascinated by

1075
01:01:53,960 --> 01:01:57,280
Speaker 1: the U. S. Navy series, the Delta Force, the Army Rangers,

1076
01:01:57,920 --> 01:02:00,360
Speaker 1: and again, just like movies, I've ready a tone of

1077
01:02:00,400 --> 01:02:04,640
Speaker 1: these books. One of my favorites Lone Survivor, which became

1078
01:02:04,680 --> 01:02:08,040
Speaker 1: a movie yea, with marks Mark Wahlberg as the guy,

1079
01:02:08,200 --> 01:02:11,240
Speaker 1: but the real guy was Marcus Lottrell. Um So, I

1080
01:02:11,240 --> 01:02:14,720
Speaker 1: actually follow his Twitter feed. Um So again, I think,

1081
01:02:14,800 --> 01:02:17,360
Speaker 1: you know, I read a lot of these books written

1082
01:02:17,440 --> 01:02:21,600
Speaker 1: by on special forces. Um in the world of technology,

1083
01:02:21,960 --> 01:02:25,240
Speaker 1: you know. Um, I tried to read not necessarily so

1084
01:02:25,320 --> 01:02:28,480
Speaker 1: many books, but a lot of the internet websites like

1085
01:02:28,640 --> 01:02:31,920
Speaker 1: you know, Mashable and tech Crunch and Fast Company, because

1086
01:02:32,240 --> 01:02:34,400
Speaker 1: one of my occupation has us is I have to

1087
01:02:34,520 --> 01:02:37,560
Speaker 1: be always updated with the latest and the best right

1088
01:02:38,200 --> 01:02:39,960
Speaker 1: and so in some ways, if I have to do that,

1089
01:02:40,160 --> 01:02:42,160
Speaker 1: I have I don't have a whole lot of time

1090
01:02:42,240 --> 01:02:46,800
Speaker 1: to actually spend reading an entire book. Um So, it's

1091
01:02:46,880 --> 01:02:49,560
Speaker 1: it's sort of a trade off there, you know. And

1092
01:02:49,760 --> 01:02:53,080
Speaker 1: early in my career I started on a trading desk

1093
01:02:53,160 --> 01:02:58,240
Speaker 1: and the head trader was a former Marine jungle combat instructor.

1094
01:02:59,000 --> 01:03:05,440
Speaker 1: And I share your um interest in special Forces and

1095
01:03:05,640 --> 01:03:09,640
Speaker 1: you hear some of the stories you hear are just astonishing,

1096
01:03:10,600 --> 01:03:14,200
Speaker 1: and it's amazing how parallel it is to other aspects

1097
01:03:14,240 --> 01:03:18,360
Speaker 1: of life. It's really and we're we're finding more and

1098
01:03:18,520 --> 01:03:21,120
Speaker 1: more things are applicable. One of the things that I

1099
01:03:21,200 --> 01:03:24,400
Speaker 1: found astonishing, and this is out of the Special Forces

1100
01:03:24,520 --> 01:03:28,160
Speaker 1: before they go on a mission, the night before, they

1101
01:03:28,240 --> 01:03:31,640
Speaker 1: go through a visualization technique where they're going through each

1102
01:03:31,840 --> 01:03:37,360
Speaker 1: step of the of the plans, whatever the insertion or

1103
01:03:37,440 --> 01:03:41,760
Speaker 1: event is, and part of the training is okay, X

1104
01:03:41,880 --> 01:03:46,440
Speaker 1: goes wrong, visualize not only what your tools and resources

1105
01:03:46,480 --> 01:03:48,800
Speaker 1: are and how you can respond to it, but imagine

1106
01:03:48,840 --> 01:03:53,880
Speaker 1: how you're gonna feel emotionally when this happens. And so

1107
01:03:54,680 --> 01:03:58,200
Speaker 1: what people have said to me is when something actually

1108
01:03:58,240 --> 01:04:01,400
Speaker 1: goes wrong and you feel that panic rising, you've already

1109
01:04:01,480 --> 01:04:03,800
Speaker 1: prepped yourself for it and you can manage it. You

1110
01:04:03,840 --> 01:04:08,080
Speaker 1: can deal with it because left unshaft, your emotions in

1111
01:04:08,160 --> 01:04:10,840
Speaker 1: those circumstances are deadly and you can't have that happened.

