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Speaker 1: You're listening to Math and Magic, a production of I

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Speaker 1: Heart Radio. To me, data isn't just numbers. Data is

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Speaker 1: also words. We struck a relationship with Twitter to be

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Speaker 1: actually the first one to anonymously connect tweets to purchasers.

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Speaker 1: What I learned is that there are words that people

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Speaker 1: use in social media that can tell you whether they're

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Speaker 1: going to go to a film six months for that

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Speaker 1: film comes out. It is astonishing. I'm Bob Pittman and

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Speaker 1: welcome to Math and Magic. On each episode, I chat

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Speaker 1: with someone I truly admire to explore their stories from

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Speaker 1: the frontiers of marketing, and we're going to cover everything

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Speaker 1: from the math of marketing all the way to the

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Speaker 1: magic of marketing, which of course the creative side of

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Speaker 1: the business. On this episode, we've got someone who covers

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Speaker 1: the range in a very unexpected way, which will come

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Speaker 1: to in a second. Just a note to listeners, this

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Speaker 1: episode was recorded in early March two thousand nineteen, before

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Speaker 1: the Fox Disney merger. I can't wait to introduce you

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Speaker 1: to today's guest. We're sitting here today with the genius

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Speaker 1: of marketing, Julie Reager. Some know her from her perch

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Speaker 1: Higah Top industry as president chief Data Strategist and had

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Speaker 1: a media at twenties century Fox. Did I get that right?

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Speaker 1: You got it right? Okay? And that sounds like to

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Speaker 1: some people are listening, I'm sure that that's a mathematician.

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Speaker 1: But wrong, really wrong. I've known Julie for a while

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Speaker 1: of the business. With her, we've done some exciting stuff

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Speaker 1: which we will talk a little bit about today from

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Speaker 1: the common stories. But she is one of the most

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Speaker 1: out of the box thinkers I know, which is surprising

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Speaker 1: with the title like chief data strategist, and she also

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Speaker 1: has a whole other dimension. I'll give you a little

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Speaker 1: preview as the ghost photographer, but will come to that.

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Speaker 1: She's a native of Oklahoma, made her way to l

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Speaker 1: A the Dallas in San Francisco, state champion golfer when

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Speaker 1: she was younger. You still play golf. I'm going to

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Speaker 1: disappoint you know, really, I have so many other things

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Speaker 1: to do. There's so many things, Okay, Julie. We're gonna

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Speaker 1: start with one of our favorite features, which is you

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Speaker 1: in sixty seconds, So here we go. Do you prefer

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Speaker 1: cats or dogs, both, beaches or mountains, mountains, pancakes or waffles?

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Speaker 1: All of the above logan or Deadpool, Deadpool Sunrise or

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Speaker 1: Sunsets Sunset Oklahoma or California, California. Sorry, Arnold Palmer or

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Speaker 1: Tiger Woods Arnold Palmers love that? Okay? Okay, now it's

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Speaker 1: gonna get a little harder. Would eat for breakfast? Vegan

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Speaker 1: protein shake? Topic you can talk about forever? The spiritual world, okay?

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Speaker 1: The other side. Smartest person you know that one's so

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Speaker 1: hard at the list. I have to say one of

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Speaker 1: the smartest people. Note it is actually Stacy Snyder because

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Speaker 1: she's creative and intelligent. Yes, childhood hero Nancy Lopez. First job?

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Speaker 1: How's a cashier at the quick stop? Two in Mima, Oklahoma?

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Speaker 1: Do you ever go back to visit occasionally? Favorite TV

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Speaker 1: show ever ever? Mm hmm. I gotta say The Golden

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Speaker 1: Girls historical idol Joan of arc most overlooked actor. Oh

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Speaker 1: my god, that's so hard in this business. Rebel Wilson.

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Speaker 1: I just love her and I hope she's listening. Best

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Speaker 1: place the photograph of ghost my backyard quote to live by. Oh,

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Speaker 1: I gotta tell you ready, we treat people how we

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Speaker 1: feel about ourselves. Where did that quote come from? Me?

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Speaker 1: I like it. We'll write that down. That's a good

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Speaker 1: bumper sticker. It is a good bumper sticker movie that

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Speaker 1: should be required viewing hidden figures. Who would play you

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Speaker 1: in a biopick? I hope it would be Tiffany Hattish

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Speaker 1: because I love her and she's really funny, but I

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Speaker 1: don't think she'd take the gig. Spirit Animal, black Panther.

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Speaker 1: Black Panther actually protects us from the underworld. Thank you. Then,

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Speaker 1: what did you want to be when you were growing up?

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Speaker 1: A professional golfer? And here you are not playing golf.

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Speaker 1: Let's get started. Let's go to the beginning. What was

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Speaker 1: it like growing up? Paint the picture a little bit

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Speaker 1: of growing up in Oklahoma. It seems like a long

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Speaker 1: way from California. It is a really long way. You

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Speaker 1: don't have the best way to describe what growing up

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Speaker 1: in Oklahoma was like is that my closest friends in

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Speaker 1: the world today are friends from there. It was like

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Speaker 1: growing up in a family of sixteen thousand and you

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Speaker 1: still say in contact with them, Yes, absolutely do share value,

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Speaker 1: share past, share ideas, share love. What is it? There

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Speaker 1: is nothing in this world that can replace history, and

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Speaker 1: sometimes of that history, are you ready? Is from another lifetime?

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Speaker 1: And I am convinced. I came back to this one

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Speaker 1: with a group of people that we've been cruising around

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Speaker 1: the world for thousands of years together. Boom. Well, I'm

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Speaker 1: glad I finally found you here because I've been missing

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Speaker 1: you for the last million years. Exactly where have you been?

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Speaker 1: I know I'm here now. Your mom was an accountant,

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Speaker 1: your dad was in the military. You followed a radically

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Speaker 1: different path. What do you think put you on this

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Speaker 1: path than what was their influence? Probably the biggest influence

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Speaker 1: was my mother, Actually not probably, it was most definitely

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Speaker 1: my mother. My mother was a feminist before it was cool,

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Speaker 1: and she has always believed that everyone was equal and

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Speaker 1: brought me up that way and told me over and

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Speaker 1: over and over again, you can be anything you want.

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Speaker 1: What she gave me was a very giant canvas. And

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Speaker 1: I think that's probably the most powerful thing, the biggest

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Speaker 1: influence that I had. I don't think it really matters

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Speaker 1: where you grow up. I mean, I think that's very

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Speaker 1: limiting for people. If you step back and if you

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Speaker 1: look at your yourself and look at the world and think,

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Speaker 1: I don't have to be one thing. I don't I know,

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Speaker 1: you don't I don't have to be one thing. I

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Speaker 1: can so go hillbilly on you, though. I mean I'll

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Speaker 1: go I'll go southern and just a minute if you

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Speaker 1: want me to. But I can go girl from Oklahoma

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Speaker 1: that sounds like she's not very smart. I can do

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Speaker 1: all that kind of stuff. But I'm not just from Oklahoma.

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Speaker 1: I'm also not just a lesbian. I am also not

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Speaker 1: just a nerd. I'm also not just a writer. I

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Speaker 1: am all of it. I am everything, and it doesn't

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Speaker 1: matter where I come from. You're star golfer, Well, first

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Speaker 1: of all, how did you get to be a star

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Speaker 1: golfer at an early age in Oklahoma? Not a lot

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Speaker 1: of grass on the greens there. I suspect we have

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Speaker 1: two things in Oklahoma. We have athletes and cattle. So

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Speaker 1: athletics is a big thing in that state. I mean

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Speaker 1: it is from football to football. You know. I grew

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Speaker 1: up in this little town and and it's kind of

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Speaker 1: a crappy town in all fairness, this little crappy town.

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Speaker 1: And we had a golf course. We had a country

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Speaker 1: club actually, and it was thirty five dollars to join.

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Speaker 1: You can't join twenty for our fitness. For what my

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Speaker 1: mom paid for us to join the country club. So

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Speaker 1: I just picked up a club. I held it like

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Speaker 1: a baseball bat, and I was hitting it further and

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Speaker 1: better than most adults. The first time I picked up

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Speaker 1: a club, I was seven years old. I had three clubs.

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Speaker 1: I had a seven of five and a nine iron.

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Speaker 1: No putter, not then eventually got one. Kim in works, right,

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Speaker 1: when did you tell yourself no to golf? And when

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Speaker 1: did you say yes to media? Oh? Gosh, in Chicago

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Speaker 1: in if you must know the place in the date,

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Speaker 1: because I remember, I was an internship at Cats Communications.

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Speaker 1: Cat I saw your eyes just then. I worked for

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Speaker 1: this wonderful woman named Maryland Moss, and I loved it.

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Speaker 1: I loved advertising. I loved media. I loved the challenge

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Speaker 1: of it. And you know, golf was one of those

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Speaker 1: things that you know, you've been playing since you were

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Speaker 1: seven and in nineteen would I say eighty eight, I

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Speaker 1: was a hair from I was around twenty. That's like

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Speaker 1: a you can be a cop for that long and

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Speaker 1: retire after that. You know, It's like there's government jobs

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Speaker 1: that are shorter length in the time I played golf,

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Speaker 1: so I think it was just my cycle. I was done.

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Speaker 1: How did you get from that moment? So here you

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Speaker 1: are today. I transferred to SMU and Dallas because it

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Speaker 1: felt like a better education for me. So I went

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Speaker 1: from University of Oklahoma, which was a full golf scholarship,

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Speaker 1: and then I felt like I really needed to actually

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Speaker 1: learn learn something new and not just how to play golf.

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Speaker 1: So went to s m U and my senior year

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Speaker 1: at s m U, Laniar Timberland, who formerly Bozell Dallas

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Speaker 1: was named after, was a professor. They kind of plucked

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Speaker 1: me out of the class in the early nineties when

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Speaker 1: we had an ad recession, when they were, you know,

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Speaker 1: twenty five people lined up for shitty job. They gave

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Speaker 1: me that shitty job and that's how that started. And

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Speaker 1: the shitty job was was insistent media planner at Bozel

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Speaker 1: Dallas and I was working on J. C. Penney. God

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Speaker 1: rested soul And did you think you had died and

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Speaker 1: gone to heaven with that job? Oh my god. I

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Speaker 1: didn't even ask how much it paid when they called

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Speaker 1: me to offer me the job. I didn't care how much?

