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Crossing the Urban Data Layer:
Mobility as a Data Generating
Activity
Herman Donner, Michael Steep and Todd Peterson
Stanford School of Engineering
Disruptive Technology and Digital Cities Program
August 2019
1
Summary of the Report
We analyze mobility within cities from the perspective of data acquisition and how
location-based-services enable companies to set demand and preferences in a
geographic context. A key aspect is that movement creates context-based-intelligence
when it becomes possible to adjust advertisement and offers based on location, activity
and social-context.
In terms of impact on on business modeling, we find that a primary impact is that on value
proposition and marketing. Smart-devices enables a constant connection and a two-way
dialog between company and customer. It will be increasingly important for retailers to
effectively reach customers with offers that are contionously updated to maximize the
likelihood of a purchase through behavioural-based pricing.
Data driven business models are transforming the insurance industry as data from
wearable devices and social-media activity determine life-insurance premiums, and car
insurance is set by where and when a car is driven. Similarly, credit-risks are now
determined through an understanding of purchasing patterns and account-flows, rather
than credit-scores.
Another category of business impact is that created by the ability to measure device
performance and usage. This impacts the entire corporate value chain – notably thorugh
outcome-based contracts and servitization when large data and increasingly advanced
analytics makes it possible decrease risks associated with guarantees, insurance and
leasing contracts.
Incrasingly, concerns are raised over both the impact on privacy and cybersecurity, in
addition to fairness when pricing is becoming increasingly individualized. We cover the
risks, implications and the challenge associated with the fact that even as consumers state
that they are concerned about privacy, they also value getting relevant content that is
enabled by consumer profiling.
2
1. Introduction: Mobility From the Perspective of Data Acquisition ............................................. 3
2. Simultaneous Trends are Changing Business, Lifestyle and Urban Environments .................. 5
2.1 Seamless Mobility is Not on the Horizon ........................................................................................... 5
2.2 All Corporations are Now in the Business of Data ........................................................................... 6
2.3 Everything is a Data Gathering Device ............................................................................................. 9
2.4 The Future is Determined by Cities .................................................................................................. 11
3. The Consumer Profile, Data Convergence and Context-Based Intelligence ....................... 12
3.1 Impact on Value Proposition and Marketing. ................................................................................ 13
3.2 Device and Usage Monitoring .......................................................................................................... 21
4. Concerns for Cybersecurity, Privacy and Fairness .................................................................. 23
4.1 Data-for-Service, the Personal Data Economy or Pay-for-Privacy? ......................................... 23
4.2 Privacy and Cybersecurity ................................................................................................................ 24
5. Industry Sector Examples and Implications ............................................................................... 27
5.1 Insurance and Healthcare .................................................................................................................. 27
5.2 Retail ...................................................................................................................................................... 28
5.3 Mobility ................................................................................................................................................. 30
5.4 Finance .................................................................................................................................................. 32
6. Summary and Concluding Comments ......................................................................................... 33
7. Endnotes ........................................................................................................................................... 36
3
1. Introduction: Mobility From the Perspective of Data Acquisition
In connected cities, the way we move is now transforming into a data generating activity
characterized by a crossing of private industry data layers – enabling both insights for
improved consumer profiling when demand and preferences are put in a geographical
context and creating new monetization opportunities. Urban environments are rapidly
being digitalized with sensors, networks, connected devices and vehicles in what is known
as the Internet of Things (IoT) revolution that is creating opportunities to collect real-time
data on preferences and activities of consumers.
1
From both a physical devices perspective, and the analysis of data, this is a commercial
and largely unregulated development – as lawmakers are unable to keep up with
technology.
2
We are therefore proposing a definition of a so-called Digital City that is
focused on commercial markets inside of urban centers, and R&D
3
, in contrast to the so-
called Smart City focusing on government public sector management.
4
This as the later
strategy depends on city government – with a culture that is uniformly bureaucratic,
inadequately funded, and lacking technical or partnering expertise required to deliver a
coherent government strategy for digitalization.
5
We believe that policymakers often fail at understanding how digitalization is planned
and implemented in urban centers, and notably lack understanding of the strategic role of
private enterprise R&D investment and expertise. Corporations such as Apple, Facebook,
Uber, and others invest a large percentage of their multi-billion R&D budgets into products
and services that are targeted at city dwellers.
6
They are the guerilla in the room that is
rarely if ever consulted by or taken into consideration by city governments in their plans
for the development of their own version of the Smart City. Yet, these same players are
sending collected data to each other, combining consumer information and creating a
perfect profile of individuals as they move about the city from home to office to event
venues.
It is private R&D and technology that is digitalizing our urban environments – as Moore’s
law has become pervasive across storage and computing, algorithms, sensors, robotics and
advanced materials science. Applications are built on top of these technologies, allowing
4
companies to both deliver new services and gather data used to understand their products
and notably their end customer. These developments are simultaneous and difficult – if not
impossible – to disentangle, in addition to occurring almost completely without public sector
involvement.
At the heart of the digitalization of cities are the mobile devices and applications that we
use for things such as giving us directions, buy goods, find housing, and communicate with
our friends and colleagues. By using these services, we share a lot of personal information,
as the typical model is that consumers trade data in return for a “free” service
7
, allowing
companies to gain insight into our preferences and learn about what content we should be
exposed to.
8
It is likely that consumers do not fully understand the full extent of data that is shared, and
what it implied when data is used to create a consumer profile.
9
When we sign up for a
service, it is seldom clear how this data is used, and with whom it is shared. Third-party
services (such as usage analytics, crash reports, and integration with social media) are
often integrated in applications and run in the background of our devices – effectively
tracking movement without any indication given to the user.
10
Sales and collaborations
based on the transfer of consumer data has turned into a billion-dollar industry.
This report outlines the broader developments that enable companies to create insight
from data and shape our urban environments. A background of key business, technology,
and societal changes that motivate this paper follows in chapter 2. Chapter 3 covers the
impact of data on business models. Chapter 4 reviews concerns for cybersecurity, privacy
and fairness. Chapter 5 summarizes implications for a few key industries. The paper ends
with conclusions and a discussion on future developments.
5
2. Simultaneous Trends are Changing Business, Lifestyle and Urban
Environments
Starting off, we believe that several closely related and simultaneous trends provide a
good illustration of the interconnectedness between how business models, consumer
preferences and cities are changing – and how exponential advances in technology drive
these developments. In the sections below, we list four trends that are shaping society at
large and consequently impacting business models.
2.1 Seamless Mobility is Not on the Horizon
Cities across the globe are facing congestion and strained infrastructure, and for the
foreseeable future, congestion is predicted to get worse across the United States – as
urban populations grow and investment in public-transit declines.
11
In fact, a likely to be
self-fulfilling cycle of declining transit ridership since 2014, has caused some experts to
conclude that there is no point to any public funding for transit at all.
12
The cost of gridlock
is enormous, estimated at $ 305 billion in 2017 for the U.S. alone, with Los Angeles, New
York, Miami and San Francisco examples of cities were traffic costs exceed $ 2000 per
driver and year.
13
Looking beyond the current state of gridlock and crumbling public transit, the single most
significant factor to consider is that of fully autonomous vehicles (AVs), which are predicted
to have a transformative impact on the automotive sector.
14
In large because they will
decrease cost and make it possible to replace car-ownership with mobility-as-a-service
(knowns as MaaS). A key benefit is also added safety, as 90% of car accidents are due
to human error.
15
However, the complexity of these technologies makes it difficult to
forecast when AVs will be pervasive
16
, and some automotive experts state that current
enthusiasm and assertions on rapid advances in AV technology is often lacking scientific
support
17
, and that “nobody in the automotive industry is anywhere close to full autonomy”.