1112
01:04:11,280 --> 01:04:14,439
Speaker 1: And it's so parallel to investing. If when you say

1113
01:04:14,480 --> 01:04:17,200
Speaker 1: to people, all right, so the market's gonna be down thirty,

1114
01:04:18,880 --> 01:04:21,320
Speaker 1: how are you gonna behave? What what's your emotional state

1115
01:04:21,400 --> 01:04:23,280
Speaker 1: going to be? If you could get people to go

1116
01:04:23,400 --> 01:04:27,680
Speaker 1: through those exercises, their behavior is better when events actually

1117
01:04:27,800 --> 01:04:31,280
Speaker 1: hit age that. That's any other books on special services

1118
01:04:31,480 --> 01:04:36,040
Speaker 1: or special forces that you think are are worth mentioning. Well,

1119
01:04:36,160 --> 01:04:39,200
Speaker 1: I really loved um. I think it's called thirteen Hours

1120
01:04:39,240 --> 01:04:42,480
Speaker 1: in Benghazi. Uh, you know came out last year to

1121
01:04:42,800 --> 01:04:44,680
Speaker 1: last year in the book and the movie has made

1122
01:04:44,920 --> 01:04:49,160
Speaker 1: awesome recommend that highly again like and I'm biased, um,

1123
01:04:49,400 --> 01:04:51,640
Speaker 1: but again, you know, you get to understand the psychology,

1124
01:04:51,720 --> 01:04:54,880
Speaker 1: psychology on the mental fortitude and the process of these

1125
01:04:55,120 --> 01:04:57,920
Speaker 1: special people. And you know that really motivates me because

1126
01:04:58,000 --> 01:04:59,760
Speaker 1: it's not just about what I have to deal with

1127
01:04:59,840 --> 01:05:02,160
Speaker 1: an mountains, but even in daily life, like you mentioned,

1128
01:05:02,640 --> 01:05:05,800
Speaker 1: you know, we have a lot of academic publication is

1129
01:05:05,840 --> 01:05:08,800
Speaker 1: a tough process. I mean, you know you've you published

1130
01:05:08,800 --> 01:05:12,080
Speaker 1: our parish, right, So it just teaches me to be

1131
01:05:12,440 --> 01:05:16,360
Speaker 1: more resilient. Um, you know, just be stronger mentally. So

1132
01:05:16,600 --> 01:05:18,840
Speaker 1: it makes it it makes it easier for me to

1133
01:05:18,880 --> 01:05:22,120
Speaker 1: deal with these things. It's really astonishing. My my office

1134
01:05:22,200 --> 01:05:25,800
Speaker 1: is about fifteen percent veterans. We find they work very

1135
01:05:25,880 --> 01:05:28,560
Speaker 1: well in the world to finance because they're dealing with

1136
01:05:28,800 --> 01:05:32,400
Speaker 1: complex logistics and and getting them done. And in our

1137
01:05:32,480 --> 01:05:34,400
Speaker 1: office there are no bullets was in by your head,

1138
01:05:34,440 --> 01:05:37,480
Speaker 1: so it's actually easy for them. What most people look

1139
01:05:37,520 --> 01:05:40,760
Speaker 1: at and say, gee, this is really hard this really complicated. Well,

1140
01:05:40,800 --> 01:05:43,120
Speaker 1: you're not in a plumbing aircraft. Nobody's shooting at you.

