the fundamentals of market share | kyle findlay

THE FUNDAMENTALS OF MARKET SHARE

Presented by Kyle [email protected] TNS Global Brand Equity CentreSCTPLS Conference 2009

2 The Fundamentals of Market Share | Kyle Findlay

THIS PRESENTATIONTOUCHES ON…

How people group together

See notes pages for further elaborations on the topics covered in this presentation

Why life isn’t fair

…and the best don’t always win

The role of social influence on

brand perceptions and market share


MARKETS ARE SOCIAL


MARKETS ARE SOCIAL

*Source:”Engaging People in Their backyards”. Research World, April 2009

“…consumers are in dialogue with each other more than ever before, creating and sharing ideas about products and services that can have as much, and sometimes more, impact on opinions as the messages paid for by advertisers.”

[Joan Lewis and Kim Dedeker of Consumer and Market Knowledge at Procter and Gamble]*


SOCIAL INFLUENCE USED TO BE CONFINED TO…

conversation peer pressure gossip


BUT, WITH TECHNOLOGY, ALL BETS ARE OFF

permanent connectivity global penetration / ubiquity

increased inter-connectedness

Roughly 10,000 social interactions daily*

social networking

*Source: Earls, M. (2007). Herd: How To Change Mass Behaviour By Harnessing Our True Nature. p.113. John Wiley & Sons

8

User reviews

9 The Fundamentals of Market Share | Kyle Findlay *Source: How Social is Amazon?http://bokardo.com/archives/how-social-is-amazon/

NOTHING IS EVER REALLY NEW


WE EITHER DEFINE OURSELVES BY SOCIETY…

Source: www.exactitudes.com


…OR AGAINST IT

goth emo

punk


EITHER WAY, WE DEFINE

OURSELVES RELATIVE TO

SOCIETY


SOCIAL INFLUENCE IS INESCAPABLE


IN A SIMILAR WAY, BRANDS ARE DEFINED BY THEIR CUSTOMERS

(and customers are defined by their brands)

15

BRANDS EXIST IN THE MINDS OF CUSTOMERS

Liverpool

16

BRANDS EXIST IN THE MINDS OF CUSTOMERS

Ferrari

17 The Fundamentals of Market Share | Kyle Findlay Read more: http://www.mediapost.com/publications/?fa=Articles.showArticle&art_aid=101402http://www.socialcustomer.com/2009/03/skittles-social-media-experiment.html

EXTREME EXAMPLE


Round 1

Probability

Round 2

Brand

1Brand

1Brand

1Brand

2Brand

2Brand

3Brand

3Brand

4Brand

4Brand

5Probability 0.1

Round 3

Probability 0.1 0.1

0.20.20.20.3

Round 1

Probability

Round 2

Probability 0.1

Round 3

Brand

1Brand

1Brand

1Brand

1Brand

2Brand

2Brand

3Brand

3Brand

4Brand

5Probability 0.1 0.10.4 0.2 0.2

Round 1

Brand

1Brand

1Brand

2Brand

2Brand

3Brand

3Brand

4Brand

4Brand

5Brand

5Probability

Round 2

Probability 0.1

Round 3

Probability 0.1 0.1

0.20.2 0.2 0.2 0.2

1 2 3 4 5

Market share

1 2 3 4 5

1 2 3 4 5

A THOUGHT EXPERIMENT…


EXPONENTIAL SPREADOF WORD-OF-MOUTH

Source: Figure 1.2 in Duncan Watt’s Six Degrees (2003): "A pure branching network. Ego knows only 5 people, but within two degrees of separation, Ego can reach 25; within three degrees, 105; and so on."


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105

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100

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Ego Step 1 Step 2 Step 3

A simple bar graph illustrating how the number ofadvocates for a brand can grow at an accelerating rate

EXPONENTIAL SPREADOF WORD-OF-MOUTH

Source: Figure 1.2 in Duncan Watt’s Six Degrees (2003): "A pure branching network. Ego knows only 5 people, but within two degrees of separation, Ego can reach 25; within three degrees, 105; and so on."


“Natural selection works like compound interest:

Source: Pinker, S. (2009). My Genome, My Self. The New York Times. Published January 7, 2009.

SMALL INITIAL ADVANTAGE >>> BIG EFFECT

A gene/brand with even a 1% advantage in the number of surviving offspring/advocates it yields will expand geometrically over a few hundred generations, and quickly crowd out its less fecund alternatives/competitors.