18
And once AVs are operational, the question of how to resolve a future transitional period
of human operated cars and AVs sharing road space also remains.
The added efficiency of AVs – basically more cars on the roads driving more closely
together and being better coordinated – is by many presented as a solution to congestion.
6
It is however far from certain that AVs will make things much better, as the positive effects
of increased efficiency is likely to be offset by increased traffic as AVs drive around
empty in search for customers, and increases demand for miles traveled due to added
convenience, putting additional strain on road infrastructure.
19
Because of this, a growing
literature points towards that earlier predictions have been overly optimistic
20
, and that
several scenarios point towards increased gridlock.
21
Much of the outcome on congestion
will depend on how AVs are regulated – as shared autonomous vehicles (SAVs) will
minimize empty vehicles driving in search for customers.
22
Existing infrastructure and
mobility patterns are also highly important, as AVs are a great first- and last-mile option
to increase the reach of existing public transit – i.e. decreasing congestion in cities with
efficient transit systems. If AVs result in a shift away from mass-transit, or if transit is lacking
as in most U.S. cities, scenarios are far less positive and often indicating a negative impact
on congestion.
23
Adding to this is that and any future scenario will need to be adjusted to current
developments – meaning that if AVs hit the market in five years, it is the future and far
worse state of congestion that serves as the reference point.
In light of the above, the aim of this report is to highlight that the main development is not
how we move, but the insights that movement creates when it is possible to cross-reference
what we know about people with locational data. Notably, how consumer profiles are
given a geographical, social and temporal context through location-based services (LBS)
that integrate geographic location with services – such as emergency services and car
navigation systems.
24
2.2 All Corporations are Now in the Business of Data
Across industries, business models are increasingly data driven, as retailers want to target
their advertising efforts, automakers collect data on usage
25
and ridesharing is managed
through monitoring of congestion and demand patterns.
26
The Economist magazine has
proclaimed that data is now the world’s most valuable resource.
27
Similarly, an oft-cited
statement by the British mathematician Clive Humby is that data is the oil of the 21st
century.
28
Increasing amounts of data available for companies to analyze is in large driven
7
by IoT, as companies can gain access to ever more detailed and sensitive data through
devices that collect information on fingerprints, heart rates and daily calorie
expenditure.
29
Even as 90% of the world’s data was generated during the last two years,
the pace is still exponentially increasing towards an amount of data that is 40 times the
size of 2017 by 2020
30
, by which IoT investment also will exceed $ 1 trillion annually.
31
Much of this is driven by so-called “Dark Data” meaning unstructured data from the
Internet, social media, connected devices, and voice.
32
Companies such as Google and Facebook have their entire business models based on the
collection of data
33
– and an industry of data brokers such as the company Acxiom
facilitates this development by collecting data on individuals and selling it to third
parties.
34
In 2012, their database was estimated to have information of about half a
billion consumers, with about 1,500 data points per person.
35
As consumer data is
collected, transfered and analyzed to guide decisions across industries, it is becoming an
increasingly valuable asset that is bought and sold like any other commodity. This is not
just a trend among technology companies, as customer databases are substantial part of
corporate value.
36
Illustrations of this value are sales of customer databases, securities
issued with consumer data as the underlying asset
37
, that companies are developing new
insurance solutions based on data transfers
38
, and massive interest in IoT and data driven
businesses from venture capital.
One might ask why companies are so interested in this information? And the answer is the
same as the one that would be provided if one asked why the five most valuable
companies in the world are tech companies of which none existed 30 years ago
39
? The
answer is that it comes down to the consumer profile, and the value of personal data which
can be described in terms of advertising. Starting off, a newspaper ad is worth very little
per view if it we can’t identify those who are exposed to it, for traditionally a common
truth among marketers was that; "I know that half of my advertising dollars are wasted…I
just don’t know which half".
40
Not only have marketing efforts by companies traditionally
been limited in terms of making sure that they are exposed to the right target audience,
but the actual effectiveness of campaigns has been difficult, if not impossible, to measure.
Looking at these issues, companies like Google have had a transformative impact – shifting
marketing spending from traditional sources towards online search advertising, as it
8
becomes possible to spend more selectively on those with a higher likelihood of responding
through profiling based on location and search history.
The more detailed the consumer profile becomes, the more targeted campaigns become
– consequently increasing the price of each individual add. An example are so-called
AdWords campaigns, were adds are shown based on defined search keywords, so that
the company offer is shown when people search for their product or service type.
41
The
ads are not only shown based on these keywords as this type of campaign typically
includes several ads with different texts are targeted at different groups of consumers
based on their geography, device segmentation, and product type.
42
This is where the
convergence of data sources and location-based information comes into play. In fact, many
applications now run in the background our devices with the purpose of tracking location
for advertising purposes.
43
Facebook now show adds based on geolocation and displays adds based on the user’s
history of viewing pages, groups and events. The ability to collect, store and analyze big
data will make it possible for say, a diaper manufacturer, to identify families with small
children, who buy a competing brand, and use targeted offers to identify their price
sensitivity and the cost of getting a specific customer to switch brand.
44
It is likely that this
will lead to guerrilla like marketing wars when companies identify and target their – and
their competitors – target customers. Consequently, every company in every industry will
need to adapt – this is no longer something that only matters for Silicon Valley.
The methods and cost of marketing, customer acquisition, and retention is now
fundamentally changing as it becomes possible to monitor the effectiveness of campaigns.
The ability to measure ROI on advertising helps companies to direct effort where it has
the most effect i.e. how many views of an add that resulted in a purchase, signup, web
page visit or lead – all of which are tracked by Google for those who purchase adds.
45
As this type of data improves the consumer profile, customer lifetime value can be
estimated and related to the cost of acquisition, customer intent and position in the buying
cycle can be identified (i.e. what products a customer wants or needs).
46
When this type
of estimation is done on data from social media interactions, comments, reviews, search
9
queries, the concept of the constantly connected consumer becomes a reality, and a resulting
shift towards a two-way dialog between companies and consumers.
47
In all, it stands clear that data is transforming industries, were insights on demand,
preferences, usage of goods and services create value through marketing, and product
offerings. The commercial value that is created by converging data is exponential, and
this is resulting in both new service offerings and new ways for marketing when information
on consumption, location, preferences and even health are merged to form an increasingly
granular consumer profile. Drugstores CVS and Walgreens gain access the number of
steps taken, blood glucose values and prescription history in exchange for discounts to
customers that participate.
48
Similarly, life insurance premiums are now set based on data
from fitness trackers
49
, and insurance companies just received regulatory approval to use
social media information to assess risk.
50
Offline and online activity is now converging in
consumer profiles, so when Google and Mastercard collaborate, online searchers can be
used to understand offline purchases – such as what browsing a certain product tells us on
subsequent purchases at physical stores.
51
Similarly, Facebook is found to buy third-party
data on your characteristics and offline activity, to create an even more granular
understanding.
52
Biometric and health data such as heart-rate and movement is creating
new opportunities and has already been found to be merged with social-media
information to target consumers.
53
2.3 Everything is a Data Gathering Device
Big data analytics and consumer profiling is made possible by advances across all aspects
of technology, that in turn results in an exponential pace of overall technological
progress.
54
Specifically, the declining cost of sensors since the early 2000s is a main
enabler of the Internet of Things (IoT)
55
, resulting in autonomous vehicles, smartphones,
tablets, buildings and infrastructure becoming data gathering devices on top of which
applications such as mapping, social media platform and applications for shopping are
built. In fact, IoT connections are expected to grow at 30% a year until 2023.
