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The H.A.T brief

general overview
by

Irene Ng

on 18 February 2014

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Transcript of The H.A.T brief

Twitter/Facebook
Social
Myfitnesspal
Food, nutrition
Withings
Weight, BP
steps
Foursquare
place/time
Other apps
variables
Personal Data Stores: Raw data repository (HAT Sink)
Internet companies
Manufacturers
Person
PRIMARY time series STUFF data
Inventories
Motion
On/off
Volume consumed
PRIMARY time series PERSON data
Emotion
Diary
Location
Clothing
Qualitative & Ethnographic data
Narratives
Stories
Rich descriptions
Tech System 1 Automagic beauty box
Tech System 2
Tech System 3
1. Shopping list & deployment plan for IoT sensors at the homes of Digital Person Zeroes (DP0) - (Cambridge)
2. Ethnographic data collection & understanding of buying/consumption behaviours of the DP0s- (Nottingham)
HAT Composite data repository & architecture (meta +Algos for base) -
Noggin

6. Map consumption attributes with buying attributes - (Warwick, Warwick Econs)
Phase 1: Discover & Understand Stakeholders of the multi-sided market
Transactional buyer data
Product data
Social CRM engine
Illustrative raw data
Name
Address
transaction date/time
product purchased
Illustrative raw data
Colour
quantity/volume
product type
illustrative inferred data
Beliefs
lifestyle
values
CRM engine
Generate
Predictions
Segmentation
Promotions
Channels & Campaigns
Advertising content
Twitter/Facebook
Social
Myfitnesspal
Food, nutrition
Withings
Weight, BP
steps
Foursquare
place/time
Other apps
variables
Social
CRM
Personal Data Stores: Raw data repository (HAT Sink)
Internet companies
Manufacturers
Person
PRIMARY time series STUFF data
Inventories
Motion
On/off
Volume consumed
PRIMARY time series PERSON data
Emotion
Diary
Location
Clothing
Qualitative & Ethnographic data
Narratives
Stories
Rich descriptions
Tech System 1 Automagic beauty box
Tech System 2
Tech System 3
HAT Composite data repository & architecture (meta +Algos for base) -
Noggin
Phase 2: Hypothesis and test relationships in the multi-sided market
Transactional buyer data
Product data
Social CRM engine
Illustrative raw data
Name
Address
transaction date/time
product purchased
Illustrative raw data
Colour
quantity/volume
product type
illustrative inferred data
Beliefs
lifestyle
values
CRM engine
Generate
Predictions
Segmentation
Promotions
Channels & Campaigns
Advertising content
3. Pull data from open APIs (secondary data of personal information) (Warwick)
4. Pull data from open APIs of manufacturers (Warwick & Project partners)
7. Hypothesize archetypes that link consumption to purchase (Exeter, Bristol)

8. Hypothesise relationship between personal data categories for human control & decisions (Exeter, Bristol)
9. Hypothesize relationships between data for personalised products (Warwick Econs)
10. Interface design (Warwick, Edinburgh)
11. Build design fiction (Edinburgh)
12. Build exemplar apps (Warwick, Edinburgh, project partners)
13. Test with DP0s (Nottingham)
how should information be displayed so that i can plan, monitor and act effectively?
what kind of data can i give so that firms can personalise their offerings for me
Continuous deployment (and data feed) (Cambridge)
Continuous data feed
Twitter/Facebook
Social
Myfitnesspal
Food, nutrition
Withings
Weight, BP
steps
Foursquare
place/time
Other apps
variables
Social
CRM
HAT Composite data repository & architecture (meta +Algos for base) -
Noggin
Internet companies
Manufacturers
Person
PRIMARY time series STUFF data
Inventories
Motion
On/off
Volume consumed
PRIMARY time series PERSON data
Emotion
Diary
Location
Clothing
Qualitative & Ethnographic data
Narratives
Stories
Rich descriptions
Tech System 1 Automagic beauty box
Tech System 2
Tech System 3
HAT Market Platform
HAT Sparkshop
Phase 3: Create multi-sided market platform
Transactional buyer data
Product data
Social CRM engine
Illustrative raw data
Name
Address
transaction date/time
product purchased
Illustrative raw data
Colour
quantity/volume
product type
illustrative inferred data
Beliefs
lifestyle
values
CRM engine
Generate
Predictions
Segmentation
Promotions
Channels & Campaigns
Advertising content
Benchmarking energy use with others,
Collaborative consumption models
e.g. Intelligent toilets derive revenues from health industry
Continuous deployment (and data feed) (Cambridge)
Continuous data feed
(3) Exchange personal data for personalised products
(2) Exchange personal data for service/advice/recommendations
(1) Exchange personal data for aggregated intelligence
(4) Exchange personal data for personalised advertising/campaigns
14. Write API spec for platform
15. HAT Fest
Disruptive business models
e.g. meds run down, triggers reorder
nutrition advice
16. Recruit New DP0s
Phase 1: Discover & Understand (Tasks 1-6)
Phase 2: Hypothesise & test (Tasks 7-13)
Phase 3: Create Market Platform (Tasks 14-16)
5.Build database architecture
Build attributes, composite attributes & meta attributes & levels i.e.(i) data dictionary & inventory system (ii) scheme for personal data stores (Warwick)
Welcome to the H.A.T project

