Cone TM Digital Marketing - Business Scenarios PDF

Digital Transformation

Throughout eternity, all that is of like form comes around again –

everything that is the same must return again in its own

everlasting cycle.....

• Marcus Aurelius – Emperor of Rome •

Digital Product Lifecycle Strategy

• Everything that goes around, comes around – everything has its’ own

lifecycle, in its’ own time. Things are born, grow, age, and ultimately

they die. It’s easy to spot a lifecycle in action everywhere you look. As

a person is born, grows, ages, and dies – then so does a star, a tree, a

bird, a bee, or a civilization – and so does a company, a product, a

technology or a market - everything goes around in a lifecycle of it own.


Investment

Product

Lifecycle

Product

Design

Product

Launch

Product

Planning

Death

Plateau

Product

Maturity

Decline

Aging

Early Growth

Migrate

Customers

to new

Products

Withdraw

Innovation Prototype / Pilot / Proof-of-concept

Cash Cow Cease

Investment


The Cone™‏ - Lifestyle Understanding

The CONE™

The CONE™ - Social Intelligence

Getting to the heart of audiences - and putting audiences back at the heart of marketing.

The CONE™ - Audience Measurement

• Due to severe competition, Communications Service Providers (CSPs) such as 3 Mobile, EE,

Talk-Talk and Vodafone, along with Mobile Virtual Network Operators (MVNOs) such as Virgin,

Tesco and Giff-gaff - no longer make significant profit from their core services (Mobile, Fixed-line

and Broadband). This has caused the dash for “Quad-play”, where CSPs now add Media and

Entertainment Packages to their core network services offering (Mobile, Fixed-line & Broadband).

• TV Set-top Boxes (Virgin, Talk-Talk, Sky, EE) are connected to the Internet and continuously

stream Audience Channel Selection data and Music Play-lists to the Communications Service

Provider (CSP) Audience Insight and Analytics servers. Similarly, Smart Phone Apps (BBC i-

player, Sky Go, Netflix, Spotify) also continuously stream Audience Channel Selection data and

Music Play-lists to the Communications Service Provider (CSP) - via Apigee to AWS Big Data.

• In a typical household (Mother, Father, two children) there may be four Smart Phones and as

many as ten other internet connected devices (Tablets, Laptops, Internet TVs, TV Set-top Boxes

and Video Games Boxes) – all streaming video, audio and data – the details of which are

captured, stored and analysed by the Communications Service Provider (CSP) using “Big Data”

Analytics techniques. This yields valuable Audience Metrics and Analytics based on intimate

understanding of consumer video, audio and internet content from which actionable audience

insights is derived from video, audio and internet streaming data – which drives Personalised

Advertising across all devices (Smart Phone, Tablet, Internet TV, Games Boxes).

The CONE™ - Social Intelligence

This revolutionary Digital Marketing approach is called the Cone™‏- a next-

generation Social Intelligence solution for real-time lifestyle understanding: -

• The Cone™‏solution uses Social Intelligence to get right to the heart of every

audience - and puts the audience back at the heart of every media organisation.

• The Cone™‏Digital‏Marketing‏solution works through Real-time Analytics –

tuning directly into the dynamic nature of people, fashion, media and culture.

• The Cone™‏solution analyses intimate audience viewing behaviour using Social

Intelligence and Real-time Insight, inspiring better digital marketing campaigns,

faster – ideas which connect directly with the widest possible network audience.

• Most importantly, the Cone™‏solution tracks and understands the changing

behaviour of viewers, fans and audiences and their propensity to engage with

different ideas, lifestyles, interests, needs, passions, aspirations and desires.

21st Century Lifestyle Understanding

Fanatics (10%) Enthusiasts (20%) Casuals (20%) Indifferent (40%)

Cone™ Fan Base Understanding©

©2013 Innovation Pipeline

The CONE™ - a New Lens

Today we can view audiences through a better lens than given by traditional segmentation. Our better lens is what we now call the Cone™. The Cone™ visualises the volume and behaviour of a user-defined audience. When an audience is viewed is this way, the behaviours and volumes are visualised across our Cone™ spectrum that segments the audience’s propensity to engage. It’s this behaviour and volume understanding that visualises the Cone™.

Scene Setters

Restless Contented


Cone™ Lifestyle Understanding

What‏is‏‘The‏Cone’?

• At its simplest, The‏Cone™‏is a visual metaphor that maps the volume of audiences across an

engagement spectrum with regards to how people connect with different passions and ideas.

• At its most sophisticated, the Cone™ delivers total entertainment digital innovation.

Why a Cone?

• The Cone™ shape is informed by the correlation between the volume of audiences and their propensity

to engage with different passions. This Cone shape proves to be universal in it’s application to brands,

ideas and industries that have ‘fans’ i.e. –

1. The thin, pointy end of the Cone™ -

• Low audience volume but incredibly high engagement and therefore high ‘purchase’ intent’

2. The fat, base end of the Cone™ -

• High audience volume but low engagement and therefore, much lower ‘purchase 'intent’

• We use our proprietary IP to produce The Cone™ in industries and clients that have fans (or at least

where people engage through ‘passionate interest’ vs mere ‘consumption’). Thus The‏Cone™‏maps

people as fans and audiences with active interests, needs and desires - not just as passive consumers.

Cone™ Lifestyle Understanding Cone™ Lifestyle Understanding© Fanatics (10%) - Core fans, including cultural arbiters, trend setters, curators, editors. Enthusiasts (20%) - Social amplifiers, restless for the new, who enjoy the discovery and social kudos of feeling and “being first”. Casuals (20%) - The wider market, happy to be influenced by others and open to engagement through social influence. Indifferent (40%) - Generally agnostic, uninterested and indifferent to ideas in question.

Fanatics 10%

Enthusiasts 20%

Casuals 30%

Indifferent 40%



How does the Cone work?

• The principle of The‏Cone™‏is firstly to understand people’s lives, and then understand

the role that different entertainment concepts and content play in their lives. Using this

narrative of understanding, we can gain unique insights, helping make better and more

incisive decisions through understanding who ideas are connecting with and why that

inspires creative marketing. We then apply The‏Cone™‏creative inspiration to innovate

compelling propositions and ideas that will connect with the widest possible audiences.

• On the surface, The‏Cone™‏profiles people’s propensity to engage with any given lens

e.g. film, reality TV, music, radio, mobile, etc. along our FECI continuum: ranging from

Fanatics, to Enthusiasts, to Casuals and finally “Indifferent”. We then use proprietary data

analytics to profile and describe groups of similar people within the FECI continuum.

• The‏Cone™‏facilitates our understanding of how groups of like-minded individuals are

connecting (or not connecting…..) with our brand and content – thus we can use intimate

personal insights to learn how to inspire the right kinds of ideas and events to better target

brand positioning and product content, influencing more receptive audiences, so delivering

new core fan connections which drives an expanding and increasingly loyal fan base …..



The CONE™ - BBC Radio 1

Cone™‏Innovation - BBC Radio 1, 2002-05

• In 2002, BBC Radio 1 - the UK’s no.1 youth radio brand (now globally streamed to millions) - was in

danger of losing its public service licence. Listener volume was in decline, with a total RAJAR audience

of circa 7 million. Radio 1 had become disconnected from its core audiences.

• We were asked to help innovate the total transformation of ideas, creativity and environment to return

Radio 1 to its pre-eminent place in youth culture.

• Central to Radio 1’s innovative revival was a new lens through which to view the Radio 1 audience. This

lens helped us understand audience engagement through behaviour - versus fixed demographics.


Sony Music: Audience Cone™ / Artist DNA

Sony Music 2007-2011 - Audience Cone™‏/ Artist DNA

• The key to success at Sony Music was using the Audience‏Cone™‏and

Artist DNA in order to help A&R Managers and Producers to understand the

role music plays in people's lives - and then understand the impact of any

particular genre or specific artist within that audience and cultural context.

• We provided a unique approach to make sense of Digital Marketing and

Social Intelligence as part of an Artists musical and career development.

We called it the Artist DNA – a tool which supports the insightful creative

foundation for all artist releases, tours, appearances and campaigns.

• Today the Cone™‏App‏- our proprietary solution using the Audience

Cone™‏and Artist DNA approach – is used by Sony Music in 32 global

territories – placing the audience back at the heart of Sony Music and putting

the artists back at the heart of their audiences - attracting new fans and re-

connecting with old fans – to give the widest possible audience and fan-base.

The Challenge – American Idol, 2014


• Analyse the Reality TV audience spectrum so that we can better understand who American Idol

fans are, and therefore gain insight into how we can halt the audience decline of 2014…..

• There is a very real and present Reality TV Cone - because there exists distinct Reality TV audience

clusters - discrete groups of people who engage with Reality TV in a variety of different ways…..

• Reality TV is a well understood lens into how people live out their own lives (they might not admit this) –

so that we can understand viewers lives and lifestyle and engage them through the Reality TV lens.

• We can map this lens through our Fanatics, Enthusiasts, Casuals and Indifferent (FECI) spectrum in

order to place each individual along a continuum of audience interest, affinity, loyalty and engagement.

• We can then profile and segment these people into different groups along the FECI spectrum – and

therefore, those within these groups who have a greater propensity and appetite for American Idol: -

– Viewers with an increased or decreased awareness of the Reality TV genre

– Viewers with a higher or lower interest in Reality TV shows / media coverage

– Viewers with a greater or lesser knowledge of Reality TV presenters / participants

– Viewers who invest more or less time in consuming Reality TV – live / streamed content

The CONE™ - American Idol, 2014

Cone™‏Innovation – American Idol, 2014

1. Fanatics - 10% : - Know about each contestant in every show, devote time to reality TV. Primarily live viewers.

2. Enthusiasts - 26%: - Buy very much into Reality TV. Have other passions. Love social media ‘second screening’.

3. Casuals - 42% : - A more diverse group. Reality TV is only one part of their busy lives. Will engage if it meets

their needs and values. American Idol, 2014 over-indexed on “Casuals”‏– but under-indexed on Audience Total

4. Indifferent - 22% : - “Indifferent”‏viewers interact with the brand when there are other brand Fans within their

social network who act as “Influencers”.‏‏AI 2014 under-indexed on both “Indifferent”‏and Audience Total

5. Unconnected. Huge marketplace. Generally, “Unconnected”‏viewers only connect with the brand if there are

other brand advocates within their social network who act as influencers or “Introducers”‏to Reality TV series.

Fanatics

10%

Enthusiasts

26%

Casuals

42%

Indifferent

22%


Analyse the Reality TV audience so that we

can better understand who American Idol

fans are, and therefore gain insight into how

we can halt the audience decline of 2014…..

