Discovering GeoDB 1. The power of place

“There are three things that matter in property: location, location, location”
Attributed to Harold Samuel [1]

The tricolon “location, location, location” is a cliché widely used by property experts, having been used in print as early as 1926 [1]. It was popularized thanks to Harold Samuel [2], who acquired a small property concern called Land Securities Investment Trust at the end of the Second World War and thanks to his ability to identify great business opportunities in the real estate sector, he was able to grow it into one of the largest companies on the London stock exchange [2, 3].

You may not know it, but several companies are using your private data right now. And there is something they’re particularly interested about, your location. To know their environment, study their competitors, discover opportunities and trends, optimize their supply chain and for many other things, companies are analyzing large volumes of data on a daily basis, and in many cases, location is absolutely crucial to contextualize the data and carry out useful analysis.

Probably the above does not surprise you, or maybe it does, but you’re likely to be unaware of the fact that several studies have proven that location is the private data that most concerns us [4, 5, 6]. And how much are companies paying you for using your most delicate private information? We don’t want to disappoint you, we know that there are several types of rewards, but we just want to ask you this question so you can think about it and about its implications.

This is the first monographic blog post of the series Discovering GeoDB. In it, we’ll review several papers that have revealed the psychological value that we associate to our private location data. Later, we’ll review other studies that have shown the great value that this information has for some companies. It’s likely that after reading this blog, your answer to the previous question changes significantly.

But, what do you know about this?

Before talking about private location, we want to write some lines about our background so that you don’t end up thinking that we don’t have a dog in this fight.

Our team is made up of members who have been working in this sector for years, with experience in platforms that are being used worldwide. We put user’s privacy first and this has allowed us to obtain very valuable information regarding users’ concerns about their privacy.

We’ve heard multiple voices in multiple languages, we’ve reflected, we’ve investigated and we’ve detected the need to develop a new paradigm to transform the current situation.

Our experience has made us identify a solution that meets a need, and this process has made us jump into this exciting project.

Demand control over your private data

“Don’t sell your soul to buy peanuts for the monkeys.”
Dorothy Salisbury Davis [7]

Surely you’ve seen some commercials of Mastercard’s Priceless campaign. It’s what happens when an advertising campaign lasts for more than two decades. And would you say that your privacy is priceless? So, why do you sell it so cheaply?

You may think you’re not selling your privacy, but we encourage you to think about it. Do you have any loyalty card? Do you use any social network? Do you use a smartphone? Do you use any internet browser? Do you use the Internet? We regret to say that whether you like it or not, while you read this line you are selling a bit of your privacy, a bit of your life.

If someone is questioned about how much money they want for their private data, they’ll probably respond with a high sum of money or they could even be offended by our offer. Is it enough to camouflage it under a loyalty program that rewards their private data with merchandising brand products to end up with a happy private data provider, with a happy customer? We did not invent it, it’s a fact [5, 8].

Companies know about this bias in our behavior and exploit it in their favor. They need our private data and they know that we’re reluctant to give it to them, so they adapt their speech so you think they give you something for free that’s very valuable. You know what they normally say, if you’re not paying for it, you’re probably the product.

Having said the above, have you ever thought about how companies use your private data? Usually they do not plot something murky, rather the opposite. It’s difficult to make a precise classification, but we could say that our data is useful for things like i) offer better services, ii) optimize supply chains, iii) increase the clientele or iv) discover new opportunities.

Things like these allow them to:

  • Offer the best mobile telephone coverage.
  • Minimize costs to give low prices.
  • Build supermarkets and gas stations in busy areas with easy access.
  • Optimize navigation routes and estimate their duration.
  • Give the best roadside assistance coverage.
  • Offer fast and economic parcel services.
  • And many other things [9, 10].

But let’s not think only about the private sector, let’s also think about everything that is possible in the public sector. Using these tools in public administration allows them to:

  • Build hospitals and schools in the best locations.
  • Create appropriate evacuation routes.
  • Find the best sites for emergency services.
  • Deploy the necessary infrastructures.

For your peace of mind, you should also know that nobody is interested in knowing your individualized data, in fact, the data of an isolated individual is not useful for this type of analysis. It’s the aggregation of data from multiple individuals that allows to respond to situations like the previous ones.