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Speaker 1: Did it? One? Six thousand bones, sixteen thousand bucks. I

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Speaker 1: used to go and eat lunch at Sam's warehouse, I

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Speaker 1: ate samples for a good year and then from there

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Speaker 1: where then I met my now wife, Suzanne. We've been

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Speaker 1: together twenty six years, congratulations, Thank you. And we decided

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Speaker 1: that we need to live a little freer and have

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Speaker 1: some liberations. So we moved to San Francisco and had

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Speaker 1: the time of our lives. It was so much fun.

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Speaker 1: Oh my gosh. And I worked at that agencies. I

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Speaker 1: worked at a small shop called Winkler McManus, I worked

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Speaker 1: at foot Cone of Building, and then I ended up

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Speaker 1: running the Hewlett Packard business globally for about it maybe

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Speaker 1: a decade sounds about right. That was quite an undertaking.

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Speaker 1: I don't think I slept for a day. And and

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Speaker 1: the go go years of Hewlett Packard, oh in the

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Speaker 1: Carly years, the big spending years. Those were fun. And

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Speaker 1: then that brought me to Los Angeles, where I pitched

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Speaker 1: the twenty century Fox business, actually on behalf of the agency.

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Speaker 1: And very quickly, like within a year, my client had

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Speaker 1: decided to move on and went back to the research

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Speaker 1: side of things, and they asked me to join, so

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Speaker 1: I did, and then here I sit with you. Wow,

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Speaker 1: that's pretty amazing. It's a great journey. Right, and what

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Speaker 1: do you think in your parents are in your background

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Speaker 1: put you on this path. They didn't even know what

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Speaker 1: the data strategist was in those days, so they didn't.

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Speaker 1: I think what's so interesting about my job is that

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Speaker 1: I am the worst technical person to grace the earth.

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Speaker 1: I miss clickers, I miss dials. Like what I would

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Speaker 1: do for an oven that just had a dial, I

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Speaker 1: would give like limbs for I think my gift to

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Speaker 1: the business world, to the entertainment industry, even when I

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Speaker 1: work at AD Agencies, was more of a vision and

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Speaker 1: a strategist. There are plenty of people who their gift

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Speaker 1: is being an expert at like writing code, analyzing you know,

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Speaker 1: tedious number. My job started out as a vision and

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Speaker 1: I think that the visionary part again came from my mother.

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Speaker 1: I also think we come into this world with jobs

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Speaker 1: to do and with gifts we already have that have

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Speaker 1: nothing to do with our parents. Jim Gianopolis, who is

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Speaker 1: a former chair person of Fox that I love very much,

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Speaker 1: said to me the day he left. You know what, Joels,

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Speaker 1: I've loved working with you. I am a little surprised.

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Speaker 1: I have to confess that you can operate so well

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Speaker 1: in the corporate world. So I was kind of like, okay,

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Speaker 1: by where's this going. What's this man gonna say to me?

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Speaker 1: And he said, because you're an iconoclast, and he goes

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Speaker 1: and we need them. They just have a hard time

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Speaker 1: working in a business structure. So the first thing I

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Speaker 1: do is I look up iconoclass because I had no

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Speaker 1: earthly idea what it meant. That's where Oklahoma comes in.

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Speaker 1: We didn't have a very good educational system, and I'm

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Speaker 1: from Mississippi. We looked up to people from oklahow It's true.

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Speaker 1: The only jokes I have are actually Nebraska jokes. Like

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Speaker 1: that's all I got, That's all I learned. I'm going

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Speaker 1: to tell you one what does the end stand for?

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Speaker 1: On the Nebraska helmet? What knowledge? So I think we'll

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Speaker 1: get a couple of calls. I think you guys, I

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Speaker 1: think you guys all sen need a laugh track? Is

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Speaker 1: there a story? This is how you found and fell

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Speaker 1: in love with data? There is What was so attractive

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Speaker 1: to me about data was what we learned about what

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Speaker 1: our customers were thinking and feeling had nothing to do

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Speaker 1: with what we actually thought. So it actually proved to

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Speaker 1: me how wrong we were. And that's how I fell

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Speaker 1: in love. We made this movie called Love Simon, and

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Speaker 1: you know, we're the first major motion picture company to

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Speaker 1: have a wide release of like a teenage gay boy

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Speaker 1: and his struggles, and we were like, oh, you know,

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Speaker 1: could we get moms to go to this? And so

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Speaker 1: I went to the creative team and said, hey, do

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Speaker 1: you guys mind making like a forty five second spot

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Speaker 1: for me so I can go in and see if

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Speaker 1: we can attract you know, moms that might want to

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Speaker 1: come to this movie. So they did that and the

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Speaker 1: spot was called Dear Mom, very clever with our titles,

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Speaker 1: And what we found was actually that moms saw this

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Speaker 1: topic of sexuality equal to sex because I saw a

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Speaker 1: lot of R rated movies that came up, and then

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Speaker 1: I looked on social and saw that they were viewing

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Speaker 1: at three times their normal rate, but they were commenting

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Speaker 1: at a third of their normal rate. And I said, oh,

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Speaker 1: this is too taboo. So let's not go spend the

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Speaker 1: company's money here, because that was a story that it

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Speaker 1: told us. So I can tell stories from their stories

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Speaker 1: because I love storytelling. The first thing I tell used

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Speaker 1: to tell my class at USC when I taught one

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Speaker 1: is if you look at your computer or your phone.

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Speaker 1: I'm going to dock you a great But the second

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Speaker 1: thing that I said to them was I do this

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Speaker 1: because I believe storytellers do for the soul what doctors

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Speaker 1: do for the physical body. So I believe in storytelling.

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Speaker 1: I believe in the ones you make up. I believe

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Speaker 1: in the ones that are real. I believe in telling

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Speaker 1: stories about our customers through their behaviors, through what they

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Speaker 1: try to tell us. And that's how I fell in

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Speaker 1: love with data is that it was just another source

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Speaker 1: for me to tell stories from. One of the reasons

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Speaker 1: I've always been drawn to you is because you don't

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Speaker 1: follow the trends. You follow the consumer, which allowed you

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Speaker 1: to back up and say, that's what's really going on

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Speaker 1: and let's figure it out. When I first started this

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Speaker 1: with Stacy, I was fearful that creative executives would see

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Speaker 1: me walk down the hall and run and hide because like,

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Speaker 1: there's the data nerd, and she's going to try to

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Speaker 1: tell me what to do. Can I tell you the

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Speaker 1: first people that lined up in front of my office

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Speaker 1: when this stuff started to take off, they were the

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Speaker 1: creative executives. They're like, Okay, I have this project can

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Speaker 1: you help me understand what it can look like? What's

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Speaker 1: our greatest potential? Where our weaknesses with this? It was them?

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Speaker 1: And what about directors? Did they it into this as

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Speaker 1: well or do they want to stay away from it?

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Speaker 1: And you just tell them how to market it. I've

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Speaker 1: dealt with more producers. Mainly what I do with them

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Speaker 1: is help understand customers likes and dislikes. I can go

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Speaker 1: look at a franchise and say, you might think this

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Speaker 1: is who as part of this franchise, but it's actually

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Speaker 1: this kind of person, you know, plan of the apes.

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Speaker 1: There is a particular type of person that loves that movie,

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Speaker 1: and it's not who you think it is. And the

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Speaker 1: funny thing it is, it's people who like movies like Tarzan,

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Speaker 1: King Kong, and ant Man, because here's what they all

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Speaker 1: have in common. Is there all retro sci fi? Retro

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Speaker 1: sci fi is not real genre. It turns out it

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Speaker 1: is though, huh, but it is in my world. I

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Speaker 1: would take, you know, the six genres, seven genres that

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Speaker 1: we have, and I would turn it into probably two

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Speaker 1: hundred because I think that's how nuanced we are. And

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Speaker 1: do you take two D four hundred? Eventually? I think

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Speaker 1: that it should go to four hundred. We are complex

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Speaker 1: creatures and we can't always identify why we do things.

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Speaker 1: But when we can look at your data, we can

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Speaker 1: start to figure out some wise. What did you learn

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Speaker 1: about how to market The Greatest Showman with Hugh Jackman.

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Speaker 1: I've learned my best lessons when I've made mistakes. So

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Speaker 1: with the Greatest Showman, we have Hugh Jackman, who we

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Speaker 1: all love and brilliant. And what happened is that when

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Speaker 1: we released our first trailer, we actually used one of

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Speaker 1: the tools that my data science team built called Nightcrawler.

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Speaker 1: What we found is those that were watching the trailer

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Speaker 1: the movies that they had bought tickets to, we're not

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Speaker 1: what we thought they were. We thought they were. You know,

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Speaker 1: this was like a musical, right What we thought it

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Speaker 1: would be would be things like lame Is because he

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Speaker 1: was in it. You know, we thought it would be

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Speaker 1: La La Land because it was a musical and it

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Speaker 1: was recent. It wasn't. It was Cinderella certainly Pitched Perfect

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Speaker 1: was in there. It was Beauting the Beast, it was

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Speaker 1: hidden figures. The ones who loved our movie from the

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Speaker 1: beginning were those that loved stories of underdogs, This is

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Speaker 1: a story of how narrow we can be in the

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Speaker 1: industry and actually how complex our customers are. We had

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Speaker 1: to adjust our messaging. We cannot go and spend the

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Speaker 1: good company money chasing after ans. It's not really going

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Speaker 1: to go. We need to spend it after those that

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Speaker 1: have already shown interest. And this is the type of

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Speaker 1: person that it is. And so we ended up, you know,

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Speaker 1: being able to shift. We clearly didn't open the movie

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Speaker 1: to big box, but we started shifting towards the end,

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Speaker 1: which I like to think helped us. And the beauty

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Speaker 1: about data is that we went back in to do

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Speaker 1: a look back, and when we first looked at those

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Speaker 1: that watched the trailer, we went and compared those that

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Speaker 1: bought tickets to our movie and then what tickets they

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Speaker 1: had bought in the past. So it's the pre in

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Speaker 1: the post and the post, it's the same list of

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Speaker 1: movies that showed up. It was Beauty and the Beast,

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Speaker 1: it was Cinderella, it was a Wonder, it was hidden figures.

