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Corporate Surveillance in Everyday Life

Report: How thousands of companies monitor, analyze, and influence the lives of billions. Who are the main players in today’s digital tracking? What can they infer from our purchases, phone calls, web searches, and Facebook likes? How do online platforms, tech companies, and data brokers collect, trade, and make use of personal data?

By Wolfie Christl, Cracked Labs, June 2017.

Contributors: Katharina Kopp, Patrick Urs Riechert.
Illustrations: Pascale Osterwalder.

In recent years, a wide range of companies has started to monitor, track and follow people in virtually every aspect of their lives. The behaviors, movements, social relationships, interests, weaknesses and most private moments of billions are now constantly recorded, evaluated and analyzed in real-time. The exploitation of personal information has become a multi-billion industry. Yet only the tip of the iceberg of today’s pervasive digital tracking is visible; much of it occurs in the background and remains opaque to most of us.

This report by Cracked Labs examines the actual practices and inner workings of this personal data industry. Based on years of research and a previous 2016 report, the investigation shines light on the hidden data flows between companies. It maps the structure and scope of today’s digital tracking and profiling ecosystems and explores relevant technologies, platforms and devices, as well as key recent developments.

While the full report is available as PDF download, this web publication presents a ten part overview.


I. Analyzing people
II. Analyzing people in finance, insurance and healthcare
III. Large-scale collection and use of consumer data
IV. Data brokers and the business of personal data
V. Real-time monitoring of behaviors across everyday life
VI. Linking, matching and combining digital profiles
VII. Managing consumers and behaviors, personalization and testing
VIII. Dragnet – everyday life, marketing data and risk analytics
IX. Mapping the commercial tracking and profiling landscape
X. Towards a society of pervasive digital social control?

In 2007, Apple introduced the smartphone, Facebook reached 30 million users, and companies in online advertising started targeting ads to Internet users based on data about their individual preferences and interests. Ten years later, a vast landscape of data companies has emerged that consists not only of large players such as Facebook and Google but also of thousands of other businesses from various industries that continuously share and trade digital profiles with each other. Companies have begun combining and linking data from the web and smartphones with the customer data and offline information that they have been amassing for decades.

The pervasive real-time surveillance machine that has been developed for online advertising is rapidly expanding into other fields, from pricing to political communication to credit scoring to risk management. Large online platforms, digital advertising companies, data brokers, and businesses in many sectors can now identify, sort, categorize, assess, rate, and rank consumers across platforms and devices. Every click on a website and every swipe on a smartphone may trigger a wide variety of hidden data sharing mechanisms distributed across several companies and, as a result, directly affect a person’s available choices. Digital tracking and profiling, in combination with personalization, are not only used to monitor, but also to influence peoples’ behavior.

„You have to fight for your privacy or you will lose it“

Eric Schmidt, Google/Alphabet, 2013

I. Analyzing people

Scientific studies show that many aspects of someone’s personality can be inferred from data on web searches, browsing histories, video viewing behaviors, social media activities, or purchases. For example, sensitive personal attributes such as ethnicity, religious and political views, relationship status, sexual orientation, and alcohol, cigarette, and drug use can be quite accurately inferred from someone's Facebook likes. Analysis of social network profiles can also predict personality traits such as emotional stability, life satisfaction, impulsivity, depression and sensationalist interest.

Analyzing Facebook likes, phone data, and typing patterns 

Similarly, personality traits can be inferred from information about the websites someone has visited, as well as from phone call records and data about mobile app usage. Browsing history can reveal information about someone’s occupation and educational level. Canadian researchers have even successfully calculated emotional states such as confidence, nervousness, sadness, and tiredness by analyzing typing patterns on a computer keyboard.

II. Analyzing people in finance, insurance and healthcare

The results of today’s methods of data mining and analytics rely on statistical correlations with certain probability levels. Although they may predict attributes and personality traits significantly above chance, they are naturally not accurate in every case. Nevertheless, such methods are already used to sort, categorize, label, assess, rate, and rank people not only for marketing purposes, but also for making decisions in highly consequential areas such as finance, insurance, and healthcare, among others.

Credit assessment based on digital behavioral data

Startups such as Lenddo, Kreditech, Cignifi and ZestFinance already utilize data from social media, web searches, or mobile phones to calculate someone’s creditworthiness without actually using data related to financial transactions. Some also draw on information on how someone fills out an online form or navigates on a website, the grammar and punctuation of one’s text messages, and the battery status on said individual’s phone. Some companies even include data about someone’s friends on a social network in calculating credit scores.

Cignifi, which calculates credit scores from the timing and frequency of phone calls, sees itself as the “ultimate data monetization platform for mobile network operators". Large companies, including MasterCard, the mobile network provider Telefonica, the credit reporting agencies Experian and Equifax, as well as the Chinese search giant Baidu, have started to partner with such startups. The larger-scale application of such services is particularly on the rise in the countries of the global south, as well as for vulnerable population groups in other regions.

Conversely, credit data also flows into online marketing. On Twitter, for example, marketers can target ads by the predicted creditworthiness of Twitter users based on data from the consumer data broker Oracle. Going a step further, Facebook has registered a patent for credit assessment based on the credit ratings of someone’s friends on a social network. Nobody knows whether it plans to turn this total integration of social networking, marketing, and risk assessment into reality.

„We feel like all data is credit data, we just don’t know how to use it yet“

Douglas Merrill, founder of ZestFinance and former chief information officer at Google, 2012

Predicting health based on consumer data

Data companies and insurers are working on programs that use information on consumers’ everyday lives to predict their health risks. For example, the large insurer Aviva, in cooperation with the consulting firm Deloitte, has predicted individual health risks, such as for diabetes, cancer, high blood pressure and depression, for 60,000 insurance applicants based on consumer data traditionally used for marketing that it had purchased from a data broker.

The consulting firm McKinsey has helped predict the hospital costs of patients based on consumer data for a “large US payor” in healthcare. Using information about demographics, family structure, purchases, car ownership, and other data, McKinsey stated that such “insights can help identify key patient subgroups before high-cost episodes occur”.