The crux of the issue is not how our private data is used, but how it’s collected. It’s inadmissible that companies can capture and exploit our private information without our consent. And it’s not ethical that, making use of psychological tricks, they can generate great economic benefits at the expense of paying the raw material with peanuts.

The collection and use of private data based on deception can not continue to be the norm. This approach generates suspicion and rejection from users and what is more important, opens privacy gaps [11].

It’s necessary to transmit a clear message to users so that they understand that providing their private data using the right channels is possible:

  1. Give them the power to decide how to use them.
  2. Avoid privacy risks.
  3. Obtain a fair economic reward.
  4. Enjoy optimized services.

We must demand control over what we sell, under what conditions and at what price.

The money that we demand in exchange for our private location data

For how much money would you sell your private location data every five minutes during the next month for? It’s a delicate question, isn’t it?

In 2013, the Financial Times published an interactive calculator [12] that allows to determine a price for our personal data based on pricing benchmarks supplied by a data brokers [13].

Playing with the calculator we obtain values ranging from half a dollar to about two dollars. For simplicity, let’s say that the average cost according to that calculator is around $1.

Buyers are not interested in individual users’ data, but large datasets with the data of millions of users. Based on the ratio, 1 person = 1 dollar, it is easy to estimate the cost of the data of a million of users.

However, the previous cost is computed based on the price given by Financial Times but, would you sell your personal data for $1?

The problem is that companies ask and respond at the same time, asking the question and telling you the answer. Setting that price is a way to self-justify the use of indirect formulas to capture user data. “How can I convince a user to give me their private data in exchange for a dollar?”.

It’s in our own interest to answer the question for ourselves, and researchers have already tried to give an honest answer to it.

In 2010, Ancient and Frog Design carried out an study to quantify the value of personal data that individuals would give up in exchange for a IT service and the results are summarized below [13].

The study shows two interesting conclusions:

  1. We want much more than a dollar in exchange for our personal data.
  2. We value certain kinds of information more highly than others.

Removing from this study the data that can only be revealed once, i.e., social security number, government id, credit card information, social profile, contact information and demographic information, we find that the most valued private data generated on a daily basis are:

  1. Digital communication history. $59
  2. Web search history. $57
  3. Physical location history. $55
  4. Web browsing history. $52

This difference in the appreciation of the value of the information has been corroborated in subsequent studies. Specifically, Staiano, J. et al. in their 2014 paper ‘Money Walks: A Human-Centric Study on the Economics of Personal Mobile Data’ [6] found evidence that “location is the most valued category of personally identifiable information … and that bulk information is valued much higher than individual information”. It should not surprise us that, with the proliferation of smartphones, which continuously accompany us wherever we go, the location has become the most valued category of private information.

But is that the price of our private data? No, that is the price for which some users were willing to disclose their private data under certain conditions.

The value for which we’re willing to disclose some of our private location information was studied in 2005 by Danezis, G. et al. [4] and in 2006 by Cvrcek, D. et al [5].

In [4] was carried out a study in which, by using deception, the authors asked several people if they would be willing to provide their private location data in exchange for money. The study allowed them to measure different factors such as how many users were interested, what amount of money they demanded or how the expected use of the data influences the price.

This study showed some interesting results, but it was done in a relatively small scale at Cambridge University so, in [5] a new study was carried out using “a sample of over 1200 people from five EU countries, and used tools from experimental psychology and economics to extract from them the value they attach to their location data”. The size of the sample allowed them to “compare [the] value across national groups, gender and technical awareness, but also the perceived difference between academic use and commercial exploitation.”.

We highlight below some interesting results found in this study:

  • Women are possibly more sensitive to what the collected data may be used for.
  • The participants did not perceive their unusual movements as more sensitive than their every-day behaviour.
  • The participants where more sensitive to the purpose of the data collection, than the duration and quantity of data collected.
  • There are huge differences among countries in the sensitivity to the time extension.
  • Basic results confirm results of the Cambridge study in the overall value of bids — e.g. medians of bids are 20 GBP and 43 EUR (i.e. about 28 GBP at the August 2006 exchange rates) for non-commercial use of data, respectively.

Well, it seems that we expect to receive much more than $1 for our personal data. Do not you think? And we should consider that these results are from 2006, since that year prices have gone up, the awareness of users regarding mobile technology has increased and location has become the most valued category of private information. We’re convinced that currently the price will be higher, but we do not want to suggest a price, you’ll decide yours.