• There is a Reality TV Cone because there

exists discrete groups of people who

engage with Reality TV in different ways.

• Reality TV is a well understood lens in

peoples lives (they might not admit this -

but we can view their lives through this

Reality TV lens).

• We can map this lens through our Fanatics,

Enthusiasts, Casuals and Indifferent

(FECI) continuum in order to place every

individual along the spectrum of audience

engagement.


Cone™ Fan Base Understanding


The Cone™ Application

• Where old-school audience analysis was retrospective and fixed, the

new Cone™ data science is lean, agile, current, fluid and predictive.

• The‏Cone™‏App takes our proven Audience Cone™‏and Artist DNA

approach and puts it on-line to render a custom lens for an audience; a

lens you can zoom, pan and focus - to reveal more hidden detail.

• The‏Cone™‏App applies data science and digital analytics principles to

generate innovative marketing insights - translated into a narrative of

real-time audience understanding - that answers the six key questions: -

1. What’s happening now ? 2. Who’s making it happen ? 3. Where is it happening ?

4. Why is it happening ? 5. When is it happening ? 6. How is it happening ?

The‏Cone™‏Application

Social Intelligence

Cloud CRM

Data

Profile

Data CRM / CEM

Big Data

Analytics

Customer Management (CRM / CEM)

Social Intelligence

Campaign Management e-Business

Big Data Analytics

The Cone™‏

Customer Loyalty

& Brand Affinity

The Cone™‏ Smart Apps

Audience Survey Data

Insights

Reports

TV Set-top Box

Proof-of-concept and Prototype

The Cone™‏approach is lean, agile, smart and creative: -

• We start by providing a custom Cone™ app as a proof of concept. We then work with client key stakeholders to scope a detailed brief which articulates a business problem domain that the Cone™ can help resolve.

• Under normal circumstances we utilise all current and past audience research and any other available internal data to first establish a baseline client Cone™.

• We then augment this by overlaying external data - Social Media Intelligence and other live streamed audience data that will provide our new real-time view for who / what / why / where / when and how fan-base and lifestyle understanding.

• Lastly, we apply this understanding social intelligence as new actionable insights to inform creative marketing campaign solutions against the agreed brief.

• Post proof-of-concept, we then agree a Cone™ app fixed term licence along with Cone™ consulting, mentoring and support – on-demand, as and when required.

The Cone™‏ – Model Design and Delivery

Phase /

Step

Description Input Design

Process

Output Cost

(estimate)

Skill Set

1 1 Cone™‏Model‏Data‏

Analysis / Design

User

Requirements

Data Analysis &

Data Modelling

Cone™ Logical

Data Model

£k Business /

Data Analyst

2 Cone™‏Data‏Design‏

– Questionnaire

User

Requirements

Data Analysis &

Data Modelling

Questionnaire

Survey Form

£k Business /

Data Analyst

3 Cone™‏Physical‏

Database Design

Logical Data

Model

Cone™

Database

Design

Physical

Cone™ Design

£k Data Analyst

/ DBA

4 Cone™‏Data‏Load‏–

Questionnaire /

Survey Forms

Physical Data

Model, Survey

Questionnaire

Cone™ Model

Calibration and

Tuning Runs

Initialised

Cone™ Model

£k Business /

Data Analyst,

DBA

2 5 Cone™‏Data‏Load‏–

In-house CRM and

Audience Data

Physical Data

Model, People

CRM Data

Cone™ Model

CRM Data Load

Populated

Cone™ Model

£k Business /

Data Analyst,

DBA

6 Cone™‏Profiling Cone™

Clustering

Algorithms

Cone™ Model

Data Profiling –

Kernel k-means

Profiled

Cone™ Model

£k Data Analyst,

DBA, Data

Scientists

3 7 Cone™‏Streaming‏

and Segmentation

Historic Sales

and CRM Data

Cone™ History

Matching Runs

Cone™ Historic

Trends

£k Data

Scientists

8 Cone™‏Real-time

Social Media Feeds

Global Social

Intelligence

Cone™ Real-

Time Analytics

Actionable

Cone™ Insights

(variable with

Cone™ total

data volume)

Data

Scientists

The Cone™‏ – Digital Marketing

The Cone™‏

The Cone™‏ – Digital Marketing

– turning Social Intelligence into Actionable Marketing Insights / Sales Opportunities…

1. Education Cone™ – Training and Education Business Scenario and Use Cases

2. Utilities Cone™ – Water, Gas and Electricity Business Scenario and Use Cases

3. Media Cone™ – Broadband, Land-line, Mobile and Entertainment Business Scenario and Use Cases

4. Music Cone™ – Brand / Genre / Label / Artists Business Scenario and Use Cases

5. Political Cone™ – Party and Voter Election Business Scenario and Use Cases

6. Fashion Cone™ – Fashion and Luxury Brands Business Scenario and Use Cases

7. Sports Cone™ – Elite Team Sports Franchise Business Scenario and Use Cases

8. Patient Cone™ – Digital Healthcare / medical Business Scenario and Use Cases

The Cone™‏ - Digital Marketing

The Education Cone™‏

The Education Cone™‏ – Student-base Understanding

– turning Social Intelligence into Actionable Educational Insights / Opportunities…

• Fanatics – (10%) Eternal Students

• Enthusiasts – (20%) Pursue multiple Training and Educational Opportunities

• Casuals – (30%) spend only on essential Training for their chosen Career Path

• Indifferent – (40%) Consume only free Training and Educational Opportunities

• Unconnected – not currently interested in Training or Educational Opportunities

Student Survey Questionnaire - Chapters

Survey Chapters – Internet of Everything (IoE) Certification Course

1. Demographics & screening (2 mins)

2. General lifestyle questions (3 mins)

3. General media & technology User & Attitude (3mins)

4. Consumer tech brand affiliations (2 mins)

5. Behavioral questions – Future of Work (3 mins)

6. IoE Category - Future of Skills interest (3 mins)

7. IoE Program focus (3 mins)

8. Personal perceptions (3 mins)

The Education Cone™‏ – Business Scenarios

Scenario 1 – Education Sector - How many Courses / Lecturers / Students do I need - to meet targets for the new Academic Year ? • An Academic Awards Body has aggressive targets to meet in launching a new

Campus, Courses and Curriculum whilst attracting sufficient new students for the following Academic Year. Senior Management needs to know the following: -

Student-base Understanding – Use Cases

– Who are our prospective students ? Where do they live ? What do they have in common ?

– where do our new conquest Student-base live and how far are they prepared to travel to attend tuition at the new Campus ? What courses do they want to register for ?

– How many Sites / Courses / Lecturers do I need to operate and how many Students do I need to attract – in order to meet performance targets for the new Academic Year

– who in our existing traditional Student-base will be lost to competitors and who will be retained over the proposed new Campus re-location, new Courses and Curriculum?

– how can we incentivise existing Education Partners to promote us to their student body ?

– how can we incentivise new Students to join the new Campus, Courses and Curriculum ?

– how do we reach out to both new and existing Education Partners and former / current students in order to canvass thoughts, influence opinions and manage communications about the benefits of the proposed new Campus, Courses and Curriculum ?

Education Cone™ – Streaming and Segmentation

Campus / Course Affinity

Education - Social Interaction

Geo-demographic Profile Experian Mosaic – 15 Groups (Streams), 66 Types (Segments)

Hybrid Cone – 3 Dimensions

The Education Cone™‏

Course Loyalty & Affinity

The Education Cone™‏ – Student-base Understanding

The Utilities Cone™‏

The Utilities Cone™‏ - Energy Customer-base Model / Understanding

– turning Social Intelligence into Actionable Marketing Insights…

• Fanatics – (10%) Buy 5 or more of our Energy / Home Security / Insurance Bundles

• Enthusiasts – (20%) Buy 3 or more of our Energy / Home Security / Insurance Bundles

• Casuals – (30%) Buy 1 or more of our Energy / Home Security / Insurance Products

• Indifferent – (40%) Former customers who have moved home / churned / defected

• Unconnected – Have no existing affinity / connection with any of our Products / Bundles

The Utility Cone™‏ – Business Scenarios

Scenario 2 – Energy Industry - How are my Marketing Teams Performing

versus each other / competitive Energy / Security / Insurance Bundles?

• Telco sector Marketing Director needs to improve market share / sales revenue.

He needs to know the following about Energy / Security / Insurance products: -

Marketing Managers Performance – Use Cases

– Which Energy / Security / Insurance Bundles are attracting increasing Sales Revenue ?

– Which Energy / Security / Insurance Bundles are declining in Sales / Market Share ?

– How are our Marketing Managers Performing - versus each other / competitive bundles ?

– How effective are our Marketing Managers in reaching out to new and existing fans in order

to canvass interest, influence opinions and manage publicity and communications about our

products, bundles and merchandising – and drive increased artist exposure / sales ?

– Which of our current Energy Products do we need to retain / loose / drop / replace ?

– Which competitive Energy Products do we need to challenge with new Media Products ?

– Which of our current Marketing Managers do we need to retain / loose / drop / replace ?

– Which competitive Marketing Managers do we need to recruit into our new Team ?

Utility Cone™ – Streaming and Segmentation

Brand / Bundle / Product Affinity

Product Spend



The Music Cone™‏

Band Loyalty & Affinity

The Utility Cone™‏ - Customer -base Understanding

The Media Cone™‏

The Media Cone™‏ - Customer-base Model / Understanding


• Fanatics – (10%) Buy 5 or more of our Media / Communications / Entertainment Bundles

• Enthusiasts – (20%) Buy 3 or more of our Communications / Entertainment Bundles

• Casuals – (30%) Buy 1 or more of our Media / Communications / Entertainment Products

• Indifferent – (40%) Former customers who have moved home / churned / defected

• Unconnected – Have no existing affinity / connection with any of our Products / Bundles

The Media Cone™‏ – Business Scenarios

Scenario 3 – Telco Industry - How are my Marketing Managers Performing

versus each other and competitive Broadband / Mobile / Media Bundles?

• Telco sector Marketing Director needs to improve market share / sales revenue.

He needs to know the following about his Broadband / Mobile / Media bundles: -

Marketing Managers Performance

– Which Broadband / Mobile / Media Bundles are attracting increasing Sales Revenue ?

– Which Broadband / Mobile / Media Bundles are declining in Sales / Market Share ?

– How are our Marketing Managers Performing - versus each other / competitive bundles ?

– How effective are our Marketing Managers in reaching out to new and existing fans in order

to canvass interest, influence opinions and manage publicity and communications about our

labels, artists, events and merchandising – and drive increased artist exposure / sales ?

– Which of our current Media Bundles do we need to retain / loose / drop / replace ?

– Which competitive Media Bundles do we need to challenge with new Media Products ?

