Outlook Business Leading Edge 2017 | Howard Moskowitz
How can Indian companies make better products? PepsiCo India's D Shivakumar inquires more from psychophysicist, Howard Moskowitz.
To know more about Outlook Business Leading Edge 2017 log onto leadingedge.outlookbusiness.com or follow #leadingedge2017 on Twitter and Facebook.
Music: www.bensound.com
#LeadingEdge2017 #Business #OutlookBusiness #OutlookMagazine #OutlookGroup
To know more about Outlook Business Leading Edge 2017 log onto leadingedge.outlookbusiness.com or follow #leadingedge2017 on Twitter and Facebook.
Music: www.bensound.com
#LeadingEdge2017 #Business #OutlookBusiness #OutlookMagazine #OutlookGroup
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NewsTranscript
00:00 [MUSIC PLAYING]
00:05 May I start by just thanking everybody for being here
00:10 and for the kind invitation and the hospitality
00:15 of wonderful people from a wonderful country.
00:18 And it's my hope and prayer that what
00:21 I'm going to talk about today and to bring to India
00:26 will provide your people, your companies, but most of all,
00:31 your younger folk with opportunities
00:35 for the next 20 years.
00:37 And may those opportunities live perhaps even longer than me.
00:42 Thank you.
00:43 Thank you.
00:44 OK, Howard Moskowski went to Queens College.
00:49 Howard, first job US Army.
00:52 A great job in the way it was described.
00:55 To get soldiers to eat the right food with the right calories.
01:00 He's worked with every single major food
01:02 company in the world, PepsiCo included.
01:05 He's won every single award in the field of market research.
01:09 And I dare say that if they were an Oscar,
01:12 he would be the first recipient of it.
01:14 Howard, I have five questions for you in our 25-odd minutes.
01:18 First, as a scientist, what got you started in this area?
01:22 And how did business people react to these new ideas
01:25 that you brought to them?
01:26 I'm going to tell you the truth.
01:28 It's not as polished as the truths you've heard before
01:32 is the wonderful words.
01:34 I got started at Harvard.
01:37 I was told by my professor, Howard,
01:39 you're not an electroniker.
01:42 That means you don't really know AC from DC
01:44 and get off my equipment.
01:46 I don't want you to burn out my transformers
01:49 when you do work on the hearing.
01:51 Howard, why don't you do something
01:53 that you can do, like taste or political polling?
01:57 And I was 23 at the time, and I heard the word taste.
02:02 And so I realized nobody knew anything.
02:05 And if I was going to get out of Harvard with a PhD,
02:09 I might do taste.
02:11 And therefore, I did work on taste.
02:14 And that's the start of my career.
02:16 The end of my career now--
02:18 I'm almost 73--
02:19 is political polling.
02:21 And we'll talk about that later.
02:23 I was in part responsible for Mr. Trump's victory.
02:27 So I've taken out extra insurance
02:30 coming to India just in case there
02:32 are a variety of Democrats.
02:34 [LAUGHTER]
02:37 I also have a New York City sense of humor.
02:39 [LAUGHTER]
02:43 One has read a lot about food optimization,
02:45 a concept that you enunciated.
02:48 What does it mean, and how does it
02:50 apply to a country like India, and what can we learn from it?
02:53 Well, that's a wonderful thing.
02:55 First of all, I want to thank you from Pepsi,
02:57 because it was Pepsi-Cola in 1974
03:01 with my associate Barry Jacobs.
03:04 And I came to Pepsi when I was a government scientist
03:09 after a meeting with the American Society of Testing
03:12 and Materials.
03:13 And Barry Jacobs said, we have some problems.
03:17 Do you think you can go to the R&D facility in Long Island
03:21 City and explain to us how we might make better products?
03:27 Make it closer, because my voice doesn't carry.
03:29 Otherwise, I'd have to yell.
03:32 So I said, OK, let me go to Long Island City.
03:35 And they were very kind.
03:37 And in fact, they said, the only way
03:39 I know how to make a better product
03:43 is to systematically vary the ingredients.
03:46 And this is very important.
03:48 Vary what is under your control scientifically,
03:51 highs and mediums and lows.
03:54 And Barry and Archie Porter were there, head of R&D.
03:59 They did it.
04:00 And it worked.
04:01 We got some winning products.
04:04 And from that, I realized that companies,
04:07 no matter how large they are--
04:09 Unilever, Pepsi-Cola, General Foods, Procter & Gamble--
04:15 really did not know how to make products better.
