Jun 19, 2013

Prices and Value of Consumer Data

The topic of personal data has interested me for a number of years because it is at the heart of new approaches to marketing and service development based on connected devices. I have written about the commercial potential of personal data from a business model perspective [1] for the GSM Association and introduced the concept of Stewardship and Platform Innovator strategies for companies in the mobile eco-system.

I was therefore pleased to see the Financial Times (FT) publish a series of articles on consumer data. Accompanying this series is an interactive calculator [2] that allows readers to determine a price for their personal data based on pricing benchmarks supplied by a data broker. After filling in the options for my own profile - in terms of demographics, family and health, property, activities of interest, and consumer behaviors - it appears that the price for my personal profile is about US$0.80.

Pricing information in the FT’s calculator shows how the market is developing in 2013. ZIP code (post code) data for a person in the USA for example can be obtained for a fee of $0.0005. In 2006, this data would have cost $0.50 according to a survey [3] of the main data brokers of that time.

The rapid price decline, a CAGR of over 60% between 2006 and 2013, is not merely due to lower costs of data collection. It also points to a significant escalation in the use of data for consumer profiling and marketing. This price-volume dynamic is a common feature of the IT and mobile industries.

In practical terms, companies that purchase consumer data sets are obliged to buy in bulk. If we assume, for illustrative purposes, an average price of $0.25 for a single profile then an extract of 10m consumers would cost $2,500,000, an amount that categorizes this as a large-enterprise undertaking. This data would then need to be analysed to identify promising targets to sell one or more offers (for now, let’s put aside the possibility that the data is used for other purposes, such as being part of a customer relationship management activity and hence an operational cost).

Of these targets, only a sub-set would make an actual purchase. A one-in-ten success rate would equate to an expenditure of $2.50 per customer, without accounting for the costs of data analysis, marketing and sales; a one-in-thousand success rate yields a figure of $250 per customer. Now, a one-in-ten success rate is quite optimistic because of the snap-shot nature of the data.

Time-series data would be much more powerful because it would improve the ‘richness’ of an individual consumer’s profile; it would also provide the seeds for contextual marketing. Location information is another dimension that would add to the ‘richness’ quotient of an individual's profile.

An even better approach would involve the use of “interactive-data” which could be used for closed-loop marketing. An example of this is where an offer could be made to a consumer based on his/her profile information. Their reaction would then be gauged ("interactive data" is derived by linking cause and effect data points) and revised offers could then be made. What this means is that if an adventurous traveller made a vacation hotel reservation in Timbuktu last June, that traveller would not be bombarded with hotel offers for Timbuktu in June of this year when he/she declines Timbuktu adverts or where there is credit card evidence of a hotel booking in June for a different destination.

For a rough idea of the value that consumers place on rich forms of data exchanged for services here are the results from a 2010 study carried out by Aricent and Frog Design [4]. This study attempted to quantify the value of personal data that individuals would give up in exchange for a “free” IT service.

The data comes from a sample of 180 individuals and needs to be used with caution. In addition to being a small sample, it is not clear to what extent the survey is representative of the general population. Also, “revealed value” does not necessarily correspond to a willingness to pay.

One opportunity that the FT and Aricent/Frog Design data does point to is the opportunity for service provider companies to engage with consumers. Specifically, there is a much higher perceived value to time-history and location forms of data compared with ‘static’ profile data.

Finally, even at price points of a few tens of cents per profile according to the FT calculator, a simple success-rate analysis of current day consumer data suggests that prices are very much on the high side.

[1] Connected Life: The Need for New Business Models, GSM Association (2012) 
[2] Companies scramble for consumer data, Financial Times, (June 12 2013) 
[3] How much are your personal details worth? Bankrate.com, (21 February 2006) 
[4] Smart Home Opportunity: Balancing Customer Data and Privacy (3 February 2010)


  1. Researchers from Telefonica and Telecom Italia report on recent work with a sample of users to assess how highly they value their personal data cross a range of categories - location, communication, apps and media.


  2. 21 Feb 2015

    Here's another data point on the 'price' of privacy - US$29/month.

    AT&T appears to be using deep-packet inspection for consumers that subscribe to its super-fast gigabit broadband service with the aim of tracking and monetizing their Internet activity. It seems that users can opt out of this arrangement for a monthly fee of $29.

    This may be a high-end figure as gigabit broadband is planned for roll-out in a select number of cities.