– Which of our current Marketing Managers do we need to retain / loose / drop / replace ?

– Which competitive Marketing Managers do we need to recruit into our new Team ?

Media Cone™ – Streaming and Segmentation

Brand / Bundle / Product Affinity

Product Spend





The Utility Cone™‏ - Customer -base Understanding


The Music Cone™‏ - Fan-base Model / Understanding


• Fanatics – (10%) Music Critics / Performers / DJ’s / Regular Clubbers / Festival-goers

• Enthusiasts – (20%) Music Consumers – spend up to 50% Disposable Income on Music

• Casuals – (30%) spend only on those Genres / Labels / Artists / Tracks that they like

• Indifferent – (40%) Tend to consume free music in the Media / via Internet Streaming

• Unconnected – Have no existing connection with our Brand / Genre / Label / Artists

The Music Cone™‏ – Business Scenarios

Scenario 4 – Music Industry - How are my A&R Managers Performing this

year, versus each other and competitive music genres / labels / artists ?

• An Independent Label recruits a new team of A&R Managers to improve artist

exposure / sales revenue. Senior Management needs to know the following: -

Music Cone™‏A&R‏Managers Performance – Use Cases

– Which genres / labels / artists are attracting increasing Sales and Public / Media exposure ?

– Which genres / labels / artists are declining in Sales and Public / Media attention?

– Which of our current contracted artists do we need to retain / loose / drop / replace ?

– Which new / emerging artists will be trending in eighteen months time?

– Which new / emerging artists do we need to sign up – and to which In-house Label ?

– How are our current A&R Managers Performing - versus each other / competitive labels ?

– How effective are our A&R Managers in reaching out to new and existing fans in order to

canvass interest, influence opinions and manage publicity and communications about our

labels, artists, events and merchandising – and drive increased artist exposure / sales ?

– Which of our current A&R Managers do we need to retain / loose / drop / replace ?

– Which competitive Label A&R Managers do we need to recruit into our new A&R Team ?

Music Cone™ – Streaming and Segmentation

Music Genre - Label / Band

/ Track Affinity

Music - Social Interaction





The Music Cone™‏ - Fan-base Model / Understanding

The Political Cone™‏

The Political Cone™‏ - Voter Model / Understanding – turning Media Data Streams into Actionable Political Insights…

• Floating Voters – (10%) Decide the outcome of General Elections • Activists – (20%) Independent, Single-issue and Minority Party Voters • Social Democrats – (30%) Centre-Left Party Supporters • Conservative – (40%) Centre-Right Party Supporters • Inactive – Politically inactive / not registered to vote

The Political Cone™‏ – Election Scenarios

Scenario 5 – General Election - How are my Candidates and their Political

Agents Performing, versus each other and against rival parties ?

• A Party Campaign Director wants to direct resources to the most winnable

constituencies. Senior Campaign Management need to know the following: -

Candidates, Political Agents and Campaign Managers Performance

– How are our current Campaign Managers Performing - versus each other / rival parties ?

– How effective are our Campaign Managers in reaching out to new and existing supporters

in order to canvass interest, influence opinions and manage publicity and communications

about our Candidates / Constituencies / Policies – and increase Public / Media exposure ?

– Which of our current Campaign Managers do we need to retain / loose / drop ?

– Which prospective Campaign Managers do we need to recruit into our Campaign Team ?

– Which prospective Candidates do we need to sign up – to stand in which Constituency ?

– Which Candidates / Political Agents are attracting increasing Public / Media exposure ?

– Which Candidates / Political Agents are declining in Sales and Public / Media attention?

– Which of our current Constituency Campaigns do we need to retain / loose / drop ?

– Which Candidates / Constituencies will be trending in Media / Publicity in 18 months time?

The Cone™‏ - Party Loyalty / Affinity

Activists - 10%

Supporters - 20%

Casuals - 30%

Indifferent - 40%

The Cone™‏

Party Loyalty & Affinity

The Cone™‏ – Political Model

Social Intelligence – Streaming and Segmentation

Political Activity

Party Affinity



The Cone™‏

Political Activity


Political Cone™ – Streaming and Segmentation

Political Influence

Party Affinity



The Cone™‏

Political Influence over

Election Outcomes

Floating Voters - 10%

Minority Party Voters - 20%

Socialists - 30%

Conservatives - 40%


UK 2010 • The United Kingdom general

election of 2010 was held on

Thursday 6 May 2010, with

45,597,461 registered voters

entitled to vote and elect

members of Parliament to

the House of Commons.

• The election took place in

650 constituencies across

the United Kingdom under

the first-past-the-post

system. None of the parties

achieved the 326 seats

needed for an overall

Parliamentary majority.

• The Conservative Party, led

by David Cameron, won the

largest number of votes and

seats but still fell twenty

seats short. The Lib Dems

joined with the Conservative

Party in a coalition – and so

together they commanded

an overall majority in the

House of Commons.

The Fashion Cone™‏

The Fashion Cone™‏ – High Street / Designer / Luxury Brand Affinity

– turning Social Intelligence into Actionable Marketing Insights / Opportunities…

• Fanatics – (10%) Fashion Critics / Designers / Celebrities / Socialites / “Fashionistas”

• Enthusiasts – (20%) Fashion Consumers – spend up to 50% Disposable Income on Fashion

• Casuals – (30%) spend only on those Brands / Labels / Designers / Ranges that they like

• Indifferent – (40%) Once followed the brand - but have become disconnected over time…..

• Unconnected – no Brand Affinity; consume High Street / Discount Store / Charity Shop Items

RETAIL 2.0 “Perfect Store” BUSINESS TRANSFORMATION

Transition - Retail 1.0 to Retail 2.0 “Perfect‏Store”‏Business‏Operating‏Model‏‏‏‏‏‏‏‏‏‏‏‏= Innovation I

Part 2

Part 4

Part 3

Part 1

Strategic Enterprise

Management Framework

Enterprise Target Operating

Model (eTOM)

Future Management

and Innovation Plans

Solution Architecture

Enterprise Architecture

Model and Roadmap


Business Programme

Plan / Project Plans

Infrastructure

Architecture

Business Operating

Model (BOM)

Business Architecture

Strategic Outcomes,

Goals & Objectives

Innovation Research

and Development

Business Programme

Management

IS / IT Strategy

Technology Strategy

Systems Planning

Enterprise Governance,

Reporting and Controls

Infrastructure Planning

Business Planning

Organisation Structure

Retail 1.0 Strategic Foresight

Strategy Development

Organisational

Change


Framework

NGE – Next-

Generation

Enterprises

Collaborative

Business

Models

Service

Convergence I

Business

Transformation

Technology Change

NGA- Next-

Generation

Architectures

Enterprise

Application

Integration

Technology

Convergence I

Buy Move Sell

Smart

Devices

Mobile

Platform

Cloud

Services Retail 2.0

I

FAST FASHION RETAILING and BRAND MANAGEMENT

In Europe, consumer spending is being re-focussed on either Value Brands or Luxury Goods Marques - squeezing out Retailers with mid-market Retail Propositions and traditional middle-of-the-road Branding Strategies. Traditional Fashion Retailers have seasons – Spring / Summer and Autumn / Winter - where popular lines are retained year-on-year. Fast Fashion Retailers (where Fast Fashion lines are only in-store for a few days or weeks, and Fast Fashion items are not subsequently repeated) are growing fast - at the expense of those conventional retailers with traditional Spring / Summer and Autumn / Winter Seasons which often feature “signature” popular repeatable core lines - always available, season on season, year on year..... Fast Fashion and Luxury Goods Retailers are now under intense competitive pressure to drive down costs by adopting a more Lean / Agile Supply Chain Model (a la mode de Wal-Mart), and by improving Supplier Relationships and Strategic Vendor Management. Fast Fashion Retailers are also required to be better at exploiting On-line and Mobile Sales Channels - which are growing much faster than traditional In-store and Catalogue Channels. Customers still like to mix-and-match Sales Channels - unwanted items purchased On-line are often exchanged In-store for replacement or refunds.

Retail 2.0 “Perfect Store” – Experience Digital Marketing – Fast Fashion

PS0004

Shelf / Space

Allocation

PS0001 Customer Offer

PS0002 Retail

Proposition

PS0003

Pricing

PS0019 Marketing

Communications (Advertise)

PS0012 Customer

Segmentation

PS0009 Global CRM

PS0011 Marketing Services -

(Analysis and Research)

PS0010 Customer

Experience and Journey

PS0006 Product

Assortment and Mix

PS0008 Forecasting and Replenishment

PS0007 Global Category

& Supplier

PS0021 Sales Analysis

and Value Chain Reporting

PS0022 Global Product

Sourcing

PS0023 Global Supply

Chain

PS0014 BUY

(Procurement)

PS0016 SELL Retail

Merchandising

PS0015 MOVE

(Logistics)

PS0017 Public Relations

PS0024 Global Shared

Services

PS0005

Business

Planning

PS00029

Analytics

PS0027

Social

Intelligence

PS0028

Digital Platforms

& Multi-channel

Retail

Digital Channels & Analytics

Retail Merchandising & Logistics Head Office

Customer Relationship Management

PS0018 Customer

Information & Services

PS0013 Customer

Loyalty Customer Services

PS0025

Global Product

Catalogue

PS0020,

Offers and

Promotions

PS0026

Local Product

Catalogue

Digital Marketing – Retail 2.0 Model


Consumers are becoming increasingly better educated. Across many urban conurbations in the Southern part of the UK, young people purchase cheap fashion items frequently and in large numbers - these items are worn for a single season (or until they fall apart.....) and are viewed by consumers almost as disposable items. Young consumers with similar disposable incomes in major Cities in Scotland and Northern Italy, for example - will spend the same amount in a season on just a few items chosen very carefully from Luxury Goods Brands - but keep them in their wardrobe for many years..... The sudden proliferation of pervasive Smart Devices communicating via the Smart Grid with the Cloud indicates that we may have just witnessed the beginning of a startling new episode in technology driven consumer behaviour – the advent of the always-on digital connected society – Smart individuals living in Smart households within the Smart Cities of the future. Smart Phones such as the Apple iPhone, HTC Desire, Google Nexus One, Windows Phones – are enabling innovative Customer Experience and Journey Stories, both in-store and mobile, including Social Media Conversations..