1141
01:05:43,640 --> 01:05:47,560
Speaker 1: It's relatively easy compared to And you know, my favorite

1142
01:05:47,680 --> 01:05:49,880
Speaker 1: NBA students or e m BAS students, and I might

1143
01:05:49,960 --> 01:05:53,760
Speaker 1: get beaten for this, are the wets because these guys

1144
01:05:53,880 --> 01:05:57,360
Speaker 1: are the most disciplined in the class, that the most ready,

1145
01:05:57,440 --> 01:06:00,840
Speaker 1: they have prepared, and that the least in so flashy

1146
01:06:00,920 --> 01:06:05,280
Speaker 1: flamboyant or showing off. Damn my favorite stuff. If I

1147
01:06:05,520 --> 01:06:07,440
Speaker 1: just teach a class with only wets, I would just

1148
01:06:07,520 --> 01:06:10,160
Speaker 1: do anything for that. Do you have a lot of

1149
01:06:10,240 --> 01:06:12,920
Speaker 1: people who come out of the Armed services? Yeah? So,

1150
01:06:13,120 --> 01:06:15,080
Speaker 1: you know all the most of the top business schools

1151
01:06:15,120 --> 01:06:20,120
Speaker 1: have special programs for um X R vets um so.

1152
01:06:20,480 --> 01:06:23,400
Speaker 1: In fact, in last fall, I actually had an ex

1153
01:06:24,200 --> 01:06:27,280
Speaker 1: X Navy Seal person in my class. No one a

1154
01:06:27,360 --> 01:06:30,960
Speaker 1: mess with Yeah, yeah, absolutely not UM but it was

1155
01:06:31,040 --> 01:06:33,040
Speaker 1: such you know, and he was one of a few

1156
01:06:33,080 --> 01:06:36,400
Speaker 1: other UM you know, X Army, X Special Forces, X Marines.

1157
01:06:37,200 --> 01:06:39,080
Speaker 1: It's just awesome to have them in the class. You know.

1158
01:06:40,040 --> 01:06:43,320
Speaker 1: I I shouldn't tell these stories, but we used to

1159
01:06:43,400 --> 01:06:46,320
Speaker 1: go out off the trading desk and one of the

1160
01:06:46,400 --> 01:06:48,280
Speaker 1: guys is a forming Army ranger. The head of the

1161
01:06:48,360 --> 01:06:53,560
Speaker 1: desk is a former Marine combat instructor. I'm like a

1162
01:06:53,640 --> 01:06:56,360
Speaker 1: skinny nothing back then, as opposed to a fat nothing now.

1163
01:06:56,880 --> 01:06:59,080
Speaker 1: And we would go to bars and and any time

1164
01:06:59,120 --> 01:07:01,840
Speaker 1: there was trouble, I always felt like I could say

1165
01:07:01,960 --> 01:07:04,520
Speaker 1: or do anything. I got these two monsters with me.

1166
01:07:05,480 --> 01:07:07,600
Speaker 1: You know, guy wants to cause trouble, It's like, all right,

1167
01:07:07,760 --> 01:07:10,760
Speaker 1: bring it, you know it was. It creates a full

1168
01:07:10,880 --> 01:07:14,920
Speaker 1: sense of of confidence right when you're when you're as

1169
01:07:14,960 --> 01:07:17,440
Speaker 1: long as they're they have your back. But it really

1170
01:07:17,640 --> 01:07:24,320
Speaker 1: is true the attitude that you develop when you think

1171
01:07:24,400 --> 01:07:30,520
Speaker 1: about these highly technical, highly dangerous missions. You know, the

1172
01:07:30,880 --> 01:07:34,200
Speaker 1: the Armed Services are really good at learning how to

1173
01:07:34,280 --> 01:07:37,640
Speaker 1: do these things, if nothing else, through just repetition and

1174
01:07:37,720 --> 01:07:40,840
Speaker 1: trial and error, and eventually they get it down to

1175
01:07:40,960 --> 01:07:43,640
Speaker 1: a science. And and these guys are really quite amazing.