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Speaker 1: It was also pitched perfect, and so you changed creative

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Speaker 1: in the direct along that take. How quickly can a

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Speaker 1: company like Fox move? Oh, Fox can be fast. It's

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Speaker 1: part of the DNA of Fox. Fox can be very

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Speaker 1: quickly more with Julie Reager right after the break, Welcome

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Speaker 1: back to math and magic. We're here with Julie Reager.

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Speaker 1: You supposedly have a proprietary data set that gives you

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Speaker 1: studio insights to million movie goers. Right, and they're named

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Speaker 1: after Marvel characters. They are X men characters x men. Okay,

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Speaker 1: and now you have an AI named Merlin. Yes, because

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Speaker 1: I like wizards. Oh, I like this, and you get

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Speaker 1: to name them. I do. See. That's the beauty you

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Speaker 1: create something. It's like if you're a builder, right, you

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Speaker 1: buy a piece of land, you put a road, and

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Speaker 1: you gotta name the road. I got to name the

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Speaker 1: road one of them. One of the most exciting meetings

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Speaker 1: I've had is I was in a meeting with you

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Speaker 1: and we were talking about my heart. Do you want

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Speaker 1: to know what I said to you? Would you tell me?

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Speaker 1: I actually looked at you and I said, Bob, you're

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Speaker 1: a tech company. What the hell is wrong with you?

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Speaker 1: You have all this great data. You're a tech company.

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Speaker 1: You're both you're an entertainment audio dada, but you're a

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Speaker 1: tech company. And so what did you do? So what

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Speaker 1: I did. We had this little movie called Bohemian Rhapsody.

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Speaker 1: You may have heard of it at a tiny movie

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Speaker 1: called Bohemian Rhapsody. And what we did we had your

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Speaker 1: data anonymized. I want to make sure I say this properly,

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Speaker 1: so nobody no lawyers start calling either one of us anyway.

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Speaker 1: So we matched folks that had downloaded your app and

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Speaker 1: used your app to our movie goers. And the reason

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Speaker 1: why we were comfortable with looking at your app dating

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Speaker 1: is because roughly nine that those that have download and

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Speaker 1: used the app also listened to Terrestrial, So I was

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Speaker 1: very comfortable with that. So once we matched it in

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Speaker 1: my pool, I had people that were movie goers, that

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Speaker 1: had people that were movie goers and also I Heart customers.

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Speaker 1: Then we created a campaign around Bohemian Rhapsody with you guys,

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Speaker 1: and so we launched the trailer with you guys. You

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Speaker 1: talked about magic and creativity, which I thought was so

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Speaker 1: brilliant because in the film, have a feeling I'm not

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Speaker 1: going to ruin anything here for any of your listeners

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Speaker 1: and no spoiler alert. There was a time when the

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Speaker 1: record producer who's played by Michael Myers, said, nobody's going

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Speaker 1: to play a song on the radio that six minutes long.

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Speaker 1: Six minutes is too long. You guys came back to us.

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Speaker 1: It was just it was like magic, and said we're

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Speaker 1: going to roadblock six minutes across our entire network. And

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Speaker 1: once I pulled myself up off the floor and was

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Speaker 1: able to utter a word, the word was genius. And

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Speaker 1: we did it right before the movie launched. So what

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Speaker 1: happened is the movie did all right. And then after

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Speaker 1: about a month, when I got four weeks of data back,

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Speaker 1: I was able to look and see who actually bought

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Speaker 1: tickets to our movie. And I already had my movie

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Speaker 1: Gover database, and then I had the database within the

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Speaker 1: database that was movies and I heart. And what was

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Speaker 1: interesting is that your customers were buying tickets twelve at

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Speaker 1: a higher rate than those that were just ongoing move

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Speaker 1: of goers. Is that good twelve percent? Because I promised

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Speaker 1: early on I wasn't going to use the F words,

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Speaker 1: so I'll say fantastic. It was fan freaking tastic. I

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Speaker 1: think what everyone in this business is dying for is attribution.

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Speaker 1: Dying because they just don't know if it's good money

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Speaker 1: or bad money that they're spending. And I also went

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Speaker 1: in to go check to see if are we just

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Speaker 1: getting a music fan. I just wanted to be able

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Speaker 1: to answer my own questions in my head. Was just

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Speaker 1: like a fluke thing. And then I went back in

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Speaker 1: and I looked at other movies that we had done

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Speaker 1: with you all in the last four years, and I

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Speaker 1: found the same pattern. Now Bow Rep was a tick higher,

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Speaker 1: which I think is where the music might play in.

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Speaker 1: We did, and the stunt was brilliant. That subsegment always

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Speaker 1: came in higher than the general movie goer segment. So

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Speaker 1: I was really confident in what we had done. Everybody

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Speaker 1: does attribution, or they think they're doing with attributions all

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Speaker 1: about digital, like I'm going to run a banner, I'm

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Speaker 1: going to do a video on YouTube or whatever. My

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Speaker 1: first attribution project was with you doing it with radio

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Speaker 1: is a likely character. Look, we were so excited about it.

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Speaker 1: One for getting someone with your kind of skill set

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Speaker 1: to believe innocent take that leap. I have to congratulate

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Speaker 1: you on doing the roadblock. Someone in the Los Angeles

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Speaker 1: market that works here took a video of them punching

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Speaker 1: between all of our radio stations in l A. And

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Speaker 1: you're hitting the same song by different artists all playing

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Speaker 1: their version of Bohebian Rapsy. You couldn't get away from

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Speaker 1: its pretty amazing. Is amazing, right, I mean all the

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Speaker 1: artists with the covers. That was so amazing is that

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Speaker 1: that became into a phenomenon. And let's not forget what

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Speaker 1: happened on the charts with the album, right, so many

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Speaker 1: amazing things that happened. Now listen, the movie is amazing.

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Speaker 1: Was the only thing that contributed to this success. The campaign?

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Speaker 1: We didn't know because the movie was incredible. We had

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Speaker 1: other great creative but was it at twelve for sent nudge? Heck,

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00:23:01,160 --> 00:23:03,439
Speaker 1: yeah it was. And I can tell you from my

429
00:23:03,600 --> 00:23:07,959
Speaker 1: data science team because they come into me very objective,

430
00:23:08,040 --> 00:23:10,880
Speaker 1: like these are the guys, and all my data scientists

431
00:23:10,920 --> 00:23:14,320
Speaker 1: are guys. I bet their sock yours are incredibly organized.

432
00:23:14,800 --> 00:23:18,040
Speaker 1: They come into me and they are clean slate every time,

433
00:23:18,080 --> 00:23:22,200
Speaker 1: and they are factual. They come into me and say, well, Julie,

434
00:23:22,240 --> 00:23:24,119
Speaker 1: here's what we found, and they say it with a

435
00:23:24,160 --> 00:23:26,520
Speaker 1: straight face. And I again, so from the time I

436
00:23:26,520 --> 00:23:29,800
Speaker 1: fell off the floor picked myself up with the stunt too,

437
00:23:29,960 --> 00:23:32,800
Speaker 1: picking myself up off the floor with the twelve percent going,

438
00:23:33,359 --> 00:23:36,240
Speaker 1: you've got to be kidding me and my wildest dreams,

439
00:23:36,359 --> 00:23:40,040
Speaker 1: it would be five. It was twelve. And do you

440
00:23:40,119 --> 00:23:43,560
Speaker 1: think it's because radio is really the conversation. It sounds

441
00:23:43,560 --> 00:23:45,840
Speaker 1: like your friends talking about the movie. Is that that

442
00:23:45,920 --> 00:23:48,720
Speaker 1: part of the brain that it sounds like I'm hearing

443
00:23:48,720 --> 00:23:50,960
Speaker 1: people talk about it? I think with anything, it's never

444
00:23:51,000 --> 00:23:52,760
Speaker 1: one answer, but I think that that's part of it.

445
00:23:52,800 --> 00:23:55,760
Speaker 1: I think another part of it, and why I have

446
00:23:55,880 --> 00:23:59,600
Speaker 1: loved radio for so long is because of again, it's

447
00:23:59,600 --> 00:24:02,320
Speaker 1: the human nature. Right. So now technology has set people

448
00:24:02,359 --> 00:24:05,280
Speaker 1: free to do what they want when they want, except

449
00:24:05,280 --> 00:24:08,720
Speaker 1: in a car, so they are A negative word to

450
00:24:08,800 --> 00:24:11,119
Speaker 1: use is trapped. I like to use the word they're surrendered.

451
00:24:11,680 --> 00:24:14,320
Speaker 1: There in a car, you have to surrender. You're forced

452
00:24:14,320 --> 00:24:17,439
Speaker 1: to surrender. And what better to surrender to than someone

453
00:24:17,520 --> 00:24:20,520
Speaker 1: you've been listening to a good part of your life.

454
00:24:20,760 --> 00:24:22,359
Speaker 1: Or you're going to hear a new story, or you're

455
00:24:22,359 --> 00:24:25,600
Speaker 1: gonna be introduced to a new song, or you're going

456
00:24:25,640 --> 00:24:27,680
Speaker 1: to hear about a movie maybe you hadn't heard about,

457
00:24:27,760 --> 00:24:29,840
Speaker 1: or get excited about one that you haven't heard about.