IBM WebSphere

SAP NetWeaver Pi and/ or IBM MQSI

SAP IS/Retail

SAP CRM

Stebo or IBM Product Centre

Internet

Contact

Centre

Mobile 3rd Party

SAP Solution Architecture

Customer Loyalty

EPOS / SEL

Sales Channels Fulfilment Channels

In-store

Home

Delivery

BI / BO / BW HANA

SAP ECC7, ERP

ATG Dynamo Oracle Fusion Oracle Retail

Oracle CRM

Stebo or Kalido

Internet

Contact

Centre

Mobile 3rd Party

Oracle Solution Architecture

Customer Loyalty

EPOS

Sales Channels

Fulfilment Channels

In-store

Home

Delivery

Oracle OBIE

Oracle e-Business Suite

Retail 2.0 “Perfect Store” – Multi-channel Architecture

E-commerce Platform

Integration Platform

Retail Platform

CRM Platform

Catalogue Platform

Internet

Contact

Centre

Mobile 3rd Party

Customer Loyalty

In-store Systems

Sales Channels Fulfilment Channels

In-store

Home

Delivery

Retail 2.0 “Perfect Store” Multi-channel Enterprise Architecture

Data Warehouse

Head Office Shared Services

Social Media Real-time Analytics

Mobile Platforms

Cloud Digital Channels Social Media

Conversations



The fastest growing sales Channels for both Fast Fashion and Luxury Goods are Smart Apps on Mobile Phones. Innovative new Retail Business Operating Models such as “Retail 2.0” and “Perfect Store” are driving the development of these new Channels. For example, when a Customer enters a store, the Retailer of the Future can detect and identify him from his Smart Phone Number, as the Customer accesses the In-store WiFi or WiMAX Network Connection. Based on vast amounts of data describing their previous consumer behaviour – we can alert the consumer to relevant In-store offers and promotions – based on Propensity Modelling –similar in content and style to those offers and promotions the customer has responded to positively in the past When a Customer Tweets that she is going to buy a “little black cocktail dress” – we can initiate a Social Media Conversation .


Fast Fashion

• ASOS • • Next • • New Look • • Primark • • Top Shop •

Luxury Brand Aggregators

• PPR • • LVMH • • Richemont•

Luxury Brands

• Channel • • Dior • • Hermes • • Gucci • • Prada •

Designer Labels

• Armani • • Burberry • • D&G • DKNY • • Ralph Lauren • • Versace •

Sports Apparel and Footwear

• Nike • • Adidas • • Columbia • • North Face •

Multi-channel Retail Architecture

Multi-channel Retail

Retail Operations – Retail Merchandising and Logistics

Head Office – Finance, Planning and Strategy

Marketing – Customer Loyalty, Experience and Journey – Offers, Promotions and Campaigns

In-store EPOS – Internet – Home Delivery

Provisioning & Replenishment

In-store

Systems

Retail

Operations

Systems

ERP

Systems

Customers

Operations

Managers

Finance

Managers

Loyalty Mart

Financial Data Warehouse

CRM and

Marketing

Systems

Marketing

Managers

Multi-channel Sales Data

Warehouse

Marketing

Customer

Analytics

Reports

Retail

Multi-channel

Sales

Analysis

Operations

Warehousing &

Logistics

Reports

Head Office

Financial

Analysis

Reports

e-Commerce

Systems

Campaign Mart

Merchandising & Logistics Data

Supplier Data

Product Data

Stores Data

Merchandising

Inventory &

Provisioning

Reports

EPOS Data

Call Centre Data

Internet Data

Customer DWH

CRM Data

Retail

Managers

ERP Data

Catalogue

Systems

Planning &

Forecasting

Systems

“BIG‏DATA”

Retail and Logistics Data

Warehouse

Planning &

Forecasting

Systems

Apache Hadoop Framework

HDFS, MapReduce, MetLab, “R”

Catalogue Data

Autonomy, Vertical

Hadoop

SAP HANA



Retail 2.0 and Perfect Store Business Operating Models and Customer Experience and Journey Business Value Propositions are being driven by technology enablement such as Multi-channel Retail (eCRM), and Social Media (sCRM), supported by Real-time Analytics @ Point-of-Sale: - • Retail Business Models – “Retail 2.0” • “Perfect Store” • • Retail Strategy – Retail Proposition • Channels • Media • • Business Value Propositions – Customer Offer, Experience and Journey • • Mobile Technologies – Mobile Computing • Smart Devices • Smart Apps • • Customer Strategy – Customer Loyalty • Offers • Promotions • Campaigns • • Retail Business Transformation – New Social Structures • Cultural Change • • Emerging Technologies – Real-time Analytics @ POS • Smart Grid • Cloud Services • Social Marketing – Internet Intelligence • Product Placement • Crowd Sourcing Events • Fulfilment – Service Access • Service Brokering • Service Provisioning • Service Delivery


LUXURY GOODS RETAILING and BRAND MANAGEMENT

Luxury Goods companies have traditionally targeted two primary “old money” customer segments – affluent fashion-conscious socialites (age range 25-35) who follow the skiing, sailing and social events seasons in major cities and exclusive resorts in either Europe or America - and retired or semi-retired individuals (age range 55-65) who have created and accumulated significant wealth during their Business and Professional careers– and who now have significant time and money available to devote towards their interests and leisure pursuits. Families are raised in the Gap Years (age range 35-55). Many familiar Luxury Goods brands now belong to just a few Luxury Brand Aggregators such as French PPR, Louis Vuiton Moet Hennessy (LVMH) and the Swiss conglomerate Richemont. In any economic downturn, these Brand Aggregators are no longer able to drive increased growth sufficient to meet their Shareholder expectations or maintain volume targets from Business Partner / Stakeholders, in traditional Markets and Customer Segments – and so are forced to expand their Market Coverage, Product Ranges and Brand Footprints (and at the same time risk suffering the dual unforeseen consequences of erosion of Product positioning, desirability and cache – along with the dilution of core Brand recognition, perception and value).

Retail 2.0 “Perfect Store” – Experience Digital Marketing – Luxury Goods

Digital Marketing – Luxury Goods Brand Status Brand Awareness Sales Volume

Luxury Brand

Aggregators

• PPR •

• LVMH •

• Richemont •

Luxury Brands

• Channel •

• Dior •

• Hermes •

• Gucci •

• Prada •

Designer Labels

• Armani •

• Burberry •

• D&G •

• Versace •

Cache Brands

• Dunhill •

• Rolex •

Star Brands

• DKNY •

• Hilfiger •

• Hugo Boss •

• Ralph Lauren •

• Tiffany•

Premium Brands

• Coach •

• Fendi •

• Swarovski •

• Valentino •

Micro Brands

• Liberty • Asprey •

• Mappin & Webb •

Esoteric Brands

• Patek Phillippe •

• Van Cleef & Arples •

Bespoke Brands

• Leviev •

• Graff •

Aspirational Brands

• Bulgari • Cherutti •

• Mont Blanc • Tods •


Today, the new Luxury Goods marketing focus has turned towards two “new money” customer segments - newly wealthy individuals in the emerging economies of the BRICS;s (Brazil, Russia, India and China) – and young Media and Entertainment Professionals and Elite Team Sports Athletes (age range 20-30) in the West. Goldman Sachs forecast that China will be buying one 3rd of the world's luxury goods in under a decade,,,,,

• Young Media and Entertainment Professionals and Elite Team Sports Athletes (age range 20-30) • New, Emerging and Developing Markets for Luxury Goods– Brazil, Russia, India China (BRICs) •

Increasingly, many Luxury Brands are also launching more accessible entry-level Product Ranges in order to attract younger, technically-savvy and fashion-aware mass-market consumers - to introduce them to a Lifestyle Experience and Journey that creates brand loyalty and lock-in with entry-level Luxury Goods Product ranges. As these young, mobile consumers careers develop and they begin to generate increased disposable income they also begin to purchase "big-ticket" Luxury Goods items from their favourite Design Guru, Role Model or Lifestyle Icon.....


Digital Marketing – Luxury Goods

Luxury Brand

Aggregators

• PPR •

• LVMH •

• Richemont •

Luxury Brands

• Channel •

• Dior •

• Hermes •

• Gucci •

• Prada •

Designer Labels

• Armani •

• Burberry •

• D&G •

• Hugo Boss •

• Versace •

Brand Status Sales Volume

Pyramid of Fashion

Esoteric Brands

• Patek Phillippe •

• Van Cleef & Arples •

Cache Brands

• Dunhill •

• Rolex •

• Valentino •

Star Brands

• DKNY •

• Hilfiger •

• Hugo Boss •

• Ralph Lauren •

• Tiffany •

Premium Brands

• Coach •

• Fendi •

• Swarovski •

Micro Brands

• Liberty • Asprey •

• Mappin & Webb •

Bespoke Brands

• Leviev •

• Graff •

Aspirational Brands

• Bulgari • Cherutti •

• Mont Blanc • Tods •


As young, mobile consumers careers develop they begin to purchase "big-ticket" Luxury Goods items from their favourite Design Guru, Role Model or Lifestyle Icon..... • Mass-market younger, technically-savvy and fashion-aware consumers • • Entry-level Luxury Goods Product Ranges – Perfume, Cosmetics, Casual Wear, Sporting Goods •

Retail 2.0 and Perfect Store Business Operating Models and Customer Experience and Journey Business Value Propositions are being driven by technology enablement such as Multi-channel Retail (eCRM), and Social Media (sCRM), supported by Real-time Analytics @ Point-of-Sale: - • A winning Customer Contact Strategy to reach out to your target audience • A stunning Customer Experience to engage and retain your target audience • Understanding of Customer Profiling and Segmentation - to define your niche • A unique Customer Offer and Journey to instil desire for your Ranges and Lines • An enthralling Customer Experience to cultivate Consumer aspiration and desire • An amazing Customer Journey Storyboard to grasp and keep Consumer attention • A compelling Retail Proposition / Channels / Media to leverage Customer interest • A mastery of Smart Devices • Smart Apps • Cloud Services to engage your Customer • Total perfection of Product and Service Delivery Management for Consumer Fulfilment • Influencer Programmes - turn Fashion Blogs into Revenue – transforming Clicks into Cash.....


The Sports Cone™‏

The Sports Cone™‏ – Fan-base Understanding

– turning Social Intelligence into Actionable Marketing Insights / Opportunities…

• Fanatics – (10%) Travel Club Members, Season Ticket Holders, buy all Club Merchandising

• Enthusiasts – (20%) Attend 10-25 Home Matches, buy Club Merchandising

• Casuals – (30%) Attend 1-10 Home Matches, buy some Club Merchandising

• Indifferent – (40%) Follow Sports Franchise in News / Media / Match Streaming only

The Sports Cone™‏ – Business Scenarios

Scenario 7 – Elite Team Sports: - Where is our Fan-base ?

• An Elite Team Sports Franchise (e.g. Premier League Football / Rugby or NBA / NFL) is re-locating its Stadium with a new Sponsor to a new location or town. Senior Management needs to know the following: -

Fan-base Understanding – Use Cases – Who are our existing Fans, what is their commitment and where do they live ?