1176
01:07:44,400 --> 01:07:47,200
Speaker 1: It's astonishing. All right, let's let's get back into uh

1177
01:07:47,760 --> 01:07:51,480
Speaker 1: no more literal war stories. Let's get back into um

1178
01:07:52,080 --> 01:07:54,240
Speaker 1: our favorite questions in the last ten minutes or so

1179
01:07:54,360 --> 01:07:59,640
Speaker 1: we have so what changes have you noticed since you

1180
01:07:59,760 --> 01:08:05,280
Speaker 1: be again focusing on mobile? How has the industry shifted

1181
01:08:06,200 --> 01:08:11,160
Speaker 1: and is it just a never ending fluxus is always

1182
01:08:11,680 --> 01:08:15,080
Speaker 1: going to be in change or what do you say? Um?

1183
01:08:15,160 --> 01:08:18,320
Speaker 1: I think the biggest change has been that the change

1184
01:08:18,400 --> 01:08:22,599
Speaker 1: from focusing on strategy to tactics, and that the reason

1185
01:08:22,720 --> 01:08:26,439
Speaker 1: that changes happened is because of the increasing accessibility of

1186
01:08:26,560 --> 01:08:30,879
Speaker 1: data that lets companies actually measure the effectiveness of their tactics.

1187
01:08:31,560 --> 01:08:33,720
Speaker 1: And now they can go back and justify the r

1188
01:08:33,760 --> 01:08:35,839
Speaker 1: O I you know, why am I spending on mobile?

1189
01:08:36,560 --> 01:08:39,080
Speaker 1: And so up until recently, the focus was do we

1190
01:08:39,200 --> 01:08:41,280
Speaker 1: have a mobile strategy, meaning like do we have a

1191
01:08:41,360 --> 01:08:43,960
Speaker 1: mobile app a mobile website? Today it's more like, well,

1192
01:08:44,280 --> 01:08:46,160
Speaker 1: obviously we have to have one of those, but is

1193
01:08:46,200 --> 01:08:49,560
Speaker 1: the investment worth it? So people are recognizing, hey, this

1194
01:08:49,760 --> 01:08:54,160
Speaker 1: is important yestely, and earlier there was ambiguity. But earlier

1195
01:08:54,200 --> 01:08:56,800
Speaker 1: there was more of a recognition that it's important, but

1196
01:08:56,880 --> 01:08:59,400
Speaker 1: there was no where to measure how important it was,

1197
01:08:59,560 --> 01:09:02,800
Speaker 1: however active it was. And that's changed now. So what's

1198
01:09:02,840 --> 01:09:05,120
Speaker 1: the next big shift we're going to see in this space?

1199
01:09:05,760 --> 01:09:09,200
Speaker 1: I think it's gonna be this intersection of mobile with

1200
01:09:09,560 --> 01:09:13,320
Speaker 1: the consumer Internet of things, right, and so you know,

1201
01:09:13,439 --> 01:09:16,320
Speaker 1: more of us, more of us varying variable devices and

1202
01:09:16,479 --> 01:09:19,280
Speaker 1: we're talking about this earlier about getting used to you know,

1203
01:09:19,400 --> 01:09:22,760
Speaker 1: you're smart refrigerator, making a phone called reminding you that

1204
01:09:22,880 --> 01:09:25,840
Speaker 1: you just run out of groceries, you know, and stuff

1205
01:09:25,840 --> 01:09:27,080
Speaker 1: like that. I think that's going to be more of

1206
01:09:27,160 --> 01:09:31,160
Speaker 1: a norm than an exception. So the likes of Alexa

1207
01:09:31,600 --> 01:09:34,679
Speaker 1: or in a Kartana or series and so on, Artificial

1208
01:09:34,760 --> 01:09:37,439
Speaker 1: intelligence is going to start taking an important role in

1209
01:09:37,520 --> 01:09:39,320
Speaker 1: our lives. You know, some may say they will take