  3. 9 April 2015

    From a Q4-2014 study conducted on behalf of Orange:

    Consumers value their personal information at up to €240 as they become more aware of its worth, but need more education on the type of data that is collected by companies, new research from Orange has revealed.

    The operator found that when sharing data with a new or unfamiliar organisation, consumers value their data at €240. This falls to €170 for companies consumers trust or have ongoing relationships with.

    Consumers attributed a value of around €15 for an individual piece of data they were willing to share with a brand they knew, rising to €19 when dealing with a new company.

    ‘Family and friends’ emails’ and ‘personal income’ were regarded as the most valuable pieces of personal data, with respondent’s pricing such information at approximately €15.


  4. 27 Jan 2016 Update

    The Federal Trade Commission ("FTC") released a report entitled "Big Data: A Tool for Inclusion or Exclusion? Understanding the Issues" ("Report") that follows the FTC's September 15, 2014 workshop on "the potential of big data to create opportunities for consumers and to exclude them from such opportunities." The Report provides a road map for insurers that use big data by:

    i) Reviewing federal laws that apply when using big data,

    ii) Setting forth legal compliance questions to be considered when using big data, and

    iii) Detailing policy considerations that should be addressed when using big data.


  5. 5 August 2016 update

    Consumers price their data at £2k – Companies pay 45p

    Research from TotallyMoney.com claims consumers put a much higher monetary value on their personal data than the companies who are using it do.

    The survey asked 1000 UK consumers how much they thought their personal data was worth, with the average coming in at £2031, however companies pay roughly 45p most of a consumers information.

    More here - http://telecoms.com/474623/consumers-price-their-data-at-2k-companies-pay-45p/

  6. 22 Nov 2016 Update

    Proximus starts selling customer data reports for €700 a time

    Proximus has started selling “market research” reports containing anonymous data about its customers’ whereabouts, movements, and countries of origin for a starting price of €700.

    The Belgium-based operator said it is making available a “treasure trove” of data about the number of customers in particular locations, mapped “at least every hour” by devices on its network.

    It is also offering information about their movements in the vicinity, as well as data revealing the “origin” of SIM cards connecting to its network, from which to ascertain where a “user comes from”.

    The new big data product, called MyAnalytics, is aimed at tourist services, event organisers, marketers and those in charge of mobility management, the company said.

    The service is available via an online portal, which Proximus claims is the first such portal "with accurate research data that are easily accessible to everyone”.


  7. 6 April 2018 update

    Cambridge Analytica bought psychological profiles on individual US voters, costing roughly 75 cents to $5 apiece, each crafted using personal information plundered from millions of Facebook accounts, according to revealed internal documents.


  8. 21 Sep 2018 update

    Can you make money from selling your data?

    Tech giants make billions from our data – why can't we do the same? Sam Harrison tries out companies which pay for personal information, with varying results.


  9. 27 Sept 2018

    Do we really want to “sell” ourselves? The risks of a property law paradigm for personal data ownership.

    "While ownership implies a property law model of our data, we argue that the legal framework for our identity-related data must also consider constitutional or human rights laws rather than mere property law rules".


  10. 12 July 2019 update

    … calculating the value of user data isn’t that simple. Estimates on what user data is worth vary widely. They include evaluations of less than a dollar for an average person’s data to a slightly more generous US$100 for a Facebook user. One user sold his data for $2,733 on Kickstarter. To achieve this number, he had to share data including keystrokes, mouse movements and frequent screenshots.


  11. 30 June 2020 update

    A few thoughts on the value of personal data via the Co-Op


    What is your data worth?

    Compensating you for your data is a thing that should work, shouldn’t it? Here’s the latest effort to do that: Data Dividend Project - “Our data is our property, and if we allow companies to use it, we should get paid for it”.

    But it generally doesn’t work, for two reasons. First, because there’s an annoying value asymmetry in personal data. If you take Facebook’s company valuation ($604bn on 29 Jun) divided by the number of Facebook users (2.6bn monthly), maybe that suggests that the average user has a value of $200-ish to Facebook’s shareholders. However, your data is worth vastly less to you because it’s really hard to commercialise it meaningfully (which is what the Data Dividend Project wants to do). Though FB would say that the value’s in the aggregation of data about many people in order to create ad-targetable slices, not in the data of any individual.

    Second, there’s another, more philosophical objection to the idea: “your” data is rarely just about you. It’s usually about you interacting with someone else, or something else, or a location, or… So maybe attributing value to an individual isn’t quite as simple as people hope. Perhaps a richer “accounting of data” could look at the societal/community level instead? Data for society not individuals. Design for society not individuals.

