How Data is used in CRM strategies

The Case / Scenario

Customer relationship management (CRM) deals with the processes and systems that support business strategies to build long-term and profitable relations with customers. Due to the rapid development of the digital world, marketing models and CRM practices have changed. Access to customers’ online data (through social networks, search engine history, cookies and other tracking systems) allow companies to gather a variety of information about customers. Such access also allows companies to create cloud systems with data, and strategize and automate CRM practices. Data engineering blends data from all sources and builds machine learning models that will accurately predict the propensity of a customer to “churn” [cease to be a customer] as well as predict the propensity to buy a new product. The Finnish telecommunications provider Elisa serves 2.8 million customers with 6.2 million subscriptions and is market leader and pioneer in new network technologies and innovations. Based on this company, the use study investigates which ethical issues arise in the use of Smart Information Systems (SIS), the combination of Big Data and artificial intelligence, in CRM and how they can be best addressed.

Ethical Issues

Ethical issues identified in the case study largely matched those found in the literature, particularly:

  • Intrusion on Autonomy: There is a concern that the autonomy of customers is being undermined through the employment of data analytics. Such techniques can expand, constrain or alter people’s choices and behaviours.
  • Privacy: Internet access using social media platforms allows for personal data to be disclosed to the platforms and their clients, without the knowledge or intention of customers, damaging their privacy. Some marketers find social media an effective means of capturing consumer data both directly and indirectly, either with or without permission.
  • Bias and Manipulation: Biases in digital systems are more difficult to recognize than in physical systems. This is problematic, given that people tend to trust the decisions of automated systems (automation bias). Furthermore, manipulation of customers can occur if there is a lack of transparency in how these systems function.
  •   Responsibility and Trust: The unequal power relationship between companies and consumers creates concerns of accountability, leading to distrust of the more powerful companies.
  •   Transparency and Company’s Vulnerability: It is difficult to make algorithms public, and most customers would not have the ability to understand those algorithms or the input data needed to run them. Furthermore, the more transparent a company is with its algorithms, the easier it is for the public to “game” the system.

Mitigation Strategies

There are several measures taken to tackle the ethical concerns, notably:

  • Intrusion on Autonomy: Potential intrusion through data collection may be mitigated by transparency and enforcing laws. The case study emphasizes that if there is no benefit for the customer, there is little point in collecting the customer’s data.
  • Privacy: Laws such as GDPR increase people’s and companies’ awareness of (the importance of) privacy.
  • Bias and Manipulation: Informed consent may increase awareness among people that data is being collected and used for nudging and/or manipulation.
  • Responsibility and Trust: Public policies (e.g. governmental regulation) and codes of conduct steer the responsibility of the data scientists.
  • Transparency and the company’s vulnerability: Greater transparency increases the trust of the customer, but this is balanced against disclosure of sensitive company information.

Lessons Learned

This case study shows valuable insights into projects related to Customer Relations Management by exemplifying how ethical concerns may be addressed by companies. Not only does the case study shed light on the use of SIS in CRM, but thanks to the interview more practical issues experienced in day-to-day involvement with the SIS. Specific insights arising from this case study include:

  • There is a need to address ethical issues in the technical sector and the importance of companies’ responsibility in this vastly changing CRM area.
  • Potential cultural differences in an international setting may be harmful in the long run, as companies developing more ethical SIS (e.g., including GDPR regulations) may be unable to develop competitively powerful algorithms, potentially leading to replacement by companies that do not follow the same ethical restrictions.
  • Intrusion on Autonomy: Potential intrusions through data collection and uses of those data may be mitigated by transparency and legal instruments.
  • Privacy: Enforcement of laws such as GDPR encourage people and companies to take privacy and data control seriously.
  • Bias and Manipulation: Effective informed consent practices may increase awareness among people that data is being collected and used for nudging and/or manipulation.
  • Responsibility and Trust: Public policies (e.g. governmental regulation) and codes of conduct should steer the behaviour of data scientists through making their responsibilities transparent.
  • Transparency and the company’s vulnerability: Greater transparency of companies’ collection and use of data increases the trust of the customer, but this needs to be balanced against disclosure of sensitive company information which may put the company at a competitive disadvantage.
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