1210
01:09:39,360 --> 01:09:41,280
Speaker 1: over our lives. I don't think it's like like that,

1211
01:09:41,439 --> 01:09:44,320
Speaker 1: but I think they'll have a very important influence. And

1212
01:09:45,200 --> 01:09:48,640
Speaker 1: so you know, this intersection of AI, consumer, IoT and

1213
01:09:48,720 --> 01:09:50,960
Speaker 1: the mobile phone is right in the middle. It's all

1214
01:09:51,040 --> 01:09:53,720
Speaker 1: going to be propelled and powered and sort of you know,

1215
01:09:54,000 --> 01:09:58,519
Speaker 1: influenced by the mobile phone. Quite quite interesting. Let me

1216
01:09:59,280 --> 01:10:01,439
Speaker 1: shift it up, knew a little bit. Tell us about

1217
01:10:01,600 --> 01:10:04,639
Speaker 1: a time you failed and what did you learn from

1218
01:10:04,680 --> 01:10:09,280
Speaker 1: that experience? So, um, just after high school. You know,

1219
01:10:09,400 --> 01:10:11,800
Speaker 1: this was in India, and you know, in India, if

1220
01:10:11,840 --> 01:10:14,320
Speaker 1: you want to do engineering, sort of the one brand

1221
01:10:14,400 --> 01:10:17,200
Speaker 1: everybody recognizes I I T. And everybody wants to go

1222
01:10:17,280 --> 01:10:21,240
Speaker 1: in an Indian Institute of Technology. Correct, right, So a

1223
01:10:21,360 --> 01:10:24,719
Speaker 1: lot of the folks in Silicon Valley or on Wall Street.

1224
01:10:24,840 --> 01:10:28,479
Speaker 1: Have I T backgrounds. I always thought, you know, I'd

1225
01:10:28,520 --> 01:10:30,320
Speaker 1: be able to get in because I had great grades

1226
01:10:30,360 --> 01:10:33,400
Speaker 1: in high school. I was like crushing it. But for

1227
01:10:33,560 --> 01:10:38,040
Speaker 1: various reasons, I didn't make it. That was devastating because

1228
01:10:39,160 --> 01:10:40,880
Speaker 1: I didn't make it. At the same time, all my

1229
01:10:41,000 --> 01:10:43,720
Speaker 1: friends who I knew would make it made it. That

1230
01:10:43,840 --> 01:10:47,680
Speaker 1: was a big failure. But basically, you know, I learned that, um,

1231
01:10:48,800 --> 01:10:51,200
Speaker 1: you know, yeah, you know, life can push you back,

1232
01:10:51,280 --> 01:10:53,080
Speaker 1: throw you out a few times, but you've got to

1233
01:10:53,160 --> 01:10:56,600
Speaker 1: just come back. And you know, momentarily there's some disappointment.

1234
01:10:57,439 --> 01:10:59,759
Speaker 1: So I promised myself in my first team of engineering,

1235
01:10:59,760 --> 01:11:02,720
Speaker 1: I'd still went to a decent engineering school. N I

1236
01:11:03,200 --> 01:11:06,320
Speaker 1: N I T y um. It's most people will stay

1237
01:11:06,560 --> 01:11:09,000
Speaker 1: it's it's great. But for me it was a disappointment.

1238
01:11:09,520 --> 01:11:11,280
Speaker 1: But you know, I'm still proud of where I went.

1239
01:11:11,400 --> 01:11:14,439
Speaker 1: And I told myself in the first year that since

1240
01:11:14,600 --> 01:11:16,560
Speaker 1: I always had a plan to do an NBA and

1241
01:11:16,680 --> 01:11:20,000
Speaker 1: in India, the big NBA brand are these I M S.