The health analytics company GNS Healthcare also calculates individual health risks for patients from a wide range of data such as genomics, medical records, lab data, mobile health devices, and consumer behavior. The company partners with insurers such as Aetna, provides a score that identifies “people likely to participate in interventions”, and offers to predict the progression of illnesses and intervention outcomes. According to an industry report, the company “ranks patients by how much return on investment” the insurer can expect if it targets them with particular interventions.

LexisNexis Risk Solutions, a large data broker and risk analytics company, provides a health scoring product that calculates health risks, as well as expected healthcare costs for individuals, based on vast amounts of consumer data, including purchase activities.

III. Large-scale collection and use of consumer data

Today’s dominant online platforms – above all Google and Facebook – have extensive information on the everyday lives of billions of people around the globe. They are the most visible, the most pervasive, and, aside from intelligence contractors, online advertisers, and digital fraud detection services, perhaps the most advanced players in the personal data and analytics industry. Many others operate behind the scenes and beyond public attention.

At its core, online advertising consists of an ecosystem of thousands of companies focused on constantly tracking and profiling billions of people. Every time an ad is displayed on a website or in a mobile app, a user’s digital profile has just been sold to the highest bidder in the milliseconds before. In contrast to these new practices, credit reporting agencies and consumer data brokers have already spent decades in the business of personal data. In recent years, they started combining the extensive information they have about people’s offline lives with the user and customer databases operated by large platforms, online advertising companies, and myriads of other businesses across many industries.

Data companies have extensive information on billions of people

Large Online Platforms
has profiles on               
Facebook users                                             
Whatsapp users
Instagram users
has profiles on
Android users
Gmail users
YouTube users
has profiles on
iOS users
Credit Reporting Agencies
has credit data on
marketing data on
„insights“ on
has data on
has data on
Consumer Data Brokers
has data on
cookies and mobile devices
it manages
consumer profiles for clients
has data on
mobile users
website visitors
provides access to
“unique” consumer IDs                                 

Facebook uses at least 52,000 personal attributes to sort and categorize its 1.9 billion users by, for example, their political views, ethnicity, and income. In order to do so, the platform analyzes their posts, likes, shares, friends, photos, movements, and many other kinds of behaviors.

In addition, Facebook acquires data on its users from other companies. In 2013, the platform began its partnership with the four data brokers Acxiom, Epsilon, Datalogix and BlueKai, the latter two of which were subsequently acquired by the IT giant Oracle. These companies help Facebook track and profile its users even better than it already does by providing it with data collected from beyond its platform.

IV. Data brokers and the business of personal data

Consumer data brokers play a key role in today’s personal data industry. They aggregate, combine, and trade massive amounts of information collected from diverse online and offline sources on entire populations. Data brokers collect publicly available information and buy or license consumer data from other companies. Generally, their data stems from sources other than the individuals themselves, and is collected largely without consumers’ knowledge. They analyze data, make inferences, sort people into categories, and provide thousands of attributes on individuals to their clients.

The profiles that data brokers have on individuals include not only information about education, occupation, children, religion, ethnicity, political views, activities, interests and media usage, but also about someone’s online behaviors such as web searches. Additionally, they collect data about purchases, credit card usage, income and loans, banking and insurance policies, property and vehicle ownership, and a variety of other data types. Data brokers also calculate scores that predict an individual’s possible future behavior, with regard to, for example, someone’s economic stability or plans to have a baby or to change jobs.

Some examples of data on consumers provided by Acxiom and Oracle 

Some examples of data on consumers provided by Acxiom and Oracle

Examples of data on consumers provided by Acxiom and Oracle (as of April/May 2017). Sources see report

Acxiom, a large consumer data broker

Founded in 1969, Acxiom runs one of the world’s largest commercial databases on consumers. The company provides up to 3,000 data elements on 700 million people from thousands of sources in many countries, including the US, the UK, and Germany. Initially a direct marketing firm, Acxiom developed its centralized consumer database in the late 1990s.

With its Abilitec Link system the company runs a kind of private population register in which every person, household, and building receives a unique ID. The company constantly updates its database with information about births and deaths, marriages and divorces, name and address changes, and, of course all kinds of other profile data. When asked about a person, Acxiom provides, for example, one of 13 religious affiliations including “Catholic”, “Jewish” and “Muslim”, and one of nearly 200 ethnic codes.

Acxiom sells access to its extensive consumer profiles and helps clients find, target, identify, analyze, sort, rate, and rank people. The company also directly manages 15,000 customer databases with billions of consumer profiles for its clients, including for large banks, insurers, healthcare organizations and government agencies. Besides marketing data services, Acxiom also provides identity verification, risk management, and fraud detection services.

Acxiom and its data providers, partners and services 

Acxiom and its data providers, partners and services

Acxiom and its data providers, partners, and clients (as of April/May 2017). Sources see report

Since the acquisition of the online data company LiveRamp in 2014, Acxiom has made major efforts to connect its decade-spanning data repository to the digital world. Acxiom was, for example, among the first data brokers to provide additional information to Facebook, Google, and Twitter in order to help the platforms better track or categorize users based on purchases and other behaviors that the platforms were still not able to track.

Acxiom’s LiveRamp connects and combines digital profiles across hundreds of data and advertising companies. At the core lies its IdentityLink system, which helps recognize individuals and link information about them across databases, platforms, and devices based on email addresses, phone numbers, smartphone IDs, and other identifiers. While the company promises that linking and matching happens in ”anonymized” and “de-identified” ways, it also states that it is able to “connect offline data and online data back to a single identifier”.

Companies that have recently been promoted as data providers by LiveRamp include the credit reporting giants Equifax, Experian, and TransUnion. Furthermore, many digital tracking services that collect data from the web, mobile apps, and even sensors placed throughout the physical world provide LiveRamp with data. Some of them use LiveRamp’s data store, which allows companies to “buy and sell valuable customer data“. Others provide data to let Acxiom and LiveRamp recognize individuals and link the recorded information with digital profiles from other sources. Perhaps most concerning is Acxiom’s partnership with Crossix, a company with extensive health data on 250 million US consumers. It is listed as one of LiveRamp’s data providers.

