If you're short on time, here's most of what you need to know about the data economy, the value of your data and data marketplaces:
Facebook's parent company Meta has recently been reported to be considering a subscription model that would allow users to use Facebook and Instagram ad-free. The measure is a direct response to new European data protection regulations, which are intended to limit the processing of personal data for advertising purposes.
A full subscription to both platforms is said to cost up to 192 euros a year. Meta is putting a price on your privacy - you pay and they leave your personal data alone. But is that a fair price?
Big tech companies like Meta and Google make billions in profits every year by using our data to serve us targeted ads. The data broker market in the US, where personal data is traded, reached a volume of 247 billion dollars last year. We provide this data to big companies virtually for free - sometimes without even knowing it. In return, we only get access to some of their services. This raises the question: How much is your personal data worth?
Finding a comprehensive answer to this question is anything but easy. The problem becomes clear when we look at the prices at which your personal data is traded. In the data broker market, your shopping data (i.e. data about your purchases and transactions) sells for a measly $0.001 . On the other hand, Amazon's Amazon Shopper Panel programme offers selected users 10 USD per month for uploading invoices from other shops.
Although in both cases essentially the same thing is being traded, the prices differ by a factor of hundreds of thousands. This is the data paradox: The value of your data seems to vary greatly depending on the context in which it is traded. By exploring this phenomenon, we gain a deeper understanding of the value of your data and gain insight into the world of the data markets.
The easiest way to gain an understanding of the value of personal data is to look at the places where it is directly traded. Data brokers sell individual pieces of information about users to companies, advertisers and other organisations interested in reaching more specific audiences or learning more about their customer base.
This is an extremely profitable business. In 2022, the global market for databroker services was estimated at $247.4 billion. By 2028, this market is expected to reach a value of $407.5 billion. Acxiom, the largest player in this market, contributes to 12% of all direct marketing revenues with its data. Another heavyweight in this industry is Equifax, which has collected payroll data from 38% of all Americans.
This data can range from general demographic information to detailed shopping habits or online activity. The selling point varies depending on the type, quality and relevance of the data.
For example, general information about an individual, such as age, gender and location, has a price of around $0.0005 per person. The data of people who are looking for a car, a financial product or a holiday, is more expensive. If you want to buy a car, companies pay around 0.0021 dollars for this information. The information that a woman is expecting a child increases is worth 0.11 dollar.
Prices for your other data vary mainly depending on demographics. For example, information on men is worth USD 0.15, slightly more than information on women (USD 0.14). Data from people with a higher income is generally worth more - With one exception, if your household earns less than $10,000 per year, companies will pay more for your data than for a family with an income of up to $120,000. This is probably because companies think you are a young student who is more likely to talk about products on social media.
The high value placed on social influence could also be observed somewhere else: Klout was a platform that used social media analytics to rate the social influence of its users through a "Klout Score", based on network size and interactions with their content. Data on users with high social influence was sold by the site for around $3 at the time. The company ceased operations in May 2018, with the introduction of new data protection regulations being a major factor.
Key determinants of it's price also include the quality and dimensionality of the data. The more I know about you, the more meaningful are the conclusions I can draw. The informative value of a data set that only contains your name is significantly lower than that of a data set that also contains your place of residence, your age, your occupation, your interests and your purchasing behaviour. High-dimensional data provides a richer picture and can be more valuable for analysis, prediction or targeted marketing.
So evidently, depending on the type, quality and richness of your data, the price companies are willing to pay for it will vary. It primarily depends on how companies assess your purchasing power and buying mood. Many companies turn to data brokers to gain a better understanding of their customers, and they are often seen as a key element of the Big Data industry. However, studies show that the data they provide is often inaccurate and insufficient.
In contrast, the data collected directly from users by the Amazon Shopper Panel is not only of high quality, but also extremely comprehensive. This is one reason why Amazon is willing to pay significantly more for your shopping data than it would cost to acquire it on the data broker market. However, it's not the only reason...
A lot of data is not openly traded. For example, personalised advertising on social media platforms is usually based on the data that Facebook or Google have collected about you on their own site. Nevertheless, the platforms assign value to your data because it allows them to serve better ads. But how do we determine this value without looking at prices?
To understand this, we first need to be clear about the difference between the price and the value of data. In economics, the value of something is a subjective measure. It is determined by the importance and the benefit that an individual or a company attaches to this good and is therefore not directly measurable. The price, on the other hand, is formed on the market where economic entities trade with each other. It can provide information about the subjective value of the good for the trading partners involved.
Facebook generates higher profits by using your personal data to run targeted advertising campaigns. A researcher, on the other hand, sees value in large data sets that help them identify patterns or trends. The subjective value that these two place on your personal shopping data will vary greatly. And so does the price they might be willing to pay.
A commonly used benchmark for the relevance of your personal data to social networks is the average revenue per user (ARPU) metric. Meta (Facebook + Instagram) generates $235 in revenue per American user annually, almost exclusively for serving personalised ads. For EU citizens, it is significantly less at around USD 70 per user, although this figure probably varies greatly depending on the income of the member state.