Why didn’t this winnowing leave […] us with the best version of every gene/product? …The world would be a duller place, but evolution doesn’t go out of its way to keep us entertained.”

[Steven Pinker]*


Mmmmmm, WHICH BRANDSHOULD I BUY?


“preferential attachment”

WHAT IS THIS MECHANISM CALLED?

“preference”

+“attachment”

(e.g. are you a dog or cat person?)


There are many potential candidates, but for the purposes of this talk, I will be focusing on…




Some quantity that…

“…is distributed among a number of individuals or objects according to how much they already have”

[Wikipedia, 2009]




Join us!

Join us!

Join us!

Join us!

Join us!

Join us!

Join us!

Join us!

Join us!

Join us!

Join us!

Join us!

HOW DOES IT WORK?


Join us!Join us!Join us!

Join us! Join us!Join us!Join us!

Join us!

HOW DOES IT WORK?

Join us! Join us! Join us! Join us!


A ROSE BY ANY OTHER NAME…


Italian economist Vilfredo Pareto in 1906

PARETO PRINCIPLE

80% of land owned by 20% of population

a.k.a. the 80/20 Rule

Source: Wikipedia, the Free Encyclopaedia


“You need money to make money”

THE RICH-GETTING-RICHER EFFECT

*Source: World Institute for Development Economics Research at the UN University (http://news.bbc.co.uk/2/hi/business/6211250.stm)

20% 80%“Richest 2% own

'half the wealth”*


“For unto every one that hath shall be given, and he shall have abundance: but from him that hath not shall be taken away even that which he hath.”

[Matthew 25:29, King James Version]

THE MATTHEW EFFECT


THE LONG TAIL

Digital retailers

Warehouse retailers

Brick and mortar

retailers

Source: Wikipedia, the Free Encyclopaedia


DOUBLE JEOPARDY

William McPhee in the 1930s

Source: TNS data


CUMULATIVE ADVANTAGE

positive social influence

negative social influence

Source: Campbell, J; Liddle, N. (2008) Why Brands Succeed: Luck or Skill?. Presented at SAMRA conference, at the Swazi Sun and Spa in Swaziland, May 2008


CUMULATIVE ADVANTAGE

positive social influence negative social influence

Source: Campbell, J; Liddle, N. (2008) Why Brands Succeed: Luck or Skill?. Presented at SAMRA conference, at the Swazi Sun and Spa in Swaziland, May 2008


Zipf’s LAW

Source: Frequencies from 336,310 documents in the 1GB TREC Volume 3 Corpus 125,720,891 total word occurrences; 508,209 unique words


OTHER EPONYMOUS ‘LAWS’

Zipf’s Law for frequency of words

The Barabási–

Albert model in

network theory

Gibrat’s Law describes

the growth of

firms and

cities

Gutenberg–Richter law

describes the magnitude

and frequency of

earthquakes

The Gompertz-

Makeham law of

mortality

Kleiber’s Law which describes

the relationship between the

metabolic rates

of animals (including

humans) and their mass

Lotka’s Law which describes the

frequency of publication by authors

Robert’s law for

executive

compensation

The Stefan–

Boltzmann law in

thermodynamics

Steven’s power law relating to

the magnitude of a wide range of

physical stimuli

such as brightness, sound,

roughness, etc.

The Yule distribution

in probability

and statistics

Bradford's Law describes

the diminishing returns

associated with widening a

search for references in

science journals


POWER LAWS

Source: Barabási, A-L. (2002). Linked: How Everything Is Connected to Everything Else. Plume. ISBN 0-452-28439-2


POWER LAWS

Stock market

activity: returns,

trading volume, and

trading frequency

Protein family and

fold occurrence in

genomes (human

and other animals)

Heart rate variability as a predictor of mortality in the elderly

Degree distribution of

nodes in a scale-free

network (such as the

internet)

Incoming links

to blogs

Guild sizes in

the massively multiplayer

online role-playing game,

World of Warcraft

Fractal geometry

(see Benoît

Mandelbrot’s work for

more on this)

The eponymous ‘laws’ listed earlier refer to specific areas where power laws apply