458
00:24:30,160 --> 00:24:33,320
Speaker 1: And our business we talked to her on their personalities

459
00:24:33,359 --> 00:24:36,520
Speaker 1: and we say, you know you're somebody's best friend riding

460
00:24:36,920 --> 00:24:39,080
Speaker 1: in that empty seat with them every day to work,

461
00:24:39,480 --> 00:24:42,280
Speaker 1: and the greatest talent we have turned out to be

462
00:24:42,320 --> 00:24:45,240
Speaker 1: a very interesting friend. And they also talked about what's

463
00:24:45,280 --> 00:24:48,560
Speaker 1: happening in your city in that moment in time, in

464
00:24:48,600 --> 00:24:51,639
Speaker 1: that moment, and you're part of that, because what we

465
00:24:51,800 --> 00:24:55,359
Speaker 1: all long to be is a part of something, whether

466
00:24:55,440 --> 00:24:57,440
Speaker 1: we want to be a part of a family, want

467
00:24:57,440 --> 00:24:58,840
Speaker 1: to be a part of a community, you want to

468
00:24:58,840 --> 00:25:00,399
Speaker 1: be a part of the city. Like that's we have

469
00:25:00,680 --> 00:25:03,720
Speaker 1: pride in our local football teams or college teams, like

470
00:25:03,760 --> 00:25:08,840
Speaker 1: it's that pride of that connection. And I think your

471
00:25:09,119 --> 00:25:14,360
Speaker 1: talent gives them another layer of connection, and we all

472
00:25:14,600 --> 00:25:17,399
Speaker 1: desperately want to be connected, to be a part. I

473
00:25:17,480 --> 00:25:19,080
Speaker 1: have a quick story for you. I don't know if

474
00:25:19,080 --> 00:25:21,879
Speaker 1: I've ever told you. For about four years I taught

475
00:25:22,119 --> 00:25:26,520
Speaker 1: entertainment marketing at USC During my tenure, I you know,

476
00:25:26,600 --> 00:25:29,240
Speaker 1: ran the gamut of talking about various parts of marketing

477
00:25:29,240 --> 00:25:31,520
Speaker 1: with them. I said, oh, you guys must only listen

478
00:25:31,600 --> 00:25:35,680
Speaker 1: to you know, Spotify or Pandora. Can I tell you?

479
00:25:35,920 --> 00:25:38,439
Speaker 1: They're like, yeah, but we listened mostly to the radio.

480
00:25:39,400 --> 00:25:42,320
Speaker 1: I go, really like, I was shocked. I go, you

481
00:25:42,359 --> 00:25:46,560
Speaker 1: do They're like yeah, And I said, well why and

482
00:25:46,600 --> 00:25:49,240
Speaker 1: they said, because I can listen to that stuff anytime,

483
00:25:49,880 --> 00:25:52,800
Speaker 1: but when I'm like driving offices, is l a right.

484
00:25:52,880 --> 00:25:55,200
Speaker 1: So we live in our cars and they're like, when

485
00:25:55,200 --> 00:25:57,000
Speaker 1: I'm going somewhere, it's like I'm out in the world

486
00:25:57,000 --> 00:25:58,639
Speaker 1: and I want to feel a part of that world.

487
00:25:58,760 --> 00:26:00,679
Speaker 1: These are college students that are telling me, is that

488
00:26:00,880 --> 00:26:03,360
Speaker 1: every put up your hand who listens to the radio live,

489
00:26:03,640 --> 00:26:07,639
Speaker 1: Every single hand went up into the air, and that's

490
00:26:07,680 --> 00:26:09,320
Speaker 1: when it dawned on. It's when it clicked with me.

491
00:26:09,359 --> 00:26:11,440
Speaker 1: And it's something that data can't ever tell you because

492
00:26:11,440 --> 00:26:14,080
Speaker 1: we want to rationalize either in or out of data.

493
00:26:14,960 --> 00:26:19,840
Speaker 1: Radio is that thing that connects everyone from thirteen to

494
00:26:20,040 --> 00:26:23,480
Speaker 1: eight plus. It's that one thing where social animals, going

495
00:26:23,520 --> 00:26:26,919
Speaker 1: back to human nature and my music collections, want to

496
00:26:26,960 --> 00:26:28,520
Speaker 1: want to be by myself. I want to sort of

497
00:26:28,560 --> 00:26:31,159
Speaker 1: be in my zone. The radio is what's going on

498
00:26:31,200 --> 00:26:33,600
Speaker 1: in the world, and we can't stay away from it

499
00:26:33,720 --> 00:26:36,359
Speaker 1: too long. It's interesting you find all this from the

500
00:26:36,359 --> 00:26:38,640
Speaker 1: other angle, and obviously we look at it in depth

501
00:26:38,680 --> 00:26:41,160
Speaker 1: and a lot. It's interesting in research studies show often

502
00:26:41,160 --> 00:26:43,480
Speaker 1: hear save where you hear about that, um it was

503
00:26:43,520 --> 00:26:46,560
Speaker 1: either a friend or on the radio. They've confused it

504
00:26:46,600 --> 00:26:48,679
Speaker 1: and it's in that same part of their brain we

505
00:26:48,720 --> 00:26:51,399
Speaker 1: talked about the car. It's interesting what alexas doing now

506
00:26:51,520 --> 00:26:54,119
Speaker 1: is opening up the home to us again. Homes used

507
00:26:54,119 --> 00:26:55,320
Speaker 1: to have a clock radio and now it is down

508
00:26:55,359 --> 00:26:59,360
Speaker 1: to about. But suddenly the new clock radio is Alexa

509
00:26:59,720 --> 00:27:02,280
Speaker 1: and to find me this surge in the home, and

510
00:27:02,320 --> 00:27:04,040
Speaker 1: I think people thought, are they're gonna listen to their music?

511
00:27:04,320 --> 00:27:06,359
Speaker 1: Turns out the number one use some Alexa's a MFM

512
00:27:06,440 --> 00:27:09,040
Speaker 1: radio for the same reasons exactly. I want somebody to

513
00:27:09,119 --> 00:27:11,600
Speaker 1: keep me company while I'm brushing my teeth in the morning,

514
00:27:11,920 --> 00:27:14,359
Speaker 1: while I'm doing some work around the house, or by

515
00:27:14,359 --> 00:27:17,280
Speaker 1: the way, go to the cash register. The person working

516
00:27:17,320 --> 00:27:19,760
Speaker 1: the cash red shirt the convenience store. They're listening to radio.

517
00:27:20,080 --> 00:27:22,560
Speaker 1: They are well listen enough hyping of us, I know,

518
00:27:22,680 --> 00:27:25,240
Speaker 1: but I think it's crucial to talk about this stuff

519
00:27:25,280 --> 00:27:27,600
Speaker 1: because it is about you know, we're not a number.

520
00:27:27,680 --> 00:27:30,960
Speaker 1: We're human. Most people looking from the outside who don't

521
00:27:30,960 --> 00:27:33,520
Speaker 1: know you personally would think you're a math person. Let

522
00:27:33,520 --> 00:27:37,000
Speaker 1: me hit one of your high profile magic moments. What

523
00:27:37,040 --> 00:27:38,639
Speaker 1: did you do with the New York Times for the

524
00:27:38,640 --> 00:27:43,000
Speaker 1: book The back in two thousand thirteen for Legendary. Oh

525
00:27:43,040 --> 00:27:45,159
Speaker 1: that was that was a great moment. That was a

526
00:27:45,160 --> 00:27:49,080
Speaker 1: great moment. So we made this little movie based on

527
00:27:49,320 --> 00:27:52,080
Speaker 1: a novel called The Book Thief written by Marcus Uzac.

528
00:27:52,320 --> 00:27:55,760
Speaker 1: It was about this young girl set in the Nazi

529
00:27:56,119 --> 00:28:00,679
Speaker 1: time in Germany. She couldn't read. Really, the heart of

530
00:28:00,720 --> 00:28:04,679
Speaker 1: what the movie was was the power of words. I

531
00:28:04,760 --> 00:28:07,000
Speaker 1: kept thinking, I remember seeing in a room, going how

532
00:28:07,000 --> 00:28:11,120
Speaker 1: do you communicate to folks? How powerful words are? I

533
00:28:11,160 --> 00:28:13,080
Speaker 1: came to the conclusion in that moment that the way

534
00:28:13,080 --> 00:28:15,320
Speaker 1: you teach somebody the power of something as you actually

535
00:28:15,320 --> 00:28:17,480
Speaker 1: take it away. So I went and talked to the

536
00:28:17,480 --> 00:28:20,240
Speaker 1: New York Times. Actually true story is, actually talked to

537
00:28:20,400 --> 00:28:22,600
Speaker 1: l a Times, and they wanted to charge so much

538
00:28:22,640 --> 00:28:25,840
Speaker 1: money from my idea, and they actually wanted to try

539
00:28:25,920 --> 00:28:27,320
Speaker 1: to hold it over my head and take it from me.

540
00:28:27,359 --> 00:28:29,520
Speaker 1: I'm just telling you that was a nasty moment. No,

541
00:28:29,680 --> 00:28:31,200
Speaker 1: I didn't apologize to the a lot of Times because

542
00:28:31,200 --> 00:28:32,840
Speaker 1: I'm still bitter. So I took it to the New

543
00:28:32,880 --> 00:28:35,719
Speaker 1: York Times, really thinking they would say no to the

544
00:28:35,760 --> 00:28:39,719
Speaker 1: idea of having blank pages and literally blank, No, this

545
00:28:39,760 --> 00:28:43,120
Speaker 1: is an advertisement on it. None of that they loved

546
00:28:43,160 --> 00:28:45,600
Speaker 1: it so much because I think that's who they are.

547
00:28:45,800 --> 00:28:47,920
Speaker 1: It's kind of like in the radio business taking a

548
00:28:48,000 --> 00:28:50,760
Speaker 1: voice away, right, It's like taking words away from a journalist.

549
00:28:51,240 --> 00:28:54,920
Speaker 1: So they did it. This was two thousand and thirteen,

550
00:28:54,960 --> 00:28:57,640
Speaker 1: maybe it's almost six years ago. Things didn't go really

551
00:28:57,720 --> 00:29:00,160
Speaker 1: viral like they do now, Like things didn't catch on

552
00:29:01,160 --> 00:29:03,600
Speaker 1: that moment when people were opening up their newspaper and

553
00:29:03,600 --> 00:29:06,000
Speaker 1: seeing it. They started to tweet about it right about it.

554
00:29:06,040 --> 00:29:09,640
Speaker 1: And what's crazy is that it is now in curriculums

555
00:29:09,680 --> 00:29:13,800
Speaker 1: and colleges around the world about having an idea that

556
00:29:14,160 --> 00:29:18,040
Speaker 1: is deeper than surface. Right, that's not just a superficial

557
00:29:18,760 --> 00:29:21,280
Speaker 1: like get somebody's attention, like it had purpose to it.

