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This presentation reviews some key aspects of changing views of strategy and sustainability as well as some basic approaches to the analysis of trends, fads, bubbles and diffusion processes in finance and fashion. - PowerPoint PPT Presentation
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In Media Res: Trends, Fads, Bubbles and Massively Scaled Analyses
Thomas Ball
Marketing Modelers Group
April 10, 2014
Highlights
• Setting the tone
• Growth is king
• Watersheds in strategic thought
• Theories of innovation and diffusion
• A few key trends in our current predicament
• Homologies between natural phenomena, finance and fashion
• Massively scaled analyses
• The arbitrage of ignorance
Setting The Tone…
Nietzsche noted that the seeds of any trend already exist, latent in the cultural sediment
Neal Gabler: “We’re a society driven by entertainment. In an entertainment culture, everything must compete with entertainment…”
Christopher Hitchens: “The pleasures and rewards of the intellect are inseparable from uncertainty, angst, conflict and even despair”
Lao Tzu, The Way of Life (Witter Bynner trans.): “Whether a man dispassionately sees to the core of life or passionately sees the surface, the core and the surface are essentially the same…”
Trends Are UbiquitousSome tentative definitions:
• A trend is a long-term or enduring influence on behavior, analogous to open ocean waves
– The Ancients had no concept of “trends” viewing existence as eternal and static
– Modern notions are typically credited as originating with Vico’s 1725 Nuova Scienza
• A fad is a short-term burst in behavior usually starting with explosive growth that rises to a single peak, followed by slower ebbing, analogous to froth on the beach from a breaker
– Bubbles are closely related to fads but are financial in nature and refer to unrealistic prices detached from intrinsic value
Wave Motion Does Not Change With Water Depth
Source: Daniel Bell, Personal communication, 2002
Froth Forms
FadsTrends
Trends And The Bottom LineHow can the analysis of trends, fads and bubbles be used to enhance business performance?
• Exploring nonlinear vs linear growth
• Can be leveraged as early warning systems for:
– Breakout ideas or new products
– Potential negative revenue surprises
– Expected timing of trends and bubbles
• Are directly relatable to key performance metrics, e.g., stock price, financials, YAG sales
• Aid in supply chain planning as analyses of this type can answer questions related to the depth of purchase orders and when to get out of a sales fad or trend for an item
– Improved predictions and forecasts: direction, magnitude, acceleration and likely ceiling
– Evaluated in terms of a prospective hit rate of actual vs predicted outcomes relative to what was previously used
• Facilitate “getting ahead of the curve” in hopes of distinguishing between smoke and mirror fads versus more durable trends in the flow of new ideas and products
– Text and image mining can be instrumental in facilitating this
Source: http://www.theworldeconomy.org/MaddisonTables/MaddisontableB-10.pdf, http://kk.org/thetechnium/archives/2008/10/the_expansion_o.php , http://smartregion.org/2011/03/creative-class Daniel Bell, The Coming of Post-Industrial Society, 1974
Neolithic ~2 million pop
1500 1560 1600 1650 1690 1725 1775 1800 1850 1885 1925 1965 2010
We find ourselves thrown into the middle of things…
Growth Is King
• Exponential growth since the Industrial Revolution (~1760+)
• The “Knowledge Society” emerged when the Services Sector eclipsed the Industrial Sector in growth (~1920s)
• The production and flow of ideas is a primary source of growth
Wealth and Population1-2010 AD
$0
$10,000
$20,000
$30,000
1 1000150016001700182018701900195019702010
0
2
4
6
Population
Year (Discontinuous)
U.S. Occupational Change 1800-2010
Million Billion
Wealth
Production of Ideas1500-2010
Year
# Books
Agriculture
Manufacturing
Low Wage Service Sector
Knowledge Workers
Millions Employed70
60
50
40
20
30
10
0
Services
Year
~Industrial Revolution
~Knowledge Society
~Industrial Revolution
Accelerating Rates Of Change
Source: Adapted from Stewart Brand, Whole Earth Review, 2000
Differential Rates of Change in Social Layers
Nature
Culture
Governance
Infrastructure
Markets/Commerce
Technology
Fashion
SOCIAL LAYERS
Cosmos
Language
Earth
In one framework society is composed of layers, each with its own rate of change
• Slower layers provide stability while faster layers drive innovation
• This hierarchical linear view, while helpful and illuminating, cannot be correct
E.g., “Competitive advantage” has been
reduced to a few months if not weeks
Acceleratin
g C
han
ge
Widespread shift towards greater uncertainty and disruptions to normative business practices
Uncertainty and The Business Landscape
Watersheds In Strategic Thought
A Clear Enough Future
What can be known?