1242
01:11:20,640 --> 01:11:22,760
Speaker 1: And I promised myself on day one, I'm going to

1243
01:11:22,880 --> 01:11:24,599
Speaker 1: get to the I M S. You know, come hell

1244
01:11:24,720 --> 01:11:27,080
Speaker 1: or or hell or heaven or whatever it is. Hell

1245
01:11:27,160 --> 01:11:30,200
Speaker 1: or high water hell or high water right? And um,

1246
01:11:30,520 --> 01:11:33,920
Speaker 1: I just religiously studied like a zombie for four years.

1247
01:11:34,200 --> 01:11:38,560
Speaker 1: Just make that happen. And it happened. So so the

1248
01:11:38,720 --> 01:11:41,800
Speaker 1: lesson learned was just double your efforts, don't give up.

1249
01:11:41,880 --> 01:11:44,320
Speaker 1: I mean, you know, life is a marathon. It's not

1250
01:11:44,479 --> 01:11:46,240
Speaker 1: just a spraint. Okay, you didn't make it to the

1251
01:11:46,280 --> 01:11:48,720
Speaker 1: I I T. But look, if you make your iams

1252
01:11:48,840 --> 01:11:51,439
Speaker 1: and then eventually do the things you want to do,

1253
01:11:51,600 --> 01:11:54,439
Speaker 1: you can still make it happen. And our last two

1254
01:11:54,720 --> 01:11:59,000
Speaker 1: favorite questions. If a millennial or recent college graduate came

1255
01:11:59,080 --> 01:12:01,160
Speaker 1: up to you and said, hey, I'm interested in getting

1256
01:12:01,200 --> 01:12:06,000
Speaker 1: into information services, mobile technology, what have you? What sort

1257
01:12:06,040 --> 01:12:08,479
Speaker 1: of career advice would you give them? So what I

1258
01:12:08,520 --> 01:12:10,559
Speaker 1: would ask them is and are you thinking it from

1259
01:12:10,600 --> 01:12:13,760
Speaker 1: an engineering perspective or from a business perspective? Okay, that's

1260
01:12:13,800 --> 01:12:17,200
Speaker 1: the first thing, because you're about to enter into an

1261
01:12:17,280 --> 01:12:20,360
Speaker 1: undergraduate program. Do you want to get into more of

1262
01:12:20,560 --> 01:12:24,559
Speaker 1: the technical engineering aspects or more of the business aspects.

1263
01:12:24,600 --> 01:12:27,360
Speaker 1: That's the first thing. Once you decide that, then it's

1264
01:12:27,360 --> 01:12:29,479
Speaker 1: a question if you get into business, you know, do

1265
01:12:29,520 --> 01:12:32,200
Speaker 1: you want to be the strategy is the big sinker?

1266
01:12:32,439 --> 01:12:34,800
Speaker 1: Or do you want to be the tactical tactician, the

1267
01:12:34,840 --> 01:12:36,920
Speaker 1: one who's going to be analyzing the data, building the

1268
01:12:37,080 --> 01:12:39,720
Speaker 1: models and that sort of thing. So you know, every

1269
01:12:39,840 --> 01:12:42,920
Speaker 1: stage there's a decision tree to be asked, and this

1270
01:12:43,080 --> 01:12:45,320
Speaker 1: is a kind of question I'll ask them. And our

1271
01:12:45,439 --> 01:12:49,920
Speaker 1: last question, what is it that you know about technology smartphones,

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01:12:50,040 --> 01:12:53,560
Speaker 1: mobile tech today that you wish you knew ten or

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01:12:53,600 --> 01:12:58,519
Speaker 1: fifteen years ago when you first we're starting out. Well,

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01:12:58,600 --> 01:13:01,360
Speaker 1: that's a great one. Actually, you know, sometimes I do wish,

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01:13:01,880 --> 01:13:07,600
Speaker 1: um I had known earlier, um, how accurate the measurements

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01:13:07,680 --> 01:13:11,040
Speaker 1: will get to be, because basically what I mean is,

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01:13:11,120 --> 01:13:14,200
Speaker 1: you know, I've always been a fan of metrics and measurement,

