Big data, big money: how companies thrive on informational resources

Information oils the economy – as we know since the path-breaking research of George Akerlof, Michael Spence and Joseph Stiglitz in the 1970s – and information can be extracted from data. Today, increased availability of “big” data creates the opportunity to access ever more information – for the good of the economy, then.

But in practice, how do companies extract value from this increasingly available information? In a nutshell, there are three ways in which they can do so: matching, targeted advertising, and market segmentation.

Matching is the key business idea of many recently-created companies and start-ups, and consists in helping potential parties to a transaction to find each other: driver and passenger (Uber), host and guest (Airbnb), buyer and seller (eBay), and so on. It is by processing users’ data with suitable algorithms that matching can be done, and the more detailed are the data, the more satisfactory the matching. Firms’ business model is usually based on taking a fee for each successful transaction (each realized match).

Targeted advertising is the practice of selecting, for each user, only the ads that correspond at best to their tastes or practices. Publicizing diapers to the general population will be largely ineffective as many people do not have young children; but targeting only those with young children is likely to produce better results. Here, the function of data is to help decide what to advertise to whom; useful data are people’s socio-demographic situation (age, marriage, children…), their current or past practices (if you bought diapers last week, you might do that again next week), and any declared tastes (for example as a post on Facebook or Twitter). How this produces a gain is obvious: if targeted adverts are more effective, sales will go up.

Market segmentation is a practice that dates back to the mid-nineteenth century and that big data enhance. It involves charging different prices to different consumers for (essentially) the same good: for example, it is customary to offer price reductions for students. The goal is to increase sales, by making a good or service more affordable to categories of the population who would not be able to pay for it otherwise, while keeping prices high for those willing to pay. With big data, segmentation can be done very finely: not just divide the population into two groups – such as students and professionals – and charge two prices, but charge a different price to each potential customer based on their ability and willingness to pay. Data, of course, must be sufficiently detailed to infer the “right” price, so perfect segmentation is still closer to a dream than to reality – but firms are getting closer and closer to it.

A whole economic ecosystem turns around these three data-intensive approaches to doing business. To facilitate targeted advertising on its platform, Google sells advertising spaces through a sophisticated system of auctions; so do Facebook and other platforms, but I will not detail this here (it is a whole, and thriving, field of research in economics).

While creating most-welcome earning opportunities, this system is not without risks. Matching solutions often involve undue appropriation of value by platforms to the detriment of users and even more, of (explicit and implicit) workers; I discussed this problem in a previous post. Targeted advertising and segmentation, in turn, raise serious privacy concerns, which again I highlighted earlier.

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