„anyone who captures data on consumers has the potential to be a data provider“

Travis May, General Manager of Acxiom's LiveRamp, 2017

Oracle, an IT giant enters the consumer data business

By acquiring several data companies such as Datalogix, BlueKai, AddThis, and CrossWise, Oracle, one of the world’s largest business software and databases vendors, has recently become one of the largest consumer data brokers as well. In its data cloud Oracle aggregates 3 billion user profiles from 15 million different websites, data from 1 billion mobile users, billions of purchases from grocery chains and 1,500 large retailers, as well as 700 million messages from social media networks, blogs, and consumer review sites per day.

Oracle aggregates data on billions of consumers 

Oracle aggregates data on billions of consumers

Oracle and its data providers, partners and services (as of April/May 2017). Sources see report

Oracle lists nearly 100 data providers in its data directory, including Acxiom and credit reporting agencies such as Experian and TransUnion, as well as companies that track website visits, mobile app usage, and movements, or that collect data from online quizzes. Visa and MasterCard are listed as data providers, too. Together with its partners, Oracle provides over 30,000 different data categories that may be assigned to consumers. Conversely, the company shares data with Facebook and helps Twitter calculate the creditworthiness of its users.

Oracle’s ID Graph identifies and combines user profiles across companies. It “unites all interactions” across databases, services and devices to “create one addressable consumer profile” and “identify customers and prospects everywhere”. Other companies can send match keys based on email addresses, phone numbers, postal addresses, and other identifiers to Oracle, which will then synchronize them to its “network of user and statistical IDs that are linked together in the Oracle ID Graph”. Although the company promises to only use anonymous user IDs and anonymous user profiles, these still refer to certain individuals and can be used to recognize them and to single them out in many life contexts.

Generally, clients can upload their own data about customers, website visitors, or app users to Oracle’s data cloud, combine it with data from many other companies and then transfer and utilize it on hundreds of other marketing and advertising technology platforms in real-time. They can use it to, for example, find and target people across devices and platforms, personalize interactions, and eventually, to measure how consumers respond after having been addressed and affected on an individual level.

V. Real-time monitoring of behaviors across everyday life

Online platforms, advertising technology providers, data brokers, and businesses in all industries can now monitor, recognize, and analyze individuals in many situations. They are able to learn what people are interested in, what they did today, what they are likely to do tomorrow, and how much they might be worth as a customer.

Data about people’s online and offline lives

A wide range of companies has been collecting information on people for decades. Before the Internet, both credit bureaus and direct marketing agencies were major points of integration between data flowing from different sources. A first big step into systematic consumer surveillance occurred in the 1990s through database marketing, loyalty programs, and advanced consumer credit reporting. Following the ascent of the Internet and online advertising in the early 2000s and the rise of social networks, smartphones, and online advertising in the late 2000s, we now see the traditional consumer data industry integrating with the new digital tracking and profiling ecosystem in the 2010s.

Mapping data collection on consumers 

Mapping data collection on consumers

Different levels, realms and sources of corporate consumer data collection.

Consumer data brokers and other firms have long been acquiring information on newspaper and magazine subscribers, book and movie club members, catalog and mail order buyers, travel agency bookers, seminar and conference participants, and consumers filling out warranty card product registrations. The collection of purchase data from loyalty programs has also long been an established practice in this regard.

In addition to data directly gathered from individuals they have been using, for example, information about the types of neighborhoods and buildings people are living in to characterize, label, sort, and categorize people. Similarly, companies now profile consumers based on metadata about the types of websites they surf, the videos they watch, the apps they use, and the geographic locations they visit. In recent years, the scale and depth of behavioral data streams generated by all sorts of everyday activities, such as web, social media, and device usage, have risen rapidly.

„That’s No Phone. That’s My Tracker“

New York Times, 2012

Ubiquitous digital tracking and profiling

One major reason that corporate tracking and profiling has become so pervasive lies in the fact that nearly all websites, mobile app providers, and many device vendors actively share behavioral data with other companies.

A few years ago most websites began embedding tracking services that transmit user data to third parties into their websites. Some of these services provide visible functionality to users. When a website shows, for example, a Facebook like button or an embedded YouTube video, user data is transmitted to Facebook or Google. Many other services related to online advertising remain hidden, though, and largely serve only one purpose, namely to collect user data. It is widely unknown exactly which kinds of user data digital publishers share and how third parties use this data. At least part of these tracking activities can be examined by everybody; by installing the browser extension Lightbeam, for example, one can visualize the hidden network third-party trackers.

A recent study examined one million different websites and found more than 80,000 third-party services that receive data about the visitors of these websites. Around 120 of these tracking services were found on more than 10,000 websites, and six companies monitor users on more than 100,000 websites, including Google, Facebook, Twitter, and Oracle’s BlueKai. A study on 200,000 users from Germany visiting 21 million web pages showed that third-party trackers were present on 95% of the pages visited. Similarly, most mobile apps share information on their users with other companies. A 2015 study of popular apps in Australia, Brazil, Germany, and the US found that between 85% and 95% of free apps and even 60% of paid apps connect to third parties that collect personal data.

An interactive map of hidden third-party tracking services on Android apps created by researchers from Europe and the US can be explored at:


Screenshot of the ISCI Haystack Panopticon available at, © Courtesy of ISCI, UC Berkeley

In terms of devices, smartphones are perhaps the biggest contributors to today’s ubiquitous data collection. The information recorded by mobile phones provides detailed insights into a user’s personality and everyday life. Since consumers generally need to have a Google, Apple, or Microsoft account to use them, much of the information is already linked to a major platform’s identifier.

Selling user data is not restricted to website and mobile app publishers. The marketing intelligence company SimilarWeb, for example, receives data not only from hundreds of thousands of direct measurement sources from websites and apps, but also from desktop software and browser extensions. In recent years, many other kinds of devices with sensors and network connections have entered everyday life, from e-readers and wearables to smart TVs, meters, thermostats, smoke alarms, printers, fridges, toothbrushes, toys, and cars. Like smartphones, these devices give companies unprecedented access to consumer behavior across many life contexts.

Programmatic advertising and marketing technology

The online advertising industry has become a pioneering force in developing sophisticated technologies that monitor and track people, as well as ones to combine and link profiles across the digital world.

Most of today’s digital advertising takes place in the form of highly automated real-time auctions between publishers and advertisers; this is often referred to as programmatic advertising. When a person visits a website, it sends user data to a variety of third-party services, which then try to recognize the person and retrieve available profile information. Advertisers interested in delivering an ad to this particular person due to certain attributes and behaviors make a bid. Within milliseconds, the highest-bidding advertiser wins and places the ad. Advertisers can similarly bid on user profiles and ad placements within mobile apps.

