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.
Digital Product Lifecycle Strategy
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
Digital Product Lifecycle Strategy
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
©2013 Innovation Pipeline
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%
©2013 Innovation Pipeline
Cone™ Lifestyle Understanding
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 …..
Cone™ Lifestyle Understanding
©2013 Innovation Pipeline
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.
©2013 Innovation Pipeline
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
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%
The Challenge – American Idol, 2014
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.
©2013 Innovation Pipeline
Cone™ Fan Base Understanding
©2013 Innovation Pipeline
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
Geo-demographic Profile Experian Mosaic – 15 Groups (Streams), 66 Types (Segments)
Hybrid Cone – 3 Dimensions
The Music Cone™
Band Loyalty & Affinity
The Utility Cone™ - Customer -base Understanding
The Media Cone™
The Media Cone™ - Customer-base Model / Understanding
– turning Social Intelligence into Actionable Marketing Insights…
• 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
Geo-demographic Profile Experian Mosaic – 15 Groups (Streams), 66 Types (Segments)
Hybrid Cone – 3 Dimensions
The Music Cone™
Band Loyalty & Affinity
The Utility Cone™ - Customer -base Understanding
The Music Cone™
The Music Cone™ - Fan-base Model / Understanding
– turning Social Intelligence into Actionable Marketing Insights…
• 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
Geo-demographic Profile Experian Mosaic – 15 Groups (Streams), 66 Types (Segments)
Hybrid Cone – 3 Dimensions
The Music Cone™
Band Loyalty & Affinity
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
Geo-demographic Profile Experian Mosaic – 15 Groups (Streams), 66 Types (Segments)
Hybrid Cone – 3 Dimensions
The Cone™
Political Activity
The Cone™ – Political Model
Political Cone™ – Streaming and Segmentation
Political Influence
Party Affinity
Geo-demographic Profile Experian Mosaic – 15 Groups (Streams), 66 Types (Segments)
Hybrid Cone – 3 Dimensions
The Cone™
Political Influence over
Election Outcomes
Floating Voters - 10%
Minority Party Voters - 20%
Socialists - 30%
Conservatives - 40%
The Cone™ – Political Model
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
Enterprise Architecture
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
Enterprise Architecture
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
FAST FASHION RETAILING and BRAND MANAGEMENT
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..
Retail 2.0 “Perfect Store” – Experience Digital Marketing – Fast Fashion
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
Digital Marketing – Retail 2.0 Model
FAST FASHION RETAILING and BRAND MANAGEMENT
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 .
Retail 2.0 “Perfect Store” – Experience Digital Marketing – Fast Fashion
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
Digital Marketing – Retail 2.0 Model
FAST FASHION RETAILING and BRAND MANAGEMENT
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
Retail 2.0 “Perfect Store” – Experience Digital Marketing – Fast Fashion
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 •
LUXURY GOODS RETAILING and BRAND MANAGEMENT
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.....
Retail 2.0 “Perfect Store” – Experience Digital Marketing – Luxury Goods
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 •
LUXURY GOODS RETAILING and BRAND MANAGEMENT
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.....
Retail 2.0 “Perfect Store” – Experience Digital Marketing – Luxury Goods
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
Geo-demographic Profile Experian Mosaic – 15 Groups (Streams), 66 Types (Segments)
Hybrid Cone – 3 Dimensions
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)
Hybrid Cone – 3 Dimensions
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.
History of Digital Epidemiology
• 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.
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.
History of Digital Epidemiology
Modern epidemiology has its origin with the study of Cholera
Broad Street cholera outbreak of 1854
History of Digital Epidemiology
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.
History of Digital Epidemiology
Broad Street cholera outbreak of 1854
Clinical Risk Types
Clinical Risk Types
Clinical Risk Group
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Morbidity Risk Types
Morbidity Risk Group
C
Legal Risk
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3rd Party Risk
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A
I D
Immunological System Risk
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Risk
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System Risk
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Toxicity Risk
Organ Failure Risk
- Airways
- Conscious
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Triage Risk
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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
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.
Pandemic Black Swan Event Types
Type Force Epidemiology Black Swan Event
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.
Pandemic Black Swan Event Types
Type Force Epidemiology Black Swan Event
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.
Pandemic Black Swan Event Types
Type Force Epidemiology Black Swan Event
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.
Pandemic Black Swan Event Types
Type Force Epidemiology Black Swan Event
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.
Pandemic Black Swan Event Types
Type Force Epidemiology Black Swan Event
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.
Pandemic Black Swan Event Types
Type Force Epidemiology Black Swan Event
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
Pandemic Black Swan Event Types
Type Force Epidemiology Black Swan Event
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.
Pandemic Black Swan Event Types
Type Force Epidemiology Black Swan Event
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.
Pandemic Black Swan Event Types
Type Force Epidemiology Black Swan Event
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.
Pandemic Black Swan Event Types
Ebola is a highly lethal Haemorrhagic Viral Biological Disease, which has
caused at least 16 confirmed outbreaks in Africa between 1976 and 2014.
Pandemic Black Swan Event Types
Type Force Epidemiology Black Swan Event
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).
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