mr and the rise of the machines

Market Research and the rise of the machines – Automa5on and AI

Market Research & The Rise of the Machines

Automa5on & Ar5ficial Intelligence

Ray Poynter April 2016


Agenda

•  Automa(on and society •  Automa(on and MR

•  Ar(ficial intelligence •  The near future •  How to be an automa(on winner – Not an automa(on loser


Automa5on – Good and Bad

When do you prefer handmade?

•  Sushi •  Haircut •  ?

When do you prefer machine made?

•  Glasses / contact lens •  Computer chips

•  ?


4 waves of automa5on 1.  Muscle is augmented (power tools, harves(ng tools,

steel factories etc) – Meiji restora(on onwards

2.  Muscle is replaced (end-‐to-‐end automa(on – ‘lights-‐out factory, e.g. FANUC robo(cs factory)

3.  Brain is augmented (Spreadsheets, accounts packages, graphics packages etc)

4.  Brain is replaced (SatNav, driverless cars, autopilot, automa(c air condi(oning, etc)


System 1 and System 2 thinking

System 1 is our automa(c, heuris(c-‐based thinking –  1+1 or picking your regular toothpaste in the store is System 1

–  System 1 is rela(vely easy to automate

System 2 is our considered thinking –  Playing Go is System 2 –  System 2 requires Ar(ficial Intelligence to automate


Impact of Automa5on -‐ Glass BoJles •  Automated glass blowing invented

Michael Owens, USA, 1905

•  Bo]les produced per person up 600% •  Price per 100 fell from $1.25 to 10 cents

•  Use of glass bo]les increased, especially for food and drink

•  Employment, choice, safety increased –  But many skilled glass blowers lost their job or had to work for less money


Automobiles •  Before Henry Ford, few cars, few workers, high costs

•  1913 Ford created first moving assembly line

•  Cars became be]er, cheaper, and available •  Millions of people were employed, the economy grew, hotels and diners were just one by-‐product of Ford’s automa(on


Impact of Automa5on – Books •  In the West, prin(ng press invented 1439 •  1450s bibles are printed – change of power! •  Books, newspapers, pamphlets – change of power –  Reading became DIY

•  Knowledge was stored, shared, and developed

•  Prin(ng was the internet of the (me Thomas Paine 1791


MR and Automa5on •  Punch card reader 1890 US Census, Hollerith

•  1960s & 70s : use of computers to produce tables, SPSS, ques(onnaire scanning

•  1980s : CATI and RDD, DeskTop Publishing, Personal Computers (word-‐processing, spread sheets, PowerPoint, SPSS)

•  1990s : Online surveys, online focus groups, bulle(n board groups

•  2000s : DIY research (e.g. Survey Monkey), Research communi(es, research panels, Social Media analy(cs, Text analy(cs, Dashboards

•  2010s : Produc(sa(on (e.g. ZappiStore), Big Data, passive data, internet of things, automated repor(ng, automa(c facial coding


Automa5on and Employment Some5mes Good

•  Glass making and cars – early 20th Century

•  IT – last 50 years •  Entertainment •  Flying •  Finance and investment services

•  ?

Some5mes Bad •  Prin(ng •  Typing •  Bank clerks •  Steel makers •  Assembly-‐line work •  Ticket people at sta(ons


Automa5on and Research Employment More –  Programmers –  Research directors –  Qual researchers –  Sales and marke(ng –  Customer success managers

–  ?

Less –  PAs and secretaries –  Interviewers –  Char(ng teams (e.g. slides, overheads, etc)

–  Junior researchers (checking tables etc)

–  ?


Automa5on and Research Quality BeJer

–  Cost –  Speed –  Fewer punching and

processing errors –  More op(ons for charts and

infographics –  Less human bias –  More analyses available –  ?

Worse –  Smaller propor(on of

popula(on, more open –  Interviewers not catching

mistakes and mo(va(ng respondents

–  Limited range of solu(ons offered – not designed to customers’ individual needs

–  DIY reducing quality –  ?


Millward Brown Link Test & Automa5on

•  Leading copy tes(ng product •  Benchmarks of over 100,000 tests

•  Predesigned – Method – Ques(onnaire – Metrics


Bespoke

Bespoke PreDesigned -‐ PreMade


Is Bespoke Research BeJer?

Bespoke designed to meet the client’s needs

But –  75% of the (me not designed by top researchers – Harder to compare with benchmarks –  Can miss essen(al ques(ons –  Slower – More expensive – Not good for ‘agile’ research – e.g. ‘fail fast’


Automa5on and DIY


Who is DIY MR for? •  People learning about research •  Small agencies

•  Client-‐side researchers, who are researchers

But, they are also used by people who do not know enough about research to be using them.


Validate.It

Designed for marketers. Proposi(on: All surveys and analyses designed by ‘best in class’ experts


ZappiStore


GRIT Report – DraV – top 7

41% 39% 35% 34% 32% 29% 24%

23% 29% 30% 28% 19% 19%

18%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Analysis of survey data

Char(ng and

infographics

Analysis of text data

Analysis of social media

Sampling Survey Design

Online focus group/IDI modera(on

In Use Under considera(on

Automa5on PlaXorms In Use or Under Considera5on

GRIT 2016, n=2144


GRIT Report – DraV – top 7

21% 20% 19% 13% 11% 10% 8%

18% 26% 16%

17% 13% 16% 13%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Report wri(ng

Image & video data

Project Design

A]ribu(on Analy(cs

Match suppliers & buyers

Biometric & nonconsc's

Match talent to projects

In Use Under considera(on

Automa5on PlaXorms In Use or Under Considera5on

GRIT 2016, n=2144


How Could Automa5on Help You?