For the most part, however, this process does not take place directly between publishers and advertisers. The ecosystem consists of a plethora of different kinds of data and technology providers interacting with each other, including ad networks, ad exchanges, sell-side platforms, and demand-side platforms. Some of these specialize in tracking and advertising alongside search results, general ads on the web, mobile ads, video ads, social network ads, or ads within games. Others focus on providing data, analytics, or personalization services.

To profile web or mobile app users, all parties involved have developed sophisticated methods to accumulate, compile, and link information from different companies in order to follow individuals across their lives. Many of them collect or utilize digital profiles on hundreds of millions of consumers, their web browsers, and devices.

Many industries are joining the tracking economy

In recent years, businesses in many industries have started to share and utilize data on their users and customers at a massive scale.

Most retailers sell more or less aggregated forms of purchase data to market research companies and consumer data brokers. The data company IRI, for example, accesses data from more than 85,000 grocery, mass merchandise, drug, club, dollar, convenience, liquor, and pet stores. Nielsen states that it collects sales information from worldwide 900,000 stores in more than 100 countries. The large British retailer Tesco has outsourced its loyalty and data activities to a subsidiary company, Dunnhumby, whose slogan is “transforming customer data into customer delight”. When Dunnhumby acquired the German ad technology company Sociomantic, they announced that Dunnhumby will “combine its extensive insights on the shopping preferences of 400 million consumers” with Sociomantic’s “real-time data from more than 700 million online consumers” to personalize and evaluate advertising.

Mapping the commercial digital tracking and profiling landscape 

Mapping the commercial digital tracking and profiling landscape

In addition to the large online platforms and the consumer data and analytics industry, businesses in
many industries have joined today’s pervasive digital tracking and profiling ecosystems

Large media conglomerates are also deeply embedded in today’s tracking and profiling ecosystems. For example, Time Inc. has acquired Adelphic, a major cross-device tracking and ad technology company, as well as Viant, a company that claims to have access to over 1.2 billion registered users. A prominent example of a digital publisher that sells data on its users is the streaming platform Spotify. Since 2016, it shares insights on their users’ mood, listening and playlist behavior, activity and location with the data division of the advertising giant WPP, which now has access to “unique listening preferences and behaviors of Spotify’s 100 million users”.

Many large telecom companies and Internet Service Providers have acquired ad technology and data companies. For example, Millennial Media, a subsidiary of Verizon’s AOL, is a mobile ad platform collecting data from more than 65,000 apps from different developers, and claims to have access to approximately 1 billion global active unique users. The Singapore-based telecom corporation Singtel acquired Turn, an ad technology platform that gives marketers access to 4.3 billion addressable device and browser IDs and 90,000 demographic, behavioral, and psychographic attributes.

Like airlines, hotels, retailers and companies in many other industries, the financial services sector started to aggregate and utilize additional customer data with loyalty programs in the 1980s and 1990s. Companies with related, complementary target groups have long been sharing certain customer data with each other, a process often managed by intermediaries. Today, one of these intermediaries is Cardlytics, a firm that runs reward programs with 1,500 financial institutions such as the Bank of America and MasterCard. Cardlytics promises financial institutions that it will “generate new revenue streams using the power of [their] purchase data”. The company also partners with LiveRamp, the Acxiom subsidiary that combines online and offline consumer data.

For MasterCard, selling products and services created from data analytics might even become its core business given that information products, including sales of data already represent a considerable and growing share of its revenue. Google recently stated that it captures approximately 70% of credit and debit card transactions in the United States through “third-party partnerships” in order to track purchases, but did not disclose its sources.

„It’s your data. You have the right to control it, share it and use it how you see fit.“

How the online data broker Lotame addresses its corporate clients on its website, 2016

VI. Linking, matching and combining digital profiles

Until recently, advertisers who used Facebook, Google or other online ad networks could target individuals based only on their online behavior. However, a few years ago data companies began providing ways to combine and link digital profiles across platforms, customer databases, and the world of online advertising.

Connecting online and offline identities

In 2012, Facebook started allowing companies to upload their own lists of email addresses and phone numbers to the platform. Although these addresses and numbers are converted into pseudonymous codes, Facebook can directly link this customer data from other companies with Facebook user accounts. In this way, companies can, for example, find and target exactly those persons on Facebook that they have email addresses or phone numbers on. They could also selectively exclude them from targeting or let the platform find people with similar attributes, interests, and behaviors.

This is a powerful feature, perhaps more powerful than it seems at first glance. It allows companies to systematically connect their own customer data with Facebook’s data. Moreover, it also allows other advertising and data vendors to synchronize with the platform’s databases and tap into its capacities, essentially providing a kind of real-time remote control for Facebook’s data universe. Companies can now capture highly specific behavioral data, such as a click on a website, a swipe in a mobile app or a purchase in a store, in real-time, and tell Facebook to immediately find and target the persons who performed these activities. Google and Twitter launched similar features in 2015.

Data management platforms

Today, most advertising technology companies continuously pass various forms of codes referring to individuals onto each other. Data management platforms allow businesses in all industries to combine and link their own data on consumers, including real-time information about purchases, website visits, app usage, and email responses, with digital profiles provided by myriads third-party data providers. The combined data can then be analyzed, sorted and categorized, and used to address certain people with certain messages on certain channels or devices. A company could, for example, target a group of existing customers that visited a certain page on its website, and are predicted to become valuable customers, with personalized content or a discount – either on Facebook, in a mobile app, or on the company’s own website.

The emergence of data management platforms marks a key moment in the development of pervasive commercial behavioral tracking. With their help, businesses in all industries across the globe are able to seamlessly combine and link the data they have collected about their customers and prospects for years with billions of profiles collected within the world of digital tracking. Companies running such platforms include Oracle, Adobe, Salesforce (Krux), Wunderman (KBM Group/Zipline), Neustar, Lotame, and Cxense.

„We will serve ads to you based on your identity, but that doesn't mean you're identifiable“

Erin Egan, chief privacy officer at Facebook, 2012

Identifying people and linking digital profiles

To monitor and follow people in many situations of their lives, combine profiles on them, and always recognize them as the same individuals again, companies collect a wide range of data attributes that identify them in some way.