This presentation takes you through the 3 phases and 16 tasks ahead of the HAT team, and how they are integrated. All 16 tasks should be completed by October 2014, which is when the HAT fest is held

e.g.
L1=cafe nero location
S1=sleep duration
T1=10 mins before arriving L2
L2=work location
e.g. Buy coffee=f{sleep duration(-), cafe nero location(<10m), before work(-)}
e.g. buy toothpaste when running low, buy toothpaste when packing
7am
11pm
Data on Emotions
Typical Time series Data layers for 1 day
1. 1-3 months of data will be compiled
2. In-person variety will be detected to understand resource contraints and needs
Data on geo-location
Data on purchase
Data on social
Data on home measurements
5 Digital Persons Zero: Wearable RFID tagged for location at home, inventory (wardrobe) tagged, temperature sensed, energy & water consumption metered, geolocation measured, steps documented; documents nutrition, water intake, purchase, emotions; provide access to foursquare, fb, twitter feeds,
Research Councils UK £1.2m (USD1.8m) project
7 academics, 9 researchers (16 total) across 6 universities in the UK from economics, social science, design, computing and business

Starts 1 June 2013 – beta deployment expected October 2014 (HATFest)

The outcome- a MARKET platform at the home
More information at http://www.hubofallthings.com

Temperature, Energy, water
coffee
Sandwich
music
12 noon
7pm
book on Amazon
Data on health - steps, blood pressure, pulse, weight
Data on nutrition - water, potassium, calcium, sugar intake
Times series, person centric
GDP/The economy
Households & Purchase Decisions (often out of context, creating market inefficiencies)
Internet-of-Things
Connected People
The markets (retail shops, high streets, online sites)
Consumption
Data (private)
Buying data
If firms knew how we consumed, products/services could be personalised e.g. personalised medicine, personalised groceries etc.
Individuals must be incentivised to collect MORE of their own data - that means consumption data must be private, owned by the individual and is a private asset to exchange
If conditions are suitable, a market could emerge (to reduce inefficiencies)
could a market in context happen?
Targeted October 2014: Viral campaign to deploy the HAT
Scaling up...........
Personal Data Stores: Raw data repository (HAT Sink)
How does consumption link to purchase
Why a Market at the Home

more than 60% of GDP is consumed at home (OECD)
Almost ALL of household purchases are out of context - opportunities abound for new business models/economic model (market inefficiency/surplus)
if only 1% of household purchase replaced by personal data can create a multiplier effect of xx%? increase in GDP? (Siloed Loyalty data is now 0.25% of spend)
Serving Home Contexts changes the nature of competition
The Internet jumps out of the box (Internet-of-things)
Collision of manufacturing and Internet companies and two very different types of business models
'Wet-wiring' will cause disruption

The H.A.T (Hub-of-All-Things)
Platform for Multi-sided Market powered by Internet-of-Things: Opportunities for New Economic & Business Models

Ownership
: All primary data generated or placed within the H.A.T is owned by the individual who owns the H.A.T (just as the collectors of data now claim ownership of data)

Resource
: Individuals must be incentivised to collect MORE of their own data, as their own resource - that means consumption data must be private, owned by the individual and is a private asset to exchange

Power
: the individual can create and consolidate more of his/her own data than it can ever be collected by firms out there. The individual can tip the power balance of information since the individual is able to integrate horizontal information without privacy infringements.