558
00:29:21,440 --> 00:29:23,680
Speaker 1: So that's actually really cool is that it's actually gone

559
00:29:23,720 --> 00:29:26,080
Speaker 1: down and a little bit of history. We had a

560
00:29:26,120 --> 00:29:28,640
Speaker 1: lot of history. Let me hit a few Let's go

561
00:29:28,680 --> 00:29:31,520
Speaker 1: to the science side of marketing here for a few minutes.

562
00:29:31,600 --> 00:29:33,960
Speaker 1: Talk a little bit about how you analyze conversations on

563
00:29:34,040 --> 00:29:39,120
Speaker 1: Twitter and what have you learned to me. Data isn't

564
00:29:39,160 --> 00:29:43,560
Speaker 1: just numbers. Data is also words. When you want to

565
00:29:43,600 --> 00:29:45,800
Speaker 1: understand what a customer is and what they think and

566
00:29:45,840 --> 00:29:47,400
Speaker 1: what they feel, you're not going to get it just

567
00:29:47,480 --> 00:29:51,280
Speaker 1: based on purchase data numbers ones and zeros. You're gonna

568
00:29:51,280 --> 00:29:52,760
Speaker 1: get it based on the language that they use and

569
00:29:52,760 --> 00:29:57,000
Speaker 1: how they communicate. We struck a relationship with Twitter to

570
00:29:57,320 --> 00:30:01,360
Speaker 1: be actually the first one to anonymously connect tweets to purchasers.

571
00:30:01,760 --> 00:30:04,760
Speaker 1: What I learned is that there are words that people

572
00:30:04,960 --> 00:30:07,960
Speaker 1: use in social media that can tell you whether or

573
00:30:08,000 --> 00:30:09,600
Speaker 1: not they're going to go to a film six months

574
00:30:09,640 --> 00:30:12,920
Speaker 1: for that film comes out. It is astonishing. There's like

575
00:30:12,960 --> 00:30:15,280
Speaker 1: a bag of words that people use, and there's another

576
00:30:15,320 --> 00:30:17,400
Speaker 1: bag of words that people use and they're not going.

577
00:30:17,920 --> 00:30:20,960
Speaker 1: There seems to be some consistency. I brought in a

578
00:30:21,040 --> 00:30:23,680
Speaker 1: brilliant woman named Dr Pam Rutledge who has her PhD

579
00:30:23,680 --> 00:30:26,240
Speaker 1: and media psychology, who helps us understand all of this.

580
00:30:26,800 --> 00:30:29,200
Speaker 1: The first movie we did it with was Logan. I mean,

581
00:30:29,240 --> 00:30:32,080
Speaker 1: Logan was a success, but I could tell based on

582
00:30:32,200 --> 00:30:34,920
Speaker 1: language that people that didn't buy tickets, And it was

583
00:30:35,000 --> 00:30:38,840
Speaker 1: really fascinating. When you get people who are all wishy washy,

584
00:30:38,840 --> 00:30:41,240
Speaker 1: and of course in our business, thinking that well we

585
00:30:41,320 --> 00:30:44,320
Speaker 1: can change their mind. No, actually no you can't. You're

586
00:30:44,320 --> 00:30:46,120
Speaker 1: not going to shift them. This is just kind of

587
00:30:46,160 --> 00:30:49,200
Speaker 1: where they sit now. Granted this is all based on Twitter, right,

588
00:30:49,400 --> 00:30:51,360
Speaker 1: and there's still more work to be done, but Twitter

589
00:30:51,560 --> 00:30:55,640
Speaker 1: when it's connected to ticket purchases a really fascinating experiment.

590
00:30:55,720 --> 00:30:57,560
Speaker 1: Can you tell us a couple of those words are

591
00:30:57,760 --> 00:31:01,840
Speaker 1: just top secret? There's a lot of them. Actually, there's

592
00:31:01,840 --> 00:31:04,320
Speaker 1: a bucket of definitive words, and then there's a bucket

593
00:31:04,360 --> 00:31:07,320
Speaker 1: of like wishy washy words. And the problem is what

594
00:31:07,400 --> 00:31:10,760
Speaker 1: marketers have They look at the wishy washy words and

595
00:31:10,800 --> 00:31:17,400
Speaker 1: they think they're definitive. Our industry, marketing, entertainment, media, all

596
00:31:17,440 --> 00:31:20,760
Speaker 1: of it. We haven't bothered to invite what I have

597
00:31:20,880 --> 00:31:25,440
Speaker 1: found to be the most pivotal expertise into hours, which

598
00:31:25,440 --> 00:31:30,200
Speaker 1: is psychology. Because once we invited psychology into our world

599
00:31:30,320 --> 00:31:32,560
Speaker 1: is when our world got big, and it's when our

600
00:31:32,600 --> 00:31:37,160
Speaker 1: heads blew up, and when we started to truly understand people,

601
00:31:38,160 --> 00:31:41,760
Speaker 1: and when we started to understand people is when we

602
00:31:41,800 --> 00:31:45,120
Speaker 1: started making better decisions. It sounds to me like you're

603
00:31:45,120 --> 00:31:50,080
Speaker 1: one of those believers that everything boils down the human nature. Oh,

604
00:31:50,160 --> 00:31:52,840
Speaker 1: I very much believe in that. And if you think

605
00:31:52,880 --> 00:31:56,600
Speaker 1: about what's happened in the television industry, you know, people

606
00:31:56,720 --> 00:31:59,440
Speaker 1: had must see Thursday, right, like must see TV and

607
00:31:59,480 --> 00:32:01,600
Speaker 1: everybody may sure they got home so they could see it.

608
00:32:01,960 --> 00:32:05,760
Speaker 1: That wasn't human nature, right. It's kind of like being

609
00:32:05,800 --> 00:32:07,760
Speaker 1: told where you're going to have a turkey leg tonight,

610
00:32:08,320 --> 00:32:09,880
Speaker 1: and so you're like, oh, I get better have a

611
00:32:09,880 --> 00:32:12,560
Speaker 1: turkeu lator, I'm not gonna eat at all. But now

612
00:32:12,680 --> 00:32:15,920
Speaker 1: there's this giant buffet and you can go watch or

613
00:32:16,000 --> 00:32:18,800
Speaker 1: listen to what you want when you want. That's human nature.

614
00:32:19,240 --> 00:32:20,880
Speaker 1: It might be tech that enabled it, but it was

615
00:32:20,960 --> 00:32:24,040
Speaker 1: human nature that made it what it is today. That's

616
00:32:24,040 --> 00:32:26,560
Speaker 1: why live viewing. It's like, if you're there, great, you'll

617
00:32:26,560 --> 00:32:29,080
Speaker 1: watch it, but you might want Lazagia tonight, so you

618
00:32:29,120 --> 00:32:30,880
Speaker 1: can save that turkey leg for when you want it.

619
00:32:31,000 --> 00:32:33,720
Speaker 1: And that's human nature. So you're saying that technology is

620
00:32:33,760 --> 00:32:39,040
Speaker 1: really doing nothing except unlocking human nature. Absolutely absolutely it is.

621
00:32:39,720 --> 00:32:41,960
Speaker 1: It also is unlocking some not great parts of our

622
00:32:42,040 --> 00:32:44,120
Speaker 1: human nature to like, I think it's unlocking a lot

623
00:32:44,160 --> 00:32:47,280
Speaker 1: of obsessiveness that we have that is built into who

624
00:32:47,280 --> 00:32:50,800
Speaker 1: we are. I think it's also unlocking some rudeness, Like

625
00:32:50,800 --> 00:32:52,960
Speaker 1: if I picked up my phone right now, that's rude.

626
00:32:54,080 --> 00:32:55,920
Speaker 1: I mean, how many restaurants do you walk into and

627
00:32:56,000 --> 00:32:58,040
Speaker 1: everybody has their phone up. I'm like, you're with a human.

628
00:32:58,560 --> 00:33:01,440
Speaker 1: But I also think that it un locks hiding for people,

629
00:33:01,640 --> 00:33:03,840
Speaker 1: you know, like people who get addicted to Facebook. I

630
00:33:03,840 --> 00:33:06,280
Speaker 1: think it's a real problem, like there's an unhealthiness to it,

631
00:33:06,600 --> 00:33:09,760
Speaker 1: and that's part of our humanness. Let me jump back

632
00:33:09,760 --> 00:33:12,640
Speaker 1: to movies. You've been on the front lines a long time.

633
00:33:13,560 --> 00:33:19,120
Speaker 1: What's the future movies, the theaters, streaming services. I think

634
00:33:19,120 --> 00:33:22,680
Speaker 1: we're going to have fewer than we have today of

635
00:33:22,800 --> 00:33:25,320
Speaker 1: films and theaters. From the time that I started working

636
00:33:25,320 --> 00:33:27,000
Speaker 1: in the business till now, I think the number of

637
00:33:27,120 --> 00:33:30,440
Speaker 1: new releases on weekends have doubled. I don't think the

638
00:33:30,440 --> 00:33:33,719
Speaker 1: industry can I don't think the consumers can support that.

639
00:33:34,280 --> 00:33:38,440
Speaker 1: One of the reasons that I believe comedies and horror

640
00:33:38,600 --> 00:33:41,680
Speaker 1: will always be such a massive part of our business

641
00:33:41,840 --> 00:33:44,800
Speaker 1: is because when you are alone and you see something funny,

642
00:33:44,840 --> 00:33:47,280
Speaker 1: you do not laugh out loud. You might just smile.

643
00:33:48,000 --> 00:33:50,440
Speaker 1: But when you're in a group and there's laughter, you laugh.

644
00:33:50,640 --> 00:33:52,960
Speaker 1: And what happens when we laugh is that our body

645
00:33:53,040 --> 00:33:57,840
Speaker 1: chemistry changes. Our enjoyment level goes through the roof because

646
00:33:57,840 --> 00:34:02,600
Speaker 1: we're releasing endorphins. That's real and horror. What happens in

647
00:34:02,680 --> 00:34:05,520
Speaker 1: horror is that we grab each other, we touch each other,

648
00:34:05,600 --> 00:34:09,040
Speaker 1: we are experiencing this together. I think family films because

649
00:34:09,080 --> 00:34:11,040
Speaker 1: you can bring everybody to it. You know, those are

650
00:34:11,120 --> 00:34:13,960
Speaker 1: things that will go on. But I just think that

651
00:34:14,040 --> 00:34:17,200
Speaker 1: the volume of movies is a lot. In any given weekend,

652
00:34:17,239 --> 00:34:20,760
Speaker 1: there's can be four or five movies opening easily easily.