56
Another key aspect of IoT is the ability transfer data. Networks such as LTE, Wi-Fi and
Bluetooth and the fifth-generation of mobile networks (5G) makes it possible for this data
10
to be collected to systems for storage and computing. Innovations within this system of
technologies will enable even greater volumes of data to be transmitted, and improved
cloud storage and computing solutions that allow for cost-efficient and fast computing, in
addition to insights through algorithms. New engineered materials enable for better radar
are coupled with network technology and AI that allow devices to communicate with each
other and make better decisions.
57
The value of data will have a significant impact on how to price and market products. A
robotic vacuum cleaner now collects “home layout data”, such as the floor plan, and where
lamps and furniture are placed
58
, and smart TVs now track everything users watch, often
selling this data to third-parties
59
. Bluetooth connected toothbrushes now collect data on
your brushing habits and cavities – and this data can be transferred to third-parties.
60
Other examples are that fridges will be able to collect data on when and how often you
open your fridge, smart door locks that know when you lock and unlock you door
61
, fitness
trackers keeping tabs on how many steps you take and your heart rate, your smartphone
on your movement and when, where and whom you spend time with, and the washing
machine will know how often you wash and how dirty your clothes were. BMW has
developed a steering wheel that can detect if the driver is stressed or otherwise
emotionally distressed
62
, and the value of the data cars collect is predicted to eventually
surpass that of the vehicle itself as in-car purchases and monitoring of surrounding
environments increases.
63
Imagine a future state were all of this is part of your consumer
profile, merged with your online search history and movement.
Those companies who know how to best value the value of this data will be able to undercut
their competitors on price and succeed in acquiring customers – why do you think a 65-
inch smart tv is so cheap? TV manufacturers only need to cover costs, the real value is
getting data on what shows are watched, what ads someone is exposed to, and any other
online activity done through the smart TV. As stated by the CEO of one manufacturer, “It's
not just about data collection. It's about post-purchase monetization of the TV.”
64
It is all
about selling shows, ads and consumer profiling data - This illustrates why all companies
will need to learn how to capture value from data, and that entire industries are shifting
towards new data-driven business models.
11
2.4 The Future is Determined by Cities
We should also bring some attention to why this report uses the term urban market place,
and why it is solely focused on cities. And the reason for this are several; First, it is where
demand is. A good illustration is that the 259 largest U.S. cities contribute 85% of GDP
65
,
and that upwards of 80% of revenue in the technology sector is generated in cities
66
–
and this disparity is increasing, as illustrated by 50% of U.S. employment growth during
2010-2017 going to 20 cities with only 30% of the population.
67
The dominance of cities
holds across the globe; the consultancy McKinsey predicts that the 600 cities that contribute
the most to global growth will account for 60% of global GDP by the year 2025, while
having 25% of the population.
68
Second, it is also in cities – notably so-called Digital Cities – where insight is enabled by
sensors on buildings, infrastructure and devices, applications for mapping, ridesharing,
restaurant reviews, and shopping, in addition to mobile networks that allow for constant
connectivity of individuals and transfer of data. As this report outlines, when movement,
consumption and preferences of individuals can be identified to form increasingly granular
consumer profiles it becomes possible to make advertising more accurate, and services
more personalized. So, that not only do we have consumers with higher incomes that are
more densely packed in cities, but also the ability to create insight.
Third, technology is playing a big role in these trends with upwards of 80% of research
and development large technology companies aimed at urban markets. Private R&D are
creating new forms of urban infrastructure for mobility, such as ridesharing to move
around, mapping that make it possible for people to find their way, LBS that allow
companies to put advertisements in a geographical context, and policymakers to track
trends such as congestion and crime.
69
12
3. The Consumer Profile, Data Convergence and Context-Based Intelligence
The ability to collect, store and analyze data has an impact spanning strategic decision
making, development of goods and services, marketing and supply chain management.
Yet, despite that companies are becoming increasingly dependent on data, the impact on
business models is still an under-researched field.
Business Intelligence (BI) is a term that referrs to methods, processes and tools for fact-
based business decision making, and is often interchangeably used with terms such as big
data, business analytics and data warehousing both within academia and private
organizations.
70
This confusion of terminology and lack of structure is illustrative of
academics still lacking knowledge on how IoT will impact business models
71
, and that
mobility has received little attention from the perspective of data acquisition. Large
corporations are finding it challenging to translate opportunities created by IoT technology
into value creation and value capture.
72
Social scientists sometimes stress the difficulty in creating insight from big data
73
, as large
datasets could tell us large scale patterns but not create contextual depth.
74
We believe
that the ability to gain insight from big data can be likened “Identification Problem”
75
of
econometric models, meaning that it is not possible to identify the best estimate of a
coefficient in a regression model.
76
And just as researchers aim to address this issue by
including controlling variables and develop new statistical techniques, data convergence
that results in increasingly granular consumer profiles coupled with better algorithms
enables for better and more reliable insights through big data. Consequently, the depth
of what can be understood with big data is increasing. With advances is Machine Learning,
the insights from data goes way beyond the surface-level of collected data, with
increasingly accurate predictions and inferences done by companies. It is not just the scale
of data being collected, it is the unprecedented intimacy of it that is creating insights and
creates privacy concerns.
77
And when companies create insight from data, and use it to drive business decisions, this
implies so-called context-based intelligence, i.e.; The ability to understand the limits of our
knowledge and to adapt that knowledge to an environment different from the one in which it
13
was developed”.
78
Context-based intelligence and the convergence of personal data are
closely related, as the later enables the former and that every time an app or service is
used, it creates additional data that feeds into services in what can be likened to a
feedback loop.
An illustrative examples of what context-based intelligence implies is that search engines
personalize search results based on search history and social activity – so when Google
knows more about my habits, preferences, location and network, it becomes possible to
tailor search results even better – and notably increase advertising revenue when the
match between product and likely buyer becomes increasingly accurate. As Google is
launching a weight loss and wellness application “Google Coach”, it is likely that the search
results for recipes will be adjusted for your specific calorie needs, or habits by time or
day.
79
Your location adds context, so it is likely that you will get a friendly reminder to
order something healthy when the application notices that you are in a restaurant.
80
If
your fitness tracker notices that you had a bad night’s sleep, you might get an offer for
coffee in the morning or have your workout routine adjusted.
81
Much of these services are
in fact based on the continuous tracking of movement, as location-based services (LBS) that
“that integrate a mobile device’s location or position with other information so as to provide
added value to a user”
82
are what enable for an ad for a coffee shop near you, or a free
coupon to the gym close to your work. As we carry our smartphones everywhere, LBS is
at the heart of monetizing consumer data.
Looking forward, LBS will be increasingly integrated into a key variety of solutions – and
be essential for autonomous applications and virtual reality
83
– as knowing the location of
various things at the same time and relating it to mapping data is essential for such systems.
3.1 Impact on Value Proposition and Marketing.
From a marketing perspective, context-intelligence is about giving people the information
they want, when they want it
84
– and advances in machine learning is now making this
possible by considering the full consumer profile in relation to the context – such as location,
time, proximity to other people, and previous activity when determining outcome (such as
what add or offer to provide or what some action tells us about a person).
14
As smartphone users value simplicity and seldom actively search information on the internet
– on average, making only 1.25 online searches
85
, while spending 3.35 hours on their
mobile devices
86
– it becomes increasingly important that applications basically spoon-
feed
87
the user with information perceived as valuable – such as when your iPhone
automatically keeps track of where you parked your car, or when you’re shown content
on Facebook or LinkedIn that you actually find worthwhile to click on.