Worth
: personal data on its own is 'silo-ed' secondary data from vertical industries - limited use. H.A.T algorithms will create worth from transforming personal data to digital assets

Trade
: Personal data as digital asset owned by the individual can be traded for benefits if a platform is created

Internalisation
: Personal data can internalise network effects of IoT back into the economy for new jobs, new businesses.
Be the disruptive platform for manufacturing/internet sector collision
H.A.T summary principles on personal data
Insights and Underpinning Theories

finally 'seeing' value in context. empowering the customer with a platform makes B2C into B2B - 3m of it. both standardisation and personalisation

Moving from an exchange market to a context market, moving from business models of value in exchange to business models of value in use - transaction boundaries?

Supply chains - postponement and mass customisation, toward indefinite postponement when customer finishes the product with personal data

Marketing and markets - analysing personal data to find in-person variety to develop the theory of latent need/demand; beyond CRM or VRM - when the product is dynamically reconfigurable and incomplete

Manufacturing and Product design/architecture: modularity for assembly, life cycle, service, and now transactions

Business models - new transaction boundaries, new VC, VP, VCap - the service changes

Economics - knightian ambiguity on choice and products; markets; friction and network effects

IS - human data architecture and service as resource planning and integration (Human resource planning v ERP v MRP)

http://valueandmarkets.com
kindle & PDF
available now

printed version
available Feb 2014

Internet-of-Things
Not sure what the new businesses/products/services are – data is anonymised (big data) therefore no value back to the person – challenges in sharing
Data is vertically siloed (no relational value to person or to new businesses)
Not privacy preserving
Trust issues
Consumers not willing to engage (fear where data is going/used)
Small market size – Market failure in some instances
The challenges
Challenge of business practice – vertical industries in an increasingly connected economy – e.g. osram, GSK
Challenge of business theories – current form of empiricism not sufficient to provide leadership for future business models, new approaches needed
Challenge of markets – being formed closer to contexts and on demand – potential new business models and services, new substrates e.g. smartphones, tablets
Challenge of policy - personal data – property rights and potential market failure
Challenge of technology/engineering design & understanding lived lives as part of design
Challenge of technology - interoperability (but this cd be a market friction issue)

Alignment between possession and use
Previously, the only route to service/outcomes was through ownership e.g. music CDs
Firms have talked, promoted and sold on the basis of benefit and use of things but benefit (outcomes) and use is not aligned to revenues – we still only buy possession and not outcomes
But outcomes/benefits come only in the context of use and experience
If firms found a way to serve contexts, ownership may not be the dominant biz model
Case study: Music

Value in exchange (Worth)
The H.A.T
Value in use
We are in a world of Stone Age emotions, medieval institutions and godlike technology: Edward O. Wilson
Market inefficiencies

Where we buy, where we consume (beer)

What we buy, what we consume (tea)

When we buy, when we consume (beer)

How we buy and how we consume

Innovation and Speed will prevail to reduce market inefficiencies?

challenge: no visibility for value-in-use

VNM utility
Ng, Irene C.L., and Laura Smith (2012), “An Integrative Framework of Value” in Review of Marketing Research Special issue on Toward a Better Understanding of the Role of Value in Markets and Marketing, Stephen L. Vargo and Robert Lusch (Eds) Vol 9, pp 207-243
Cambridge Service Week, 2013
Irene Ng
Professor of Marketing & Service Systems
Director, International Institute for Product & Service Innovation
University of Warwick

The HAT Business Model
HAT Foundation
Privacy, trust and ethics certification rating (legal and technology framework - AAA to DDD
DP0s continuing research and development of algorithms, clusters and new APIs

HAT startup
Licenses platform to
contributing x% license fee
Certified Hard HATs
- Intel board + sensors
Certified Sun HATs
- Mydex/PDS + IoT hub
Certified Straw HATs
(Nymote etc.)
HAT app
(other sinks)
contribute new algo/APIs
commercial agreements
Fridges, set top boxes, smart meters, game machines, PCs, Other IoT sinks
Internet companies and retailers
Manufacturers
Brick & Mortar retailers
Individuals & households
Multisided market platform
The H.A.T
We are in a world of Stone Age emotions, medieval institutions and godlike technology: Edward O. Wilson
Irene Ng
Professor of Marketing & Service Systems
Director, International Institute for Product & Service Innovation
University of Warwick
Alignment between possession and use
Previously, the only route to service/outcomes was through ownership e.g. music CDs
Firms have talked, promoted and sold on the basis of benefit and use of things but benefit (outcomes) and use is not aligned to revenues – we still only buy possession and not outcomes
But outcomes/benefits come only in the context of use and experience
If firms found a way to serve contexts, ownership may not be the dominant biz model
Case study: Music
Market inefficiencies

