Speaker 1: Pushkin. For roughly five thousand years, people call themselves doctors
Speaker 1: and pretended to know all sorts of things that they
Speaker 1: didn't know, and were as likely to kill you as
Speaker 1: to cure you. These doctors existed because sick people desperately
Speaker 1: wanted to believe in them. Coaching feels the same way
Speaker 1: to me. For decades, people just sort of hoped that
Speaker 1: if a man was hollering at them, he must be
Speaker 1: helping them to win. Maybe he was sometimes, but that's
Speaker 1: not my point. My point is that even if coaches
Speaker 1: have no effect on performance, even if they're doing more
Speaker 1: harm than good, we might still insist on having them
Speaker 1: because we need someone on our side to believe in.
Speaker 1: But coaching's changing the same way medicine changed one hundred
Speaker 1: years ago. Coaches are discovering science, and science is discovering them.
Speaker 1: I'm Michael Lewis and This is Against the Rules, a
Speaker 1: show about various authority figures in American life. This season
Speaker 1: is about the rise of coaches, and this episode is
Speaker 1: about data and pitching. A while back in two thousand
Speaker 1: and three, I published a book called Moneyball. It's about
Speaker 1: how the Oakland A's baseball team had used data analysis
Speaker 1: to get an edge on everyone else. They were a
Speaker 1: poorly funded team in a small market. They had no
Speaker 1: money to spend on players, but their new and better
Speaker 1: statistics enable them to value baseball players more accurately, so
Speaker 1: they could sell the players that were overvalued and trade
Speaker 1: for the ones that were undervalued. I remember at the
Speaker 1: time being shocked at the notion that baseball players could
Speaker 1: be misvalued. I mean, baseball players have been doing the
Speaker 1: same job for a century, out in the open, in
Speaker 1: front of millions of people. But suddenly, all over a
Speaker 1: thing that had been done a certain way forever was
Speaker 1: now being done a totally different way. Everyone in baseball
Speaker 1: started using data and getting all sorts of insights from it,
Speaker 1: and the insights led not just to better valuations of
Speaker 1: baseball players, it eventually led to a new kind of coaching.
Speaker 1: In the past, it used to be that many coaching
Speaker 1: positions were almost a sine cure. That's Ben Lindberg, co
Speaker 1: author of a book called The MVP Machine. It's about
Speaker 1: a revolution in how the world's best baseball players get coached.
Speaker 1: It was the coaches who were the manager's palace, his
Speaker 1: drinking buddies would become the coaches, and there wasn't that
Speaker 1: much coaching going on at the major league level. It
Speaker 1: was sort of reinforcing lessons that had already been taught
Speaker 1: and keeping guys in line, but there wasn't that much
Speaker 1: expectation that coaches would improve players once they got to
Speaker 1: that level. But that was about to change big time.
Speaker 1: Ben was part of a huge and growing crowd of
Speaker 1: data geeks outside of baseball who spent lots and lots
Speaker 1: of time analyzing players and building models to try to
Speaker 1: predict their performance. As sophisticated as the statistical projection models are,
Speaker 1: they'll only really look at the player's past performance and
Speaker 1: his age and maybe some comparable players from the past,
Speaker 1: and they'll spit out a projection that say, well, he
Speaker 1: was this good in the past few years, and we'll
Speaker 1: adjust for the ballpark, and here's how old he is,
Speaker 1: and so here's our median projection for him, and so
Speaker 1: all of a sudden, there players who are just busting
Speaker 1: out of those projections exactly, And a projection system would
Speaker 1: never forecast that. It might say that someone's going to
Speaker 1: get a bit better, a bit worse, but typically it
Speaker 1: won't say that someone is going to do something that's
Speaker 1: completely out of line with their past performance. In other words,
Speaker 1: teams had gotten really good, or at least a lot
Speaker 1: better at evaluating the potential of all their players, but
Speaker 1: the players were still playing better than expected, so much
Speaker 1: better that the analysts were a bit suspicious. But the
Speaker 1: last time you saw this was with the steroids era,
Speaker 1: that all of a sudden, people players were performing in
Speaker 1: ways that the projection models would never have guessed. And
Speaker 1: you're so you're kind of seeing it, but without an
Speaker 1: explanation as obvious as steroids exactly. Yeah, then started looking
Speaker 1: into what these players were doing. The ones who were
Speaker 1: dramatically exceeding the analysts expectations, the overperformers, all had something
Speaker 1: in common. Coaches who use new technology. One of the
Speaker 1: really big innovations has been the high speed camera. So
Speaker 1: a company called Edgrotronic, which developed these cameras for scientific purposes,
Speaker 1: has found that much of its business has come from
Speaker 1: baseball teams because baseball teams have found that if you
Speaker 1: train these high speed cameras on players, you can perceive
Speaker 1: things about players movement that they didn't know about themselves.