Linear forecasts drive risk-based strategies
Intuition works well hereLinear systemsChange is gradualBehavior is deterministic and predictable
Alternate Futures
A few discrete outcomes define the future
A Range of Futures
A range of possible outcomesNo natural scenariosIntuitive, gut decisions less effective
True Uncertainty
Shrinking evidence that forecasts workNonlinear systemsExtreme changes in behavior can occur
abruptly and without warningBehavior is deterministic but not predictableLearning to live with uncertainty, doubt,
approximate or imprecise answers
From Risk to Greater Uncertainty and ComplexityLow High
1
2
3
?
• Anomalies regarding the assumptions of competitive advantage and sustainability of growth
• Trend towards hypercompetition in a widened arena of business operations versus an industry-specific focus
• Risk is known and quantifiable, uncertainty is neither
Source: Courtney and Kirkland, Strategy Under Uncertainty, HBR, 1996 Richard D’Aveni, Hypercompetition, 1994 Rita McGrath, The End of Competitive Advantage, 2013 Olivier Compte and Andrew Postlewaite, Uncertainty, Ignorance and Strategy, 2014 Kate Raworth, Royal Society for the Arts, Growth Is Not Enough, 2014
Models Of Innovation And Diffusion Theories of innovation and diffusion are rooted in analysis of nonlinear logistic growth curves
Source: Jesse Ausubel, DRAMs as Model Organisms for Study of Technological Evolution, 2001 Steven Johnson, Where Good Ideas Come From, 2010 Jonah Berger, Contagious, 2013 Alex Pentland, Social Physics : How good ideas spread, 2014
• Classic models are built up from an individual time series with models focused on a single diffusion curve based on cumulative data possessing a known origin or zero start value
• More recent theorizing focuses on extensions of the classic model to networks, social learning, flows of ideas, crowdsourcing and quantification of virtually everything – with less emphasis on the individual’s role
• So, from a “classic” innovation perspective Thomas Edison was a visionary
More recent views as the spotlight grabbing manager of a lab employing hundreds of scientists
Classic Diffusion and S-Shaped CurvesEight Generations of DRAM Chips, 1970-2000
Cu
mu
lati
ve DR
AM U
nit S
hip
me
nts (M
illio
n)
8000
6000
4000
2000
4K
16K
64K
256K
1M
4M
16M
64M
1970 1975 1980 1985 1990 1995 2000
Useful Books on Social Networks
Year
From Hierarchies To Heterarchies
A Hierarchical Structure
Heterarchical Structures
Social Networks Fractal Heterarchy
Hierarchical, top-down, Tayloristic organizational structures were ubiquitous in the 20th c with clear – if static -- lines of control, authority and division of labor
Visualizing the Shift In Social Structure
- This structure allows for much greater flexibility: job profiles overlap as talent replaces skill, the corporate “ladder” flattens out or disappears entirely, human capital flows as needed
Note: “Heterarchy” was coined in1945 by Warren McCullough, a neurophysiologist. From Wiki: A heterarchy is a system of organization where the elements of the organization are unranked (non- hierarchical) or where they possess the potential to be ranked a number of different ways. The two kinds of structure are not mutually exclusive. A heterarchy may be parallel to a hierarchy, subsumed in a hierarchy, or it may contain hierarchies. In fact, each level in a hierarchical system is composed of potentially heterarchical groupings which contains its constituent elements.
