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So many Big Brothers… II

The amount of data I am providing to Amazon is staggering … and most of it is being done below of my level of perception. Image credit: Sarah Grillo/Axios


“Born to Shop”. I remember seeing this on a t-shirt long long time ago in Carnaby Street, London, at a time when having writings on a t-short was new and eye catching. At those times Internet was yet to come and computers were nowhere to be seen in everyday life. Today the sentence is truer then ever but now we have merged shopping with the cyberspace and as we shop, be it in the cyberspace or in a store we create data that are no longer under our control.

Cyberspace shopping is obviously creating data and I’ll come back to this in a moment, but also shopping in a brick and mortar store is creating data and this may not be perceived. You enter into an apparel store and start “surfing” the shelves. Most likely there are security cameras watching you and it is possible, probable actually, that the images captured are also used by a machine learning software aiming at profiling you (what you seem to be interested in) and at assessing the impact of specific apparels and placement of those apparels on customers. The aim is two fold: prompting you with some shopping tease and find out the best way to display the goods. I have been working with retailers in the past to find ways to make the best use of the physical presence of a customer in the store. That included recognising the customer (face recognition but nowadays this is also being done through electronic tagging, using the store fidelity card given to the customer or through the “store” app on the customer smartphone) and informing the store assistant of the name of the person (if on file, which for customer having a store fidelity card is always the case) of previous shopping and of the interest she is showing in looking at certain apparels. Also, there is the possibility to “customise” large displays around the shop so that when that customer is nearby the most appropriate commercial is shown to grab her attention.

As you see, just by being in a store (and even window-watching) we generate data about ourselves (the time we are there, what we are looking at… and of course if we buy something what we have bought). These data are recorded in our personal data record (personal in the sense that it is about us but those date are not under our control).  This is not just about apparels store. When you check out at a supermarket your credit card (i.e. you name) is associated with the stuff you bought and this, over time, creates a quite accurate profile (when you are usually shopping at that supermarket, what you buy and the frequency of buyiing a certain product…). All these data are crunched by data analytics and may result in mails just the day before you are planning to return to that supermarket with some teasing on some products (the goal is clearly to increase your spending and possibly increase it on products with higher margin. Actually, this is not true: a supermarket director I spoke with recently told me that I have it all wrong: they just want to deliver better service, suggesting what we may like and could have missed. It is not about increasing their bottom line but about making me happier. How nasty of me to have thought differently).

Store fidelity cards, biometric identification, credit card identification are the starting point that activate data mining, data crunching, machine learning and smart selling. In the process the store (often store chain) accrue a tremendous amount of data on myself. Special query languages like Hive and Pig have been crafted to make the most of this growing set of customer retail data. I am starting to think that it would be just fair if these guys would share my data with me!

Now, if a brick and mortar store can get so many data on myself just imagine how many data are harvested by on-line store!

Let’s consider Amazon as the iconic on-line store. In these last two months they have become even bigger as people turned to the web as the only open shop in their neighbourhood.

On-line stores are not constrained, in general, by geography, their marketplace is the world. More than that. Also their portfolio is not constrained by a physical space. Their warehouses can serve a broad area and therefore stock is easier to handle. Besides, large on-line stores may not need a warehouse for many  products in their portfolio: they can have the manufacturer send the product directly to the customers, no need for a warehouse in between. Amazon has some 12 million products in its direct sales, over 350 million if one takes into account the thousands and thousands of resellers using the Amazon platform.
In terms of “my data” this means that large on-line stores can harvest much more data from me since I will be using their store for a broader set of products (not necessarily buying them, browsing their virtual shelves is as good as buying from the point of view of harvesting data).

Amazon, I am using it as an example, harvest my data from:

  • my profile – when I register on Amazon I am providing info like my name, my home address, my contact -phone/mail, my card/payment method….;
  • my browsing of their website (when you come back to their website you’ll see listed the items you explored in previous visits). They accrue data not just on what I see, also on what info I look at (I am interested in reviews of the product, on their technical data, how much time I spend on a page….), on when I look at them, from where, from what device;
  • my search queries (how I call something, how the answers to my queries meet my needs…);
  • my wish lists – what and when, when I order an item from a wish list…;
  • my reviews and comments to other people’s review;
  • my order history (when, what, from where, how I paid, where I asked the product to be delivered, complains, returns, how was the order related to some suggestions from Amazon, how the order is in line with the spending trend in a community…);

Amazon is selling more than “physical” products. They sell (prime) music, (prime) movies, (Kindle) books. They get to know what I like, when I have free time to dedicate to music/video… In addition they have now gained access to my home with Alexa that listen to what I say through the day. Amazon declares that Alexa is not storing queries nor listening to my chatting unless it is directed to it, and this is likely to be true. However, my queries are not processed locally, in my home, they are processed in the Amazon clouds…

Take all this into account and Amazon likely they know much more about me than myself!

About Roberto Saracco

Roberto Saracco fell in love with technology and its implications long time ago. His background is in math and computer science. Until April 2017 he led the EIT Digital Italian Node and then was head of the Industrial Doctoral School of EIT Digital up to September 2018. Previously, up to December 2011 he was the Director of the Telecom Italia Future Centre in Venice, looking at the interplay of technology evolution, economics and society. At the turn of the century he led a World Bank-Infodev project to stimulate entrepreneurship in Latin America. He is a senior member of IEEE where he leads the New Initiative Committee and co-chairs the Digital Reality Initiative. He is a member of the IEEE in 2050 Ad Hoc Committee. He teaches a Master course on Technology Forecasting and Market impact at the University of Trento. He has published over 100 papers in journals and magazines and 14 books.