Speaker 1: And the coaches using these cameras were very different from
Speaker 1: the old school baseball coaches, the Sinecure guys. For a start,
Speaker 1: the new coaches weren't former big league players. In some cases,
Speaker 1: they didn't even know any big league players. My name
Speaker 1: is Kyle Bodie. Like this guy, I was twenty two
Speaker 1: years old started coaching little league and I realized quickly
Speaker 1: that I just didn't know anything about coaching. Kyle had
Speaker 1: just moved from Ohio to Seattle, where he landed a
Speaker 1: part time job as a little league coach. Yep, that's
Speaker 1: how he started in life as a little league baseball coach.
Speaker 1: Ed played some my father was a coach, but I
Speaker 1: figured I owed it to the kids to learn just
Speaker 1: a little bit more about keeping their arms healthy. And
Speaker 1: he had some questions like how many pitches should a
Speaker 1: kid be allowed to throw? And what was the best
Speaker 1: way to throw him? I mean, I was once a pitcher,
Speaker 1: and I spent half my life with my arm in
Speaker 1: an ice bucket. To this day, I can't sleep on
Speaker 1: my right shoulder throwing a ball overhand. It might look
Speaker 1: like a natural and healthy thing to do, but it's not.
Speaker 1: Kyle body looked around for research on the subject, but
Speaker 1: unfortunately it was all very nonspecific kind of very academic research,
Speaker 1: and then the training programs and the coaching programs that
Speaker 1: were out there were very bland, not based on any
Speaker 1: sort of evidence. It really shocked me, So Kyle started
Speaker 1: to do research on his own. Then he got a
Speaker 1: promotion from Little league to the freshman team at a
Speaker 1: Seattle high school. But he found himself at war with
Speaker 1: the JV and varsity coaches at the high school. They
Speaker 1: were coaching the old fashioned way, telling players what to do,
Speaker 1: hollering at them when they screwed up, praising them when
Speaker 1: they didn't. Actively coaching athletes just typically makes them worse.
Speaker 1: Just intervention is typically one of the worst things you
Speaker 1: can do. What are the points of friction with the
Speaker 1: old coaching model, Like, what specifically kind of things would
Speaker 1: you do that were heretical. So informing the athlete that, like,
Speaker 1: whatever they're doing is not good enough and then just
Speaker 1: seeing how they change over time and how they will
Speaker 1: self organize was a real heretical idea, right because most
Speaker 1: coaches think that they have a lot to give to
Speaker 1: the athlete, and my view on it still is today
Speaker 1: is that they're good enough, like we just need to
Speaker 1: give them the right direction and let them figure it
Speaker 1: out for the most part. You ever read a book
Speaker 1: called The Inner Game of Tennis? I have, Yeah, it's
Speaker 1: one of my favorites. Anyway, In his first year coaching,
Speaker 1: Kyle's freshman team won as many games as they lost,
Speaker 1: which was actually pretty great. That season, the school's varsity
Speaker 1: and JV teams were losing most of their games. But
Speaker 1: at the end of the year, the head coach fired
Speaker 1: Kyle because the other coaches hated his methods. They thought
Speaker 1: he had no clue how to coach. That didn't stop Kyle.
Speaker 1: He doubled down on his approach, and he wound up
Speaker 1: building what amounted to a baseball bionic manfactory drive line Baseball,
Speaker 1: he called it. So describe to me this laboratory you build.
Speaker 1: In its current incarnation, it's about fifteen high speed motion
Speaker 1: tracking cameras. There's force plates in there to measure ground
Speaker 1: reaction forces. Attracts every movement two hundred forty times per second,
Speaker 1: and it's submillimeter accurate, so every movement can be tracked
Speaker 1: down to at least one millimeter of accuracy, and usually
Speaker 1: much better. Let's go one hundred point four ninety three
Speaker 1: point seven the high speed cameras allow Kyle to measure
Speaker 1: the speed of a pitcher's arm, among other things, the
Speaker 1: faster a pitcher's arm, the faster the ball comes out
Speaker 1: of it in theory one hundred three point zero. In practice,
Speaker 1: not all pictures are able to translate their arm speed
Speaker 1: into ball speed. Two guys with the exact same arm
Speaker 1: speeds might throw very different fastballs. If someone's arm speed
Speaker 1: is extremely high and the ball comes out at like
Speaker 1: a lower predictive velocity based on like a regression algorithm,
Speaker 1: that we know that there's some inefficiencies there that we
Speaker 1: should be able to easily clean up. That is, you
Speaker 1: could identify people with the god given talent to throw
Speaker 1: a baseball because you had new insight into where that
Speaker 1: talent came from. So when that happens, are you thinking,
Speaker 1: there's this pool of players out there who have high
Speaker 1: arm speed, low velocity that we can just fix. That's
Speaker 1: all that I think about pretty much every day. And
Speaker 1: could you like drive around with a little truck and
Speaker 1: put people in the back of your truck and test
Speaker 1: their arm speed? Is that what you would do? Yeah,
Speaker 1: that's actually funny. We almost butt an RV to do
Speaker 1: exactly that to drive around the country with our lab.
