Introduction
Customer analytics can be a useful tool for organizations to meet the needs of their clients more effectively. However, when handled unethically or irresponsibly, it could create a backlash from consumers who would opt out of the loyalty program and even abandon the grocery chain. The case study entails a grocery chain, which sold customer data to an insurance firm.
The insurance firm then created a profile of the grocery chain’s consumers and found that a relationship existed between their buying habits and the number of HIV claims. A lot of ethical issues, concerning the availability of this information to the insurance company and its effect on the consumer, arise out of this predicament. Both organizations must exercise a lot of caution in leveraging the information.
Violation of trust
Nowadays, it is possible for companies to possess a lot of information about their consumers. Developments in information technology make it possible to track basic customer features like sex, age, and gender as well as complex information like purchasing habits and buying frequency. Some of the most intimate details of a person’s life can be revealed through customer analytics.
However, many buyers are growing uneasy about the availability and use of their personal information by other parties. As a result, several of them are taking legal action against firms that have violated their right to privacy. Google and several other corporations have faced law suits in which customers object to the sale of their personal information without their consent (Omer 2008).
In this case study, a grocery chain has sold consumer data to a third party. It is not clear whether client consent was sought before this happened, but it is likely that very few would have entered the loyalty program if they knew what the grocery chain was going to do with it. Buyers often accept reward programs to access certain perks or benefits from selected stores.
They trust that the company which hosts the loyalty program will be responsible for the information. Therefore, if they learn that a third party had access to their personal information, then this could violate the principle of mutuality and reciprocity. They could opt out of the loyalty program and even abandon the grocery chain altogether. One of the experts in the case study –Katherine Lemon – states that “angry customers will speak with their wallets if their trust is violated” (Davenport & Harris 2007).
Therefore, the grocery chain would lose buyers who are the key reason for its survival. Additionally, this backlash could spread across the entire retail industry. Several buyers could become aware of the misuse of their information. They would push legislators to restrict information-sharing or even data collection. Consequently, the irresponsible use of the data could hurt the grocery chain more severely than the insurance firm.
Another expert in the case study – David Norton – suggests doing a positive spin on this discovery by giving loyalty points to those buyers who make healthy purchases, like buying condoms. However, the main challenge with this approach is that customers will demand to know the source of that information. If the insurance firm rewards clients for making healthy choices, buyers will wonder where the insurance firm obtained the information.
They would raise questions about the company’s respect for privacy and its morality as well. Buying personal data undermines the very essence of mutuality and reciprocity. The insurance company may have valid reasons for knowing more about their clients, but the way they went about it is quite unethical. It would send a message to its buyers that it watches their every move and invades their privacy.
The best thing for the insurance firm to do would be to refrain from applying the new information about HIV in their business processes. They should not encourage more positive lifestyles among those who do not purchase condoms and neither should they penalize the ones that make these perceived unhealthy choices.
No single person responds to the invasion of privacy positively. One of the main instincts that humans have is self-preservation. If someone is perceived as a threat, then the subject is likely to respond by lashing out against the aggressor. Many customers in the insurance company will experience a loss of control about what others know about them.
If they learn that their insurer purchased personal information about them, even for their benefit, they would feel highly insecure. Many may wonder whether other organizations could access their profile and use the information against them. For instance, mortgage providers may classify them as at-risk groups and deny them opportunities to own homes.
Alternatively, employers may avoid hiring them after learning that they have a permanent ailment. All these insecurities may arise out of that one special offer from the insurance firm. Every action reacts, so alterations in the service provisions of the firm could be traced to the grocery chain which could lose its buyers (Mitchell 2009).
Dehumanization of clients
An implicit value is attached to the use of customer data to benefit an organization. When doing customer analytics, customer data is reduced to nothing more than a financial asset. This approach dehumanizes clients and makes third-party analytics quite unethical. Accounting and other calculations do not prioritize the social aspects of the buyer, yet that is what counts.
The insurance firm’s use of customer data to create patterns about their claims focuses on the financial value of the client over and above anything else (Andon et al., 2001). As such, it shifts attention away from meeting clients’ needs and diverts it to taking value from them. This approach means that customers are only valuable to an organization about how much value they can bring to the organization.
It treats them like exploitable assets rather than real people. In essence, if the insurance firm were to create different policies to correspond to the condom purchasing habits of its clients, then it would be devaluing them. In literature, some experts have realized that nowadays companies categorize customers into low yielding and high yielding portfolios.
They argue that low-value customers do not warrant high investment and resources since the contributions they can make to a firm are minimal. These approaches consider buyers as inventories as investments rather than people. As one of the experts in the case study – Katherine Lemon – stated, this approach creates a battered customer syndrome.
In essence, it alienates certain categories of buyers because they are perceived to be of less value. As such, they get a few special prices and offers to owe to the top tier syndrome. Customers may eventually learn about such stereotyping and could thus take their business to other firms that treat them more humanely (Lloyd 2000).