88
As brand loyalty is declining and consumers increasingly value simplicity, the ability for a
company to have enough data points and contextual understanding will be essential for
customer acquisition and retention. In fact, the single most important factor for making a
customer “sticky” in the sense that they follow through on intended purchases, make
continuous purchases, and recommend the products to others is “decision simplicity”,
meaning how easy it is to get information about the product or service that is deemed
trustworthy and allows for an efficient comparison of options.
89
A consequence of these
consumer preferences is that what the customer wants perfectly aligns with the business
model of search advertising based on consumer profiling.
Another example of how consumers actively participate in creating insight on their demand
and references is the trend of what is referred to as the “quantified self”
90
and
“lifelogging” – people gaining self-knowledge through collected data about themselves.
Typical examples are fitness trackers, “smart” scales
91
, applications aimed at tracking
locations of interest
92
, identification of DNA and heritage
93
, and identification of human
microbiomes related to behavior.
94
A key aspect of increasing consumer understanding is integration between applications
from the same company, as it creates opportunities for companies to understand who their
customer is, spanning habits, needs and desires. When Apple adds payment solutions and
streaming services additional pieces of information users are gathered, just as when
Facebook adds a dating function. Similarly, collaborations and third-party data
transactions - enables for better insights and linkage of offline and online activity is linked
– explaining why social media companies are increasingly either buying or collaborating
with companies that provide additional data points.
95
Among many other data points,
15
Facebook can now offer advertisers the ability to filter on; “1. Location 2. Age 3…Gender
5. Language 6. Education level 7... School 9. Ethnic affinity 10. Income and net worth
11. Home ownership and type 12...14. Square footage of home 15…16. Household
composition…… 21. Users in new relationships… 29. Mothers, divided by “type” (soccer,
trendy, etc.)… 33. Employer… 39. Users who plan to buy a car (and what kind/brand of
car, and how soon)… 50. Users who have donated to charity (divided by type)… 61.
Early/late adopters of technology… 65. Number of credit lines… 66. Users who are active
credit card users… 69. Users who carry a balance on their credit card… 71. Preference in
TV shows. 80. Users who buy groceries (and what kinds)… 85. Users whose household makes
more purchases than is average… 87. Types of restaurants user eats at.”.
96
In addition to
activity on the platform, virtually all of your online activity is tracked while logged in, and
data on finances are provided from actors such as Experian. Any online publisher has the
option of installing Facebook Pixel that allows for tracking of any user with a Facebook
account.
97
With this information, companies can have their content or campaigns displayed for the
most relevant audience. Another industry example is what Google describes as their
“Customer Match”. A tool for companies to; “use your online and offline data to reach and
re-engage with your customers across Search, Shopping, Gmail, and YouTube. Using
information that your customers have shared with you, Customer Match will target ads to
those customers and other customers like them.”.
98
Yet another illustration of the value of converging data is that the data broker Acxiom
offers any company the ability to purchase “Consumer Insights Packages”, which are
described as; “Consumers expect a connected experience. That means you have to understand
their offline and online presence, buying behaviors, and interests. Acxiom offers the industry’s’
most comprehensive data and models, and we can help you choose the most relevant and
effective audiences to drive better marketing results both offline and online.”.
99
among other
packages, the company offers solutions for Valentine’s Day – identifying consumers that
prefer jewelry over flowers, or candy; or those who plan a romantic dinner at home and
those that plan to eat out.
100
Another offering is aimed at the “Back to School” market, to
identify demographics such as the “Stylish Student” for companies that “Want to target
campus trendsetters who are likely to be out spending big on the latest apparel and
16
accessories? We can help you identify them for perfect message placement. If your client sells
trendsetting apparel to children, teens, or college students, we have a segment to match.“.
101
However, It is not only about the initial matching of campaigns with potential customer –
as it is now possible to track effectiveness of a particular campaign when marketers can
link exposure to an add and a subsequent action or purchase. Identifying if a campaign
was effective and on whom a dollar spent on coupons, promotions or any other marketing
effort made a difference, and on whom it was wasted. This has the potential to change
the entire revenue model for the ad industry, as companies can identify the value of
marketing for the first time.
Despite spending upwards of 20% of revenue on campaigns, large companies have
historically had little insight into their effectiveness.
102
Marketing efforts are typically
analyzed in isolation, and without knowledge about any counterfactual outcome.
Consequently, most marketers often misattribute outcomes to marketing efforts, and
finance departments tend to doubt if marketing spending is worthwhile as the returns are
double counted – so when added together, the marketing ROI sometime adds up to twice
the actual sales.
103
The need for looking at the whole picture when analyzing return on marketing investment
(ROMI) is amplified by companies marketing their products through several touch points
and sales channels
104
- so when a consumer is exposed to car reviews, paid adds, YouTube
content, billboards and mail campaigns, the question of how to attribute a final sale arises.
And this is where companies take advantage increasingly data-driven strategies as it
becomes possible to track who that got exposed to what, and use algorithms to determine
optimal marketing strategies.
105
IoT and data convergence is central for the ability to
identify target audiences and measure ad effectiveness. An illustration is that the
effectiveness of Facebook ads can now be tested by seeing how exposure in your feed
translates into in-store purchases, phone orders and bookings through their “Offline Events”
service that also measures offline return on ad spend and allows companies to reach
people based on thief actions they take offline, in addition to audiences believed to be
similar to those they have offline data for.
106
17
3.1.1 Implications of the constantly connected customer
As customers carry their devices at all times, they create data trails from activity such as
searches, purchases and movement, companies will be able to continuously follow their
changing needs and preferences over time.
As an illustrative example, think of a consumer, Lisa, joining a loyalty program at a grocery
store chain. When Lisa enters a store, the LBS enabled app wakens up her smartphone
and suggests purchases based on Lisa’s past purchasing habits and might even create a
promotion on the ingredients for dinner – and since she has two small children, Lisa is shown
targeted offers for diapers – thus getting relevant content that increases her satisfaction
with the service. As an offer is sent to her smartphone, the customer relationship
management program (CRM), keeps track if whether or not Lisa took up on the offer. So,
if a discount of $ 1.5 on a new brand of pasta sauce doesn’t work this time around,
perhaps a $ 2 offer will be offered next week. Over-time, it will be possible to identify
how price-sensitive Lisa is, and break it down by product, so that offers can be tweaked
to maximize the likelihood of a purchase. It will be possible to identify what promotions
Lisa responds to, what personalized pricing offers that are driving her loyalty behavior –
so that an exact customer value can be assigned to Lisa based on her expected
expenditure and contribution to profit over time, and Lisa’s experience will continually
improve, as she receives increasingly accurate content. The relationship will seamlessly
change as her needs change, such as offerings for diapers changing towards school
supplies when her kids grow older.
107
Consequently, a constant two-way interaction
between the company and consumer is created through devices, offerings and suggestions.
Once a company has this information in a fully integrated CRM system it becomes possible
to track real time shopper behavior and influence it on both a macro and micro scale.
ROMI will be continuously monitored and spending will only be spent were results are
maximized
108
.
If targeting of customers is sufficiently cheap, companies should in theory focus more
attention towards their competitors’ customers.