653
00:34:21,040 --> 00:34:24,040
Speaker 1: I think the future of streaming services is going to

654
00:34:24,080 --> 00:34:26,799
Speaker 1: be fascinating to watch. Oh my gosh, I mean, and

655
00:34:26,880 --> 00:34:29,279
Speaker 1: we have front row seats. I think there's going to

656
00:34:29,280 --> 00:34:32,480
Speaker 1: be a lot of fighting with what looks like parody products.

657
00:34:32,600 --> 00:34:35,200
Speaker 1: I think only a few will win. Everybody's just getting

658
00:34:35,239 --> 00:34:37,120
Speaker 1: their gloves on right now. And I think that streaming

659
00:34:37,200 --> 00:34:39,680
Speaker 1: is going to be fascinating to watch. So the high

660
00:34:39,760 --> 00:34:44,440
Speaker 1: budget limited series hurting movies, new form of movies, how

661
00:34:44,480 --> 00:34:48,720
Speaker 1: do you see it? I think that everything has hurt movies.

662
00:34:49,200 --> 00:34:52,160
Speaker 1: Gaming right. Gaming is something that has taken people away

663
00:34:52,200 --> 00:34:55,080
Speaker 1: from theaters. I think that TV is better than it's

664
00:34:55,080 --> 00:34:58,000
Speaker 1: ever been. Game of Thrones is better than most movies

665
00:34:58,000 --> 00:35:01,560
Speaker 1: you've ever seen in your life. I kind of wish

666
00:35:01,600 --> 00:35:03,480
Speaker 1: they'd show it in a theater, to tell you the truth,

667
00:35:04,280 --> 00:35:06,200
Speaker 1: makes you want to go buy a much bigger television

668
00:35:06,560 --> 00:35:10,240
Speaker 1: and the greatest shows I've ever seen, I mean visually storytelling,

669
00:35:10,680 --> 00:35:13,200
Speaker 1: so you can get the experience in home and a

670
00:35:13,280 --> 00:35:18,680
Speaker 1: longer experience. Okay, future of ad agencies LOI gosh, that's

671
00:35:18,719 --> 00:35:20,960
Speaker 1: a tough one. I love ad agencies where I started.

672
00:35:21,560 --> 00:35:24,799
Speaker 1: I think, what's the intersection between a consulting company now

673
00:35:24,840 --> 00:35:27,960
Speaker 1: like the Anderson's and ad agencies? Right? I mean, that

674
00:35:28,239 --> 00:35:31,760
Speaker 1: seems to be an interesting competition that's happening. I think

675
00:35:31,840 --> 00:35:35,480
Speaker 1: that neither of them have the greatest advantage, which is data,

676
00:35:36,280 --> 00:35:39,640
Speaker 1: because the best data cannot be held by them. The

677
00:35:39,680 --> 00:35:42,840
Speaker 1: best data can really only be held by the client.

678
00:35:44,000 --> 00:35:47,320
Speaker 1: So for me, because I am disintermediated from a customer,

679
00:35:47,400 --> 00:35:49,840
Speaker 1: I have to go rent that data. So when you

680
00:35:49,880 --> 00:35:51,640
Speaker 1: rent a data, it's like the difference between renting and

681
00:35:51,719 --> 00:35:53,440
Speaker 1: own and a home. I can't go paint the walls

682
00:35:53,480 --> 00:35:56,200
Speaker 1: without asking permission. I can't go take a wall down

683
00:35:56,280 --> 00:35:58,799
Speaker 1: or add a new bathroom because I'm renting the data,

684
00:35:58,880 --> 00:36:01,479
Speaker 1: which in the rental agreement with data means I can't

685
00:36:01,520 --> 00:36:05,480
Speaker 1: let other parties have it, including the agency. Including the agency,

686
00:36:05,520 --> 00:36:08,040
Speaker 1: and I think that they all are going to struggle

687
00:36:08,080 --> 00:36:12,160
Speaker 1: with that because you don't have the purchase data. So

688
00:36:12,239 --> 00:36:13,960
Speaker 1: I think they're all in trouble for that. And I

689
00:36:13,960 --> 00:36:16,560
Speaker 1: think they're all in trouble because the business is based

690
00:36:16,600 --> 00:36:19,360
Speaker 1: on paying for head count. There needs to be some

691
00:36:19,440 --> 00:36:21,880
Speaker 1: more machine learning that they can get with the media business.

692
00:36:21,880 --> 00:36:24,600
Speaker 1: So you think the compensations sort of hurting their structure.

693
00:36:24,760 --> 00:36:26,520
Speaker 1: I think it's killing all of them. And I think

694
00:36:26,600 --> 00:36:29,799
Speaker 1: that recessions were terrible for the agencies because they just

695
00:36:29,800 --> 00:36:31,840
Speaker 1: wanted to hold onto business and clients were in trouble,

696
00:36:31,880 --> 00:36:34,440
Speaker 1: and they just want to pull every cost down. I

697
00:36:34,440 --> 00:36:36,680
Speaker 1: don't think agencies make the money they should make in

698
00:36:36,680 --> 00:36:39,279
Speaker 1: all honesty to to provide the service that they need

699
00:36:39,320 --> 00:36:41,879
Speaker 1: in this fragmented world we live in. You know, back

700
00:36:41,880 --> 00:36:43,920
Speaker 1: in the day when we only had the three networks,

701
00:36:43,920 --> 00:36:45,719
Speaker 1: then we had the fourth network, right, and then we

702
00:36:45,760 --> 00:36:48,440
Speaker 1: had MTV. You know, to buy a TV ad was

703
00:36:48,480 --> 00:36:51,799
Speaker 1: a big deal because it was expensive, right, and the

704
00:36:52,000 --> 00:36:54,920
Speaker 1: talent to make those there was only a small group

705
00:36:54,920 --> 00:36:59,200
Speaker 1: of talented creative types to make those ads. I think

706
00:36:59,239 --> 00:37:01,879
Speaker 1: what's happened, it's hurt the creative side of the ad

707
00:37:01,880 --> 00:37:04,040
Speaker 1: business is that there's not enough talent to make all

708
00:37:04,120 --> 00:37:06,799
Speaker 1: the ads that are necessary. Now it costs you a

709
00:37:06,840 --> 00:37:09,600
Speaker 1: buck if you want to put a video on right,

710
00:37:09,760 --> 00:37:12,879
Speaker 1: you can actually buy media with a dollar now, where

711
00:37:12,920 --> 00:37:15,400
Speaker 1: before it was like a million, now it's a dollar.

712
00:37:15,760 --> 00:37:19,040
Speaker 1: The problem is there's not enough creative executives to make

713
00:37:19,120 --> 00:37:23,279
Speaker 1: great work anymore. It became the costco right where it

714
00:37:23,320 --> 00:37:26,040
Speaker 1: was once a boutique. You can't fill up the costco

715
00:37:26,120 --> 00:37:29,600
Speaker 1: with talent. You can't just manufacture talent. I've not heard

716
00:37:29,640 --> 00:37:33,000
Speaker 1: that before, but you're right, it fits the facts. I

717
00:37:33,040 --> 00:37:36,080
Speaker 1: think it's a problem with clients too, because how much

718
00:37:36,080 --> 00:37:39,319
Speaker 1: bad work have you seen or heard? A lot? A lot,

719
00:37:40,719 --> 00:37:44,480
Speaker 1: sadly sadly. So let's jump to TV for a minute.

720
00:37:45,080 --> 00:37:47,840
Speaker 1: TV reach has dropped a lot for TV with ads

721
00:37:47,840 --> 00:37:51,120
Speaker 1: in it. How is that shifting media plans? TV used

722
00:37:51,160 --> 00:37:53,359
Speaker 1: to be the reach medium foundation of every body. Then

723
00:37:53,360 --> 00:37:56,000
Speaker 1: we'll add all the other stuff to it. Is that changing?

724
00:37:56,800 --> 00:37:59,600
Speaker 1: I think it's definitely changed, And think there's a tremendous

725
00:37:59,640 --> 00:38:04,120
Speaker 1: amount out of denial from the older folks in the business.

726
00:38:04,640 --> 00:38:08,200
Speaker 1: You know, when I first started, the industry itself was

727
00:38:08,239 --> 00:38:11,520
Speaker 1: probably spending upwards of their budget to open a film

728
00:38:11,560 --> 00:38:15,439
Speaker 1: on television. Last I looked, I think that it has

729
00:38:15,480 --> 00:38:17,759
Speaker 1: fallen to probably fifty or sixty. I think when the

730
00:38:17,760 --> 00:38:20,680
Speaker 1: biggest problems is that, you know, the cost of television

731
00:38:20,719 --> 00:38:24,040
Speaker 1: has gotten so high as well, some of it is fantastic.

732
00:38:24,320 --> 00:38:27,560
Speaker 1: There's still some really great television out there. People still

733
00:38:27,600 --> 00:38:30,799
Speaker 1: love TV. They be careful. I'm not saying people don't

734
00:38:30,840 --> 00:38:33,600
Speaker 1: love TV, because they do. They reaches down and it

735
00:38:33,719 --> 00:38:35,640
Speaker 1: is so I think that it has a hard time

736
00:38:35,680 --> 00:38:38,560
Speaker 1: finding its space. Also, they're losing talent, just how like

737
00:38:38,600 --> 00:38:41,319
Speaker 1: ad agencies lost talent and I've watched them lose some

738
00:38:41,360 --> 00:38:44,360
Speaker 1: great talent to go work at a Facebook. They go

739
00:38:44,440 --> 00:38:46,760
Speaker 1: work at a Google, to go work at a Twitter,

740
00:38:47,000 --> 00:38:50,439
Speaker 1: because it's a brighter, shinier object. Here we are doing

741
00:38:50,440 --> 00:38:53,480
Speaker 1: a podcast. Some people are saying, instead of doing the

742
00:38:53,480 --> 00:38:55,839
Speaker 1: TV or instead of putting the video to it, I'll

743
00:38:55,880 --> 00:38:58,120
Speaker 1: just do the story without the video. We just did

744
00:38:58,200 --> 00:39:00,920
Speaker 1: a podcast with ron Berg and d He's back and

745
00:39:01,000 --> 00:39:03,920
Speaker 1: doing by the Way extraordinarily well. But it sort of

746
00:39:03,960 --> 00:39:06,920
Speaker 1: feels like that's like the next version of the movie

747
00:39:07,480 --> 00:39:10,280
Speaker 1: is there in the podcast, and that I've got everything

748
00:39:10,280 --> 00:39:12,800
Speaker 1: in my head. I need to make that story come alive.