• The 21st c organization is increasingly focused on flatter networks and is labeled heterarchical
Source: Alex Pentland, Social Physics: How good ideas spread, 2014 Duncan Watts, Computational Social Science, 2013 Eli Pariser, The Fikter Bubble, 2011
Leading Us to The Many, Many Megatrends…#
Me
as
ure
me
nts
pe
r p
ers
on
pe
r u
nit
of
tim
e
Traditional Information Sources and Media:E.g., print, broad demographics,Self-reported and scanner data
Emergence of ConnectivityIPv4
GlobalizationSocial Networks
Mobile TechnologyKlondike-like Wealth Bubbles
Cross-Section Time Series
Key Demographic Trends:
- Increased urbanization
- Birth rates decline
- Aging of the population
- Growth in disposable income
creates a world middle class
As well as many unknowns, uncertainties and questions with no current answers…
Singularity?Sustainability?
Time
Today? Internet of ThingsUbiquitous Computing
HyperDataLiving Laboratories
Wearable Tech
HyperconnectivityIPv6
Nano-Monetization Increasing Complexity/Fragility
Quantified Selves, Cities, SocietiesSurveillance and Control in the Panopticon
43%
13%
23%
33%
14%
3.4%
20%
51%
0
25
50
75
100
1900 1920 1935 1950 1960 1975 1985 1995 2000 2012
Trade-offs in the allocation of household expenditures are a Darwinian, zero-sum game
Trends In The US Household Budget
Sources: US Bureau of Economic Analysis, 2008, http://www.bls.gov/opub/uscs/home.htm, the Census Bureau revised Stat Abstracts after 2008 making pre-1990 information less accessible *All Other expenditures include (as % of 2012 All Other): Transportation (36%), Insurance and Pensions (22%), Healthcare (14%), Entertainment (10%), Religion and Charities (8%), Personal Care (2%), Alcohol (2%), Tobacco (1%), Miscellaneous All Other (1.7%)
US Household Expenditures by Category% of total expenditures, 1900-2012, discontinuous years, current $
%
All Other* $3.3 trill
Housing $2.1 trill
Food $820.4 bill
Apparel $215.6 bill
% Change In Hhold Expenditures
1900-2012
+151%
-76%
+41%
-70%
Necessities $3.1 trill
49% of Total -39%
• Along with significant shifts between categories since the turn of the 20 th c, expenditures on necessities – housing, food and apparel -- saw a large decline (-39%) as a percent of total expenditures with a corresponding shift into the All Other* category
All Other* $3.3 trill
51% of Total +151%
Year (Discontinuous)
From Linear to Nonlinear Models and Assumptions
Technology played a key role in this trend with the adoption of, e.g., telephones, radio and TVs as well as increasing transportation options
• This trend flattens out in the late 80s
Average Household Size in the US1890-2010
Source: US Statistical Abstracts, http://hypertextbook.com/facts/2006/StaceyJohnson.shtml http://www.nationmaster.com/graph/peo_ave_siz_of_hou-people-average-size-of-households
Household Size Shrank 50% In The Past Century
2
3
4
5
1890 1940 1970 1995 2000
2.12.22.22.2
2.32.3
2.42.4
2.52.52.5
2.62.62.6
2.72.8
3.1
0 1 2 3
Sweden Denmark
Norway Germany
SwitzerlandHolland
BelgiumBritain
France Finland Austria
USA Australia
Canada Italy
Japan Ireland
Average Household Size in the Developed World
~2010
Avg=2.5
Year (Discontinuous)
Average # People
Women are working more and spending less time with their families while the opposite is true for men
• Free time as a percent of total time has contracted since the 60s
Where Did The Time Go?
Source: University of Maryland, Scientific Research on the Internet, Base=168 hours per week
% Utilization of Total Time2000
Overall % Change1965-2000
% Change: Men vs Women1965-2000
% Women % Men
16%
19%
22%
44%
0% 20% 40%
Family
Free Time
Work
Personal Care,Sleep
-10.5%
-1.4%
11.9%
-0.2%
-15% -5% 5% 15% 25%
55%
5%
-14%
-2%
-27%
-8%
104%
1%
-40% -10% 20% 50% 80% 110%
%%
A less active, more passive American emerged along with the Internet as dramatic declines are to be seen in arts attendance and leisure pursuits from pre-web days to the present
• An overall metric of adult arts attendance declined 18% from 1982 to 2008 from 39% to 33%
- Exercise and Volunteering are the only categories that show increases in participation
*Arts activities tracked since 1982 are attendance at jazz, classical music, opera, musical plays, non-musical plays, ballet performances, and visits to art museums or art galleriesSource: NEA, Survey of Public Participation in the Arts, 1982-2008
Did The Internet Change Behavior?