It seems that we’ve raised a bit the amount of money that user should receive in compensation for providing their private location data, but is user’s private location so valuable?

Why your private location is valuable

The big data market was worth $125.000.000.000 in 2015 [14], which provides a sense of just how much financial capital enterprises are pouring in to data operations. But big data is not just about collecting and processing huge volumes of data. If the data you are storing and analyzing is full of inconsistencies, inaccuracies or other issues, the analytic results you obtain will be misleading [15].

It’s estimated that 80% of business data contains a location component so it’s critical to understand how it affects businesses. Their analysis can provide insights that supports and improves decision-making procedures in a lot of business aspects. Analyzing data by location allows businesses to ask and accurately answer questions such as “where are my customers?” or “how far are my customers from my location?” as well as “how well does my supply chain service those customers?”. [10]

Thanks to the ubiquity of smartphones, it’s possible to bring order to the data to be analyzed by adding location information to it; if you’re able to contextualize your data in this way, you can “reveal relationships between data sets that might not have otherwise been obvious or easy to ascertain and, through location analytics, arrive to the kind of insights that get reflected in the bottom line”. [9]

The last part of the previous paragraph is taken from an article published in 2017 by Forbes Insight in which they conducted several interviews with different executives who are using localization in their big data analysis. In the article, the executives talk openly about how this information is, in many cases, a critical element to perform an analysis that does not provide misleading results.

We would like to highlight some of the parts of the interview with Nigel Lester, managing director for Pitney Bowes in the Australia-New Zealand region, since we believe that they masterfully defined the importance of localization for big data analysis.

“The real strength of location data is that it becomes a common link between seemingly disconnected silos of business data … Data that doesn’t seem to have any obvious relationship can be contextualized by location. It could be your customer locations versus your competitor’s locations — data sets with no obvious link, but if you start to geo-enrich them, you may find that relationships begin to emerge and you’ll be able to build out a more holistic and valuable view of your customers.” [9]

We finish the previous section with the question “is user’s private location so valuable?”. We let you answer this question for yourselves.

In the next entry we’ll deepen on how it’s possible to find the balance between the economic interests of the users and the economic interests of the companies.

References:

  1. https://www.nytimes.com/2009/06/28/magazine/28FOB-onlanguage-t.html
  2. https://en.wikipedia.org/wiki/Harold_Samuel,_Baron_Samuel_of_Wych_Cross
  3. http://www.theprisonerandthepenguin.com/?p=558
  4. Danezis, G. et al. How much is location privacy worth? In WEIS, volume 5. Citeseer, 2005.
    http://infosecon.net/workshop/pdf/location-privacy.pdf
  5. Cvrcek, D. et al. A study on the value of location privacy. In Proceedings of the 5th ACM workshop on Privacy in electronic society, pages 109–118. ACM, 2006.
    https://www.esat.kuleuven.be/cosic/publications/article-845.pdf
  6. Staiano, J. et al. Money Walks: A Human-Centric Study on the Economics of Personal Mobile Data, 2014 ACM Conference on Ubiquitous Computing, pp. 583–594. ACM, Seattle. https://arxiv.org/pdf/1407.0566.pdf
  7. https://www.brainyquote.com/quotes/dorothy_salisbury_davis_221682
  8. https://medium.com/@dfcatch/the-psychology-of-loyalty-programs-3a9741c43f41
  9. The Power of Place: How Location Intelligence Reveals Opportunity in Big Data.
    https://www.forbes.com/forbesinsights/pitney_bowes_power_of_place/
  10. The eureka moment: Location intelligence and competitive insight.
    https://www.forbes.com/forbesinsights/location_intelligence/index.html
  11. https://en.wikipedia.org/wiki/Facebook%E2%80%93Cambridge_Analytica_data_scandal
  12. http://ig-legacy.ft.com/content/f0b6edc0-d342-11e2-b3ff-00144feab7de#axzz5MudwqLJm
  13. http://www.more-with-mobile.com/2013/06/prices-and-value-of-consumer-data.html
  14. https://www.forbes.com/sites/gilpress/2014/12/11/6-predictions-for-the-125-billion-big-data-analytics-market-in-2015/
  15. http://blog.syncsort.com/2017/03/big-data/quality-data-big-data-worth/