Because of its ambiguity a person's legal name has always been a bad identifier for data collection. The postal address, in contrast, has long been, and still is, a key attribute that allows combining and linking data about consumers and their families from different sources. In the digital world, the most relevant identifiers used to link profiles and behavioral data across different databases, platforms, and devices are email addresses, phone numbers, and unique codes that refer to smartphones or other devices.

User account IDs of the large platforms such as Google, Facebook, Apple, and Microsoft also play an important role in following people across the Internet. Google, Apple, Microsoft, and Roku assign “advertising IDs” to individuals, which are now widely used to match and link data from devices such as smartphones with other information from all over the digital world. Verizon uses its own identifier to track users across websites and devices. Some large data companies such as Acxiom, Experian, and Oracle have introduced globally unique IDs for people, which they use to link their decades-old consumer databases and other profile information from different sources with the digital world. These corporate IDs mostly consist of two or more identifiers that are attached to different aspects of the online and offline life of someone and can be linked to each other in certain ways.

Identifiers used to track people across websites, devices and areas of life 

Identifiers used to track people across websites, devices and areas of life

How companies identify consumers and link profile information about them. Sources see report


Tracking companies also use more-or-less temporary identifiers, such as cookie IDs that are attached to users surfing the web. Since users may disallow or delete cookies in their web browser, they have developed sophisticated methods to calculate unique digital fingerprints based on various attributes of someone’s web browser and computer. Similarly, companies compile fingerprints for devices such as smartphones. Cookie IDs and digital fingerprints are constantly synchronized between different tracking services, and then linked with other, more permanent identifiers.

Other companies provide cross-device tracking services that are based on using machine learning to analyze large amounts of data. For example, Tapad, which has been acquired by the Norwegian telecom giant Telenor, analyzes data on 2 billion devices around the globe and uses behavioral and relationship-based patterns to find the statistical chance that certain computers, tablets, phones and other devices belong to the same person.

“Anonymous” profiling?

Data companies often remove names from their extensive profiles and use hashing to convert email addresses and phone numbers into alphanumeric codes such as “e907c95ef289”. This allows them to claim on their websites and in their privacy policies that they only collect, share, and use “anonymized” or “de-identified” consumer data.

However, because most companies use the same deterministic processes to calculate these unique codes, they should be understood as pseudonyms that are, in fact, much more suitable for identifying consumers across the digital world than real names. Even if the profiles companies share with one another only contain “hashed” or “encrypted” email addresses and phone numbers with each other, a person can still be recognized again as soon as he or she uses another service linked with the same email address or phone number. In this way, even though each of the tracking services involved might only know a part of someone’s profile information, companies can follow and interact with people at an individual level across services, platforms, and devices.

„If a company can follow and interact with you in the digital environment – and that potentially includes the mobile phone and your television set – its claim that you are anonymous is meaningless, particularly when firms intermittently add offline information to the online data and then simply strip the name and address to make it ‘anonymous’.“

Joseph Turow, marketing and privacy scholar in his book “The Daily You”, 2011

VII. Managing consumers and behaviors, personalization and testing

Based on the sophisticated methods of linking and combining data across different services, businesses in all industries can utilize today’s ubiquitous behavioral data streams to monitor and analyze a wide range of consumer activities and behaviors that might be relevant to their business interests.

With the help of data vendors, companies try to capture as many touchpoints across the whole customer journey as possible, from digital ones to in-store purchases, direct mail, TV ads, and call center calls. They try to record and measure every interaction with a consumer, including on websites, platforms, and devices they do not control themselves. They can seamlessly collect rich data about their customers and others in real-time, enhance them with information from third parties, and utilize the enriched profiles within the marketing and ad technology ecosystem. Today’s consumer data management platforms allow for the definition of complex sets of rules that dictate how to automatically react to specific criteria such as certain activities, people, or some combination thereof.

Consequently, individuals never know whether their behavior triggered a reaction from any of these continuously updated, interconnected, opaque networks of tracking and profiling, and, if so, how this affects the options they get across communication channels and life situations.

Tracking, profiling and affecting people in real-time 

Tracking, profiling and affecting people in real-time

Every interaction triggers a wide range of data flows between multiple companies.

Mass personalization

The data streams that are shared between online advertisers, data brokers, and other companies are not only used to display precisely targeted ads on websites or within mobile apps to users. They are increasingly used to dynamically personalize the contents, options, and choices offered to consumers on, for example, a company’s website. The data technology firm Optimizely, for instance, can help personalize a website for first time visitors, based on digital profiles on those visitors provided by Oracle.

Online stores might, for example, personalize how someone is addressed, which products are displayed prominently, which discounts are offered, and even the prices of products or services can differ based on who is visiting a website. Online fraud detection services assess users in real-time and decide which payment and shipping methods someone sees.

Companies have developed technologies to constantly calculate and rate someone’s potential long-term value based on information about a said person’s browsing, search, and location history, as well as app usage, product purchases, or friends on a social network. Every click, swipe, like, share, or purchase might automatically influence how someone is treated as a customer, how long someone has to wait when calling a phone hotline, or whether someone is excluded from marketing efforts or services at all.

„The rich see a different Internet than the poor“

Michael Fertik, founder of, 2013

Three types of technology platforms play an important role for this kind of instant personalization. First, companies use advanced Customer Relationship Management systems to manage their data on customers and prospects. Second, they use Data Management Platforms to connect their own data to the digital advertising ecosystem, and gain additional profile information about their customers. Third, they can use Predictive Marketing Platforms, which help them compile the right message to the right person at the right time by calculating how to persuade someone by exploiting personal biases and weaknesses.

The data company RocketFuel, for example, promises its clients to “bring together trillions of digital and real-world signals to create individual profiles and deliver personalized, always-on, always-relevant experiences to the consumer”, based on 2.7 billion unique profiles in its data store. RocketFuel says that it “scores every impression for its propensity to influence the consumer”.

The predictive marketing platform TellApart, which belongs to Twitter, creates a customer value score for each shopper and product combination, a “compilation of likelihood to purchase, predicted order size, and lifetime value”, based on “100s of online and in-store signals about a particular anonymous customer”. Subsequently, TellApart helps automatically assemble content such as “product imagery, logos, offers and any metadata” for personalized ads, emails, websites, and offers.