– Which of our existing traditional Fan-base will be lost and who will be retained over the proposed Stadium re-location ?

– How can we incentivise existing Fans to remain loyal Club Supporters ?

– Who will our new Fans be, what is their motivation and where do they live ?

– where do our new conquest Fan-base live and how far are they prepared to travel to attend events at the proposed new Stadium ?

– How can we incentivise new Fans to join the Supporters Club ?

– How do we reach out to both new and existing fans in order to canvass thoughts, influence opinions and manage communications about the benefits of the proposed new Sponsor and Stadium re-location?

Sports Cone™ – Streaming and Segmentation

The Cone™‏ Sports Team -

Fan-base Loyalty / Affinity

Sports Team Affinity

Geographic Location



The Sports Cone™‏ – Fan-base Understanding

4D Geospatial Analytics • The profiling and analysis of

large aggregated datasets in

order to determine a ‘natural’

structure of groupings provides

an important technique for many

statistical and analytic

applications. Cluster analysis

on the basis of profile similarities

or geographic distribution is a

method where no prior

assumptions are made

concerning the number of

groups or group hierarchies and

internal structure. Geo-

demographic techniques are

frequently used in order to

profile and segment populations

by ‘natural’ groupings - such as

common behavioural traits,

Clinical Trial, Morbidity or

Actuarial outcomes - along with

many other shared

characteristics and common

factors.....

The Flow of Information through Time

• String Theory predicates that Space-Time exists in discrete packages, with Time Present always in some way inextricably woven into both Time Past and Time Future – yielding the intriguing possibility of glimpses through the mists of time into the path and outcome of future events. Any item of Data or Information (Global Content) may contain faint traces which offer insights into the trajectory of Clusters of linked Past, Present and Future Events. If the future timeline were linear, then all events would unfold in an unerringly predictable manner towards a known and certain conclusion. The future may be viewed as both unknown and unknowable (Hawking Paradox) . Future outcomes are uncertain – future timelines are non-linear (branched) with a multitude of alternative futures. Chaos Theory suggests that even the most subliminal inputs, originating from unknown forces so minute as to be undetectable, may become amplified through numerous system cycles to grow in influence and impact over time - so deviating Space-Time trajectories far away from their predicted path - thus fundamentally altering the outcome of future events.

• Every item of Global Content in the Present is somehow connected with both Past and Future temporal planes. Space-Time is a Dimension Cluster consisting of the three Spatial dimensions (x, y and z axes) plus Time (the fourth dimension - t) – which together flow in a single direction – relentlessly towards the future. Space-Time does not flow uniformly – the “arrow of time” may be deflected by unknown factors. There may be “unforeseen external forces” (random events) that create disturbance in the temporal plane stack which marks the passage of time - with the potential to create eddies, vortices and whirlpools along the flow of Time (chaos, disorder and uncertainty) – which in turn posses the capability to generate ripples and waves (randomness and disruption) – thus changing the course of the path of the Space-Time continuum. “Weak Signals” are “Ghosts in the Machine”- echoes of these subliminal temporal interactions – with the capacity to carry information about possible future “Wild card” or “Black Swan” random events .

4D Geospatial Analytics – The Temporal Wave

• The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration

of Geospatial “Big Data” - simultaneously within a Time (history) and Space (geographic)

context. The problems encountered in exploring and analysing vast volumes of spatial–

temporal information in today's data-rich landscape – are becoming increasingly difficult to

manage effectively. In order to overcome the problem of data volume and scale in a Time

(history) and Space (location) context requires not only traditional location–space and

attribute–space analysis common in GIS Mapping and Spatial Analysis - but now with the

additional dimension of time–space analysis. The Temporal Wave supports a new method

of Visual Exploration for Geospatial (location) data within a Temporal (timeline) context.

• This time-visualisation approach integrates Geospatial (location) data within a Temporal

(timeline) dataset - along with data visualisation techniques - thus improving accessibility,

exploration and analysis of the huge amounts of geo-spatial data used to support geo-

visual “Big Data” analytics. The temporal wave combines the strengths of both linear

timeline and cyclical wave-form analysis – and is able to represent data both within a Time

(history) and Space (geographic) context simultaneously – and even at different levels of

granularity. Linear and cyclic trends in space-time data may be represented in combination

with other graphic representations typical for location–space and attribute–space data-

types. The Temporal Wave can be used in roles as a time–space data reference system,

as a time–space continuum representation tool, and as time–space interaction tool.

4D Geospatial Analytics – London Timeline

4D Geospatial Analytics – London Timeline

• How did London evolve from its creation as a Roman city in 43AD into the crowded, chaotic cosmopolitan megacity we see today? The London Evolution Animation takes a holistic view of what has been constructed in the capital over different historical periods – what has been lost, what saved and what protected.

• Greater London covers 600 square miles. Up until the 17th century, however, the capital city was crammed largely into a single square mile which today is marked by the skyscrapers which are a feature of the financial district of the City.

• This visualisation, originally created for the Almost Lost exhibition by the Bartlett Centre for Advanced Spatial Analysis (CASA), explores the historic evolution of the city by plotting a timeline of the development of the road network - along with documented buildings and other features – through 4D geospatial analysis of a vast number of diverse geographic, archaeological and historic data sets.

• Unlike other historical cities such as Athens or Rome, with an obvious patchwork of districts from different periods, London's individual structures scheduled sites and listed buildings are in many cases constructed gradually by parts assembled during different periods. Researchers who have tried previously to locate and document archaeological structures and research historic references will know that these features, when plotted, appear scrambled up like pieces of different jigsaw puzzles – all scattered across the contemporary London cityscape.

Social Intelligence – Fan-base Understanding

CONES

• Multiple Cones can be created and cross-referenced using Social Intelligence and Brand

Interaction / Fan-base Profiling and Segmentation in order to deliver actionable insights for any

genre of Brand Loyalty and Fan-base Understanding as well as for other Geo-demographic

Analytics purposes - Digital Healthcare, Clinical Trials, Morbidity and Actuarial Outcomes: -

– Music (BBC and Sony Music)

– Broadcasting (Radio 1 / American Idol)

– Digital Media Content (Sony Films / Netflix)

– Sports Franchises (Manchester City / New York City)

– Fast Fashion Retailers (ASOS, Next, New Look, Primark)

– Luxury Brands / Aggregators (Burberry / LVMH, PPR, Richemont)

– Multi-channel Retail – Loyalty, Campaigns, Offers and Promotions

– Financial Services – Brand Protection and Reputation Management

– Travel, Leisure and Entertainment - Destination Events and Resorts

– MVNO / CSPs - OTT Business Partner Analytics (via Firebrand / Apigee)

– Telco, Media and Communications - Churn Management / Conquest / Up-sell / Cross-sell Campaigns

– Digital Healthcare – Private / Public Healthcare Service Provisioning: - Geo-demographic Clustering and

Propensity Modelling (Patient Monitoring, Wellbeing, Clinical Trials, Morbidity and Actuarial Outcomes)

Social Intelligence – Fan-base Understanding

The Patient Cone™‏

The Patient Cone™‏ - Model / Understanding – turning Biomedical Data Streams into Actionable Medical Insights…

• Acute – (10%) Active Patient Monitoring – Alerts and Alarms • Chronic – (20%) Passive Monitoring – Biomedical Data Streaming • Casuals – (30%) Walk-in for Treatment On-demand – 1-5 times a year • Indifferent – (40%) See once a year– Annual Health-check / Review • Unconnected – Not Registered with any Primary Healthcare Provider

The Patient Cone™‏ – Medical Scenarios

Scenario 8 – Digital Healthcare: - Patient Monitoring / Biomedical Analytics

• A Public Health Body is charged with providing improved and more efficient Healthcare – at

reduced cost. The chosen solution is Digital Healthcare service provisioning – Biomedical Data

Streaming, Patient Monitoring, Medical Data Science, Propensity Modelling and Predictive

Analytics. Senior Healthcare Management need to understand the following: -

Patient Understanding – Use Cases

– How can we move patients safely from the Operating Theatres into Intensive Care,

General Wards, Convalescence facilities and back into their own Homes - 20% Faster ?

– Which existing Medical facilities can be de-commissioned, and what new Medical facilities

do we need to build – whilst providing improved Biomedical Data Streaming / Patient

Monitoring / Predictive Analytics service provisioning, all at reduced cost ?

– Where should old Medical Facilities be closed and new Medical Facilities built ?

– Which Chronic / Acute Patients do we need to focus on for maximum value-for-money in

Biomedical Data Streaming / Patient Monitoring service provisioning ?

– Which Patients need Active Patient Monitoring – Alerts and Alarms – and which Patients

only need Passive Monitoring – Biomedical Data Streaming and Analytics ?

– Which Patients are Walk-in cases, and need Treatment On-demand – and which Patients

only need to be seen once a year, for an Annual Health-check / Screening / Review ?

The Cone™‏ - Patient Types

Acute- 10%

Chronic- 20%

Casuals - 30%

Indifferent - 40%

The Cone™‏Patient

Biomedical Analytics‏

Actionable Medical Insights

Biomedical Clustering

Clinical Presentation

Biomedical Profile Biomedical Analytics – Groups (Streams), Types (Segments)


The Cone™‏ – Patient Model

The Biomedical Cone™ Converting Data Streams into Actionable Insights

Salesforce

Anomaly 42

Cone

Unica

End User

BIG DATA

ANALYTICS

BIOMEDICAL DATA

Patient Monitoring

Platform

INTERVENTION

• Treatment

• Smart Apps

The Cone™‏Patient

Biomedical Analytics‏

Actionable Medical Insights

Electronic Medical Records

(EMR)

• Geo-demographics

• Streaming

• Segmentation

• Households

PATIENT RECORDS

• Medical History

• Key Events

Insights

Insights Insights

Anomaly

42 Unica

Biomedical

Data Streaming

People, Places

and Events

Health

Campaigns

• Clinical and Biomedical Data

• Images – X-Ray, CTI, MRI

• Procedures and Interventions

• Prescriptions and Treatment

Social

Media

EXPERIAN

Mosaic

4D Geospatial Analytics • The profiling and analysis of

large aggregated datasets in

order to determine a ‘natural’

structure of groupings provides

an important technique for many

statistical and analytic

applications. Cluster analysis

on the basis of profile similarities

or geographic distribution is a

method where no prior

assumptions are made

concerning the number of

groups or group hierarchies and

internal structure. Geo-

demographic techniques are

frequently used in order to

profile and segment populations

by ‘natural’ groupings - such as

common behavioural traits,

Clinical Trial, Morbidity or

Actuarial outcomes - along with

many other shared

characteristics and common

factors.....