Speaker 1: It turned out he didn't need to drive around looking
Speaker 1: for people with this weird arm talent. They found him.
Speaker 1: I real that I needed to make a change going
Speaker 1: into the season. I was helping out my dad's team
Speaker 1: and trying to make money in every way. That's Matt
Speaker 1: Boyd Back in twenty seventeen. He was an unknown minor
Speaker 1: league pitcher coming off a terrible season. He had a
Speaker 1: below average fastball around eighty nine miles an hour. Oh,
Speaker 1: and he had an arm injury. The way he was going,
Speaker 1: he's about to be spending a lot more time with
Speaker 1: his mom and dad. Over to the Christmas break, one
Speaker 1: of my dad's players came back and he was ninety
Speaker 1: two guy in high school and he was at Oregon State,
Speaker 1: and after the fall he was ninety eight miles per hour. Oh,
Speaker 1: I went, whoa, whoa, whoa, I go, what were you
Speaker 1: doing down there? He's like, I did the drive Line program.
Speaker 1: Drive Line. That's Kyle Bodie's lab. So at that point,
Speaker 1: Kyle would have been kind of a local secret. So
Speaker 1: someone all of a sudden has this kind of miraculous
Speaker 1: jump in velocity. You hear about it, and you go see,
Speaker 1: you call Kyle had had you ever heard of him
Speaker 1: at that point? No? I hadn't heard of him. Matt
Speaker 1: goes to seek Kyle in his Bionic manfactory and here
Speaker 1: we have cameras, great our guns. I go up a
Speaker 1: little stairwell and here's you know, a little eight foot
Speaker 1: wide by ten foot high probably pitching lane tunnel created
Speaker 1: with all this technology in there. And there's Kyle behind
Speaker 1: a computer and he runs me through the program. At
Speaker 1: that point, had you ever seen the technology that was there? No?
Speaker 1: And I honestly I didn't. I couldn't even told you
Speaker 1: right today what I saw in there. I couldn't even
Speaker 1: told you what was going on a bunch of fucket
Speaker 1: lids for some for balldrills. It looks like this, a
Speaker 1: lot of gizmos, a lot of gizmos, a lot of wires,
Speaker 1: a lot of interesting looking baseballs, a lot of lines
Speaker 1: on a pitching mound and stuff. And you know, Kyle
Speaker 1: explained to me what the concept of what he does,
Speaker 1: and we talked about it, and then we just started
Speaker 1: the program. Kyle put Matt Boy through a bunch of tests.
Speaker 1: The big one was to test his arm speed, but
Speaker 1: he never used the phrase arm speed. Kyle actually never
Speaker 1: told Matt what he was testing for. He in our
Speaker 1: lab tested higher than pretty much everyone and still almost
Speaker 1: everyone to this day. He's just an excellent athlete. And
Speaker 1: yet the ball velocity wasn't where it needed to be
Speaker 1: or where it predicted it should be. So what was
Speaker 1: the inefficiency? Like? What was what was he doing that
Speaker 1: caused the ball to come out more slowly than what
Speaker 1: you would have predicted? Right, it's really hard. We don't
Speaker 1: know the rude cause yet. That's that's the actual interesting
Speaker 1: thing is we're still studying, like why this happens. At
Speaker 1: the lab, Kyle hands Matt these really heavy balls to throw.
Speaker 1: He's found that when people throw a heavy ball, their
Speaker 1: body naturally finds the most efficient way to do it
Speaker 1: because it's so painful and uncomfortable to throw it inefficiently.
Speaker 1: Matt basically moves into the Bionic manfactory and throws heavy
Speaker 1: balls the entire off season. Then rejoins his minor league
Speaker 1: team and you know, I get down there, I tell
Speaker 1: him I'm I'm full progression off the mount and they're like, okay,
Speaker 1: well let's see it. My first bullpen I'm I'm ninety
Speaker 1: two to ninety five. Oh, and I think everyone's going,
Speaker 1: what the heck happened? You know, and even I am,
Speaker 1: I'm going, man, I'm throwing the baseball up in the zone. Now,
Speaker 1: this is amazing. This is so cool. Like when all
Speaker 1: of a sudden, you've got this new weapon. Yeah, when
Speaker 1: you got this fastball that's coming out of your hand
Speaker 1: three or four miles an hour faster than it usually does.