749
00:39:13,680 --> 00:39:15,759
Speaker 1: So what did you learn from all of that that

750
00:39:15,800 --> 00:39:18,399
Speaker 1: you brought to what you are today? Oh my god,

751
00:39:18,440 --> 00:39:20,719
Speaker 1: I've learned everything. I don't even know where to begin

752
00:39:20,800 --> 00:39:24,400
Speaker 1: with that, Bob, that's such a magical question. I have

753
00:39:24,600 --> 00:39:27,680
Speaker 1: to tell you. The best learning I've had were in

754
00:39:27,680 --> 00:39:31,160
Speaker 1: the worst times, right, because it's contrasts, so it's like

755
00:39:31,200 --> 00:39:33,560
Speaker 1: all the shitty stuff that happened is when I really learned.

756
00:39:33,880 --> 00:39:36,360
Speaker 1: All the dumb things I've done is when I've learned.

757
00:39:36,719 --> 00:39:39,040
Speaker 1: The fun parts are fun, but not the most learning,

758
00:39:39,360 --> 00:39:42,040
Speaker 1: and that the best education that you get. I think

759
00:39:42,080 --> 00:39:44,279
Speaker 1: probably the one thing that I really have taken away

760
00:39:44,320 --> 00:39:47,600
Speaker 1: from all of it is I am fearless to do

761
00:39:48,280 --> 00:39:52,120
Speaker 1: anything and everything. So what's the dumbest thing you've done? God,

762
00:39:52,120 --> 00:39:54,279
Speaker 1: I've done something. Okay, like one dumb thing, and tell

763
00:39:54,360 --> 00:39:56,200
Speaker 1: us what you learned from the dumb thing. Dumb things

764
00:39:56,200 --> 00:39:59,000
Speaker 1: that I've done. Um, I bought a house without looking

765
00:39:59,040 --> 00:40:01,319
Speaker 1: at it. That's pretty dumb. It was dumb. Oh. The

766
00:40:01,360 --> 00:40:05,239
Speaker 1: best dumb one, though, was when I traveled for HP.

767
00:40:05,920 --> 00:40:08,800
Speaker 1: I started taking ambient so I was traveling all around

768
00:40:08,800 --> 00:40:11,400
Speaker 1: the world and I couldn't get off of the stuff.

769
00:40:11,840 --> 00:40:14,680
Speaker 1: One night, Susanne and I went to go see a movie.

770
00:40:14,760 --> 00:40:16,880
Speaker 1: We went to go see Where the Millers. They have

771
00:40:16,960 --> 00:40:21,760
Speaker 1: an RV and They're like drug mules across Mexico. And

772
00:40:21,880 --> 00:40:24,000
Speaker 1: the next morning I wake up and I have a

773
00:40:24,040 --> 00:40:27,759
Speaker 1: notification on my phone this is congratulations you've won, And

774
00:40:27,840 --> 00:40:32,400
Speaker 1: the text was from eBay I apparently and an ambient.

775
00:40:32,480 --> 00:40:35,759
Speaker 1: Hayes bought a thirty five ft nineteen eight six airstream.

776
00:40:36,520 --> 00:40:39,319
Speaker 1: You still have it? Uh No, I redid it, but

777
00:40:39,440 --> 00:40:42,719
Speaker 1: actually donated it to victims of the first blaze in

778
00:40:42,760 --> 00:40:45,640
Speaker 1: California up north because there were teachers that were sleeping

779
00:40:45,640 --> 00:40:48,040
Speaker 1: in sporting good stores. Now that makes me look like

780
00:40:48,040 --> 00:40:52,640
Speaker 1: I'm a saint. That was pretty dumb and awesome. She

781
00:40:52,719 --> 00:40:53,879
Speaker 1: got an r V. It's got to be the first

782
00:40:53,960 --> 00:40:55,560
Speaker 1: RV you had, right, it was the first RV and

783
00:40:55,600 --> 00:40:58,080
Speaker 1: it was amazing, And I stopped taking Ambian That a

784
00:40:58,160 --> 00:41:01,960
Speaker 1: two fur jumping again. Your quota is saying being a

785
00:41:02,040 --> 00:41:05,320
Speaker 1: lesbian in the professional world as a gift, not a handicap.

786
00:41:05,840 --> 00:41:08,120
Speaker 1: You've been very honest and very open about who and

787
00:41:08,280 --> 00:41:11,200
Speaker 1: what you are. But what's been the reaction to the

788
00:41:11,200 --> 00:41:15,120
Speaker 1: revelation of your psychic abilities. Yeah, it was way easier

789
00:41:15,120 --> 00:41:16,960
Speaker 1: to come out as a lesbian than it was a

790
00:41:17,000 --> 00:41:21,520
Speaker 1: crazy ghost photographer, slatch, psychic ish human. I will tell you.

791
00:41:21,560 --> 00:41:24,040
Speaker 1: I am so lucky to be in the industry that

792
00:41:24,080 --> 00:41:27,200
Speaker 1: I am, because we are the land of misfit toys.

793
00:41:27,400 --> 00:41:29,239
Speaker 1: We just are. You go talk to any of them,

794
00:41:29,280 --> 00:41:31,799
Speaker 1: and none of them, by the way, were like the

795
00:41:31,840 --> 00:41:36,160
Speaker 1: homecoming queen and king and the you know, quarterback of

796
00:41:36,200 --> 00:41:38,719
Speaker 1: the football team, and none of them were. We were

797
00:41:38,760 --> 00:41:41,800
Speaker 1: all weird. The best place for me to be weird

798
00:41:42,160 --> 00:41:46,359
Speaker 1: was in this industry. So let's talk about the ghost photographer.

799
00:41:47,000 --> 00:41:50,600
Speaker 1: What's the journey on the recognition from you had these abilities?

800
00:41:50,920 --> 00:41:53,640
Speaker 1: When did you feel like you could tell somebody else? Uh,

801
00:41:53,680 --> 00:41:55,799
Speaker 1: this is going on in my head and I think

802
00:41:55,840 --> 00:41:58,760
Speaker 1: I have these abilities. Mine was a journey through grief.

803
00:41:59,280 --> 00:42:02,840
Speaker 1: I lost my I'm to Alzheimer's. I was absolutely positively devastated.

804
00:42:03,160 --> 00:42:05,360
Speaker 1: Five months later lost one of our closest friends to

805
00:42:05,400 --> 00:42:08,760
Speaker 1: a car accident. It just compounded grief. I am normally

806
00:42:08,800 --> 00:42:11,279
Speaker 1: a pretty funny human being. I was not funny. I

807
00:42:11,360 --> 00:42:15,440
Speaker 1: was sad. Met this amazing woman named Brenda Via, an

808
00:42:15,520 --> 00:42:19,239
Speaker 1: amazing psychic who did a group reading for us. That

809
00:42:19,280 --> 00:42:22,279
Speaker 1: moment was very pivotal change in my life. Shortly after

810
00:42:22,440 --> 00:42:26,240
Speaker 1: meeting Brenda, I started seeing ghosts and photographs. That was weird.

811
00:42:26,560 --> 00:42:28,880
Speaker 1: I'm gonna say it. It was weird, and it was

812
00:42:28,880 --> 00:42:31,720
Speaker 1: in my backyard. And I got nine thousand of them,

813
00:42:31,760 --> 00:42:34,360
Speaker 1: if not more. Now I think I've lost count. So

814
00:42:34,400 --> 00:42:37,120
Speaker 1: why did they come to your house? At first, I

815
00:42:37,160 --> 00:42:39,480
Speaker 1: thought that I had opened a ghost portal, because we remember,

816
00:42:39,520 --> 00:42:41,480
Speaker 1: movies have kind of ruined everything for all of us.

817
00:42:41,680 --> 00:42:44,839
Speaker 1: We made Poltergeist, and so I'm like, a crap, I'm

818
00:42:44,840 --> 00:42:47,600
Speaker 1: gonna be like carry Anne, I'm dead or Caroline or

819
00:42:47,640 --> 00:42:49,400
Speaker 1: whatever the hell her name was. I thought like it

820
00:42:49,440 --> 00:42:51,680
Speaker 1: was going to suck our house down into I don't know,

821
00:42:51,719 --> 00:42:55,520
Speaker 1: the great hell abyss. And then I later started to

822
00:42:55,640 --> 00:42:59,040
Speaker 1: learn that they're just everywhere. Were some of them coming

823
00:42:59,040 --> 00:43:02,279
Speaker 1: to visit me? I think, so, how could you not, like,

824
00:43:02,360 --> 00:43:04,560
Speaker 1: if you're a ghost or I'm going to say the

825
00:43:04,600 --> 00:43:06,640
Speaker 1: A word, even an alien. I mean, I'm sorry, people,

826
00:43:06,640 --> 00:43:08,560
Speaker 1: but you read the book. It's it's in there. If

827
00:43:08,600 --> 00:43:10,960
Speaker 1: you're one of them, and you're like, hey, this lesbian

828
00:43:11,000 --> 00:43:13,360
Speaker 1: in this corner over here on Sherman Oaks taking pictures

829
00:43:13,360 --> 00:43:15,279
Speaker 1: of ghost I want to go be in one. They

830
00:43:15,360 --> 00:43:17,640
Speaker 1: must have a club or like at least like a

831
00:43:17,640 --> 00:43:20,040
Speaker 1: phone tree to let him know what's going on. And

832
00:43:20,120 --> 00:43:22,040
Speaker 1: I think I was like the top of that phone tree.