The Flow of Information through Time

• String Theory predicates that Space-Time exists in discrete packages, with Time Present always in some way inextricably woven into both Time Past and Time Future – yielding the intriguing possibility of glimpses through the mists of time into the path and outcome of future events. Any item of Data or Information (Global Content) may contain faint traces which offer insights into the trajectory of Clusters of linked Past, Present and Future Events. If the future timeline were linear, then all events would unfold in an unerringly predictable manner towards a known and certain conclusion. The future may be viewed as both unknown and unknowable (Hawking Paradox) . Future outcomes are uncertain – future timelines are non-linear (branched) with a multitude of alternative futures. Chaos Theory suggests that even the most subliminal inputs, originating from unknown forces so minute as to be undetectable, may become amplified through numerous system cycles to grow in influence and impact over time - so deviating Space-Time trajectories far away from their predicted path - thus fundamentally altering the outcome of future events.

• Every item of Global Content in the Present is somehow connected with both Past and Future temporal planes. Space-Time is a Dimension Cluster consisting of the three Spatial dimensions (x, y and z axes) plus Time (the fourth dimension - t) – which together flow in a single direction – relentlessly towards the future. Space-Time does not flow uniformly – the “arrow of time” may be deflected by unknown factors. There may be “unforeseen external forces” (random events) that create disturbance in the temporal plane stack which marks the passage of time - with the potential to create eddies, vortices and whirlpools along the flow of Time (chaos, disorder and uncertainty) – which in turn posses the capability to generate ripples and waves (randomness and disruption) – thus changing the course of the path of the Space-Time continuum. “Weak Signals” are “Ghosts in the Machine”- echoes of these subliminal temporal interactions – with the capacity to carry information about possible future “Wild card” or “Black Swan” random events .

4D Geospatial Analytics – The Temporal Wave

• The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration

of Geospatial “Big Data” - simultaneously within a Time (history) and Space (geographic)

context. The problems encountered in exploring and analysing vast volumes of spatial–

temporal information in today's data-rich landscape – are becoming increasingly difficult to

manage effectively. In order to overcome the problem of data volume and scale in a Time

(history) and Space (location) context requires not only traditional location–space and

attribute–space analysis common in GIS Mapping and Spatial Analysis - but now with the

additional dimension of time–space analysis. The Temporal Wave supports a new method

of Visual Exploration for Geospatial (location) data within a Temporal (timeline) context.

• This time-visualisation approach integrates Geospatial (location) data within a Temporal

(timeline) data along with data visualisation techniques - thus improving accessibility,

exploration and analysis of the huge amounts of geo-spatial data used to support geo-

visual “Big Data” analytics. The temporal wave combines the strengths of both linear

timeline and cyclical wave-form analysis – and is able to represent data both within a Time

(history) and Space (geographic) context simultaneously – and even at different levels of

granularity. Linear and cyclic trends in space-time data may be represented in combination

with other graphic representations typical for location–space and attribute–space data-

types. The Temporal Wave can be used in roles as a time–space data reference system,

as a time–space continuum representation tool, and as time–space interaction tool.

History of Digital Epidemiology

• Doctor John Snow (15 March 1813 – 16

June 1858) was an English physician and a

leading figure in the adoption of anaesthesia

and medical hygiene. John Snow is largely

credited with sparking and pursuing a total

transformation in Public Health and epidemic

disease management and is considered one

of the fathers of modern epidemiology in part

because of his work in tracing the source of

a cholera outbreak in Soho, London, in 1854.

• John Snows’ investigation and findings into

the Broad Street cholera outbreak - which

occurred in 1854 near Broad Street in the

London district of Soho in England - inspired

fundamental changes in both the clean and

waste water systems of London, which led to

further similar changes in other cities, and a

significant improvement in understanding of

Public Health around the whole of the world.

http://en.wikipedia.org/wiki/Anaesthesia

http://en.wikipedia.org/wiki/Hygiene

http://en.wikipedia.org/wiki/Epidemiology

http://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak

http://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak


• The Broad Street cholera outbreak of

1854 was a major cholera epidemic or

severe outbreak of cholera which

occurred in 1854 near Broad Street in

the London district of Soho in England .

• This cholera outbreak is best known for

statistical analysis and study of the

epidemic by the physician John Snow

and his discovery that cholera is spread

by contaminated water. This knowledge

drove improvement in Public Health with

mass construction of sanitation facilities

from the middle of the19th century.

• Later, the term "focus of infection" would

be used to describe factors such as the

Broad Street pump – where Social and

Environmental conditions may result in the outbreak of local infectious diseases.

http://en.wikipedia.org/wiki/London

http://en.wikipedia.org/wiki/Soho

http://en.wikipedia.org/wiki/England

http://en.wikipedia.org/wiki/John_Snow_(physician)

http://en.wikipedia.org/wiki/John_Snow_(physician)

http://en.wikipedia.org/wiki/Cholera

http://en.wikipedia.org/wiki/Focus_of_infection

History of Digital Epidemiology • It was the study of

cholera epidemics, particularly in Victorian England during the middle of the 19th century, which laid the foundation for epidemiology - the applied observation and surveillance of epidemics and the statistical analysis of public health data.

• This discovery came at a time when the miasma theory of disease transmission by noxious “foul air” prevailed in the medical community.


Modern epidemiology has its origin with the study of Cholera

Broad Street cholera outbreak of 1854


Modern epidemiology has its origin with the study of Cholera.

• It was the study of cholera epidemics, particularly in Victorian England

during the middle of the 19th century, that laid the foundation for the science

of epidemiology - the applied observation and surveillance of epidemics and

the statistical analysis of public health data. It was during a time when the

miasma theory of disease transmission prevailed in the medical community.

• John Snow is largely credited with sparking and pursuing a transformation in

Public Health and epidemic disease management from the extant paradigm

in which communicable illnesses were thought to have been carried by

bad, malodorous airs, or "miasmas“ - towards a new paradigm which would

begin to recognize that virulent contagious and infectious diseases are

communicated by various other means – such as water being polluted by

human sewage. This new approach to disease management recognised that

contagious diseases were either directly communicable through contact with

infected individuals - or via vectors of infection (water, in the case of cholera)

which are susceptible to contamination by viral and bacterial agents.

History of Digital Epidemiology • This map is John Snow’s

famous plot of the 1854 Broad Street Cholera Outbreak in London. By plotting epidemic data on a map like this, John Snow was able to identify that the outbreak was centred on a specific water pump.

• Interviews confirmed that outlying cases were from people who would regularly walk past the pump and take a drink. He removed the handle off the water pump and the outbreak ended almost overnight.

• The cause of cholera (bacteria Vibria cholerae) was unknown at the time, and Snow’s important work with cholera in London during the 1850s is considered the beginning of modern epidemiology. Some have even gone so far as to describe Snow’s Broad Street Map as the world’s first GIS.


Broad Street cholera outbreak of 1854

Clinical Risk Types

Clinical Risk Types

Clinical Risk Group

Employee

Patient

B

A

Human Risk Process

Risk

D

Morbidity Risk Types

Morbidity Risk Group

C

Legal Risk

F

3rd Party Risk

G

C

Technology Risk

Trauma Risk

E

Morbidity Risk

H E

J

G

A

I D

Immunological System Risk

Sponsorship

Stakeholders Disease

Risk

Shock Risk

Cardiovascular

System Risk

Pulmonary System Risk

Toxicity Risk

Organ Failure Risk

- Airways

- Conscious

- Bleeding

Triage Risk

- Performance

- Finance

- Standards

Compliance Risk

H

Patient Risk

Neurological

System Risk F

B

Predation Risk

Risk Complexity Map

Pandemics‏•‏Study‏Case‏•

Pandemics‏•‏Study‏Case‏•

• Pandemics - during a pandemic episode, such as the recent Ebola outbreak, current

policies emphasise the need to ground decision-making on empiric evidence. This section

studies the tension that remains in decision-making processes when their is a sudden and

unpredictable change of course in an outbreak – or when key evidence is weak or ‘silent’.

• The current focus in epidemiology is on the ‘known unknowns’ - factors with which we are

familiar in the pandemic risk assessment processes. These risk processes cover, for

example, monitoring the course of the pandemic, estimating the most affected age groups,

and assessing population-level clinical and pharmaceutical interventions. This section

looks for the ‘unknown unknowns’ - factors with a lack of, or silence, of evidence, of which

we have only limited or weak understanding in the pandemic risk assessment processes.

• Pandemic risk assessment shows, that any developing, new and emerging or sudden and

unpredictable change in the pandemic situation does not accumulate a robust body of

evidence for decision making. These uncertainties may be conceptualised as ‘unknown

unknowns’, or “silent evidence”. Historical and archaeological pandemic studies indicate

that there may well have been evidence that was not discovered, known or recognised.

This section looks at a new method to discover “silent evidence” - unknown factors - that

affect pandemic risk assessment - by focusing on the tension under pressure that impacts

upon the actions of key decision-makers in the pandemic risk decision-making process.

Antonine Plague (Smallpox ) AD 165-180

Pandemic Black Swan Events Black Swan Pandemic Type / Location Impact Date

Malaria For the entirety of human history,

Malaria has been a pathogen

The Malaria pathogen kills more

humans than any other disease 20 kya – present

Smallpox (Antonine Plague) Smallpox Roman Empire / Italy Smallpox is the 2nd worst killer 165-180

Black Death (Plague of Justinian) Bubonic Plague – Roman Empire 50 million people died 6th century

Black Death (Late Middle Ages) Bubonic Plague – Europe 75 to 200 million people died 1340–1400

Smallpox Amazonian Basin Indians 90% Amazonian Indians died 16th century

Tuberculosis Western Europe, 18th - 19th c 900 deaths per 100,000 pop. 18th - 19th c

Syphilis Global pandemic – invariably fatal 10% of Victorian men carriers 19th century

1st Cholera Pandemic Global pandemic Started in the Bay of Bengal 1817-1823

2nd Cholera Pandemic Global pandemic (arrived in London in 1832) 1826-1837

Spanish Flu Global pandemic 50 million people died 1918

Smallpox Global pandemic 300 million people died in 20th c Eliminated 20th c

Poliomyelitis Global pandemic Contracted by up to 500,000

persons per year 1950’s/1960’s 1950’s -1960’s

AIDS Global pandemic – mostly fatal 10% Sub-Saharans are carriers Late 20th century

