This chapter is a continuation of the preceding chapter, where the author discussed the obstacles encountered by established companies when wishing to transform their business models and provides suggestions for improvement of their corporate governance to better navigate the digital transformation. In this chapter, the author provides practical rules of the road for how established companies can monetize their data including some pitfalls for established companies and discusses a number of ethical dilemmas that companies have encountered in practice when implementing new digital technologies and services.
TopProvide Added Value With Digital Services And Customers Will Provide More Data
Despite all obstacles, established companies certainly do not have an impossible starting position compared to the newcomers. They usually have large numbers of established customer relationships with associated customer data and often enjoy the trust of their customers. Training algorithms requires a great deal of high-quality data, something that established companies have a head start on. With the Cambridge Analytica data analysis scandal, many people are now aware that the price tag of new digital business models and personalised offers is usually their privacy. Here are opportunities for established companies. You can use data in a fair way. This requires a different mindset, whereby the higher expectations of consumers vis-à-vis established companies are not perceived as an (unjustified) double standard (see section ‘Uneven Playing Field Or Lack Of Sympathy?’ in the preceding chapter “The impact of IR4 on corporate governance of listed companies”), but are embraced as a positive. High expectations mean a high level of confidence, which is a positive distinguishing factor in the competition with newcomers (Accenture, 2017).1
The most important thing is that customers feel that their data are being used to make the services better for them, and not just for companies to earn extra income from them through personalised advertising. Trust, good security and privacy are strong assets online. Research shows that people are willing to share data when personalised value-added services are provided in return (Accenture, 2019, p. 32). In contrast, lack of data security and privacy are the main causes of loss of trust (Accenture, 2019, p. 36).
A study by the Dutch Consumers’ Association (Consumentenbond, 2019) shows that consumers are relatively satisfied with their banks, although there are major differences, and the big three (Rabobank, ABNAMRO and ING) score the lowest. The main reason for this is the ratio of costs and the fact that customers of these banks feel that the bank’s self-interest and making a profit are paramount. This outcome is really the worst possible combination of the old and new worlds and therefore is a recipe for disruption. Rather than capitalising on their existing relationships and their trust, their online services actually undermine their potential competitive advantage. The Dutch online bank Knab stands out in a positive way. It is no coincidence that this online bank was set up outside the established company (Aegon) with a user-centric business model. Knab, for example, gives a notification if a better deal is available for its customers (for example, higher interest rates for savings accounts, a better mortgage rate or insurance premium), even if that other product does not belong to Knab itself.
In any event, established companies will have to follow this route. The consent requirements have been tightened under GDPR. Permission must be given freely, which means that, for example, access to an online service may not be made dependent on giving permission for profiling for commercial purposes (for example, by including this in the website conditions or privacy policy) (Art. 7(4) GDPR). If permission must really be freely given, then you must make it very attractive for the consumer to give permission. In other words, you will have to offer an added-value service that consumers want so much that they give you permission to collect and process their data for it. Large tech companies are constantly inventing new services with the central aim of generating even more data and thus being able to profile customers. This used to be mainly online, but now these companies are focusing on services and connected products in the offline world to generate data, such as our cars (connected and soon autonomous cars), our living environments (smart homes) and our bodies (wearables and implantables).