109
If, as an example, Proctor & Gamble
(P&G) wishes to analyze baby product sales, Lisa would be identified as a valuable
18
customer as she spends $ 1585 a year on such products. Knowing how this spending is
distributed across various supermarkets – say between Walmart and Target– and by
brand – such as Proctor & Gamble, Johnson & Johnson and Kimberly-Clark – P&G can
now see that Lisa spends less than 10% of that sum on their products. Having this insight,
P&G can identify non-P&G high-value customers and create personalized campaigns
aimed at changing their purchasing behavior. Offers would be based both on this macro
level identification, and what we know since before about Lisa. So initially, an offer for a
discount for P&G diapers is sent to Lisa in an effort to make her change brand. And if it
does not work a new offer will be tested. In relation to costs of customer acquisition and
retention, perfectly identifying preferences and price-sensitivity of consumers and
optimizing for lowering marketing cost could result in dramatic decreases in cost for
companies.
The ability to communicate with consumers through smart-devices is an essential enabler of
the constantly connected consumer, and the way an application creates insight can be
thought of in terms of two main categories. First, the data that is created when the
application is used that becomes additional data-points in the overall consumer profile.
Second, the device is also a medium of communication and a channel that enables for
additional sales and marketing. So, with the insight from the consumer profile, offers and
the ability to make purchases are given on the device. Similarly, it is through the device
that personalized adds are displayed.
3.1.2. Individualized pricing
Consumer profiling is not only a way to reach a desired audience, as it also can provide
insight into the purchasing power of consumers, so that a company can individualize price.
This is already seen in the insurance industry – with premiums set by individually assessed
risk, such as health insurance set by fitness tracking data such as how many steps that are
taken
110
, or car insurance set by where and when someone drives.
111
Similarly, it is now
possible to identify financial risk using spending habits and bank account flow data, which
is improving risk management in finance
112
, and machine learning is now “taking credit-risk
scoring to the next level” according to the company SAS.
113
Not only is risk management
19
improved, companies also have the ability to individualize interest rates and insurance
premiums – i.e. prices – to a much higher degree.
For companies, consumer profiling is also about how much you are likely to spend and the
ability to set prices through“behavior-based price discrimination”.
114
Researchers have
given some attention to the possibilities of price discrimination created by targeted
advertising, having found that it has the potential to increase business sector profits under
certain conditions.
115
Other studies indicate that targeted advertising leads to increased
market fragmentation resulting in local monopolies, and that some scenarios point towards
that consumers benfit more than companies.
116
This is consistent with studies of
individualized smartphone-based offers, finding that profits increase from unilateral price
differentiation, but that these returns are likely to be mitigated by competitors engaging
in similar practices.
117
Similarly, the ability to set higher prices for consumers with a strong
preference of the product is offset by increased price competition for value conscious
shoppers that compare price.
118
3.1.3. Persuasive technologies and psychological profiling
As the amounts of marketing efforts and online content increases – it becomes increasingly
difficult to create trust with consumers; therefore, new technologies aim at shaping
preferences in more subtle ways.
Looking ahead, there will be a shift from not just identifying the right offer for the right
person (as described above), as it becomes possible to find far more refined ways of
getting people to buy your product or service when the demarcation between advertising
and content is blurred. Context-based intelligence is not just about your observable habits
– it is also about your relationships, social context and psychology. When your virtual
assistant knows that your wife is feeling down, it might give you coupon for flowers or a
dinner for two special. Social media platforms are already covering the emotional state
of users
119
, and from a marketing perspective, persuasive technology – machines designed
to influence human beliefs and behaviors
120
– offer massive opportunity to identify the
psychology of intent, and the triggers that turn intent into action. One type of application
is that of creating games and promoting healthy behavior through peer-pressure. This type
20
of technology does however not only apply to promotion of healthy behaviors. If Facebook
knows that you are in the market for a car (the age of your car is part of the consumer
profile, and provided from government records
121
), it is not impossible to imagine that
your social media will show you pictures of people in your circle of friends with a particular
brand of car in the background – reminding you that driving, say a BMW, is a way of
fitting in to your social circle. Once the data is large enough, it will be possible to identify
which of your friends that have large influence on your behavior and tweak the algorithm
further. Knowing your habits, such as regularly driving in snowy conditions (through tracking
your location), you will get a tailored offer to lease a 4-wheel drive BMW based on your
likely price-point based on other purchases, responses to offers and how that has
correlated with auto spending for other consumers. Companies that have access to
consumers, i.e. the ability to influence, are going to become increasingly important as a
new and more subdued way of marketing develops – were consumers are unable to
distinguish between advertising and other content or know why they are shown certain
content.
As the limits of big data analysis decrease, it makes it possible to gain unprecedent insight
into human behaviors and prediction of actions when the data is both deep in insight and
large in numbers. Examples of studies within this sphere of data-driven psychology are
that psychologists now can predict if someone is entering a depression through tracking
location through a smartphone – as people that are depressed tend to move less.
122
At
Stanford, researchers are working on a large-scale smartphone sensing study aiming at
examining what smartphone data, and notably mobility patterns, tells us about the persons
psychological state. There is in fact a startup with an app that tracks your mood based on
movement
123
, and Stanford researchers has developed an app that detects autism in
children.
124
There is also research aimed at linking personality with spending habits
125
,
which in theory would enable for identification of personality traits and likely spending
from credit card data. Imagine a future when similar applications run in the background
on your devices, with psychological insights becoming part of your consumer profile.
21
3.2 Device and Usage Monitoring
IoT is transforming businesses not only through identification of consumer characteristics.
The ability to gather, store and process big data can have a profound impact on all
aspects of the corporate Value Chains, i.e. the set of activities conducted to deliver a
service or product
126
, and create competitive advantage through more efficient logistics,
operations, marketing and service.
For supply chains, knowing the location of all products, supplies and deliveries enables for
precise estimates of estimated time of arrival. Smart devices create the ability to monitor
the performance of a product, such as a car, which enables for better life cycle
management by predicting when the car needs servicing and what parts that will break –
based on observations from hundreds of thousands of other cars – which in turn can be
used to optimize capacity and inventory at local service centers. It also enables for new
and more efficient ways of contracting across the supply-chain, such as outcome-based
contracts
127
when the manufacturer can identify if a particular part performed in
accordance to specification. And when it is possible to gain this much data – quality can
be improved through the insights provided – say how a breakdown correlates with usage
and weather – and lower risk by more precise predictions of how a piece of equipment
will perform.
For capital goods, this ability to create insight from data enables for a shift towards
product-service system (PSS) business models that are focused on as a system of products
and services that are continuously updated to meet customer needs.
128
PSS has been
driving profits for goods manufacturers as services increase margins.
129
This entails a
completely new value proposition and business model
130
. Similar to how software as a
service (SaaS) changed how enterprise applications are sold, companies across all
industries transition of from a product-centric business model towards a continuous service-
centric business model
131
, through what is known as Servitization.
132
Data is not only transforming consumer facing industries. For advanced – business-to-
business – products, companies have used performance and usage data to optimize
complex maintenance contracts and extended warranties, thus shifting towards a greater
22
focus on service as IoT enables for better risk management. Traditionally, risks have been
too high for servitization of the core product.
133
However, better management of assets
and the ability to monitor performance is changing this. Prominent examples of companies
that have managed to do this shift are Rolls-Royce Aerospace, offering power-by-the-
hour, so that the buyer buys say 20,000 hours of operation rather than an airplane engine,
Xerox having shifted from selling printers and copying machines to selling complete
solutions for document management, and Alstom selling train-life services spanning
installation and servicing of a train over several years.
134
Typically, this implies a 10-year
contract, were the manufacturer shares some of the risk that the equipment works, and the
buyer has a payment scheme that is linked to actual usage.
135
The long-term nature of this
type of business model, and the necessity to understand the customer business model,
naturally leads to much closer business relationships – leading to both business model and
organizational impact.