Personalized pricing and election campaigns

Similar methods can be used to personalize prices in online shops by, for example, predicting how valuable someone might be as customer in the long term or how much someone is probably willing to pay in a moment. Strong evidence suggests that online shops already show differently priced products to different consumers, or even different prices for the same products, based on their individual characteristics and behaviors. A similar field is the use of personalization during election campaigns. Targeting voters with personalized messages that are adapted to their personality and to their political views on certain issues has already raised massive debates about the potential for political manipulation.

Utilizing data, analytics, and personalization to manage consumers 

Utilizing data, analytics, and personalization to manage consumers

Businesses in all industries can utilize today’s networks of digital tracking and profiling
to find, assess, address, sort and manage customers.

Testing and experimenting on people

Personalization based on rich profile information and pervasive real-time monitoring has become a powerful tool set to influence consumer behavior such as visiting a website, clicking on an ad, registering for a service, subscribing to a newsletter, downloading an app, or purchasing a product.

To further improve this, companies have started continuously experimenting on people. They conduct tests with different variations of functionalities, website designs, user interface elements, headlines, button texts, images, or even different discounts and prices, and then carefully monitor and measure how different groups of users interact with these variations. In this way, companies systematically optimize their ability to nudge people into acting how they want them to act.

News organizations, including large outlets such as the Washington Post, use different versions of article headlines to test which variant performs better. Optimizely, one of the major technology providers for such tests, offers its clients the ability to “experiment broadly across the entire customer experience, on any channel, any device, and any application”. Experimenting on unaware users has become the new normal.

Facebook stated in 2014 that it runs “over a thousand experiments each day” in order to “optimize specific outcomes” or “inform long-term design decisions”. In 2010 and 2012, the platform conducted experiments on millions of users and showed that manipulating Facebook’s user interface, functionalities, and displayed content can significantly increase voter turnout for groups of people. The platform’s notorious mood experiment on nearly 700,000 users involved secretly manipulating the amount of emotionally positive and negative posts in users’ news feeds, which ended up influencing how many emotionally positive and negative messages the users then posted themselves.

After massive public criticism of Facebook’s experiments, the online dating platform OkCupid released a provocative blog post defending such practices, stating that “we experiment on human beings” and “so does everybody else”. OkCupid reported on an experiment wherein they had manipulated the “match” percentage shown to pairs of users. When they showed a 90% match to actually bad-matching pairs, these users exchanged significantly more messages with each other. OkCupid claimed that when they “tell people” they are a “good match”, they “act as if they are”.

All these ethically highly questionable experiments clearly demonstrate the power of data-driven personalization to influence behavior.

VIII. Dragnet – everyday life, marketing data and risk analytics

Data about people’s behaviors, social relationships, and most private moments is increasingly applied in contexts or for purposes completely different from those for which it was recorded. In particular, it is increasingly used to make automated decisions about individuals in crucial areas of life such as finance, insurance, and healthcare.

Risk data for marketing and customer management

Credit reporting agencies and other key players in risk assessment in fields such as identity verification, fraud prevention, healthcare and insurance analytics mostly also provide marketing solutions. Furthermore, most consumer data brokers trade many kinds of sensitive information – e.g. about an individual’s financial situation – for marketing purposes. The use of credit scores for marketing purposes to either focus on or exclude vulnerable population groups has evolved into products that unify marketing and risk management.

The credit reporting agency TransUnion provides, for example, a product for data-driven decisions in retail and financial services that allows clients to “implement marketing and risk strategies tailored for customer, channel and business goals”, including credit data and promising “unique insights into consumer behavior, preferences and risk”. Companies can let consumers “choose from a suite of offerings that are tailored to their needs, preferences and risk profile” and “evaluate a customer for multiple products across channels, and then only present the offer(s) that are most relevant to them, and profitable” for the company. Similarly, Experian provides a product that combines “consumer credit and marketing information that is compliantly available from Experian”.

„Surveillance is not about knowing your secrets, but about managing populations, managing people“

Katarzyna Szymielewicz, Vice-President EDRi, 2015

Online identity verification and fraud detection

In addition to the real-time surveillance machine that has been developed within online advertising, another forms of pervasive tracking and profiling has emerged in the fields of risk analytics, fraud detection and cyber security.

Today’s online fraud detection services use highly invasive technologies to evaluate billions of digital transactions and collect vast amounts of information about devices, individuals, and behaviors. Traditional vendors in credit reporting, identity verification, and fraud prevention have started to monitor and evaluate how people surf the web and use their mobile devices. Furthermore, they have started to link digital behavioral data with the vast amounts of offline identity information that they have been collecting for decades.

With the rise of technology-mediated services, verifying consumer identities and preventing fraud have both become increasingly important and challenging issues, especially in light of cybercrime and automated fraud. At the same time, today’s risk analytics systems have aggregated giant databases with sensitive information on entire populations. Many of these systems cover a wide range of use cases, including proof of identity for financial services, assessing insurance and benefits claims, analyzing payment transactions, and evaluating billions of online transactions.

Such risk analytics systems may decide whether an application or transaction is accepted or rejected or which payment and shipping options are available for someone during an online transaction. Commercial services for identity verification and fraud analytics are also used in areas such as law enforcement and national security. The line between commercial applications of identity and fraud analytics and those used by government intelligence services is blurring more and more.

When people are singled out by such opaque systems, they might get flagged as suspicious and warranting special treatment or investigation – or they may be rejected without explanation. They might get an email, a phone call, a notification, an error message, or the system may simply withhold an option without the user ever knowing of its existence for others. Inaccurate assessments may spread from one system to another. It is often difficult or impossible to object to such negative assessments that exclude or deny, especially because of how hard it is to object to mechanisms or decisions that someone does not know about at all.

Examples of online fraud detection and risk analytics services

The cyber security firm ThreatMetrix processes data on 1.4 billion “unique user accounts” across “thousands of global websites”. Its Digital Identity Network captures “millions of daily consumer transactions including logins, payments and new account originations”, and maps the “ever-changing associations between people and their devices, locations, account credentials, and behavior” for identity verification and fraud prevention purposes. The company partners with Equifax and TransUnion. Its clients include Netflix, Visa, and firms in fields such as gaming, government services, and healthcare.