So not showing ads to an average user from a high-income country costs Meta around $200. In fact, this figure is strikingly close to the price the company charges users for an ad-free experience.
But is that really the value Meta places on your data? In Facebook's advertising model, your personal data is just one piece of the puzzle. Advertisers pay for the data to accurately target their audience, but they also pay just for the access to that audience, i.e. your attention. It's important to distinguish what part of the ARPU is attributable to the data itself and what part the companies are paying just to have an ad show up in your feed.
According to a study by the Network Advertising Initiative, advertisers are willing to pay 2.68 times more for personalised advertising than for non-personalised advertising. This is because targeted advertising is twice as effective at converting users who click on the ad into buyers. This figure can be used for a rough estimate of the value of your personal data to the online advertising industry. Applied to Meta's $235 ARPU, this results in an annual value of about $147 (or $12.25 per month) for the average user's personal data. With an ARPU of around USD 420 for the entire online advertising industry , this results in a value of USD 263 per year (or USD 22 per month).
However, the digital advertising industry is only one sector where your data is being used (or could potentially be used). The data broker market also serves a variety of other industries that need user data to make predictions or decisions. In most cases, the data does not even have to be personalised, i.e. attributable to a specific person.
For example, foot traffic data helps inform key decisions in the real estate, retail and investment sector by measuring customer visits and origin and providing insights for shop location selection, investment decisions and marketing campaigns. The New York-based startup Thasos Group charges some hedge funds up to $1 million a year for "alternative data," such as pseudonymised location data, to help securities traders predict stock trends.
Medical data, especially when combined with advanced analytics and artificial intelligence, can not only generate profits but also contribute significantly to the development of new drugs and treatments. According to a study by EY, the value of a single patient record is estimated at over £100. Combining genomic and phenotypic data can increase this value to over £1000 per record. A curated dataset from the UK's public health system , the NHS, is estimated to be worth around £5 billion per year, or £91 (~$110) per patient.
To summarise: the price companies are willing to pay for your data depends largely on what they can expect to earn using that data. In terms of our data paradox, this means that Amazon values your purchase data at at least $10. Which makes sense - Amazon is probably the company with one of the highest ARPUs in the world. By linking your purchase data to existing information, they can not only optimise their personalised advertising for you, but also improve their overall advertising strategy for all users on the platform.
So far we've talked a lot about how your data is used and traded without your explicit knowledge or consent. Does this make you a little uncomfortable? That's understandable. We've also left out one important aspect: You.
Your opinion or concerns about your privacy are usually not taken into account when your data is being traded. However, you probably have a certain reservation value for your data and would not sell it for a lower price.
Although this value is highly individual and thus varies greatly from person to person, there are studies and estimates on how most users value their own personal data. For example, in an Italian study, the average user showed themselves willing to auction off their daily location data for €3. For app usage data, on the other hand, it was only €2. If you're reading this article, your data is probably worth more to you than it is to the average user. Studies have shown that just knowing that your data is being traded and money is being profited from, increases people's personal valuation of their data.
Data brokers sell your data for dirt cheap because they don't have to pay the costs associated with the damage to your privacy. Instead, they only incur the cost of obtaining the data, which is usually extremely low. This allows them to sell an indiviual's data for under a penny. But your privacy is still being violated with every transaction.
In economics, this is known as a negative externality. It occurs whenever a particular transaction has a negative impact on someone who had no control over the exchange. In this case, the price of a product does not reflect its true cost. This almost always leads to a market failure, i.e. a sub-optimal allocation of resources. A company profits while third parties, in this case you as an individual, pay the price. As a result, your data is sold and shared en masse without regard to the true social cost.
Regulatory or legislative intervention is often needed to correct market failures and protect individual rights. This has happened in the EU, but also in several other countries and individual states in the US. In the EU, the General Data Protection Regulation (GDPR) aims to strengthen the rights of individuals with regard to their personal data. It defines not only how companies can collect and use data, but also how they must report on it and what permissions they must obtain.
What can we learn from this for our data paradox? For data brokers, the cost of obtaining data is usually very low because they often have access to large amounts of data that they acquire at low prices or even for free. So they do not need to charge a very high price. Amazon, on the other hand, has to motivate users to provide certain information about themselves and therefore pays an incentive cost.
Businesses value your data because they can use it to generate profits. For advertisers, the data you generate in a year is worth about $263, for the medical industry potentially around $110, maybe much more. However, the price that companies pay to buy your data (e.g. from data brokers) is far below the value they derive from it, and also below the personal value you are likely to place on your own data.
The economic value that can be derived from personal data is immense. Companies are therefore increasingly willing to offer users money for their data if they are unable to obtain it in other ways, for example because regulation prevents them from doing so. As property rights shift from the companies that collect data to the users who generate it, the price of personal data is likely to increase.
Our navigation through the digital world is increasingly informed by a recognition that our data is not only personal, but also a commodity with a specific, albeit variable, value. The future potentially holds a scenario where users are not just passive participants but informed actors, actively navigating, negotiating and perhaps even capitalising on the value of their digital selves. Data marketplaces where users sell their data directly to interested companies could help create a fairer and more transparent data economy.
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