Phenomenon Assumed exponent

Frequency of use of word 1.2

Number of hits on websites 1.4

Number of books sold in the U.S. 1.5

Telephone calls received 1.22

Magnitude of earthquakes 2.8

Diameter of moon craters 2.14

Intensity of solar flares 0.8

Intensity of wars 0.8

Net worth of Americans 1.1

Number of persons per family name 1

Population of U.S. cities 1.3

Market moves 3 (or lower)

Company size 1.5

People killed in terrorist attacks 2 (but possibly a much lower exponent)

A list of several power law scaling exponents, relating to specific phenomena*

POWER LAWS

*Sources:Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. p.264. Allen Lane. Newman, M.E.J.. (2005). Power Laws, Pareto Distributions, and Zipf’s Law. Complexity Digest 2005.02. p.1-27


Step 1: Rank the data

SPOTTING A POWER LAW

rank data


Step 2: Log-log linear transformation


log-log transformation


Step 3: Curve fitting


NOTE: THIS IS AN EXAMPLE FROM

GOOGLE IMAGES.


THE QUESTION:

DO POWER LAWS EXIST IN OUR

MARKET DATA?


Types of cheeseTypes of mustard

FINDINGS


k = 2.08 k = 3.23

Types of cheeseTypes of mustard

FINDINGS


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A chart summarising the goodness of fit scores we found across the 46 datasets. A smaller distance indicates a better fit. Average distance = 0.32

FINDINGS

Goodness of fit

Studies

BEST FIT

WORST FIT

46 datasets


WHEN DO POWER LAWS FORM?

…when the “cost” of distributing a quantity is low

Or, in network terms, when the cost of creating new links/edges between nodes is low

Image source: Figure 1.3 in Duncan Watt’s Six Degrees (2003)



“The essential limitation of scale-free networks is that everything is assumed to come for free …without regard for the difficulty of creating or maintaining them [links between nodes]”.

[Duncan Watts, 2003]*

*Source: Watts, D. (2003). Six Degrees: The Science of a Connected Age. W. W. Norton & Company



Sources: Wikipedia, the Free EncyclopaediaBarabási, A-L. (2002). Linked: How Everything Is Connected to Everything Else. Plume. ISBN 0-452-28439-2



Sources: Wikipedia, the Free EncyclopaediaBarabási, A-L. (2002). Linked: How Everything Is Connected to Everything Else. Plume. ISBN 0-452-28439-2

Scale-free networks are not random

Non-random process? Preferential attachment?



*Source: Ball, P. 2005. Critical Mass: How One Thing Leads to Another. Random House

“In this computer simulation, black regions would represent a liquid and white regions a gas… The size of these regions spans all scales – from the size of a single particle to the size of the entire system. There is no typical scale to this unevenness: it is scale-free.”*


BRINGING IT BACK TO BRANDS

Business results

Power in the market

Power in the mind


THE ROLE OF FRICTIONIN THE MARKET


Attitudinal/Preferential

End result

THE ROLE OF FRICTIONIN THE MARKET

Friction


Power law

WHAT HAPPENS WHEN THERE IS NO FRICTION?

Attitudinal


WHAT DOES IT MEAN FOR MARKETS?

Double Jeopardy is most apparent in our data

Requirements:

1. The market needs to have reached a level of stability and maturity

2. Market big enough to support the formation of customer groups

3. The benefit of branding needs to outweigh the cost

4. Difference between the brands in the market

5. The market cannot be a monopoly market

Sources: Ehrenberg, et.al., 1990; Ehrenberg, 2005, Date unknown; Wikipedia, 2009


THE NEXT QUESTION(S)

A measure of friction

Its correlation with goodness of fit?

12


AREAS OF THE FRAMEWORKTO ELABORATE ON…

*See notes pages for further elaborations on the topics covered in this presentation

1. Generative mechanism – preferential attachment is simplistic

e.g. it does not capture the decay of network nodes.

2. Accounting for interaction effects – this framework looks at

brands/ideas in isolation when we know that they are

continually interacting with other systems in their environment

(“meta-networks”)


…power laws are unlikely to be clearly evident in markets.

What is more interesting is the biasing process influencing these constantly evolving systems.

This bias affects how people group around brands and other ideas!

THE IMPORTANT THING TO REMEMBER IS THAT…


IN SUMMARY

Markets are social

Preferential attachment*

Power laws

Role of friction

1

2

3

4

Join us!

*As a candidate mechanism

THANK YOU!

Comments, questions, suggestions?E-mail me if you would like a copy of this deck

[email protected]

mailto:[email protected]