    But the wider ideas Data Dividend represents are interesting. What if Big Tech funded schemes that paid citizens? (This sounds a bit like turning users into shareholders.) Or what if organisations with high levels of consumer trust took roles as “data trusts” on behalf of people?

  12. 9 July 2020

    Unlock the Hidden Value of Your Data via Harvard Business Review and The GovLab, based at the NYU Tandon School of Engineering.

    To help maximize data for the public good, we need to:
    i) Develop methodologies to measure the value of data
    ii) Develop structures to incentivize collaboration
    iii) Encourage data collaboratives
    iv) Identify and nurture data stewards


  13. 31 July 2020 update

    Your Company’s Data May Be Worth More Than Your Company

    Recently, with their businesses stricken by the Covid-19 pandemic, both United Airlines and American Airlines have secured multi-billion dollar loans by collateralizing their MileagePlus and AAdvantage customer loyalty programs, respectively. The third-party appraisals of their data suggest that it is worth two to three times more than the market value of the companies themselves. United’s customer data was valued at $20 billion while its market cap at the time was about $9 billion. Similarly, American’s data was valued at a minimum of $19.5 billion and up to a jaw-dropping $31.5 billion, whereas its own market cap was hovering at less than $8 billion.


  14. 19 Aug 2020 update

    The Economics of Social Data

    A data intermediary pays consumers for information about their preferences and sells the information so acquired to firms that use it to tailor their products and prices. The social dimension of the individual data - whereby an individual’s data are predictive of the behavior of others - generates a data externality that reduces the intermediary’s cost of acquiring information. We derive the intermediary’s optimal data policy and show that it preserves the privacy of the consumers’ identities while providing precise information about market demand to the firms. This enables the intermediary to capture the entire value of information as the number of consumers grows large.


    Bergemann, Dirk and Bonatti, Alessandro and Gan, Tan, The Economics of Social Data (March 2, 2020). Cowles Foundation Discussion Paper No. 2203R, March 2020, Available at SSRN: https://ssrn.com/abstract=3548336 or http://dx.doi.org/10.2139/ssrn.3548336

  15. 24 July 2022 update

    Information Frictions and Heterogeneity in Valuations of Personal Data

    As policymakers explore introducing data dividends and companies experiment with new business models around data markets, it is essential to understand the economic valuations of consumers’ personal data. In this paper, we provide evidence documenting substantial dispersion and heterogeneity by gender, race, and income in users’ data valuations for their social media data through incentive compatible studies on a representative sample of US internet population as well as a data conscious sample. Marginalized individuals (women, Black, and lower income) have significantly lower data valuations in both samples even after controlling for income and education. Through a randomized intervention, we find evidence that participants respond to information giving them a signal about the value of their data from legal settlements and revenue projections.

    Specifically, we find that low WTA users in both samples revise their valuations upwards towards the settlement amount while high WTA users do not revise downwards. These revisions significantly reduce the observed heterogeneity in baseline valuations for marginalized individuals. Dispersion and heterogeneity in valuations, however, persist following the information treatments, consistent with theories of data and privacy valuations that construed them as amalgams of objective and subjective factors.

    Our research is not without limitations. First, we explore data from only one platform, that is, Facebook, and analyze the value for the entire stock of data, which includes both sensitive data (private messages, photos) and less sensitive data (public likes). Although studying Facebook data is important because it is the largest social media platform in the world, it would be informative to replicate these results for other types of data that might range in their degree of sensitivity.

    The aim of the paper is not to focus on an exact dollar valuation for data; rather, we focus on comparing valuations across different demographic groups. Future research could study how firms value user data and how these valuations vary based on user characteristics.


  16. 17 February 2023 update

    Capitalizing on the pandemic explosion in telehealth and therapy apps that collect details of your mental health needs, data brokers are packaging that information for resale, a new study finds. There’s no law stopping them.

    One company advertised the names and home addresses of people with depression, anxiety, post-traumatic stress or bipolar disorder. Another sold a database featuring thousands of aggregated mental health records, starting at $275 per 1,000 “ailment contacts.”