Ebola West African epidemic – 50% fatal Sub-Saharan Africa epicentre Late 20th century

http://en.wikipedia.org/wiki/Pathogen

http://en.wikipedia.org/wiki/Antonine_Plague



http://en.wikipedia.org/wiki/Roman_Empire

http://en.wikipedia.org/wiki/Italy

http://en.wikipedia.org/wiki/Plague_of_Justinian

http://en.wikipedia.org/wiki/Late_Middle_Ages

http://www.sciencemuseum.org.uk/broughttolife/themes/publichealth/sti.aspx

For the entirety of human history, Malaria has been the most lethal pathogen to attack man

Pandemic Black Swan Event Types

Type Force Epidemiology Black Swan Event

1 Malaria Parasitic

Biological

Disease

The Malaria pathogen has killed more humans than any other disease. Human

malaria most likely originated in Africa and has coevolved along with its hosts,

mosquitoes and non-human primates. The first evidence of malaria parasites

was found in mosquitoes preserved in amber from the Palaeogene period that

are approximately 30 million years old. Malaria may have been a human

pathogen for the entire history of the species. Humans may have originally

caught Plasmodium falciparum from gorillas. About 10,000 years ago, a period

which coincides with the development of agriculture (Neolithic revolution) -

malaria started having a major impact on human survival. A consequence was

natural selection for sickle-cell disease, thalassaemias, glucose-6-phosphate

dehydrogenase deficiency, ovalocytosis, elliptocytosis and loss of the Gerbich

antigen (glycophorin C) and the Duffy antigen on erythrocytes because such

blood disorders confer a selective advantage against malaria infection (balancing

selection). The first known description of malaria dates back 4000 years to 2700

B.C. China where ancient writings refer to symptoms now commonly associated

with malaria. Early malaria treatments were first developed in China from

Quinghao plant, which contains the active ingredient artemisinin, re-discovered

and still used in anti-malaria drugs today. Largely overlooked by researchers is

the role of disease and epidemics in the fall of Rome. Three major types of

inherited genetic resistance to malaria (sickle-cell disease, thalassaemias, and

glucose-6-phosphate dehydrogenase deficiency) were all present in the

Mediterranean world 2,000 years ago, at the time of the Roman Empire.

http://en.wikipedia.org/wiki/Coevolution

http://en.wikipedia.org/wiki/Mosquito

http://en.wikipedia.org/wiki/Primate

http://en.wikipedia.org/wiki/Amber

http://en.wikipedia.org/wiki/Paleogene


http://en.wikipedia.org/wiki/Plasmodium_falciparum

http://en.wikipedia.org/wiki/Plasmodium_falciparum

http://en.wikipedia.org/wiki/Gorilla

http://en.wikipedia.org/wiki/Neolithic_revolution

http://en.wikipedia.org/wiki/Natural_selection

http://en.wikipedia.org/wiki/Sickle-cell_disease



http://en.wikipedia.org/wiki/Thalassaemias

http://en.wikipedia.org/wiki/Glucose-6-phosphate_dehydrogenase_deficiency








http://en.wikipedia.org/wiki/Ovalocytosis

http://en.wikipedia.org/wiki/Elliptocytosis

http://en.wikipedia.org/wiki/Glycophorin_C



http://en.wikipedia.org/wiki/Duffy_antigen_system

http://en.wikipedia.org/wiki/Red_blood_cells

http://en.wikipedia.org/wiki/Blood_disorders

http://en.wikipedia.org/wiki/Balancing_selection

http://en.wikipedia.org/wiki/Balancing_selection

http://en.wikipedia.org/wiki/Genetic_resistance_to_malaria

http://en.wikipedia.org/wiki/Mediterranean




2 Smallpox Viral

Biological

Disease

The history of smallpox holds a unique place in medical history. One of the

deadliest viral diseases known to man, it is the first disease to be treated by

vaccination - and also the only disease to have been eradicated from the

face of the earth by vaccination. Smallpox plagued human populations for

thousands of years. Researchers who examined the mummy of Egyptian

pharaoh Ramses V (died 1157 BCE) observed scarring similar to that from

smallpox on his remains. Ancient Sanskrit medical texts, dating from about

1500 BCE, describe a smallpox-like illness. Smallpox was most likely

present in Europe by about 300 CE. – although there are no unequivocal

records of smallpox in Europe before the 6th century CE. It has been

suggested that it was a major component of the Plague of Athens that

occurred in 430 BCE, during the Peloponnesian Wars, and was described

by Thucydides. A recent analysis of the description of clinical features

provided by Galen during the Antonine Plague that swept through the

Roman Empire and Italy in 165–180, indicates that the probable cause was

smallpox. In 1796, after noting Smallpox immunity amongst milkmaids –

Edward Jenner carried out his now famous experiment on eight-year-old

James Phipps, using Cow Pox as a vaccine to confer immunity to Smallpox.

Some estimates indicate that 20th century worldwide deaths from smallpox

numbered more than 300 million. The last known case of wild smallpox

occurred in Somalia in 1977 – until recent outbreaks in Pakistan and Syria.

http://en.wikipedia.org/wiki/Plague_of_Athens

http://en.wikipedia.org/wiki/Peloponnesian_War

http://en.wikipedia.org/wiki/Thucydides

http://en.wikipedia.org/wiki/Galen





http://en.wikipedia.org/wiki/Italy



3 Bubonic

Plague

Bacterial

Biological

Disease

The Bubonic Plague – or Black Death – was one of the most devastating

pandemics in human history, killing an estimated 75 to 200 million people

and peaking in Europe in the years 1348–50 CE. The Bubonic Plague is a

bacterial disease – spread by fleas carried by Asian Black Rats - which

originated in or near China and then travelled to Italy, overland along the Silk

Road, or by sea along the Silk Route. From Italy the Black Death spread

onwards through other European countries. Research published in 2002

suggests that the Black Death began in the spring of 1346 in the Russian

steppe region, where a plague reservoir stretched from the north-western

shore of the Caspian Sea into southern Russia. Although there were

several competing theories as to the etiology of the Black Death, analysis of

DNA from victims in northern and southern Europe published in 2010 and

2011 indicates that the pathogen responsible was the Yersinia pestis

bacterium, possibly causing several forms of plague. The first recorded

epidemic ravaged the Byzantine Empire during the sixth century, and was

named the Plague of Justinian after emperor Justinian I, who was infected

but survived through extensive treatment. The epidemic is estimated to have

killed approximately 50 million people in the Roman Empire alone. During

the Late Middle Ages (1340–1400) Europe experienced the most deadly

disease outbreak in history when the Black Death, the infamous pandemic

of bubonic plague, peaked in 1347, killing one third of the human population.

http://en.wikipedia.org/wiki/Pandemic

http://en.wikipedia.org/wiki/History_of_the_world

http://en.wikipedia.org/wiki/Common_Era

http://en.wikipedia.org/wiki/Etiology_(medicine)

http://en.wikipedia.org/wiki/Yersinia_pestis



http://en.wikipedia.org/wiki/Plague_(disease)

http://en.wikipedia.org/wiki/Byzantine_Empire

http://en.wikipedia.org/wiki/Plague_of_Justinian

http://en.wikipedia.org/wiki/Justinian_I

http://en.wikipedia.org/wiki/Late_Middle_Ages



4 Syphilis Bacterial

Biological

Disease

Syphilis - the exact origin of syphilis is unknown. There are two primary

hypotheses: one proposes that syphilis was carried from the Americas to

Europe by the crew of Christopher Columbus, the other proposes that

syphilis previously existed in Europe but went unrecognized. These are

referred to as the "Columbian" and "pre-Columbian" hypotheses. In late 2011

newly published evidence suggested that the Columbian hypothesis is valid.

The appearance of syphilis in Europe at the end of the 1400s heralded

decades of death as the disease raged across the continent. The first

evidence of an outbreak of syphilis in Europe were recorded in 1494/1495

in Naples, Italy, during a French invasion. First spread by returning French

troops, the disease was known as “French disease”, and it was not until

1530 that the term "syphilis" was first applied by the Italian physician and

poet Girolamo Fracastoro. By the 1800s it had become endemic, carried by

as many as 10% of men in some areas - in late Victorian London this may

have been as high as 20%. Invariably fatal, associated with extramarital sex

and prostitution, syphilis was accompanied by enormous social stigma. The

secretive nature of syphilis helped it spread - disgrace was such that many

sufferers hid their symptoms, while others carrying the latent form of the

disease were unaware they even had it. Treponema pallidum, the syphilis

causal organism, was first identified by Fritz Schaudinn and Erich Hoffmann

in 1905. The first effective treatment (Salvarsan) was developed in 1910

by Paul Ehrlich which was followed by the introduction of penicillin in 1943.

http://en.wikipedia.org/wiki/Syphilis

http://en.wikipedia.org/wiki/Americas

http://en.wikipedia.org/wiki/Christopher_Columbus


http://en.wikipedia.org/wiki/Naples

http://en.wikipedia.org/wiki/Girolamo_Fracastoro



http://en.wikipedia.org/wiki/Fritz_Schaudinn

http://en.wikipedia.org/wiki/Fritz_Schaudinn

http://en.wikipedia.org/wiki/Erich_Hoffmann

http://en.wikipedia.org/wiki/Salvarsan

http://en.wikipedia.org/wiki/Paul_Ehrlich

http://en.wikipedia.org/wiki/Penicillin



5 Tuberculosis Bacterial

Biological

Disease

Tuberculosis - the evolutionary origins of the Mycobacterium tuberculosis

indicates that the most recent common ancestor was a human-specific

pathogen, which encountered an evolutionary bottleneck leading to

diversification. Analysis of mycobacterial interspersed repetitive units has

allowed dating of this evolutionary bottleneck to approximately 40,000 years

ago, which corresponds to the period subsequent to the expansion of Homo

sapiens out of Africa. This analysis of mycobacterial interspersed repetitive

units also dated the Mycobacterium bovis lineage as dispersing some 6,000

years ago. Tuberculosis existed 15,000 to 20,000 years ago, and has been

found in human remains from ancient Egypt, India, and China. Human

bones from the Neolithic show the presence of the bacteria, which may be

linked to early farming and animal domestication. Evidence of tubercular

decay has been found in the spines of Egyptian mummies, and TB was

common both in ancient Greece and Imperial Rome. Tuberculosis reached

its peak the 18th century in Western Europe with a prevalence as high as

900 deaths per 100,000 - due to malnutrition and overcrowded housing with

poor ventilation and sanitation. Although relatively little is known about its

frequency before the 19th century, the incidence of Scrofula (consumption)

“the captain of all men of death” is thought to have peaked between the end

of the 18th century and the end of the 19th century. With advent of HIV there

has been a dramatic resurgence of tuberculosis with more than 8 million

new cases reported each year worldwide and more than 2 million deaths.