1278
01:13:15,200 --> 01:13:18,920
Speaker 1: and ten years back, the accuracy of the data and

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01:13:19,080 --> 01:13:21,720
Speaker 1: the quality of the data was nowhere near as good

1280
01:13:21,760 --> 01:13:25,679
Speaker 1: as where it is today, right um. And so along

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01:13:25,720 --> 01:13:28,600
Speaker 1: the way, you know, I had to build models or

1282
01:13:28,680 --> 01:13:31,920
Speaker 1: design projects, and these are all long term things. So

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01:13:32,000 --> 01:13:34,800
Speaker 1: if I knew five years from now how accurate the

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01:13:34,920 --> 01:13:37,280
Speaker 1: data would be, how great the metric would be, I

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01:13:37,280 --> 01:13:42,880
Speaker 1: would design it differently. So, you know, hindsight is we

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01:13:43,040 --> 01:13:45,800
Speaker 1: have been speaking with Professor and India Ghosts of n

1287
01:13:45,960 --> 01:13:50,000
Speaker 1: y U Stern School of business and author most recently

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01:13:50,080 --> 01:13:53,560
Speaker 1: of tap Unlocking the Mobile Economy. If people want to

1289
01:13:53,600 --> 01:13:56,880
Speaker 1: find some of your own other researcher papers, they can

1290
01:13:56,920 --> 01:13:59,400
Speaker 1: go to the n y U Stern site to find

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01:13:59,479 --> 01:14:02,760
Speaker 1: your list, your CV and your list of publications. That's right.

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01:14:02,800 --> 01:14:04,760
Speaker 1: I have a homepage that's also on the end yu

1293
01:14:04,800 --> 01:14:08,000
Speaker 1: Stone website, so please go there and and again my

1294
01:14:08,120 --> 01:14:10,360
Speaker 1: emails there as well. If somebody has to reach out

1295
01:14:10,400 --> 01:14:13,000
Speaker 1: to me, I'm on email. My Twitter hashtag is a

1296
01:14:13,200 --> 01:14:16,559
Speaker 1: g h y C. I'm on LinkedIn, so either way

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01:14:16,600 --> 01:14:19,799
Speaker 1: it's well. Thank you in India for being so generous

1298
01:14:20,200 --> 01:14:24,640
Speaker 1: with your time. We love your comments, feedback and suggestions.

1299
01:14:24,880 --> 01:14:28,360
Speaker 1: Be sure to write to us at m IB podcast

1300
01:14:28,560 --> 01:14:31,760
Speaker 1: at Bloomberg dot net. Be sure and check out any

1301
01:14:31,840 --> 01:14:35,160
Speaker 1: of the other hundred and fifty or so prior podcast

1302
01:14:35,280 --> 01:14:38,040
Speaker 1: we've had. Just looked up or down on iTunes or

1303
01:14:38,160 --> 01:14:40,720
Speaker 1: SoundCloud or Bloomberg dot com and you could see the

1304
01:14:40,760 --> 01:14:43,680
Speaker 1: full run of all of our guests. I would be

1305
01:14:43,760 --> 01:14:47,120
Speaker 1: remiss if I did not thank my head of research,

1306
01:14:47,680 --> 01:14:52,360
Speaker 1: Michael bat Nick. Taylor Riggs is our producer, and our

1307
01:14:52,560 --> 01:14:59,200
Speaker 1: recording engineer and audio producer is Medina Parwanna. I'm Barry

1308
01:14:59,280 --> 01:15:03,240
Speaker 1: Rihults have been listening to Manasters and Business on Bloomberg Radio.

1309
01:15:08,360 --> 01:15:10,600
Speaker 1: Our world is always moving, so with Mery Lynch, you

1310
01:15:10,680 --> 01:15:13,680
Speaker 1: can get access to financial guidance online, in person or

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Speaker 1: through the app. Visit mL dot com and learn more

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01:15:15,920 --> 01:15:18,479
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Speaker 1: PC