Participation In Leisure Activities% of Adults, 2008**
26
28
31
32
33
42
53
0 15 30 45 60
Playing Sports
Outdoor Activities
Sports Events
Volunteer/Charity
Overall
Gardening
Exercise
-33%
-22%
-36%
14%
-18%
-31%
4%
-50% -40% -30% -20% -10% 0% 10% 20%
Total % Change1982-2008
IncreasedDeclined
Foraging, Exploration And EngagementAs hives grow or food sources decline, bees will migrate to new locations
• Bees alternate between random foraging and new hive spotting
• Once a bee finds a good possibility, they return to the hive and perform a "waggle" dance indicating the precise direction and distance of the new location
• Other bees watch the dance and elect to propagate that source
How Do Bees Do It? Is There A Human Analogue?
• New hive location is a kind of exploratory market lottery distinguished by a collaborative and decentralized process leveraging bees unique ability to find nectar
• At the risk of anthropomorphizing distinctly nonhuman behavior, the analogy might be to long-run, aggregate human behavior
• Human creativity is the wild card in the survival (or not) of the species
Source: Thomas Seeley, The Wisdom of the Hive: The Social Physiology of Honey Bee Colonies, 1996
Modeling Fads And Explosive Self-Generating DemandPredictive modeling of behaviors from phenomena such as the explosive flow of water out of a breached dam to search activity for the term “Justin Bieber”
• An inverse t-distribution fits both phenomena (ex Bieber’s burstiness)
Water Flowing From A Breached Dam vs Justin Bieber Keyword Search
Source: Google Trends, Marcch 17, 2014 “Justin Bieber” as a keyword exploded in the 12 months from May 2009 to May 2010 but did not keep pace with the growth in overall Google search activity after. GT output is normalized to mask the real, underlying raw numbers. This process creates a relative, dimensionless, opaque, ipsative metric that ranges between 0 and 100 where “0” does not mean “no activity” but activity below an unknown, inconsistent and arbitrary level that GT does not report and “100” refers to the maximum level of keyword activity for the time window after normalization. The results can and do change constantly for any number of reasons. All of this makes firm interpretation of any GT chart impossible. While one can assume that a rising trend shows increase, a down trend does not imply decline. What one can infer from a down trend is that keyword use has not grown at the same rate as the denominator (all Google search activity). The burstiness is driven by discrete events with wide media coverage in his celebrity/notoriety.
0
25
50
75
100
May-09 Nov-09 May-10 Nov-10 May-11 Nov-11 May-12 Nov-12 May-13 Nov-13
“Ju
sti
n B
ieb
er”
Ke
yw
ord
Financial Bubbles: Tulip Mania In Renaissance HollandA price bubble occurred during the 17th c Dutch Renaissance or “Golden Age” involving speculation in tulip bulbs• Accounts vary but one sale of 40 bulbs was recorded at 100,000 florins
- To put that in mid-17th c Dutch perspective, a ton of butter cost around 100 florins, laborers might earn 150 florins a year and "eight fat swine" cost 240 florins
- One story has it that someone cut up a rare tulip bulb, not his own, like a shallot for his breakfast, leaving its real owner apoplectic with rage
Dutch Tulip Bulb Prices1634-1637
Source: http://en.wikipedia.org/wiki/Tulip_mania
Financial Bubbles And Shiller’s Irrational ExuberanceThe past three decades of financial history contain several examples of the destruction in wealth that can be wreaked when asset price bubbles burst • Nobel Laureate Robert Shiller was an early whistle-blower regarding the potential ravages of the Dot Com Bubble as well as the subsequent real
estate bubble leading into the Downturn
- His book may have “created” the Dot Com Bubble recession with its nearly wholesale adoption by then Federal Reserve chairman, Alan Greenspan, pulling the rate levers
Source: Robert Shiller, Irrational Exuberance, 2001,2006, http://aida.econ.yale.edu/~shiller/data.htm
The S&P 500 vs the Consumer Price IndexAnnualized, 1871-2013
0
400
800
1,200
1,600
1871 1900 1929 1958 1987
0
100
200
Year
S&
P 5
00
CP
I, 1983=
100
CPI S&P
2013
Bubbles?