Speaker 1: What do you notice in what happened and how hitters
Speaker 1: respond and how the effectiveness hitters have against you. I
Speaker 1: remember going in to Double A that season and I did.
Speaker 1: We have a catcher named Jack Murphy who was about
Speaker 1: four or five years older than me, and he went
Speaker 1: up and told me, he goes, Maddie, you have a
Speaker 1: new fastball. You need to pitch off it. I remember,
Speaker 1: I was kind of scared. I was like, what, I've
Speaker 1: never done that in my life. I'm always fastball, change up.
Speaker 1: And then I mixed my curveball, and you know, but
Speaker 1: he challenged me, and all of a sudden, I'm like
Speaker 1: striking guys out on three fastballs in Double A, and
Speaker 1: I'm going like, is it this easy? Like this is
Speaker 1: all it took. By the end of that season, Matt
Speaker 1: Boyd had been called up to the major leagues to
Speaker 1: the Detroit Tigers to be a starting pitcher. Paints in
Speaker 1: and then paints away both fastballs. Want to pitch to
Speaker 1: Bird another strikeout for Matthew boy to start the form.
Speaker 1: They now pay him five point three million dollars a
Speaker 1: year and feel like they're getting a deal. And Kyle Body, well,
Speaker 1: now he has a new job too. I'm the president
Speaker 1: and founder of drive Land Baseball and the director of
Speaker 1: Pitching Initiatives of the Cincinnati Reds. Somebody asks a question,
Speaker 1: how do you throw a ball faster? They gathered data.
Speaker 1: They measure everything that the human body does when it
Speaker 1: throws a ball. They test theories and find answers rooted
Speaker 1: in science. All of a sudden, there's a new way
Speaker 1: to coach, and it's getting adopted in sports. But not
Speaker 1: just in sports, because there are people all over who
Speaker 1: don't know why they're good at something or how to
Speaker 1: get better. About five years ago, I was CEO and
Speaker 1: at a software company and we're growing pretty quickly. This
Speaker 1: is a meet bendoff. Five years ago, he was just
Speaker 1: another Silicon Valley entrepreneur trying to get his product out
Speaker 1: the door by using salespeople. But I was puzzled why
Speaker 1: some of our people were more successful than others. And
Speaker 1: every time we wanted to understand why, we had to
Speaker 1: go and interview people and see what they think. And
Speaker 1: he didn't want just a collection of stories. He wanted data.
Speaker 1: Why did some sales pitches work while others didn't? Think
Speaker 1: about like football right or baseball right? If the coach
Speaker 1: never sees the game and the only thing is to
Speaker 1: understand how to get better is by interviewing some of
Speaker 1: the players what they think has happened. So I wanted
Speaker 1: to have like something like a game tape and game
Speaker 1: stats for sales and AI to understand what really separates
Speaker 1: the top performers from everybody else. You know that message
Speaker 1: that you get on customer service calls, This call may
Speaker 1: be recorded for quality assurance. We appreciate your patience. Calls
Speaker 1: for quality. Companies were recording their sales calls, but no
Speaker 1: one was really listening to them. A met wanted to
Speaker 1: listen to all the game tape and analyze it. And
Speaker 1: I then started asking a bunch of other people, you know,
Speaker 1: if we build a system kind of shine a light
Speaker 1: on conversation and give you insights from that. Would you
Speaker 1: buy how much you're willing to pay a meat? Created
Speaker 1: a new company he called it Gong. He went to
Speaker 1: people and said, hand me all the recordings of all
Speaker 1: your sales calls, plus a list of your salespeople in
Speaker 1: order of how good they are. We'll analyze it. And
Speaker 1: so if you'd gone to just one of those top
Speaker 1: sales people and ask what you're doing, it works. No, no,
Speaker 1: absolutely not, because they don't know. People think it's an art, right.
Speaker 1: It's like they're not aware that's something that they're doing
Speaker 1: because they don't know what the other people are doing,
Speaker 1: so they don't know what the differences are. The Gong
Speaker 1: artificial intelligence, had no theory about what worked and what
Speaker 1: didn't in sales. It just had millions of sales pitches.
Speaker 1: It searched for patterns in both the calls they got
Speaker 1: results and the calls that didn't. The Gong actually learns
Speaker 1: like it looks at the salespeople and says, like, which
Speaker 1: one is closing more deals, and then it starts to
Speaker 1: analyze the difference. So the software learns automatically. You just
Speaker 1: connect to the calls. If there was an art to
Speaker 1: any of this, it was in the questions that Gong
Speaker 1: asked if the phone call data. I mean, the very
Speaker 1: simple one is like percent of talk time? Right, You
Speaker 1: and I are talking right now, and by the end
Speaker 1: of the call, Gong say, well, a meat was talking
Speaker 1: fifty six percent of the time. It turns out there's
Speaker 1: lots of things that separated great sales pitch from a
Speaker 1: bad one. The simple virtues. They're sort of obvious, but
Speaker 1: they could now be quantified. On average, forty six percent
Speaker 1: talk time is ideal. Thirteen questions is optimal? Right? Not less?