136
Often, new offerings emerge, with an example being that the
network manufacturer Ericsson has shifted from selling network equipment towards
solutions for telecoms providers spanning maintenance and data insights through AI.
137
When customer needs are understood, typically services adapt, such as a logistics
company using trucks on a pay-per-mile model, with costs and maintenance as part of the
contract
138
– in contrast to just buying trucks. This shift across industries is often driven by
outside forces. Notably, technologies that fundamentally change an industry – such as AVs
that are predicted to change the automotive industry. Automakers are responding, as Ford
now stating that they have shifted from selling cars to selling mobility and investing in
ridesharing applications and AV technology.
139
Similarly, Volvo has increased focus on
monthly car plans rather than just selling cars.
140
And data is central in the ability to tailor
the product to customer needs, optimize risk management, supply chain contracts, and
manage the inbound and outbound logistics.
As devices and applications collect ever more data, a key aspect that will change business
models is the monetization of this data, shifting the model from making money from selling
a product or a service towards gathering data, with the initial product and service being
an enabler that is optimized for creating insight.
141
23
It will be possible to better manage both revenue and costs, and risk will be shared across
a greater number of parties that will be bound for longer-periods of time. This provides
incentives to increase trust and fundamentally change how sales are done – illustrating
how both operations and business models change.
4. Concerns for Cybersecurity, Privacy and Fairness
Analysis of the underlying economic model for getting access to consumer data, the impact
on privacy and the consequences of algorithmic decision-making has been primarily
analyzed within the field of law.
142
Several regulatory issues emerge relating to consumer
profiling; such as that of fairness and transparency when consumers trade personal data
in return for services, that of heightened risk associated with security breaches and the
consequences of algorithmic bias as algorithms become pervasive in determining
increasingly important commercial and social aspects of life.
4.1 Data-for-Service, the Personal Data Economy or Pay-for-Privacy?
The current model were consumers trade their personal data in return for using a service
has been criticized, yet, there has been almost no analysis of the relationship between the
utility gained by consumers and the value of the data they provide – the “Return on
Data”.
143
As such, it is impossible for consumers to compare data-for-service deals. Thus,
some researchers suggest that this return needs to be analyzed in conjunction with privacy
laws
144
, while others suggest entirely new models for transacting data. Notably, the
personal-data-economy (PDE) model that implies that companies would buy data from
individuals, giving every persona a piece of the action when data is monetized. Another
alternative is the pay-for-privacy (PFP) model were users of a service would pay extra in
return for not giving up data and receiving personalized adds.
145
Although promoted by
some, actual implementation of these models would be highly complex. There are also
concerns that these models might exacerbate existing inequality issues as lower income
and less educated consumers would be unfairly targeted
146
, and studies show that lower
income individuals have lower confidence in their ability to protect their digital data, in
addition to also experiencing higher degrees of monitoring.
147
24
Algorithms are “Unseen and almost wholly unregulated”
148
, so that when the consumer sees
the service – but not the underlying consumer profiling data that enables it – it can be
likened to the tip of an ice-berg. Questions relating the impact of targeted offers and
individualized pricing in relation to fairness arise when offerings are based on a
consumer’s perceived willingness to pay and psychological traits
149
(see section 3.2.1).
Potentially, companies would be able to identify psychological traits associated with bad
financial decision-making and make offers that take advantage of those traits.
4.2 Privacy and Cybersecurity
Almost every time an app is installed or even a device is used, some information is traded
in return for this service – so the company gets access to location, photos, search activity,
music listened to and so forth.
150
As data is becoming more valuable and the amount of it
that is collected increases, regulation is increasing, with the European Union through the
General Data Protection Rights law (GDPR), leading in increasing individual rights coupled
with an enforcement regime. This while the United States is characterized by a
decentralized regime with little ability to enforce those few regulations that exist, in
addition to a greater focus on commercial needs.
151
Americans are increasingly concerned about advertisers and companies getting access to
their social media information, with 61% of respondents in a study of U.S. adults wanting
to do more to protect their privacy.
152
So, while individuals are increasingly concerned
about their social media data being shared, consumers also demand services that offer
simplicity and ease of use
153
– which is enabled by consumer profiling.
At the moment, it is the privacy agreement or user agreement that regulates what data a
company is allowed to collect, and what to do with it (such as transferring it to third-
parties).
154
However, only about 26% of free mobile apps and 40% of paid apps have
such policies
155
, and most privacy agreements allow for transfer of data to third parties
in anonymized form
156
, or a transfer of data the case of a company acquisition, merger,
or bankruptcy.
157
It is also increasingly difficult for consumers to keep track of what is
collected, as many third-party applications being built in as part of applications, and these
collect our data without active consent or privacy agreements
158
, and if a policy does not
25
exist, a company is often free to monetize consumer data without risk relating to privacy
violations.
159
Consumers are also unlikely to read or understand this type of documentation – in 2014
half of internet users did not know what a privacy policy was
160
– and if they do,
researchers question if they understand what they sign up for when installing an app or
making a payment through their phone, or even when they buy a device such as smart TV
that tracks usage
161
, or a toothbrush that tracks brushing habits.
162
Even when providing
active consent, consumers are unlikely to understand the full scope of profiling and its
implications.
163
About half of U.S. adults state that they do not fully understand what
happens with their data when they share information with companies
164
, and many
privacy agreements take over 20 minutes to read
165
, and sometimes require reading skills
at the level of a senior college student.
166
Even if a consumer declines a service, or if new regulation makes it more difficult to collect
data – technology is now able to fill in the data gaps, so that a key question is if consent
even matters? For if a company knows four of your friends, they know much about you
through so-called probabilistic inference – meaning that non-consenting consumers are
assigned characteristics from similar consumers for which there is a representative sample.
Machine learning can now accurately identify romantic partners in 55% of cases with only
anonymized relationship data and Facebook can identify social relationship even the users
are unaware of.
167
Another study was able to identify 90% of consumers from anonymized
credit card transactions, stating that even data with very little information provide limited
anonymity.
168
As companies are collecting increasing amounts of data – some of it highly sensitive
biometric data – the risks associated with cybercrime increase. Large corporations have
seen data breaches of millions of customer records with sensitive information such as social
security numbers, address and credit card information – with one example being the credit
scoring company Equifax.
169
The risks associated with biometric data are even higher, as
unlike a credit card, the characteristics of your iris or fingerprint cannot be changed.
170
Transfers of health and biometric data are increasing. In 2014, the Federal Trade
Commission found that 12 mobile health applications transferred information to 76 third-
26
parties, with some 18 parties receiving device-specific identifiers, 14 receiving user-
specific identifiers, and 22 receiving other types health information.
171
As IoT devices are become pervasive, the vulnerability and number of attacks will increase,
making security solutions even more important.
172
It is predicted that devices will be used
to attack routers and networks through botnets
173
, i.e. a third-party controlling a large
number of compromised devices, which can be used for gaining access to data through
spyware, making a computer make purchases without the real user’s knowledge, or denial-
of-service attacks. In 2017, the cyberattack that made Twitter, Netflix and the New York
Times inaccessible was initiated through IoT devices.
174
Analyzing cybersecurity and threats for the year 2025, researchers at the University of
California at Berkley found that cyberthreats are now evolving into protection against
increasingly advanced devious manipulation, rather than just brute force data theft. States
and criminal organizations could potentially use deep fakes (i.e. fake audio and video
that looks convincingly real) and adversarial machine learning for malign purposes and
make small changes in datasets to infuse bias in algorithms.
