Stream Two: Marketing

Stream Leaders:

Kirstie Ball and Colin Bennett

As she approached the downtown railway station, Jessica heard the email alert from her smartphone. She noticed that the billboards outside the station were promoting political candidates in the forthcoming parliamentary election. She hadn’t yet decided how to vote – they all seemed the same these days. To her surprise the message was from a local candidate for parliament, encouraging her to check out his party’s position on the environment. She had not supported that party in the past; and she didn’t intend to now. She had, however, signed a petition through LEADNOW.CA protesting oil pipeline development, and had included her e-mail address. A political consultancy for the party had purchased consumer datasets from a data broker. It had linked Jessica’s email to her twitter feed and other social media accounts and had profiled her as having an interest in the environment. The party was sure that environmental issues might engage Jessica, and other people like her, with their policies.

This stream examines how the massive data accumulation, analytical techniques and applications associated with big data have reconstituted practices of marketing and political campaigning. We seek to find out how big data analytics used in the consumer world are now spreading into the political realm and shape ongoing political campaign activity. Numerous white papers about big data practices highlight that the key elements of the big data ‘trail’: the diffusion of personal devices which stream data to business organizations; data intensive organizations which buy and sell data to users; scientific spin offs which develop algorithms and seek practical applications for them; the world of political and business practices which seek to reach individuals and persuade them to buy or vote in a particular way. In this phase we view big data simultaneously as a scientific innovation, as a management practice and as a technique that seeks to modify human behaviour and constitute everyday life. Our research involves two case studies: consumer data donation and political campaigning.

‘Consumer data donation’ involves consumers parting voluntarily, even altruistically, with their consumption data in return for customized products and services. Such rapidly expanding initiatives exist in energy, transport and healthcare sectors. Data donation adds to the multiple data streams now analyzed by ‘big data’ marketers, but promises to circumvent ethical problems around consent, purpose specification and data limitation. This workstream determines whether socially responsible commercial big data practices, beginning with data donation and ending with procedural and distributive justice, can be achieved.

Big data resources are the key resource not only for marketing but also for political and electoral analysis. CRM has extended to ‘voter relationship management.’ Consumer demand for customization, interaction and dialogue and the reconfiguring of commercial space has entered the political realm. Citizens’ personal data are collected and processed to regulate fair and efficient elections but also in order to influence behaviours and decisions. Despite the dynamism and secrecy of this, some significant trends exist: the development of voter management databases; the integration of personal data from commercial data brokerage firms; the decentralization of data to local campaigns; the targeted sharing through social media and the ‘micro-targeting’ of increasingly refined segments of an electorate. Each trend is facilitated by big data analytics.

In both case studies, urgent questions are raised about how data are obtained, analyzed, and alter the way in which business and politics are conducted. Big data analytics compromises traditional fair information principles, particularly those of purpose specification and consent that form the foundation for national and international data protection standards. In fact, our MCRI research shows that certain data protection principles may support big data by improving data quality. Data minimization, e.g., can help organizations get the most out of their data and analyze it more accurately.

Research Workshops:

New Lines of (In)Sight? Big Data Surveillance & the Analytically-Driven Organization, June 2018

Data-driven Elections: Implications and Challenges for Democratic Societies, April 2019

Project Partners