Speaker 1: No more so, this alone is food for thought that
Speaker 1: there's a maximum amount of time to be talking. If
Speaker 1: I were to talk only forty six percent of the time,
Speaker 1: would my wife stop telling me that I don't listen?
Speaker 1: What if I counted the number of questions I asked
Speaker 1: in a particular conversation and stopped myself at thirteen. I mean,
Speaker 1: it's not that if you ask fourteen you lose that deal.
Speaker 1: But more than that, people might lose their patience, and
Speaker 1: too less it means you're talking too much. Patience factor,
Speaker 1: that's like a pretty big challenge for a lot of people,
Speaker 1: they're too quick to respond, so it's a good practice
Speaker 1: to pause think in any reply. We all know that
Speaker 1: in theory, at least, Gone could act just like a
Speaker 1: game coach, just last instructions into a person's ear as
Speaker 1: they pitched the product. Shut the hell up, ask another question.
Speaker 1: You're at seventy three percent, and Meek decided it was
Speaker 1: better if he didn't do this. If he offered the
Speaker 1: feedback after the call instead, you know, will alert the
Speaker 1: coaches to hear like three conversations that have room for improvement,
Speaker 1: and then the coach will open them up just like
Speaker 1: a game tape and start breaking it down to start commenting, Oh,
Speaker 1: minute three ZH six, I really love the way you
Speaker 1: phrased that question. Have you considered X man? If I
Speaker 1: were in sales, all of this would drive me batshit,
Speaker 1: which is why I'm not in sales and also probably
Speaker 1: why everyone but my mother is still making fun of
Speaker 1: my podcast ad Reads. But for people who actually make
Speaker 1: their living selling stuff, well, Gong is their new best coach.
Speaker 1: So when I'm engaging with a customer, especially in introductory calls,
Speaker 1: I want them to do as much of the talking
Speaker 1: is possible. That's Megan Dorner, who sells software for a
Speaker 1: Canadian company called AVIC. I have no idea what the
Speaker 1: software it does, but that's not important. The key fact
Speaker 1: about it is that it needs to be sold. The
Speaker 1: more the customers talking, the more that I'm learning, especially
Speaker 1: like I said on that first call. And so the
Speaker 1: longest customer story again is how long they're telling a
Speaker 1: you know, an interactive I guess one long tail. So
Speaker 1: that the customers monologuing, that's good, that's correct, that's correct.
Speaker 1: But if you're monologuing, it's bad. Megan now pays attention
Speaker 1: to who's monologuing, because Gong forces her too. Five minutes
Speaker 1: after every call, she gets a note from Gong filled
Speaker 1: with stats and a color coded report card. One of
Speaker 1: the grades is based on the length of her monologues.
Speaker 1: When the customer monologues, Gong doesn't call it that, They
Speaker 1: call it a story. If you've got a woman of
Speaker 1: few words on the other end of the line and
Speaker 1: there's not much you can do to tease a big
Speaker 1: story out of her, you totally right. And that's when
Speaker 1: I find that my longest monologue, longest customer story, and
Speaker 1: talk ratio could be all in yellow. Yellow is Gong's
Speaker 1: color grade for a C. Green is an A red
Speaker 1: is an F. Megan's never in the red. She's one
Speaker 1: of Avic's top salespeople and also almost by definition, a
Speaker 1: salesperson that Gong thinks is great. The only yellow card
Speaker 1: she's ever likely to get from Gong is for interrupting.
Speaker 1: I'm somebody who likes other people. I like talking, I
Speaker 1: like engaging. I also like to be right. I like
Speaker 1: to help. I like to give the information I think
Speaker 1: is going to be helpful, and so I'm eager to
Speaker 1: do that. So, in a funny way, you had to
Speaker 1: be a different kind of person when you were a
Speaker 1: salesperson than you were just out in the world. That's correct.
Speaker 1: All this lead It's to an obvious thought. If Gone's
Speaker 1: report cards work for sales, why stop there? So after
Speaker 1: I'll be with my friends or with family, I'll think
Speaker 1: to myself, after I leave, God, I interrupted a lot,
Speaker 1: and it's just it's always in the back of my
Speaker 1: mind now because I see it so often that my
Speaker 1: patience is low. Is it Gone that made you aware
Speaker 1: of it more so than before? Absolutely? Does anybody around
Speaker 1: you sense that your approach is changing at all? Is
Speaker 1: anybody noted to you that you know, you're all of
Speaker 1: a sudden listening better to me, my spousehays does that count?