175
Cyber-threats will become potentially more harmful as algorithms become pervasive in
determining key aspects of everyday life such as if a mortgage will be approved and
even if a suspected criminal is going to get bail and the length of sentencing. With
advances in machine learning, risks rise in relation to how such insights will be used when
it is possible to identify if a young child has autism through how they use an app
176
,
depression and mood from how we move
177
, and spend
178
, linking personality to eye
movement
179
, the risk of insuring your car through how we drive
180
, the likelihood to default
on your debt based on spending habits
181
, if you are likely to get fired on your new job
based on assessment of cultural fit based on the language you use.
182
27
5. Industry Sector Examples and Implications
In the below section, we provide few examples of how the ability to create insight from
data is shifting business models across industries.
5.1 Insurance and Healthcare
Insurance is an industry at the forefront of using this type of data as customer knowledge
is essential for assessing and pricing risk.
183
Examples are that life insurance risk and
premiums are based on fitness tracker data
184
, car insurance related to driving behavior
and the location of timing of driving
185
, and insurance companies are currently exploring
the use of social media to create risk profiles.
186
This shifts the industry focus from risk
management through pooling, towards a greater focus on risk management through
individualized risk assessment.
Smart devices that we carry, have at home, at work, in vehicles and across public space
are creating what the consultancy McKinsey refers to as an “avalanche of data” that will
transform the insurance industry.
187
In fact, in a report on the impact of IoT, the insurance
giant AIG states that; “At the center of this new universe of data will be the insurance industry,
which has been using massive amounts of data to understand and mitigate risk. It’s only a
slight exaggeration to say that insurers invented the idea of Big Data. Naturally, as IoT
objects proliferate and permeate all levels of our economy, it will be the insurers who are best
placed to analyze this data and extract meaningful and actionable insights – insights that
could make our world a safer and more productive place than we could ever have
imagined.”.
188
In terms of converging data, State Farm, in 2014 patented a platform for aggregating
and combining data from smart home devices, vehicles and personal health data, so that;
“…Based on the determined underlying factors and correlations for each of the determined
patterns, the method and system may provide the individual with various benefits such as
personalized recommendations, insurance discounts, and other added values or services that
the individual can use to better manage and improve his or her life.”
189
28
Private companies have never before had access to health data.
190
Data from wearables
have the ability to track how many steps taken walking and running, physical activity,
caloric intake, blood oxygen, blood sugar and heart rates
191
– infusing health data into
the consumer profile. That companies collect and transfer this type of sensitive data raises
increasing privacy concerns – it is however almost wholly unregulated. Notably, most laws
aimed at regulating health data only applies to health providers – not technology
companies.
192
This creates opportunity to assess risk and set insurance premiums. As an
example, the insurance company John Hancock now sets life insurance premiums based on
data collected from fitness trackers such as Fitbit or Apple Watch
193
, stating that; “…in a
departure from the traditional life insurance business model, all John Hancock life insurance
policies will come with Vitality – a behavior change platform that rewards customers for the
everyday steps they take to live longer, healthier lives. Built on the convergence of behavioral
economics and consumer technology, John Hancock Vitality policies incentivize healthier
choices linked to physical activity, nutrition and mindfulness.”
194
And their CEO, Brooks
Tingle, statest that; "We have smart phones, smart cars and smart homes. It's time for smart
life insurance that meets the changing needs of consumers. We believe offering Vitality on all
life insurance policies, at no additional cost, is the right thing to do for our customers, our
business and society. We believe this is the future of our industry, and I encourage other
insurance companies to follow suit.".
195
The insurance industry is moving toward data-driven strategies, and have recently
received regulatory approval in the state of New York to use social media information for
assessment of risk and set insurance premiums.
196
5.2 Retail
Consumer profiling has been transformative for the retail sector, most notably in relation
to development of the value proposition and marketing (see section 3).
Retailers have typically collected data on consumers through opt-in programs to build
consumer loyalty and gather information on purchasing habits, which is now becoming more
refined as smart devices enable for collection of more types of data – such as the
drugstore chains CVS and Wahlgreens now have smartphone applications that give
29
consumers discounts in return for health data constantly streamed to their systems.
Additional discounts are provided if customers agree to relinquish their right to health care
privacy.
197
As the LBS app provides locational data through smartphones, health is given
geographical context and consumers are given offers for products they are likely to buy
while in the store.
198
When increasing amounts of information are collected, it becomes
possible to merge information on the number of steps taken and how often someone works
out. Heart-rate and other health information has already been found to be merged with
social-media information to target consumers.
199
The potential impact of consumer data on how to drive sales is well-illustrated by a
statement by Fabrizio Freda, who is CEO of the global cosmetics company Estée Lauder
who attributed annualized sales- and earnings-growth of 9% and 61% respectively to
improved data analytics
200
, and relating to the company’s digital strategy the CEO states;
We treat data and information as key strategic assets that power a world-class,
interconnected analytics “ecosystem” across our brands, regions and functions. Using cutting-
edge tools and techniques, we are connecting the dots from data and predictive insights to
implications and actionable recommendations. Our enhanced marketing analytics capabilities
give us access to consumer journey insights across global markets, providing a deeper
understandding of when, how and where our consumers are purchasing and re-purchasing,
further enabling the shift from trial to loyalty.
201
This is not just about marketing, data analytics will transform all aspects of retail, with
companies that provide retail technology for inventory management, cashiers and
marketing impacted by a fundamental shift in how stores function, likely causing
widespread disruption.
202
Automated payment and supervision are making stores more efficient and cheaper to run
when we are automatically tracked within an Amazon Go store that allows customers to
simply walk out of the store with their merchandise as long as they have the app
downloaded.
203
Cameras and advanced algorithms identify who is in the store, and what
that is taken with payment being automatic. Data convergence and movement is central
to this – with the LBS Amazon app as the key enabler. The consumer profile becomes a
key foundation for an entire ecosystem spanning from marketing to payment.
30
Across retail, the fundamental economics will shift when costs for employees is dramatically
reduced, as are costs associated with shoplifting. Couple this with targeted advertising
and offers and a continuous digital dialogue with consumers and what we are seeing is an
almost fundamentally new industry.
Looking ahead, the field of of consumer neuroscience offers a lot of promise and is likely
to have significant impact on retail. It covers research on how emotion and brain activity
relates to offers and consumption.
204
Related to behavioral economics and how consumers
make decisions, the technology is described by the company Nielsen as something that
enables retailers to; “Capture non-conscious aspects of consumer decision-making with the
most complete set of neuroscience tools at a global scale.”
205
, which is enabled by EEG
sensors that measure attention, emotion and memory; facial coding that identify emotions
and reactions; Biometrics that track skin conductance and heart rate to identify emotional
reactions; and eye tracking that keeps track of visual focus.
206
It is not unthinkable that
data from wearables is coupled with in-store CCTV cameras to allow for algorithms to
create insight from this type of data, which can be used to change store outline, inventory,
and understand how real-world in-store browsing relates to online activity.
5.3 Mobility
Technological advances will change the auto industry economics, as the ability to analyze
real-time road data can improve sales and marketing, while digitalization and simulation
can save money on R&D and manufacturing productivity.
207
Ownership models are also
changing, as technologies have enabled industries to shift towards service provision.
Mobility-as-a-service – in contrast to car ownership – can be put in this context.
208
Auto
manufacturers are shifting from selling cars to business of mobility, and that of data.
Notably, ridesharing is an example of LBS and smartphones that track when, where and
how we move, which is used by providers of mapping, ridesharing, and public transport
agencies to determine congestion, travel patterns and demand.