Where we buy, where we consume (beer)

What we buy, what we consume (tea)

When we buy, when we consume (beer)

How we buy and how we consume

Innovation and Speed will prevail to reduce market inefficiencies?

challenge: no visibility for value-in-use
freemium
Me
Favourites
Homes
Discover
Understand
Learn
Plan
Monitor
Act
Me
Calendar
About
Social
Body
Wellbeing
Healthcare
Monitor
Things
Stuff
Fridge
Understand
Usage
Alerts
Learn
Match
Analyse
Consumption
Environment
Search
Advice
Buy widgets
Compare
Plan
Shopping list
Subscriptions
Data streams
Usage
Bathroom 1
All users
1 week | 2 weeks | 1 mth | 6 mths | YTD | 1 yr | 2 yrs | 5 yrs|
Shampoo
Conditioner
Toilet roll
in room
Inventory
Volume
Weight
in room
Inventory
in room
Inventory
Search: Search for offers, search for data stream buyers

Match: Match usage of product with offers, with complementary products etc.

Compare: compare usage of this product with other products, other users, other whatever

Analyse: analyse usage of product with other products and across time, or analyse depletion rates of the product, analyse product with your attributes (shampoo with your hair type etc.)

Advice: seek advice on the use of this product (could be bundled with personal attributes)

Pull data from Withings, FB, Twitter, calendar etc.
Match
Choose Match widget
In-app Market
in list
in list
in list
Search
Choose Search widget
Choose Compare Widget
Compare
Analyse
Choose Analyse Widget
Advice
Choose Advice Widget
Products
Usage
Bathroom 1
All users
1 week | 2 weeks | 1 mth | 6 mths | YTD | 1 yr | 2 yrs | 5 yrs|
Water
Ventilator
Light 1
Volume
Duration
Utiility
Brandrefill consumption analysis
User 2
1 week | 2 weeks | 1 mth | 6 mths | YTD | 1 yr | 2 yrs | 5 yrs|
Water
Light 1-Energy
Shampoo
Toilet paper
Conditioner
Toothpaste
User 1
Total Consumption
[15.00]
Total Consumption
[17.50]
6 parameters
Settings
Homes
Integrate your IoT Sink
Intel
Alertme
SmartThings
Geras
Usage
Bathroom 1
All users
1 week | 2 weeks | 1 mth | 6 mths | YTD | 1 yr | 2 yrs | 5 yrs|
Vacuum Cleaner
Washing Machine
Oven
Duration
Equipment
Duration
Export usage
Sell Data Stream
Analyse
Search
Match
Compare
Advice
Design fiction towards
Use Case
Analyse
Mathwiz
Find your Contextual archetypes
Brandrefill
Analyse your Consumption
Buy more analysis widgets
Discovery
Compare
Contexts
Consumption
Buy more comparison widgets
Mathwiz Contextual archetypes
1 week | 2 weeks | 1 mth | 6 mths | YTD | 1 yr | 2 yrs | 5 yrs|
Dyson use
occupants
6 parameters
Export usage
Sell Data Stream
Analyse
Search
Match
Compare
Advice
Temperature
Humidity
Match
Confused.com
- matches your shopping list to offers
Which?
- matches your usage to products
Buy more match widgets
Act
Shop for Products
Stuff
Wardrobe
Store Cupboard
Fridge
Environment
Temperature
1 week | 2 weeks | 1 mth | 6 mths | YTD | 1 yr | 2 yrs | 5 yrs |
Room 1
Room 2
Room 3
Room 4
Room 5
Export history
Export Data Stream
Analyse
Search
Match
Compare
Buy
Add to Shopping list
Advice
Export history
Export Data Stream
Analyse
Search
Match
Compare
Buy
Add to Shopping list
Advice
Export history
Export Data Stream
Analyse
Search
Match
Compare
Buy
Add to Shopping list
Advice
Export history
Export Data Stream
Analyse
Search
Match
Compare
Buy
Add to Shopping list
Advice
Visualise
these are learning widgets that the user can buy in-app (in-app market for widgets)
Pull data from Sinks
market for sinks to integrated into the HAT market platform
market for personal data apps
market for retailers
Pull product data from retailers
Full transcript