833
00:43:22,360 --> 00:43:24,800
Speaker 1: I see ghosts everywhere. Do I see one in here? No?

834
00:43:24,920 --> 00:43:26,360
Speaker 1: I don't. Just for the record, I know that's what

835
00:43:26,400 --> 00:43:28,759
Speaker 1: you wanted to ask me, right, I was gonna ask

836
00:43:28,840 --> 00:43:32,439
Speaker 1: with spirit animals. Spirit animals? You love that You told

837
00:43:32,440 --> 00:43:34,600
Speaker 1: me I would say it was an eagle? Was my

838
00:43:34,640 --> 00:43:38,040
Speaker 1: spirit animal? You called my kids, you called everyone in

839
00:43:38,080 --> 00:43:41,719
Speaker 1: my family, and when I looked it up, well, yep

840
00:43:42,080 --> 00:43:44,719
Speaker 1: they are. It's a weird thing, right they are. So

841
00:43:44,760 --> 00:43:46,480
Speaker 1: please ask me, Julie, how do you see this? How

842
00:43:46,480 --> 00:43:48,799
Speaker 1: does this happen from you? Pretend I said that. I'm

843
00:43:48,800 --> 00:43:50,439
Speaker 1: going to pretend you said that, because it's a really

844
00:43:50,440 --> 00:43:52,600
Speaker 1: strange thing. I get asked a lot about you know,

845
00:43:52,640 --> 00:43:55,160
Speaker 1: how do you see the other side? And what I see?

846
00:43:55,360 --> 00:43:58,200
Speaker 1: It's another dimension with the spirit animals, especially sometimes I

847
00:43:58,200 --> 00:44:00,399
Speaker 1: can see with my eyes open. It's a best when

848
00:44:00,440 --> 00:44:03,520
Speaker 1: I close because they always show up over your head.

849
00:44:04,200 --> 00:44:07,840
Speaker 1: And sometimes they'll show up like I had somebody with

850
00:44:07,960 --> 00:44:09,720
Speaker 1: my spirit animal, and I'm like, will hold on a second,

851
00:44:09,719 --> 00:44:11,279
Speaker 1: I see a fin. I can't tell if it's a

852
00:44:11,280 --> 00:44:13,680
Speaker 1: whale fin or a dolphin fin. Hold on a second.

853
00:44:13,719 --> 00:44:16,000
Speaker 1: They need to show me. And it happened to have

854
00:44:16,080 --> 00:44:18,560
Speaker 1: been a dolphin at that time. So I see them

855
00:44:18,600 --> 00:44:22,080
Speaker 1: above people's head and they're not stagnant, they're moving. Having

856
00:44:22,120 --> 00:44:25,839
Speaker 1: disabilities it changed your own sense of life, what life is,

857
00:44:25,880 --> 00:44:29,480
Speaker 1: and mortality and life and death and what's on the

858
00:44:29,520 --> 00:44:33,720
Speaker 1: other side. It has changed everything in all the best ways.

859
00:44:34,360 --> 00:44:39,040
Speaker 1: I think it really plays into the fearlessness, right It's like,

860
00:44:39,080 --> 00:44:41,560
Speaker 1: whatever happens, I'm going to be okay. But I think

861
00:44:41,560 --> 00:44:45,480
Speaker 1: what has changed the most for me is now that

862
00:44:45,560 --> 00:44:49,320
Speaker 1: I have these abilities, it's what I do with them,

863
00:44:49,480 --> 00:44:53,160
Speaker 1: how I can help and serve other people. People have

864
00:44:53,200 --> 00:44:56,040
Speaker 1: found me from my book. A couple have become friends.

865
00:44:56,040 --> 00:44:59,040
Speaker 1: One has become a very close friend. And I just

866
00:44:59,080 --> 00:45:02,600
Speaker 1: recently spoke to her great grandmother. I said, she had

867
00:45:02,600 --> 00:45:05,360
Speaker 1: a nickname for you, but it's not a derivative of

868
00:45:05,400 --> 00:45:08,560
Speaker 1: your name. It's like, there's a nickname she keeps showing

869
00:45:08,600 --> 00:45:10,359
Speaker 1: me my dog. So all I can tell you is

870
00:45:10,400 --> 00:45:12,400
Speaker 1: that maybe it was Pudding and she was That's what

871
00:45:12,440 --> 00:45:14,920
Speaker 1: she called me, was Pudding, because that's my nickname for

872
00:45:15,000 --> 00:45:17,000
Speaker 1: my dog. Your great grandmother was smart enough to show

873
00:45:17,000 --> 00:45:19,840
Speaker 1: me my dog to come up with your nickname that

874
00:45:19,840 --> 00:45:24,800
Speaker 1: she called you through your life isn't that crazy, that awesome,

875
00:45:24,920 --> 00:45:27,840
Speaker 1: that just happened two nights ago, and for her to

876
00:45:27,880 --> 00:45:30,839
Speaker 1: feel more connected is the greatest gift I think that

877
00:45:30,920 --> 00:45:33,480
Speaker 1: I could ever give. If I am able to connect

878
00:45:33,520 --> 00:45:36,919
Speaker 1: with someone's loved one who has passed, it's very woo woo.

879
00:45:37,000 --> 00:45:41,800
Speaker 1: We remember, this is math and magic. We believe in

880
00:45:41,840 --> 00:45:45,520
Speaker 1: them both. You do both. So let's go to the

881
00:45:45,640 --> 00:45:51,120
Speaker 1: last questions. We go to beside you, who's the greatest mathematician?

882
00:45:51,200 --> 00:45:55,320
Speaker 1: You know, there is a guy named Peter Masanagro who

883
00:45:55,520 --> 00:45:59,880
Speaker 1: is the head of operam Are Marketing Science agency at

884
00:46:00,200 --> 00:46:03,120
Speaker 1: who I believe is the best mathematician that I have

885
00:46:03,200 --> 00:46:06,960
Speaker 1: ever met. He is Steve Jobs, Like, Wow, what is

886
00:46:07,000 --> 00:46:11,640
Speaker 1: the greatest magician? Who is it? Oprah Winfrey? I think

887
00:46:11,760 --> 00:46:15,760
Speaker 1: that she has turned her own life and other lives

888
00:46:15,840 --> 00:46:18,719
Speaker 1: into gold because I think storytellers are the ones who

889
00:46:18,719 --> 00:46:21,360
Speaker 1: actually run the world. She is a brilliant storyteller. And

890
00:46:21,360 --> 00:46:23,200
Speaker 1: you're right, that is a magician. By the way. We

891
00:46:23,200 --> 00:46:25,359
Speaker 1: could go on forever, but I'm gonna tell people read

892
00:46:25,400 --> 00:46:28,799
Speaker 1: your book The Ghost Photographer. I had two of them

893
00:46:28,800 --> 00:46:31,120
Speaker 1: on my desk and I can't tell you how many

894
00:46:31,160 --> 00:46:33,840
Speaker 1: people walked in my office sort of picked up the

895
00:46:33,880 --> 00:46:36,480
Speaker 1: book and thumbed through it. A couple of borrowed the book.

896
00:46:36,520 --> 00:46:38,120
Speaker 1: A couple of them said, I'm getting my own book,

897
00:46:38,400 --> 00:46:40,600
Speaker 1: so whatever it is, there's a great interest in it.

898
00:46:40,640 --> 00:46:43,279
Speaker 1: And everybody enjoyed the book, including me. Do you need

899
00:46:43,400 --> 00:46:46,760
Speaker 1: some more? Always need more? Three books is like free food,

900
00:46:47,000 --> 00:46:51,040
Speaker 1: right and you had gally copy number one brilliant today.

901
00:46:51,160 --> 00:46:55,200
Speaker 1: Thank you for joining us. Thank you. Here's three lessons

902
00:46:55,239 --> 00:46:58,280
Speaker 1: I take away from this episode with Julie Reager. One

903
00:46:58,600 --> 00:47:02,080
Speaker 1: data isn't just looking at numbers. Look at the language

904
00:47:02,120 --> 00:47:05,279
Speaker 1: people use around your product and the ways they communicate

905
00:47:05,360 --> 00:47:09,200
Speaker 1: it for deeper insight. Two data doesn't have to be

906
00:47:09,239 --> 00:47:12,600
Speaker 1: restrictive for creatives. If you frame it correctly, it should

907
00:47:12,600 --> 00:47:16,200
Speaker 1: open you up to new potential ideas and spaces that

908
00:47:16,280 --> 00:47:20,960
Speaker 1: aren't being explored. Three. Challenge yourself to look for the stories.

909
00:47:21,360 --> 00:47:24,040
Speaker 1: It's how Julie fell in love with Dada, looking at

910
00:47:24,040 --> 00:47:28,040
Speaker 1: our customers through their behaviors. Julie Reager's book is called

911
00:47:28,040 --> 00:47:32,200
Speaker 1: The Ghost Photographer, Hollywood Executive's true story of discovering the

912
00:47:32,239 --> 00:47:35,360
Speaker 1: real world of make believe. It's out from Simon and Schuster.

913
00:47:35,400 --> 00:47:45,240
Speaker 1: Now check it out. That's it for today's episode. Thanks

914
00:47:45,280 --> 00:47:47,840
Speaker 1: so much for listening to Math and Magic, a production

915
00:47:47,840 --> 00:47:50,920
Speaker 1: of I Heart Radio. The show is hosted by Bob Pittman.

916
00:47:51,360 --> 00:47:54,000
Speaker 1: Special thanks to Sue Schillinger for booking and wrangling our

917
00:47:54,080 --> 00:47:57,400
Speaker 1: wonderful talent, which is no small feat. Nikki Etre for

918
00:47:57,440 --> 00:48:00,480
Speaker 1: pulling research bill plaques, and Michael as Are for their

919
00:48:00,520 --> 00:48:04,440
Speaker 1: recording help, our editor, Ryan Murdoch, and of course Gayle Raoul,

920
00:48:04,640 --> 00:48:07,880
Speaker 1: Eric Angel, Noel Mango and everyone who helped bring this

921
00:48:07,920 --> 00:48:13,800
Speaker 1: show to your ears. Until next time, m