Similarly, the data company ID Analytics, which was recently acquired by Symantec, runs an ID Network with “100 million identity elements coming in each day from leading cross-industry organizations”. The company aggregates data on 300 million consumers, including on sub-prime loans, online purchases and credit card and wireless phone applications. Its ID Score evaluates digital devices, as well as names, social security numbers, and postal and email addresses.

Trustev, an online fraud detection company based in Ireland, which was acquired by the credit reporting agency TransUnion in 2015, evaluates online transactions for clients in financial services, government, healthcare, and insurance, based on the analysis of digital behaviors, identities, and devices such as phones, tablets, laptops, game consoles, TVs, and even refrigerators. The company offers corporate clients the ability to analyze how visitors click and interact with websites and apps, and uses a wide range of data to assess users, including phone numbers, email and postal addresses, browser and device fingerprints, credit checks, transaction histories across merchants, IP addresses, mobile carrier details and cell locations. To help “approve future transactions” every device receives a unique device fingerprint. Trustev also offers a social fingerprinting technology that analyzes social media content, including “friend list analysis” and “pattern identification”. TransUnion has integrated Trustev technology into its own identity and fraud solutions.

Trustev uses a wide range of data to assess people, according to its website: 

Trustev uses a wide range of data to assess people, according to its website:

Screenshot of Trustev’s website, June 2, 2017 © Trustev

Similarly, the credit reporting agency Equifax states that it has data on nearly 1 billion devices and can validate “where a device really is and whether it is associated with other devices used in known fraud”. By combining this data with “billions of identity and credit events to find suspicious activity” across industries, and with information about employment and about relationships between households, families and associates, Equifax claims to be able to “identify devices as well as individuals”.

I am not a robot

Google’s reCaptcha product actually provides similar functionality, at least in parts. It is embedded into millions of websites and helps website providers decide whether a visitor is a legitimate human being or not. Until recently, users had to solve several kinds of quick challenges such as deciphering letters on a picture, choosing objects in a grid of pictures, or simply clicking on an “I’m not a robot” checkbox. In 2017, Google introduced an invisible version of reCaptcha, explaining that from now on “human users will be let through” without any user interaction in contrast to “suspicious ones and bots”. The company doesn’t disclose which kinds of user data and behaviors it uses to identify humans. Investigations suggest that Google doesn’t only use IP addresses, browser fingerprints, the way user’s type, move their mouse, or use their touchscreen "before, during, and after" a reCaptcha interaction, but also several of Google’s cookies. It is not clear whether people without user accounts face a disadvantage, whether Google is able to identify specific individuals rather than only “humans”, or whether Google also uses the data recorded within reCaptcha for purposes other than for bot detection.

Digital tracking for advertising and fraud detection?

The ubiquitous streams of behavioral data recorded for online advertising increasingly flow into fraud detection systems. The marketing data platform Segment, for example, offers clients easy ways to send data about their customers, website, and mobile app users to many different marketing technology services, but also to fraud detection companies. One of them is Castle, which uses “customer behavioral data to predict which users are likely a security or fraud risk”. Another one, Smyte, helps “prevent scams, spam, harassment and credit card fraud”.

The large credit reporting agency Experian offers a cross-device tracking service that provides universal device recognition across mobile, web, and apps for digital marketing. The company promises to reconcile and associate their client’s “existing digital identifiers”, including “cookies, device IDs, IP addresses and more”, providing marketers with a “ubiquitous, consistent and persistent link across all channels”.

Experian’s device identification technology comes from 41st parameter, an online fraud detection company that Experian acquired in 2013. Based on 41st parameter’s technology, Experian also offers a device intelligence solution for fraud detection during online payments, which “establishes a reliable ID for the device and collects rich device data”, “identifies every device on every visit in milliseconds” and “gives unparalleled visibility into the person behind the payment”. It is not clear whether Experian uses the same data for its device identification services in fraud detection and marketing.

IX. Mapping the commercial tracking and profiling landscape

In recent years, pre-existing practices of commercial surveillance have rapidly evolved into a vast landscape of corporate players that constantly monitor entire populations. Some actors in today’s pervasive tracking and profiling ecosystem, such as the large platforms and other companies with large numbers of customers, have a unique position in terms of the scale and depth of their consumer profiles. Nevertheless, the data used to make decisions on people in many areas of life is mostly not held in one place, but rather assembled from several sources in real-time, as needed.

A wide range of data and analytics companies in marketing, customer management and risk analytics seamlessly gather, analyze, share, and trade consumer data as well as combine it with further information from thousands of other companies. While the data and analytics industry provides the means to deploy these powerful technologies, businesses in many industries equally contribute both to intensifying the amount and detail of the collected data and the ability to utilize it.

Mapping the commercial digital tracking and profiling landscape 

Mapping the commercial digital tracking and profiling landscape

In addition to the large online platforms and the consumer data and analytics industry, businesses in
many industries have joined today’s pervasive digital tracking and profiling ecosystems

Google and Facebook, followed by other large platforms such as Apple, Microsoft, Amazon and Alibaba have unprecedented access to data about the lives of billions of people. Although they have different business models and therefore play different roles in the personal data industry, they have the power to widely dictate the basic parameters of the overall digital markets. The large platforms mostly restrict how other firms can directly obtain their data; in this way, they force them to utilize the platform’s data on users within their own ecosystems and gather additional data from beyond the platforms’ reach.

Although the large multinationals in different sectors that have frequent interactions with hundreds of millions of consumers are in a somewhat similar position, they not only acquire consumer data collected by others, but often also provide data. While parts of the financial services and telecoms sectors, as well as crucial societal areas such as healthcare, education, and employment, are subject to stronger privacy regulation in most jurisdictions, a wide range of companies has started to utilize or contribute data to today’s networks of commercial surveillance.

Retailers and other companies that sell products and services to consumers mostly also sell data about their customers’ purchases. Media conglomerates and digital publishers sell data about their audiences, which is then utilized by companies in most other sectors. Telecom and broadband providers have started following their customers through the web. Large companies in retail, media and telecom have acquired or are acquiring data, tracking, and advertising technology firms. With Comcast acquiring NBC Universal, and AT&T most likely acquiring Time Warner, the large telecoms in the US are also becoming giant publishers, creating powerful portfolios of content, data, and targeting capabilities. With its acquisition of AOL and Yahoo, Verizon also became a “platform”.