04:20 They had a lot of data on their products.
04:22 But they never systematically created different new test
04:27 products, evaluated them, and built models.
04:31 So to answer your question, Pepsi accepted it.
04:37 Unilever accepted it.
04:40 I was bodily thrown out of a variety of companies, many
04:43 of which are here today.
04:45 And I won't mention their names because it's embarrassing.
04:49 But it was a mixed reaction.
04:51 There were a number of companies that really
04:53 accepted systematic variation and a lot of companies
04:57 that did not.
04:58 But every company, whether it was Pepsi or Tropicana--
05:03 you've all had the orange juice with pulp, I think--
05:08 or Maxwell House Coffee, any company which systematically
05:12 did the experiments, which varied the products,
05:15 ended up winners.
05:17 That's how I got into the business.
05:18 Give us how you developed both Cherry Vanilla Dr. Pepper,
05:27 which was completely off the charts for Cadbury Schweppes,
05:30 as well as Prego Chunky.
05:32 What led you to really these rockbuster innovations?
05:37 Well, first of all, I'm not an innovative person.
05:42 I was a scientist.
05:45 I still am a scientist.
05:47 And I simply was able to convince the people at Cadbury
05:52 Schweppes, Dr. Pepper, the people at Campbell's Soup,
05:56 Prego, to systematically vary the products.
06:00 That was the biggest achievement.
06:02 And once they made the different products
06:05 and tested them with 50 or 100 people per product,
06:09 they found out these remarkable developments.
06:13 That's also something that I want to leave with India,
06:17 that India can become a powerhouse.
06:21 All of the world's food companies,
06:25 and in fact, many of the consumer product companies,
06:27 not food--
06:28 health and beauty aids--
06:30 are what we say in America, sitting ducks.
06:34 I like to repeat that in case that wasn't clear.
06:38 All of the companies in the world in food, many of them
06:43 in health and beauty aids, are sitting ducks.
06:46 That is to say, India can become a powerhouse of product
06:52 development and design, not so much by outspending
06:57 these companies as by doing simple experiments,
07:01 the way I did it for Campbell's Soup.
07:04 Understanding the ingredients, varying them systematically--
07:09 and I have some papers to share with you--
07:13 testing these in combinations with consumers--
07:17 I hope I'm not being too technical--
07:19 test these combinations with consumers, get the reactions,
07:24 and understand the dynamics of the product.
07:27 Can you imagine a country as great as India putting
07:33 its companies through this training
07:36 and letting them become powerhouses?
07:38 It's all in your hands.
07:40 So that's the positive words I have to say.
07:46 The other big concept you're known for,
07:47 Howard, is mind genomics.
07:50 Can you give us a 101 course on what this is?
07:52 What is mind genomics?
07:54 Yes.
07:55 That's the second half of my life.
07:59 The first half was devoted to--
08:01 and still is devoted to--
08:03 making better products.
08:05 But then I realized that there was something else going on.
08:09 The people that I was coming in contact with,
08:12 the younger people, weren't able to think quite strongly,
08:18 quite creatively, quite critically.
08:22 And so I realized that with God's gift of this science,
08:27 that I'd have to not only spend time in the commercial world,
08:33 but redevelop a science of thinking,
08:36 a science of the mind.
08:38 And so what I did is create a system
08:41 by which you could figure out what's important in an area.
08:45 And for example, take a product like the toothbrush.
08:52 What happens when you put ideas together
08:55 about mechanical movement and toothbrushing?
08:59 You end up with new synthesis of ideas.
09:02 And this became the mechanical toothbrushing industry.
09:06 So mind genomics is simply putting together ideas,
09:10 combining those ideas, testing them with consumers,
09:15 getting at the reactions, and building
09:17 a science of any topic, any experience area
09:22 from the bottom up.
09:24 What it means is that any young person today
09:28 can learn to think critically.
09:30 It's simply mind genomics.
09:32 It's simply a method for understanding
09:35 how ideas fit together.
09:38 Imagine a kid 10 years old, or 12 years old, or 15 years old
09:44 becoming a scientist.
09:46 It's the same kind of revolution as we
09:49 had in the digital period when kids 10 years old
09:52 were given computers.
09:54 Let's give them something better.
09:56 Let's give them the discipline to think.
09:59 And that's what mind genomics is about.
10:02 You mentioned millennials.
10:03 And I know that you have a book on it also.
10:07 A lot of people talk a lot about millennials.
10:09 We do.
10:09 We're a very young country.
10:11 330 millennials, a million millions in here.