209
Modern cars collect upwards of 25gb of data every hour
210
identifying what the driver is
looking at, level of concentration, and mood, in addition to locations traveled, driving
31
behavior and usage of the in-car systems. This is driven by the current state of level 3
autonomy – cars that can drive themselves under certain conditions – which require sensors
that collect data on the surroundings. Just as data is converging, cars will become an
integral part of a wider mobility system – sending and receiving data from other IoT
devices and vehicles so that efficiency and safety increases. This will be increasingly
important as a driver of profits for automanufacturers as consumers no-longer pay for a
greater number of gadgets in their cars – which has been the main way of competing
between auto manufacturers.
211
The data that is captured could amount to three-quarters of a trillion dollars in value by
the year 2030, as automakers are exploring new revenue streams, such as selling data
gathered on the surrounding environment to mapping companies and applications that
map traffic conditions.
212
In-car activity is a potential source of revenue, with General
Motors offering “GM marketplace” that connects the car’s information on its location and
available range to other companies
213
, so that the car can make hotel reservations and
take orders coffee at a nearby Starbucks.
214
This type of LBS allows for notifications when
you are close to a regular stop and the car asking if an order should be made.
215
As GM
receives fees for each transaction, this is one new revenue stream that illustrates how
mobility is blending with the overall urban marketplace.
From a manufacturing perspective, cars that continuously keep track of performance and
usage enable for new ways of contracting across the entire supply-chain through outcome-
based contracts and servitization (as described in section 3.2). This will span from parts
suppliers to end consumers with an example of the latter that Tesla keeps track of all
aspects of usage. (although not sharing the data with the car owner) – sometimes making
the insights public when crashes occur.
216
BMWs will note a fender-bender and notify local
dealerships that a car will be coming in for repairs while also sending a quote of the cost
to the owner by text shortly after the accident. IoT makes it possible to identify the context
of breakdowns and accidents, in addition to quantifying cost and assigning responsibility.
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5.4 Finance
As an example of the trade of data for service, free digital payments (such as the
payments company Finja) are given to consumers in the hope that they will be buying
additional services such as insurance, which are marketed using data gathered from the
platform. It is likely that more of this type of “free” financial services will be offered as
the value of consumer data is highly valuable, as the financial services industry is similar
to insurance in that it is about identifying and pricing risk.
Data is making risk-assessments, credit decisions and pricing more refined, such as using
in-and-out flow from bank accounts and the types of spending someone does is used as a
substitute for credit-scoring. Starting in early 2019, a new type of “UltraFICO” credit-
score will be available to a few U.S. lenders, giving them the opportunity to analyze in-
and-out flow in bank accounts rather than credit-history.
217
A new platform in Latin
America aggregates utilities payments, geography and socio-economic information to
assess credit risk
218
, and is already collaborating with large local banks.
219
This type of
data could potentially shift finance towards a more adaptive type of consumer
interactions, with a constant bi-directional information between the financial institution and
the customer when credit decisions are automated and continuous, so that transaction
history, cash flows and use result in continuously adjusted risk-based credit limits.
220
Machine learning and LBS is a key aspect of this development, as illustrated by a
hypothetical consumer credit scenario described by the company SAS; “Suppose you have
a customer who has opted into location- based awareness and typically uses 90 percent of
his credit card limit. You see that he is going to a business where he normally makes purchases
of $150 to $300, but he only has $120 credit remaining on his card. Since he has a good
payment history and good cash flow in his other accounts, the system automatically sends him
an SMS with a limited-time offer to increase his credit limit by $500 for one month. The bank
may have just earned (or reaffirmed) the customer’s loyalty.”
221
Data is improving decision-making across all aspects of finance. Fraud detection is enabled
from knowing when, where and on what someone spends, similarly, banks now can scan
documents for insight automatically rather than have employees do the work manually.
222
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6. Summary and Concluding Comments
In this report, we have outlined simultaneous and closely related trends that relate to how
data is impacting business models and how convergence of various data layers from
devices and applications create increasingly granular consumer profiles. A key aspect of
this development is that location-based-services set demand and preferences in a
geographical context. Notably, this is a primarily urban phenomena, for not only is it in
cities that wealth is concentrated, it is also in cities that insight is created through connected
devices and networks.
We find two primary channels through which data is transforming business models. First,
an impact on value proposition and marketing, as companies can identify the right target
audience and track the efficiency of marketing campaigns – which is at the center of
turning data into a tradable asset. The convergence of data is now driving a development
towards a constantly connected consumer – with insight created by target offers and
individualized pricing. Responses and reactions can now be monitored, so that behavioral
based pricing becomes a reality. Companies will be able to identify how consumers react
to various incentives and effectively target their competitors’ customers – potentially
leading to commercial guerilla like warfare. In terms of industry impact, it is likely that
increased profits on consumers with a high willingness-to-pay are offset to various degrees
by increased competition for lower-end consumers. This will make retail more
unpredictable and competition increasingly cut-throat.
Consumer profiling is not just about retail. Insurance and finance are at the forefront of
this development, as the industry builds on assessment and pricing of risk. Unregulated and
ill-understood, algorithms coupled with data from social-media, smart-home appliances,
fitness trackers, credit card transactions, and smartphones now enable for individual risk
assessments of life- and car-insurance and credit card debt. Consequently, the industries
are shifting away from models based on pooling risk, towards a data-driven strategy of
identifying individual risk characteristics. What is framed as discounts is a likely first step
towards increasingly individualized pricing
34
The second category of impact on business models is that created by the ability to monitor
devices usage and performance, as everything is becoming data gathering devices. This
enables for new ways of contracting across the supply-chain, such as outcome-based
contracts with suppliers and more refined risk-sharing when relationships in-between
companies become shift from far-in-between transactions of goods to ongoing
partnerships as IoT enables for what is known as servitization, when sales of airplane
engines is replaced with contracts for hours of operation, and auto manufacturers can keep
tabs of how parts perform to predict breakdowns and handle risks associated with selling
mobility rather than cars. Not only does this change the fundamental business model but
also the organizational needs – so that companies will need re-focus their organizational
structure and employee competencies when all aspects of the value chain are changing,
spanning the way contracts are formulated to the value proposition for the end consumer.
Overall, a key aspect of data-driven business models is that the transaction of goods for
cash is shifting towards a transaction of often “free” services in return for data, making it
essential for companies to determine the value of data and have a strategy for data
collection. Part of this is that everything is becoming a data gathering device – so that a
large part of the value of a car, smart-tv, or any application lies in the information that it
collects and how it can be used to create insight. Consequently, as the value of customer
data increases, pricing and costs of customer acquisition will need to be reevaluated.
Another key aspect is that the fundamental way of conducting R&D is changing, as
companies now need to be keeping track of technology being developed outside of the
company to identify relevant applications before the competition does – rather than the
other way around when a business need or problem prompts development of a new
technology to address that specific need or problem.
As biometric data and psychological insights become part of the consumer profile, risks
associated with cybersecurity increase – as a fingerprint cannot be replaced like a credit
card number. As data is becoming a traded asset, and algorithms become pervasive in
determining pricing, credit and even the probability of getting bail, concerns on fairness
and privacy are raised as consumers only sees the service and not the data that enables
it. Basically, it is only the tip of the ice-berg of personal data that is visible. However,
35
consumer preferences perfectly align with consumer profiling as simplicity and the
provision of relevant content is highly valued and a key determinant of what makes a
customer “sticky”.
Convergence and the creation of context-based-intelligences are also addressing previous
limitations of big data, as big data is also becoming deep data when additional data-
points are added. It is likely that this will create unprecedented opportunity to learn about
psychology and human behavior.
36
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91
Such as the Fitbit Aria scale
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Such as RoadGoat and Swarm by Foursquare
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Such as through 23&Me
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