Extreme Value Models Used To Control Flood RiskMuch of Holland is below sea level and survives only due to an extensive battery of sea dikes• In the early 50s, the worst sea surge in their history far exceeded then current flood controls, killing thousands
• Using a 400+ year record of maximum annual storm surge height, Dutch mathematicians estimated the minimum required sea wall that would protect them against a 1 in 10,000 year event…the levees were then rebuilt to that specification
• With global warming, the Dutch are again rebuilding…this time for a 1 in 100,000 year event
Source: http://news.nationalgeographic.com/news/2001/08/0829_wiredutch.html D van Dantzig, Economic Decision Problems for Flood Prevention, 1956 Paul Embrechts, Claudia Kluppelberg, Modelling Extremal Events: for Insurance and Finance (Stochastic Modelling and Applied Probability ), 2012 Mary Mapes Dodge, Hans Brinker and the Silver Skates, 1865
Landscape Sculpture Commemorating the “Little Dutch Boy”In Madurodam, The Netherlands
The Importance And Relevance Of FashionFashion is one of the world's most important creative industries
• It is the major output of a global business with annual U.S. sales of more than $200 billion—larger than those of books, movies, and music combined
• Fashion has provided economic thought with canonical examples of consumption, conformity, diffusion, networks, trends and fads
• Social thinkers have long treated fashion as a window into social class, change and culture
• Cultural theorists have focused on fashion to reflect on its symbolic meaning and social ideals
• It is a greenhouse for the analysis of trends and fads
Source: Hemphill and Suk, The Law, Culture and Economics of Fashion, 2009
FEEDBACK/PROPAGATE
3 Years of Sales
Decline
“Blooming, Buzzing Confusion” in Idea FlowsDecisions Analogous to Bees and New Hive
Location
Vis
ion
ari
es
Pla
ce
Be
ts
Sources ToolsWellesP K DickBellWintourJobsDavosMeekerArtistsAcademics
Long-Tailed
Product?Growth
Maturity Decline
Decelerating Sales
Pe
rform
an
ce
Sales DataMarketing SpendCompetitive Info from
Comparison Engines
Tools
Pro
du
cti
on
of
the
Po
rtfo
lio
Market Mix ModelingData MiningPredictive ModelingNetwork and Diffusion ModelsRecommender SystemsMachine LearningTest, Learn and Refine
Sources
Introduction
Accelerating Sales
0
Innovation Pipeline Sales Curves and the Product Life Cycle Execution
Social MediaPatentsHiring TrendsFilm, Books, Art, etc.VC InvestmentsDemographics (Youth
and Agelessness)Tech ConferencesBlogs
Go – No Go
Visionaries
Visionaries Originate While Markets Imitate, Diffuse and Drive
Fragmented Market Arenas And Proliferating, Rapidly-Cycling Products Drive Need For Massive-Scale Monitoring And Analysis
Evidence-Based Decision-Making
R&D-Tacit KnowledgeText and Image MiningPrediction MarketsContinuous TrackingCompetitive Info from
Comparison Engines
La
un
ch
Time
Sc
an
, M
on
ito
r, O
rig
ina
te,
Imit
ate
Trends and fads are driven by evanescent, black box creative ferment and idea flows that are difficult to capture, quantify and predict
*Word-of-mouth and Ready-to-WearSource: Teri Agins, Personal communication, The End of Fashion, 2000 David Wolfe, Doneger Group, Personal Communication, 2014
Cascades in Fashion Industry Trends
Premiere Vision Summarizes the Latest Ideas
Paris’ fabric and textile show is a first look at latest trends based on fabric purchases
Social media WOM plays a tacit role in diffusion of the latest designs
Networks Diffuse the Latest Ideas
Runways reflect a surprising degree of consistency
Fashion Week Presents the Latest Designs
Buyer purchases play a huge role in shaping what consumers see
Retail Buyer Purchases Shape Consumer Choice
Emerge: Haute Couture
Cascade: Couture
Consolidate: Mass Luxe
Diffuse: RTW or Pret-a-Porter
Decoding Trends And Fads: Artists As Cultural AntennaH
aute
Co
utu
re a
s t
he
Av
ant-
Gar
de
Ho
w d
ec
od
e e
me
rge
nc
e a
s w
ell
as
im
po
rta
nc
e,
pro
mis
e o
r p
rete
ns
e?