10:16 We always talk about how different they are.
10:18 But one of the things I really want to prove with you
10:20 is, what can we do for them?
10:23 Well, the millennials.
10:25 First of all, are they really different?
10:28 Do they have different values?
10:31 I don't want to say that they're different or not.
10:33 What I do is I do the experiment.
10:36 Remember this mind genomics.
10:38 Take a topic like what kind of beverages.
10:41 You're in the beverage business.
10:43 Or what kind of foods?
10:44 Mix and match ideas.
10:47 Give these ideas to millennials.
10:50 And let's see what pops, what ideas pop.
10:53 And let's build a machine of ideas
10:56 that combines features for millennials.
10:59 You'll find that the millennials are not
11:01 as different as you think from the older people.
11:04 They may talk differently.
11:06 But there's just the same kinds of divisions
11:10 among these millennials as there are in older people.
11:14 But because companies don't do the science,
11:20 you look at this new cohort of young people,
11:23 and you say, my god, they're all different.
11:25 They're speaking a different language.
11:27 They're not.
11:29 They're really not too different.
11:31 All you have to do is be able to speak with them
11:36 and know what makes them tick.
11:38 And it's not so different.
11:39 We do work, for example, in wine.
11:42 We thought that the millennials are
11:44 very different in what they like.
11:46 They're not different in what they like.
11:48 They just will buy it differently.
11:50 They respond to different messages.
11:52 This is something that Howard did which
11:58 most people didn't expect.
12:00 He went to 102 black people and asked them,
12:03 what is it that you expect of the next president of America?
12:07 And he said they expected the following--
12:09 lowering of taxes, wanted a strong leader,
12:12 wanted to create more jobs, change the tax code,
12:16 help small companies win, make college affordable,
12:20 and improve the US financial health.
12:23 And Howard was one of the first people who said,
12:25 you must vote for Trump and not Hillary Clinton.
12:28 So I want to know, what prompted you to do this?
12:30 Because in that article, it said that Trump team
12:33 refused to comment on this.
12:34 Yes.
12:36 It was July 2016.
12:40 And along with Mind Genomics, this effort
12:44 to teach people how to think, we also
12:47 have an effort called Vox Populi to use Mind Genomics to make
12:53 governance open and visible to all people.
12:58 And we had one of the campaign people, Daniel Gulbenovich,
13:04 from Trump say, yeah, we'd be interested in seeing the issue.
13:09 And the question we have is, what do we
13:11 say to blacks, African-Americans?
13:15 And without their help, we got all the issues together.
13:18 We used Mind Genomics, mixing and matching these ideas,
13:22 giving them to the blacks, getting responses
13:26 from these combinations, and then figuring out
13:29 exactly the kinds of elements that worked and not,
13:33 and dividing the black population,
13:36 even with 100 people, not so much by who they were
13:41 as by the way they thought.
13:43 And from that, we were able to identify exactly what
13:48 Mr. Trump should say.
13:50 And if you look at Mr. Trump's actions around Labor Day,
13:54 the beginning of September, he all of a sudden
13:57 went from being off strategy and talking about everything
14:01 to talking for two weeks about facts,
14:04 the facts that we gave him.
14:06 And again, I'm an iconoclast, as you might be able to sense.
14:12 And all of this is 10 to 15 years ahead
14:16 of when it will be accepted, just like the work with Pepsi
14:19 was 15 years ahead.
14:21 So I don't expect to live to see the real implementation of Mind
14:27 Genomics to help government or Mind Genomics
14:31 to help the Indian youth think more critically and become
14:35 powerhouses.
14:36 But I'm planting the seed anyway.
14:40 You didn't recommend the wall.
14:41 Pardon me?
14:42 You didn't recommend the wall to Mr. Trump.
14:45 Well, Mr. Trump has got his own agenda.
14:51 And would it surprise you to know that I'm apolitical?
14:55 I'm really not interested in politics.
14:57 I'm interested in people and a better world,
15:01 but not at all in politics.
15:03 You think the work you've done right now predicting this
15:07 will have a big future in the political landscape
15:09 of many other countries?
15:11 I'm hoping, with God's help, that it will.
15:14 I know that already in the United States
15:17 and in the state of Wisconsin, they're
15:19 seriously considering using this to help frame public policy.
15:25 And I believe that once the citizens can see on a computer,
15:30 on a table, a statistical table, what
15:34 are the ideas that are important,
15:37 what are the ideas that are popular and not popular,
15:40 they'll be able to be better informed.
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