  17. 18 Sep 2023 update

    Estimating the Value of Offsite Data to Advertisers on Meta


    We study the extent to which advertisers benefit from data that are shared across applications. These types of data are viewed as highly valuable for digital advertisers today. Meanwhile, product changes and privacy regulation threaten the ability of advertisers to use such data. We focus on one of the most common ways advertisers use offsite data and run a large-scale study with hundreds of thousands of advertisers on Meta. Within campaigns, we experimentally estimate both the effectiveness of advertising under business as usual, which uses offsite data, as well as how that would change under a loss of offsite data. Using recently developed deconvolution techniques, we flexibly estimate the underlying distribution of treatment effects across our sample. We find a median cost per incremental customer using business as usual targeting techniques of $43.88 that under the median loss in effectiveness would rise to $60.19, a 37% increase. Similarly, analyzing purchasing behavior six months after our experiment, ads delivered with offsite data generate substantially more long-term customers per dollar, with a comparable delta in costs. Further, there is evidence that small scale advertisers and those in CPG, Retail, and E-commerce are especially affected. Taken together, our results suggest a substantial benefit of offsite data across a wide range of advertisers, an important input into policy in this space.


  18. 2 May 2024 update

    Estimating Consumer Welfare Gains from Free Online Services

    In The Digital Welfare of Nations: New Measures of Welfare Gains and Inequality (NBER Working Paper 31670), Erik Brynjolfsson, Avinash Collis, Asad Liaqat, Daley Kutzman, Haritz Garro, Daniel Deisenroth, Nils Wernerfelt, and Jae Joon Lee use a survey-based experiment to estimate the welfare impacts of digital goods. The researchers use Facebook’s internal survey platform to administer a large-scale incentivized online choice experiment to 39,717 Facebook users across 13 countries. They query users about their preferences regarding ten digital goods — Facebook, Twitter, Instagram, WhatsApp, Snapchat, TikTok, Google Search, Google Maps, YouTube, and Amazon Shopping — as well as the amount of money they would be willing to accept in exchange for deactivating their Facebook accounts for one month. They use the resulting survey data to calculate the consumer welfare gains generated by each of these products.

    The survey data suggest that among Facebook users, the ten digital goods generate $2.52 trillion in consumer welfare across the 13 countries, corresponding to 5.95 percent of the countries’ total GDP, and ranging from $1.29 trillion in the United States to $13 billion in Romania. The gains represent a higher share of income in lower-income countries as well as a higher share of income among individuals with lower income and wealth. The researchers therefore conclude that freely available digital goods reduce disparities in consumer welfare both within and across nations.

    The results suggest that most of the welfare gains from using these digital goods accrue to consumers and not to the platforms. For example, the researchers estimate that the user value generated by Facebook is $284 billion for the 13 countries studied, more than twice as much as Meta’s $115 billion in advertising revenue from Facebook, Instagram, and WhatsApp globally.

    Because free digital goods generate substantial welfare for consumers but are not included in GDP, economic growth and labor productivity — typically defined as GDP per hour worked — have been underestimated in recent years, at least for the countries in the study’s sample. Traditional measures of output and productivity do not reflect the full contribution of digital goods.

    The researchers’ findings are not driven by consumers who spend an outsize amount of time on digital platforms. The estimated welfare gains are distributed across a broad range of users, not concentrated among those who are very active online.


  19. 8 May 2024 update

    Walmart shopper data will soon feed targeted ads on Disney Plus and Hulu

    The new Walmart and Disney deal will theoretically match users’ data without violating their privacy.

    Advertisers can now use Walmart’s shopping data to target Disney’s streaming portfolio, which includes Disney Plus and Hulu, Adweek reports. The news comes after the companies announced a partnership between Disney Advertising and Walmart Connect, the retailer’s media business, on Wednesday.

    As a part of the deal, Walmart advertisers will be able to match the retailer’s shopper data with Disney’s proprietary Audience Graph tools, helping them target audiences and measure data better. Datasets will be combined using clean-room technology so that — theoretically — user data can’t be shared with other external parties.


  20. 7 June 2024 update

    Pluralistic: Surveillance pricing (05 Jun 2024)

    For example, Plexure boasts that it can predict what day a given customer is getting paid on and use that information to raise prices on all the goods the customer shops for on that day, on the assumption that you're willing to pay more when you've got a healthy bank balance.

    The surveillance pricing industry represents another reason for everything you use to spy on you – any data your "smart" TV or Nest thermostat or Ring doorbell can steal from you can be readily monetized – just sell it to a surveillance pricing company, which will use it to figure out how to charge you more for everything you buy, from rent to Happy Meals.