Speaker 1: Oh my God, tell me about that. Yes. So I
Speaker 1: We've had the conversation after we've left a social setting
Speaker 1: where I've said, god, I did I was interrupting a
Speaker 1: lot at dinner or whatever, and she has said to me,
Speaker 1: you know you've been actually better than you have been previous.
Speaker 1: You know you're getting better at listening, You're getting better
Speaker 1: at not interrupting. So she has noticed that, Yeah, I don't,
Speaker 1: I don't do it quite as often. Did you then
Speaker 1: explain to her why you weren't doing it as often? No,
Speaker 1: absolutely not. Life is a market. Everyone's always looking for
Speaker 1: edges that other people don't have. Even a nice Canadian
Speaker 1: woman knows better than to give her secrets away. Do
Speaker 1: you think the world would be a better place if
Speaker 1: we were all gone in all of our conversations. I
Speaker 1: think it depends on the level of you know, give
Speaker 1: a shit that the person has to want to do
Speaker 1: better or be better, right. But I think a lot
Speaker 1: of people who spend a lot of time just talking
Speaker 1: about themselves are unaware how much time they're spending talking
Speaker 1: about themselves. That's very true. Yeah, there's no question that
Speaker 1: the future of this is gone trying to figure out
Speaker 1: and reduced to little color coded bars. What it is
Speaker 1: that causes a uson to like another person? Oh yeah,
Speaker 1: which is scary when you think about it, but incredibly useful.
Speaker 1: Gong's now being used by fifty thousand salespeople at almost
Speaker 1: a thousand different companies. We grab Megan sort of at random,
Speaker 1: and she's a sample size of one. But it's not
Speaker 1: hard to see how this new coaching tool might shape behavior.
Speaker 1: It creates new stats, Gong stats. The stats capture something
Speaker 1: true about the performance, the same way that I don't
Speaker 1: know on base percentage captures something true about a baseball
Speaker 1: hitter's performance. The management pays more to the people with
Speaker 1: good stats, and so soon everyone's just adjusting their game.
Speaker 1: Baseball hitters are learning plate discipline, and salespeople are learning patients.
Speaker 1: Most salespeople would say like Gong is like change my
Speaker 1: game forever. This is a meet bendoff. Again, they use
Speaker 1: those were like a game changer. It is possible if
Speaker 1: if your product isn't good or a competitive in the market,
Speaker 1: it doesn't matter if he has like thirds question, right.
Speaker 1: I mean, it's like, so there's only so much that
Speaker 1: you can do. I mean there's like, uh, this is
Speaker 1: not some kind of magic, it's just like facts. But
Speaker 1: there's something else that happens with data. In the right
Speaker 1: analysis of it, it causes all kinds of folk beliefs
Speaker 1: to just disappear. Let me give you an anecdote. So,
Speaker 1: a lot of the managers and the coaches are often
Speaker 1: obsessed with the filler words. When we introduce the product, Oh,
Speaker 1: can you track like fuller words, you know, like oombs
Speaker 1: and arms and like and so and like. It drives
Speaker 1: them nuts. But you know what, we ran the research
Speaker 1: and turns out there's zero correlation between like usage of
Speaker 1: filler words and success. Huh, So, so that parameter doesn't
Speaker 1: really matter. The managers were thinking that the more filler
Speaker 1: words the worse. Yes, right, it is annoying to hear
Speaker 1: them when you listen to recording and there's and people
Speaker 1: say like a hundred times it drives you nuts, right right.
Speaker 1: The fact is it doesn't matter. The fact is you
Speaker 1: never know any of this without all these facts. Gong
Speaker 1: is screwing up everybody's assumptions. Like the recent one was
Speaker 1: using swear words on calls, does it help, does it
Speaker 1: get in a way or does it even matter? And
Speaker 1: what's the answer. Um, So it depends what the correlation,
Speaker 1: what the research shows. I said, it depends on what
Speaker 1: you're selling, Like if you're selling bibles, it probably doesn't help. Yeah, yeah,
Speaker 1: we didn't have any Bible sellers over there in the stats.
Speaker 1: But it's run over like, you know, fifty thousand people,
Speaker 1: so it's it's a very large number. But the corollary
Speaker 1: is that it depends who starts first. If the buyer
Speaker 1: starts with curse words and you match that that that's
Speaker 1: actually works better. But if you start it it doesn't help.
Speaker 1: You might fucking believe that, or you might fucking not.
Speaker 1: The point is that it doesn't matter what you believe.
