Data collection is a market process that developed particularly in recent years, even though firms always needed information about their customers in order to manage their products and to create new ones, as well as to choose the best price (or prices) to apply. Until a few years ago, however, this collection was made in a “discrete” way: every time the customer came into contact with the firm, the latter collected information about the former. This contact could be a physical encounter, a telephone call, an email exchange. However, this discrete paradigm is disappearing, and a continuous paradigm is emerging. Data collection cannot be considered a step-by-step process anymore, since it is now a flux. The collection of data is continuous, since consumers share their information and indirectly communicate their preferences in almost every moment, thanks for example to smartphones and smartwatches, but also to laptops and computers.
Firms also use data for targeting their advertising and products to groups of customers, namely treating in the same way consumers with “similar enough” characteristics. This strategic action is not new, either: individual data is (and was) used to create group-specific strategies, so individual data is (and was) social data, as claimed by Bergemann & Bonatti (2019). However, the flux of data makes this strategic behaviour more and more convenient for the firms, since the groups of customers can be more precisely defined thanks to the everyday larger quantity of data, and this leads to better pricing, advertising, and product-design strategies.
Data has a value that is not directly exchanged in the market, since the firms enjoy benefits coming from the information shared by customers and this value is either not paid to the customers or paid only in part (Bergemann & Bonatti, 2019). This is the case when the firms offer services to their customers in exchange for their data, which are freely shared with the firms when the latter use a smartphone app or when they search something in a search engine site. The “price” of data – its value for the customers – will decrease as the ability of the firm to create groups of customers increases, or as the customers present more similar characteristics, since the profile of a group or of a customer will have some traits that can be used to create and study the profile of another group/customer; this will lead to an over-sharing of data, given the low price of data and of privacy (Acemoglu et al., 2019).
The bigger the firm, the larger its ability to process data and to identify groups of individuals and their specific needs. In addition to that, the services a big firm offers for free to its customers have a cost which is very small with respect to how much the firm itself can gain from the data it can collect from a larger number of customers (Bergemann & Bonatti, 2019). Consequently, a big firm can extract a higher value from data and can use it to become even bigger.
But which are the firms’ strategies that are particularly improved by the right use of data?
Availability of data allows the firms to create pricing strategies that are able to extract value from the customers. These pricing strategies consist, for example, in a segmentation of the market, namely the ability of a firm to apply different prices to different groups of agents, exploiting the similar traits of the customers that define their willingness to pay for a certain product or service. In particular, the exclusivity of data – the fact that a firm owns data which are not shared with other firms – plays a key role in this kind of strategy, giving to the firm with an advantage of data availability the ability to act as the leader in a market in fixing the prices, leading to an increase in competition, even though in some cases it could also lead to the emergence of cooperative behaviour among the firms (Gu et al., 2019).
Thanks to the large amount of data firms can collect, new products can be developed too. In particular, customers can get involved in sharing their data, since firms can use it to determine the customers’ needs and how to meet these needs with their current product or with newly developed ones. Data generated by customers can be used to develop new ideas, design new products, and test how these products perform in the market (Zhan et al., 2018).
Finally, data availability permits the development of new marketing and innovation strategies. Data and Big Data analytics are the (new) tools that managers have at their disposal to study how their customers behave, in order to adapt the strategies of their firms in regard to marketing and advertising, as well as to defend their brand. In other words, data is a decision tool for what concerns both communication and product innovation, especially for digital native firms, developing from a pure tool of descriptive analysis to a tool of prediction of market behaviour (Johnson et al., 2019).
All these strategies use the data generated as a by-product of other production and advertising actions of the firm, i.e. as an asset that can be exploited by a firm in order to obtain better market performance, to create better products, or to apply higher prices. However, data may also be seen as the final goal of production, as in the case of some particular service firms and platforms, for which data is at the base of the whole development process of their products. The (free) work of the customers that share their data, in particular, makes it possible to perfect the product itself and, at the same time, creates an asset of data that can be used and/or sold by the firm (Trabucchi & Buganza, 2019).
Thus, data is used (and treated) by the firms as an asset, as capital, even though it comes from the labour of customers that create it. The switch from the data as capital view to the data as labour view could be beneficial for the customers, but this transition should result from a collaborative series of action of firms, customers, and government (Arrieta Ibarra et al., 2018), and it should also consider indirect effects that may indeed reduce wellbeing (Acemoglu et al., 2019).
Acemoglu, D., Makhdoumi, A., Malekian, A., & Ozdaglar, A. E. (2019). Too much data: Prices and inefficiencies in data markets, NBER Working paper series No. 26296.
Arrieta Ibarra, I., Goff, L., Hernández, D. J., Lanier, J., & Weyl, E. G. (2018). Should We Treat Data as Labor? Moving Beyond “Free.” American Economic Review, 108, 38–42.
Bergemann, D., & Bonatti, A. (2019). The Economics of Social Data: An introduction, Cowles Foundation Discussion Paper No. 2171R.
Gu, Y., Madio, L., & Reggiani, C. (2019). Exclusive Data, Price Manipulation and Market Leadership, CESifo Working Papers No. 7853.
Trabucchi, D., & Buganza, T. (2019). Data-driven innovation: switching the perspective on Big Data. European Journal of Innovation Management, 22(1), 23–40.
Johnson, D. S., Muzellec, L., Sihi, D., & Zahay, D. (2019). The marketing organization’s journey to become data-driven. Journal of Research in Interactive Marketing, 13(2), 162–178.
Zhan, Y., Tan, K. H., Li, Y., & Tse, Y. K. (2018). Unlocking the power of big data in new product development. Annals of Operations Research, 270(1–2), 577–595.