6 Cholera Bacterial

Biological

Disease

Cholera is a severe infection in the small intestine caused by the bacterium

vibrio cholerae, contracted by drinking water or eating food contaminated

with the bacterium. Cholera symptoms include profuse watery diarrhoea and

vomiting. The primary danger posed by cholera is severe dehydration, which

can lead to rapid death. Cholera can now be treated with re-hydration and

prevented by vaccination. Cholera outbreaks in recorded history have

indeed been explosive and the global proliferation of the disease is seen by

most scholars to have occurred in six separate pandemics, with the seventh

pandemic still rampant in many developing countries around the world. The

first recorded instance of cholera was described in 1563 in an Indian medical

report. In modern times, the story of the disease begins in 1817 when it

spread from its ancient homeland of the Ganges Delta in the bay of Bengal

in North East India - to the rest of the world. The first cholera pandemic

raged from 1817-1823, the second from 1826-1837 The disease reached

Britain during October 1831 - and finally arrived in London in 1832 (13,000

deaths) with subsequent major outbreaks in 1841, 1848 (21,000 deaths)

1854 (15,000 deaths) and 1866. Surgeon John Snow – by studying the

outbreak cantered around the Broad Street well in 1854 – traced the source

of cholera to drinking water which was contaminated by infected human

faeces – ending the “miasma” or “bad air” theory of cholera transmission.



7 Poliomyelitis Viral

Biological

Disease

The history of poliomyelitis (polio) infections extends into prehistory.

Ancient Egyptian paintings and carvings depict otherwise healthy people

with withered limbs, and children walking with canes at a young age.[3] It is

theorized that the Roman Emperor Claudius was stricken as a child, and this

caused him to walk with a limp for the rest of his life. Perhaps the earliest

recorded case of poliomyelitis is that of Sir Walter Scott. At the time, polio

was not known to medicine. In 1773 Scott was said to have developed "a

severe teething fever which deprived him of the power of his right leg." The

symptoms of poliomyelitis have been described as: Dental Paralysis,

Infantile Spinal Paralysis, Essential Paralysis of Children, Regressive

Paralysis, Myelitis of the Anterior Horns and Paralysis of the Morning.

In 1789 the first clinical description of poliomyelitis was provided by the

British physician Michael Underwood as "a debility of the lower extremities”.

Although major polio epidemics were unknown before the 20th century, the

disease has caused paralysis and death for much of human history. Over

millennia, polio survived quietly as an endemic pathogen until the 1880s

when major epidemics began to occur in Europe; soon after, widespread

epidemics appeared in the United States. By 1910, frequent epidemics

became regular events throughout the developed world, primarily in cities

during the summer months. At its peak in the 1940s and 1950s, polio would

maim, paralyse or kill over half a million people worldwide every year

http://en.wikipedia.org/wiki/Prehistory

http://en.wikipedia.org/wiki/Ancient_Egypt

http://en.wikipedia.org/wiki/Roman_Emperor

http://en.wikipedia.org/wiki/Claudius

http://en.wikipedia.org/wiki/Sir_Walter_Scott

http://en.wikipedia.org/wiki/Teething

http://en.wikipedia.org/wiki/Epidemic

http://en.wikipedia.org/wiki/Paralysis

http://en.wikipedia.org/wiki/Millennia

http://en.wikipedia.org/wiki/Endemic_(epidemiology)



8 Typhus Bacterial

Biological

Disease

Typhoid fever (jail fever) is an acute illness associated with a high fever that

is most often caused by the Salmonella typhi bacteria. Typhoid may also be

caused by Salmonella paratyphi, a related bacterium that usually leads to a

less severe illness. The bacteria are spread via deposition in water or food

by a human carrier. An estimated 16–33 million cases of typhoid fever occur

annually. Its incidence is highest in children and young adults between 5 and

19 years old. These cases as of 2010 caused about 190,000 deaths up from

137,000 in 1990. Historically, in the pre-antibiotic era, the case fatality rate of

typhoid fever was 10-20%. Today, with prompt treatment, it is less than 1%.

9 Dysentery Bacterial /

Parasitic

Biological

Disease

Dysentery (the Flux or the bloody flux) is a form of gastroenteritis – a type

inflammatory disorder of the intestine, especially of the colon, resulting in

severe diarrhea containing blood and mucus in the feces accompanied by

fever, abdominal pain and rectal tenesmus (feeling incomplete defecation),

caused by any kind of gastric infection. Conservative estimates suggest

that 90 million cases of Bacterial Dysentery (Shigellosis) are contracted

annually, killing at least 100,000. Amoebic Dysentery (Amebiasis) infects

some 50 million people each year, with over 50,000 cases resulting in death.

http://en.wikipedia.org/wiki/Case_fatality_rate

http://en.wikipedia.org/wiki/Gastroenteritis

http://en.wikipedia.org/wiki/Inflammation

http://en.wikipedia.org/wiki/Intestine

http://en.wikipedia.org/wiki/Colon_(anatomy)

http://en.wikipedia.org/wiki/Diarrhea

http://en.wikipedia.org/wiki/Blood

http://en.wikipedia.org/wiki/Mucus

http://en.wikipedia.org/wiki/Feces

http://en.wikipedia.org/wiki/Fever

http://en.wikipedia.org/wiki/Abdominal_pain

http://en.wikipedia.org/wiki/Rectal_tenesmus

http://en.wikipedia.org/wiki/Rectal_tenesmus

http://en.wikipedia.org/wiki/Infection



10 Spanish

Flu

Viral

Biological

Disease

In the United States, the Spanish Flu was first observed in Haskell County,

Kansas, in January 1918, prompting a local doctor, Loring Miner to warn the

U.S. Public Health Service's academic journal. On 4th March 1918, army cook

Albert Gitchell reported sick at Fort Riley, Kansas. A week later on 11th March

1918, over 100 soldiers were in hospital and the Spanish Flu virus had now

reached Queens New York. Within days, 522 men had reported sick at the

army camp. In August 1918, a more virulent strain appeared simultaneously

in Brest, Brittany-France, in Freetown, Sierra Leone, and in the U.S, in Boston,

Massachusetts. It is estimated that in 1918, between 20-40% of the worlds

population became infected by Spanish Flu - with 50 million deaths globally.

11 HIV / AIDS Viral

Biological

Disease

AIDS was first reported in America in 1981 – and provoked reactions which

echoed those associated with syphilis for so long. Many of the earliest cases

were among homosexual men - creating a climate of prejudice and moral

panic. Fear of catching this new and terrifying disease was also widespread

among the public. The observed time-lag between contracting HIV and the

onset of AIDS, coupled with new drug treatments, changed perceptions.

Increasingly it was seen as a chronic but manageable disease. The global

story was very different - by the mid-1980s it became clear that the virus had

spread, largely unnoticed, throughout the rest of the world. The nature of this

global pandemic varies from region to region, with poorer areas hit hardest. In

parts of sub-Saharan Africa nearly 1 in 10 adults carries the virus - a statistic

which is reminiscent of the spread of syphilis in parts of Europe in the 1800s.

http://en.wikipedia.org/wiki/Haskell_County,_Kansas

http://en.wikipedia.org/wiki/Haskell_County,_Kansas

http://en.wikipedia.org/wiki/Fort_Riley,_Kansas

http://en.wikipedia.org/wiki/Queens

http://en.wikipedia.org/wiki/Brest,_France

http://en.wikipedia.org/wiki/Freetown

http://en.wikipedia.org/wiki/Boston

http://en.wikipedia.org/wiki/Boston






12 Ebola Haemorrhagic

Viral

Biological

Disease

Ebola is a highly lethal Haemorrhagic Viral Biological Disease, which has

caused at least 16 confirmed outbreaks in Africa between 1976 and 2014.

Ebola Virus Disease (EVD) is found in wild great apes and kills 50% to 90% of

humans infected - making it one of the deadliest diseases known to man. It is

so dangerous that it is considered to be a potential Grade A bioterrorism agent

– on a par with anthrax, smallpox, and bubonic plague. The current outbreak

of EVD has seen confirmed cases in Guinea, Liberia and Sierra Leone,

countries in an area of West Africa where the disease has not previously

occurred. There were also a handful of suspected cases in neighbouring Mali,

but these patients were found to have contracted other diseases

For each epidemic, transmission was quantified in different settings (illness in

the community, hospitalization, and traditional burial) and predictive analytics

simulated various epidemic scenarios to explore the impact of medical control

interventions on an emerging epidemic. A key medical parameter was the

rapid institution of control measures. For both epidemic profiles identified,

increasing the rate of hospitalization reduced the predicted epidemic size.

Over 4000 suspected cases of EVD have been recorded, with the majority of

them in Guinea. The current outbreak has currently resulted in over 2000

deaths. These figures will continue to rise as more patients die and as test

results confirm that they were infected with Ebola.


Ebola is a highly lethal Haemorrhagic Viral Biological Disease, which has

caused at least 16 confirmed outbreaks in Africa between 1976 and 2014.



13 Future

Bacterial

Pandemic

Infections

Bacterial

Biological

Disease

Bacteria were most likely the real killers in the 1918 Flu Pandemic - the vast

majority of deaths in the 1918–1919 influenza pandemic resulted as a result of

secondary bacterial pneumonia, caused by common upper respiratory-tract

bacteria. Less substantial data from the subsequent 1957 and 1968 Flu

pandemics are consistent with these findings. If severe pandemic influenza is

largely a problem of viral-bacterial co-pathogenesis, pandemic planning needs

to go beyond addressing the viral cause alone (influenza vaccines and

antiviral drugs). The diagnosis, prophylaxis, treatment and prevention of

secondary bacterial pneumonia - as well as stockpiling of antibiotics and

bacterial vaccines – should be high priorities for future pandemic planning.

14 Future

Viral

Pandemic

infections

Viral

Biological

Disease

What was Learned from Reconstructing the 1918 Spanish Flu Virus

Comparing pandemic H1N1 influenza viruses at the molecular level yields key

insights into pathogenesis – the way animal viruses mutate to cross species.

The availability of these two H1N1 virus genomes separated by over 90 years,

provided an unparalleled opportunity to study and recognise genetic properties

associated with virulent pandemic viruses - allowing for a comprehensive

assessment of emerging influenza viruses with human pandemic potential.

There are only four to six mutations required within the first three days of viral

infection in a new human host, to change an animal virus to become highly

virulent and infectious to human beings. Candidate viral gene pools for future

possible Human Pandemics include Anthrax, Lassa Fever, Rift Valley Fever,

EVD, SARS, MIRS, H1N1 Swine Flu (2009) and H7N9 Avian / Bat Flu (2013).

http://www.microbemagazine.org/index.php?option=com_content&view=article&id=2902:what-we-learned-from-reconstructing-the-1918-influenza-pandemic-virus&catid=675:featured&Itemid=896

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