Speaker 1: The data generates the knowledge, and the knowledge allows you
Speaker 1: to coach people how to do better at the most
Speaker 1: basic human activity talking. Can you imagine a future like
Speaker 1: that distant future where human interactions will have changed pretty
Speaker 1: meaningfully because we'll all have been coached up in how
Speaker 1: to have conversations. Absolutely, the device that we're using today,
Speaker 1: like namely like speech or the English language hasn't changed
Speaker 1: much for thousands of years, where the amount of information
Speaker 1: that we need to exchange today and the amount of
Speaker 1: noise and clutter and the environment is so different that
Speaker 1: it could definitely use an upgrade. An upgrade. Well, one
Speaker 1: thing hasn't changed. If they're better ways to manipulate people,
Speaker 1: salespeople will find them first and the rest of us
Speaker 1: will just follow. We focus on things that are teachable,
Speaker 1: are coachable, all right, we can't teach anybody how to
Speaker 1: be funny. Let me stop, Let me stop, let me
Speaker 1: stop you for a second. I'm sorry, I know I interrupted,
Speaker 1: but this is important. You don't think you could teach
Speaker 1: people to be funny. Well, we haven't been able to
Speaker 1: crack that code yet. Maybe a meat hasn't cracked that code,
Speaker 1: But that doesn't mean that it can't be cracked. Well,
Speaker 1: you can measure precise things contained in the conversation, the
Speaker 1: words that people are saying to each other, and how
Speaker 1: it influences outcomes. Allison wood Brooks is an associate professor
Speaker 1: at the Harvard Business School. Like, we're on a date,
Speaker 1: do you want to go out with me again? We're
Speaker 1: on a sales call? Did it convert to a sale?
Speaker 1: All kinds of things that you can connect the content
Speaker 1: of the conversation with things that really matter, with outcomes
Speaker 1: that really matter. Professor Brooks takes all these conversations and
Speaker 1: analyzes them. She's using the same new machine learning that
Speaker 1: Gong uses, but she's looking for different things. Give me
Speaker 1: an example, one example of something that you can you
Speaker 1: learn from this technology, Like something someone says that leads
Speaker 1: the other person to want to go on a date.
Speaker 1: Let's give you that exact example. We have a data
Speaker 1: set of people doing speed dating. Each person went on
Speaker 1: like twenty dates, okay, quick four to five minute speed
Speaker 1: dates in round robin fashion. At the end of each date,
Speaker 1: you say are you willing to go out with that
Speaker 1: person again? And they record and transcribe all the interactions.
Speaker 1: So now we see exactly what people are saying on
Speaker 1: their dates, and we can measure what are the things
Speaker 1: that people are doing and saying that make them more
Speaker 1: likely to be more datable in the future. And one
Speaker 1: thing that really matters is question asking. So asking more
Speaker 1: questions for both men and women on these heterosexual dates
Speaker 1: leads to better dating outcomes. Really, especially follow up questions.
Speaker 1: How do you know that the people who are asking
Speaker 1: the questions aren't just naturally more attracted to the people
Speaker 1: who they're asking the questions of. Because so two things.
Speaker 1: One we can control for other aspects of attractiveness observational
Speaker 1: data too. We then come back to Harvard and run
Speaker 1: experiments where we tell people ask a lot of questions
Speaker 1: in one condition or another condition where we say we
Speaker 1: don't tell them anything. In the condition where they're asking
Speaker 1: a lot of questions, they also are more attractive and likable.
Speaker 1: This new line of research has uncovered the various ways
Speaker 1: that people gain power and authority in conversation. This professor
Speaker 1: can prove that they work, and it's just made her
Speaker 1: want to ask even more questions. Even if you know
Speaker 1: conceptually what a charismatic, smooth, productive conversationalist looks like. Is
Speaker 1: there any way that you can train people to actually
Speaker 1: get better at executing it? Brooks has all this data,
Speaker 1: some of it from Gong, but also from doctor's appointments
Speaker 1: and work meetings and parole interviews and speed dating sessions
Speaker 1: and on and on and on. She uses the data
Speaker 1: to test theories about conversations, and one of those theories
Speaker 1: is about humor. There's this great work that sends of
Speaker 1: play right is like the key to psychological safety and
Speaker 1: thriving and creativity. It's the only way that you can
Speaker 1: really be creative in the presence of others is if
Speaker 1: you feel safe to say something stupid and silly. That
Speaker 1: all sounds sensible to me, But do you have any
Speaker 1: data to back up absolutely? What's the data? Let me
Speaker 1: tell you about one paper. It's a bidirectional finding, meaning
Speaker 1: people who have high status tend to be more free
Speaker 1: and use humor more freely. But the more interesting direction
Speaker 1: is if you are of low status, if you can
Speaker 1: land a joke, people perceive you as higher status. A
Speaker 1: lot of polite laughter happens. So but if other people
Speaker 1: think what you said is actually funny, appropriate for the circumstances,
Speaker 1: and at least one person laughs at it, your status
Speaker 1: takes this huge jump. Totally true, right, So yes, it
Speaker 1: pays to be funny. Funny gets you status, and status
Speaker 1: get you money. But the mystery remains, can funny be coached?