Art, Innovation,
Imitation
CommoditizationMonetization
Trend Setting
Trend Is Set
Fast Fashion, e.g., Zara, H&M
Fast Fashion Skips Over Haute Phase
The evolution of Chinese fashion street styles based on image mining of thousands of pictures taken in Shanghai and Beijing in the last five years suggests:
Identifying Fads And Trends With Machine Learning Algorithms
Source: http://www.jingdaily.com/from-social-status-to-self-expression-the-rapid-evolution-of-chinas-street-style/42059/?utm_source=twitterfeed&utm_medium=twitter# Svante Jerling, Personal communication, P1.cn, 2014
Tracking Handbags With LVMH Logo in China2008-2013
• Tastes may have shifted away from impersonal, conspicuous status statements using “logo” brands such as LVMH to more personal, “niche” brands
• Slowing economic growth as well as a crackdown on corruption and pirating are also factors
• These include tracking social mobilization and civil unrest, epidemic forecasting, real-time prediction of stock market moves, rate of picture postings on Flickr during Hurricane Sandy that correlated with the storm’s barometric pressure, networks of A-listers and their entourage
- After Currid-Halkett, fashion is part of the celebrity network and could be decoded as such
Leveraging Social Media To Predict Emergent PhenomenaOpen Source Indicators (OSIs) such as text and image mining of social media have seen wide use in the prediction of emergent social phenomena
The Fame Game: Celebrity Networks
Source: IARPA program on OSIs, enter these search terms into a Google Scholar search window: D12PC00337 OR D12PC00285 OR D12PC00347 Elizabeth Currid-Halkett, Starstruck: The Business of Celebrity, 2012, she posits five tiers in the celebrity system: first, celebrities and aspirants, second, PR reps, agents and handlers working directly for the first tier, third, the supporting machinery of lawyers, chauffeurs, bodyguards, couriers and attendants, fourth, “preppers,” e.g., stylists, beauty salons and fifth, media
The Arbitrage Of IgnoranceRediscovering the value inherent in ignorance, uncertainty, diffidence, cultivation of doubt, error and insecurity as modes of learning, motivation and discovery
We’re All Drinking From Fire Hoses Now
Source: Stuart Firestein, Ignorance: How it drives Science , 2012 Rita McGrath, The End of Competitive Advantage, 2012
• Sherlock is dead! Long live Sherlock!
– It isn’t possible to keep up with everything. Who was the last intellectual that could?
• Hype, hubris and disinformation in massive quantities of information: our modern Babbits
• Approximation versus false precision
– What does it mean to “optimize” inaccurate and incomplete data?
– Can some grad student in decision theoretics develop a “Law of Bad Data?”
• In the midst of a paradigm shift
– Disruptions are everywhere
– Anomalies in Porterian assumptions of the sustainability of growth
Appendix
Vulnerability, Blind Spot
Enhanced Johari’s WindowKnown to Self Not Known to Self
Known to Others
Not Known to
Others
Façade/Insight
Johari’s Window
Hidden, Façade, Private
or Tacit Knowledge,Advantage
Unknown to Self or Others,
Area with Greatest Potential
Knowledge Ignorance
Developed in the 50s as a framework visualizing the structure of interpersonal knowledge
• Still sees wide use in corporate training events
• Adapting and extending the original, symmetric boxes to the wider arena of what is known vs not known, a potentially more representative and asymmetric framework emerges
Source: JosephLuft and Harrington Ingham, The Johari window, a graphic model of interpersonal awareness, 1955 Lowell Bryan, McKinsey Director, “~80% of a corporation’s knowledge assets are tacit,” 2004 Associate Lunch talk
Johari’s Window
Open Arena, Mano a Mano,
Trench Warfare
Hic Leonem,Uncertainty,
Black Swans, Exploration,Serendipity,
Greatest Potential,Generally Not Quantifiable
But Not Unknowable
Competitive Arena
Advantage, Private,Tacit,
Façade, Insight
Vulnerability, Blind Spot
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