Before you in the pipeline?

•  ? AVer you in the pipeline?

•  ?


Agile Research

•  High speed, low cost, learn/fail quickly •  Tradi(onal projects were like climbing Everest –  Base camp, camp 2, 3, 4 and then push for the summit

•  Modern business thinking is lean –  Trial and Error (Trial and Learn)

•  Needs faster and cheaper tools to allow mul(ple research phases.


Standards

•  Sir Joseph Whitworth

•  Before 1841 every blacksmith made their own nuts, bolts, etc

•  Whitworth created Whitworth Standard

•  Allowed flexibility, collabora(on, and agile development (and repair)


The New Standards

•  R •  JSON •  Ruby on Rails •  Seman(c Web

•  Hadoop


What (if anything) is holding your organisa5on back from being ‘Agile’

Do you even aspire to Agile?


AI is Automa5on applied to the mind

Especially to System 2 thinking


AI in our daily lives

•  Virtual Personal Assistants (Siri, Google Now, Cortana)

•  Video games

•  Smart cars (e.g. auto park) •  Purchase predic(on (e.g. Amazon)

•  Online customer support


Google Photos


Other Recogni5on Technology


Transla5on and Text Recogni5on •  Text recogni(on for analysis is already common

•  Voice recogni(on for sales and support is also common

Skype Recogni(on Transla(on Speech Skype


Sen5ment Analysis

iPhone comments on Facebook before iPhone 4 launch


MR Implica5ons of Text, Photos and Pictures

•  At the moment most quant studies avoid open-‐ended comments, photos, and videos

•  1 hour of video takes a person 3 hours to process •  In the future? – More open-‐ended ques(ons, fewer closed scales – More images – More video


Associated Press and bot-‐journalism •  Before AI – their team of journalists were crea(ng about

300 finance stories a quarter –  Topics such as net income, sales etc

•  With AI (since 2014) – producing 3,700 stories per quarter –  Journalist now check and tweak

•  These stories have standard types of input data and a predictable range of stories

•  Sounds a lot like a tracking report or a concept test report!


Chess, Go, and the implica5ons for AI •  In 1997 IBM’s Deep Blue beat the Gary Kasparov (best player in the world) at chess

•  2016 Google’s AlphaGo beat Lee Sedol (best player in the world) at Go

•  Deep Blue was an expert system and used brute force – the key was programming

•  AlphaGo used machine learning – the key was training


Can Computers be Crea5ve?


MicrosoV’s foul-‐mouthed, racist chatbot

•  March 23, 2016 – Microsop create Tay an AI chatbot

•  Very quickly Twi]er users train Tay to be racist, foul-‐mouthed and to talk about sex

•  March 25 – Tay is taken down

•  We’ve learned a lesson about AI and about the nega(ve side of people


Intellec5on Expert System Automa5on for MR Reports


When does automa5on aid employment? When the cost and/or speed improvements grow the market by more than the increased produc(vity per person

Market = 100 widgets per day. 10 people produce 10 widgets each

Automa5on & Market A 1 person can produce 50 widgets Market grows to 200 widgets per day 4 people employed

Automa5on & Market B 1 person can produce 50 widgets Market grows to 2000 widgets per day 40 people employed


Where will AI make a difference 1.  Project design

–  Sample, scales, method, ques(onnaire, discussion guide –  Fewer people, be]er designs, fewer mistakes

2.  Project management –  Booking the fieldwork, placing contracts, monitoring, adjus(ng, crea(ng top-‐line reports –  Fewer people, fewer mistakes

3.  Standard reports –  Tracking, customer sa(sfac(on, concept tests –  Fewer people, easier to use reports, less impact when a big story is found

4.  Analysis of open-‐ends, pictures, video –  Leading to an increase in the use of all three, fewer closed ques(ons –  More people, more insight, more storytelling


Where will AI make a difference -‐ 2 5.  Managing and modera(ng large research communi(es

–  Member queries, engagement, chatbot moderators –  Larger communi(es, more insight, similar number of researchers

6.  Advanced analy(cs –  Social media, big data, longitudinal analysis, programma(c marke(ng, a]ribu(on analysis –  More analy(cs, growth in the number of analysts, people skilled at using the AI

7.  Research bots –  Sopware that will search exis(ng data sources, e.g. studies, public data, social media and find

answers to research ques(ons –  Will replace some researchers, but will lead to growth in people running research bots, new

agencies?

•  Overall, over 10 years, I expect 60% of current MR jobs to go, and perhaps 20-‐30% new jobs to be created


Be an automa5on winner Not an automa5on loser

•  Client success managers •  Bespoke researchers –  Qual and quant

•  Entrepreneurs / Interpreneurs •  The people introducing and u(lising Automa(on and AI •  Performers –  Storytellers, illustrators, video makers, sales people, writers, nego(ators …


Thank You!

Follow me on TwiJer @RayPoynter

Or sign-‐up to receive our weekly mailing at hJp://NewMR.org


Q & A

mr and the rise of the machines

Marketing