Speaker 1: There's a part of me that wonders if there's a
Speaker 1: version of what's going on in baseball coaching that doesn't
Speaker 1: apply to what you're doing. And what's going on baseball
Speaker 1: coaching is the technology has generated all this data about
Speaker 1: how a pitcher's body moves, and they're able to identify
Speaker 1: people who have an aptitude for doing things that is
Speaker 1: untapped because they have whatever. The fundamental attribute is arm
Speaker 1: speed that you can't teach, but translating the arm speed
Speaker 1: into a speed on a fastball is a different thing.
Speaker 1: And I wonder if there's an equivalent in conversation where
Speaker 1: you could identify the core traits that lead to conversational
Speaker 1: excellence and you can figure out who's sort of maxing
Speaker 1: out and who's not and why totally. And not only that,
Speaker 1: Allison's taken everything she's learned about human speech, humor, and
Speaker 1: the rest and built it into a new course at
Speaker 1: the Harvard Business School. It's called how to Talk Gooder
Speaker 1: in Business and in Life. It's trying to turn conversation
Speaker 1: into a science. It's like having the right arm for
Speaker 1: the fastball and not doing it quite right. You've got
Speaker 1: the brain space and the ability to do it, you
Speaker 1: just didn't think to do it. That's the dream scenario,
Speaker 1: and that's really the hope with these HBS students, right
Speaker 1: These are super smart people who maybe just haven't heard
Speaker 1: the right strategies. Hundreds of Harvard students tried to get
Speaker 1: into Alison's new class. She accepted seventy seventy of the
Speaker 1: world's most ambitious people, hoping that the science of conversation
Speaker 1: will offer them yet another edge in life. Many people
Speaker 1: would find it odd to approach a conversation seeking to
Speaker 1: maximize the profits of it. Exactly, They'll love it. They
Speaker 1: want to perform optimally in every way in their lives,
Speaker 1: and this is the moment to moment way that you
Speaker 1: would achieve that moment to moment, and in each moment,
Speaker 1: there's now data which can be used by a coach.
Speaker 1: I'll be checking in with Alison's students next week and
Speaker 1: asking a new question. When you start to get an
Speaker 1: edge like this, a data edge, does the coaching start
Speaker 1: to shade into something else, something that's maybe against the rules.
Speaker 1: I'm Michael Lewis, Thanks for listening to Against the Rules.
Speaker 1: Against the Rules is brought to you by Pushkin Industries.
Speaker 1: The show's produced by Audrey Dilling and Catherine girodo Or
Speaker 1: Girardo or girodot Or Girardo I've Never Gotten It right,
Speaker 1: with research assistance from Lydia Genecott and Zooe Wynn Our
Speaker 1: editor is the magnificent Julia Barton, who finds my every mistake.
Speaker 1: Mia Loebell is our executive producer, and she disapproves of
Speaker 1: me half the time. Our theme was composed by Nick Brittell,
Speaker 1: who is really slumming it working here, with additional scoring
Speaker 1: by Stellwagon Symphonette. We got fact checked by Beth Johnson,
Speaker 1: which was totally unnecessary because our facts are always right.
Speaker 1: And our show was recorded by tofur Ruth and Trey
Speaker 1: Schiltz in spite of enduring the coronavirus in the studio,
Speaker 1: which is the Northgate Studios in Berkeley, as always thanks
Speaker 1: to Pushkin's founders Jacob Weisberg, who I think of as
Speaker 1: my brain, and Malcolm Gladwell, who I think of as
Speaker 1: my goal. Do you sense that the challenge with men
Speaker 1: is different from the challenge with women. We do have
Speaker 1: a good amount of research on gender in conversation. For example,
Speaker 1: we know Matthias Melt the University of Arizona has this
Speaker 1: great paper showing that men and women are equally talkative
Speaker 1: and even though women have the stereo type of being chattier,
Speaker 1: what he finds is that men and women both speak
Speaker 1: about sixteen thousand words per day, so that's about similar.
Speaker 1: But what we do know from other researches that men
Speaker 1: and women speak at different times, especially in the workplace.
Speaker 1: You know, I find I speak when a woman needs
Speaker 1: something explained to her, exactly fifteen and a half thousand
Speaker 1: and of my words every day just to do that,
Speaker 1: and for some reason, for some reason, they don't appreciate it.
Speaker 1: This just is Could I learn how to get them
Speaker 1: to appreciate it by coming to your class? No, No,
Speaker 1: what kind of class is this? Oh my god, it's
Speaker 1: too funny.