Financial institutions have long used data on consumers for risk management, such as credit scoring and fraud detection, as well as for marketing, customer acquisition, and retention. They supplement their own data with external data from credit reporting agencies, data brokers and marketing data companies. PayPal, the biggest name in online payments, shares personal information with more than 600 third parties including other payment providers, credit reporting agencies, identity verification and fraud detection companies, as well as with the most advanced players within the digital tracking ecosystems. While credit card networks and banks have shared financial data on their customers with risk data providers for decades, they have now started selling transactional data for marketing purposes.

A myriad of smaller and larger firms providing websites, apps, games, and other applications are closely connected to the marketing data ecosystem. They use services that allow them to easily transmit data about their users to hundreds of third-party services. Many of them sell their users’ behavioral data streams as a core part of their business model. Even more worryingly, companies that provide new kinds of devices such as fitness trackers also seamlessly embed services that transfer user data to third parties.

The pervasive real-time surveillance machine that has been developed for online advertising is rapidly expanding into other fields including politics, pricing, credit scoring, and risk management. Insurers all over the world have started to offer their customers programs involving real-time tracking of behaviors such as car driving, health activities, grocery purchases, or visits to the fitness studio. New players in insurance analytics and financial technology predict individual health risks based on consumer data, as well as the creditworthiness of individuals based on behavioral data on phone calls or web searches.

Consumer data brokers, customer management companies, and advertising agencies such as Acxiom, Epsilon, Merkle or Wunderman/WPP play a major role in combining and connecting data between platforms, multinationals, and the advertising technology world. Credit reporting agencies like Experian that provide many services in very sensitive fields such as credit reporting, identity verification and fraud detection also play a major role in today’s pervasive marketing data ecosystem.

Particular large companies that provide data, analytics, and software services have been named as “platforms” as well. Oracle, a large database and business software provider, has become a consumer data broker in recent years. Salesforce, the market leader in customer relationship management that is managing the customer databases of millions of clients, yet having many customers each, has acquired Krux, a major data company connecting and combining data all over the digital world. The software company Adobe also plays an important role in profiling and advertising technology.

In addition, most major companies in business software, analytics and consulting, such as IBM, Informatica, SAS, FICO, Accenture, Capgemini, Deloitte, and McKinsey, or even intelligence and defense firms such as Palantir, also play a significant role in the management and analysis of personal data, from customer relationship management to identity management to marketing to risk analytics for insurers, banks, and governments.

X. Towards a society of pervasive digital social control?

This report finds that the networks of online platforms, advertising technology providers, data brokers, and other businesses can now monitor, recognize, and analyze individuals in many life situations. Information about individuals’ personal characteristics and behaviors is linked, combined, and utilized across companies, databases, platforms, devices, and services in real-time. With the actors guided only by economic goals, a data environment has emerged in which individuals are constantly surveyed and evaluated, categorized and grouped, rated and ranked, numbered and quantified, included or excluded, and, as a result, treated differently.

Several key developments in recent years have rapidly introduced unprecedented new qualities to ubiquitous corporate surveillance. These include the rise of social media and networked devices, the real-time tracking and linking of behavioral data streams, the merging of online and offline data, and the consolidation of marketing and risk management data. Pervasive digital tracking and profiling, in combination with personalization and testing, are not only used to monitor, but also to systematically influence people’s behavior. When companies use data about everyday life situations to make both trivial and consequential automated decisions about people, this may lead to discrimination, and reinforce or even worsen existing inequalities.

In spite of its omnipresence, only the tip of the iceberg of data and profiling activities is visible to individuals. Much of it remains opaque and barely understood by the vast majority of people. At the same time, people have ever fewer options to resist the power of this data ecosystem; opting out of pervasive tracking and profiling has essentially become synonymous with opting out of modern life. Although corporate leaders argue that privacy is dead (while caring a great deal about their own privacy), Mark Andrejevic suggests that people do indeed perceive the power asymmetries of today’s digital world, but feel “frustration over a sense of powerlessness in the face of increasingly sophisticated and comprehensive forms of data collection and mining”.

In light of this, this report focused on the actual practices and inner workings of the contemporary personal data industry. While the picture is becoming clearer, large parts of the systems in place still remain in the dark. Enforcing transparency about corporate data practices remains a key prerequisite to resolving the massive information asymmetries between data companies and individuals. Hopefully this report’s findings will encourage further work by scholars, journalists, and others in the fields of civil rights, data protection, consumer protection, and, ideally, also of policymakers and the companies themselves.

In 1999, Lawrence Lessig famously predicted that left to itself, cyberspace will become a perfect tool of control shaped primarily by the “invisible hand” of the market. He suggested that we could “build, or architect, or code cyberspace to protect values that we believe are fundamental, or we can build, or architect, or code cyberspace to allow those values to disappear”. Today, the latter has nearly been made reality by the billions of dollars in venture capital poured into funding business models based on the unscrupulous mass exploitation of data. The shortfall of privacy regulation in the US and the absence of its enforcement in Europe has actively impeded the emergence of other kinds of digital innovation, that is, of practices, technologies, and business models that preserve freedom, democracy, social justice, and human dignity.

On a broader level, data protection legislation alone will not mitigate the consequences that a data-driven world has on individuals and society, whether in the US or Europe. While consent and choice are crucial principles to resolve some of the most urgent problems of intrusive data collection, they can also produce an illusion of voluntariness. Besides additional regulatory instruments such as anti-discrimination, consumer protection, and competition law, it will generally require a major collective effort to realize a positive vision for a future information society. Otherwise, we might soon end up in a society of pervasive digital social control, where privacy becomes – if it remains at all – a luxury commodity for the rich. The building blocks are already in place.

Further reading:

  • A more comprehensive take on the issues covered in the web publication above, as well as references and sources, can be found in the full report, available as a PDF download.
  • The 2016 report "Networks of Control" by Wolfie Christl and Sarah Spiekermann, which the current report is largely based on, is available as a PDF download and as a printed book.

The production of this report, web materials, and illustrations was supported by the Open Society Foundations.