If the insurance firm decided to engage in levels of analytics that give precedence to certain clients over others, then it could be dehumanizing consumers. If it used the information to charge higher insurance premiums to those that do not purchase condoms, then it could be stereotyping this group. The company would be saying that only a few of its clients (in this case, the ones that buy condoms) are worth attracting or nurturing.
As a result, the insurer would give less attention to the ‘unprofitable’ group as they would be perceived as a liability. Such a philosophy causes businesses to treat some clients as bad, yet they are the epicenter of the company’s mission. The insurance firm should leverage this information responsibly by treating all its clients equally. It could take a proactive stance by preaching healthy choices among all clients as this would not raise suspicions about where they got the information in the first place.
Any other strategy that would prioritize one claims group over another would be tantamount to stereotyping individuals. Furthermore, many analysts have looked into ethical and unethical practices within the insurance industry. They have found that increasing rates to ‘at risk’ groups, like cancer or HIV patients, is discriminatory. All persons have a right to access insurance regardless of their age, gender or their health condition.
It would be legal to charge differential premiums between HIV patients and healthy clients, but it is unethical (Ericson et al. 2000).It should be noted that sometimes the sale of customer data has yielded positive returns for society in general. For instance, US federal agents have bought customer data from data brokers and used them to capture terrorists or unwanted criminals. In essence, this has made society safer for all.
However, when data is sold to profit-making institutions, then it only serves their profit-making interests and undermines the interests of the consumers. Data selling is an ethical dilemma when the parties involved are non-profit-making (Cooper et al. 2000).
Conversely, when firms buy information to meet their own needs at the expense of others, then the act becomes unethical. In the case study, the insurance firm, as well as the grocery chain, are benefitting from customer profiles by trading them. These are entities that are putting their interests first and curtailing the rights of their buyers.
The inaccuracy of the information and homogenization
The insurance firm in the case study is using data about another company’s clients. Therefore, the patterns it obtained were directly applicable to the grocery chain and not necessarily the insurance company. Some of the patterns found in one group of people could be untrue for another set. Therefore, they could be coincidental or inaccurately portray a certain group of people.
The insurer would be undermining its interests if it assumed that all non-condom users would lead to an increase in HIV claims. Therefore, denying them claims or increasing their premiums would be punishing them for a baseless claim. The truth is that a lot of variables come into play whenever one is analyzing such sensitive matters. Medical experts state that condom use is not 100% effective against the spread of HIV so that the insurance company could be oversimplifying a very complex problem (Evangelista 2012).
Many western nations suffer from such forms of clustering. Companies, government agencies, and other stakeholders like to place people in segments where members possess similar characteristics. They presume that the members in those groups are homogenous and they have similar needs. Even government census can segment people based on their geographical locations.
For instance, it is often assumed that people who live in certain neighborhoods have certain income streams and are thus likely to possess other traits. For instance, their health, voting choices and their reading patterns will be determined by where they live. Such clustering reduces society to homogeneous groups which are far removed from reality.
The only real relationship between members of a certain neighborhood is probably their income streams. Other inferences may be highly inaccurate. Likewise, the use of client information to make inferences about their insurance claims could be highly misguided. The firm will be basing its decisions on matters that have little to do with a certain client, and this would be detrimental to the organization.
In the real world, many individuals have suffered from these misjudgments tremendously. A case in point is an e-company called Choice point. The firm has a database of over 19 million customers, and these are often sold to government agencies, banks, insurance firms, and several other businesses. In one instance, a customer lost his job because Choicepoint’s database indicated that he was a former convict.
A later investigation revealed that a mistake had been made in the system. In another scenario, an insurance client paid high insurance premiums because the data broker’s system indicated that he had had several accidents. However, this was a mistake on the part of the data broker since it had mixed up the victim’s profile with another driver’s record.
The patterns deduced from databases strongly depend on the quality of data management by the firm. Information can get mixed up, and calculations could be wrong. It thus makes little sense for companies to penalize individuals for mistakes that they did not make. The insurance firm in the case study could certainly fall victim to any of the above situations, so it must stay not misuse the information (Prentice 2010).
Conclusion
Customer analytics may appear to be a creative solution for several firms. Calculations and patterns are done at a distance such that the decision maker does not have to confront the people who are affected by the decision. In the case study, the insurance firm is not even known to the grocery chain customers who probably think that the chain will protect their personal information amply.
This approach objectifies clients and causes third-party profile users to absolve themselves from personal responsibility for the information. Such companies reduce their clients to nothing more than assets that can either be disposed of or reduced. If the insurance company charges higher premiums because of the trends it obtained from the database; then it will be propagating alienation of customers.
The practice is a direct violation of customer rights as none of them would willingly agree to such misuse of their personal information. The responsible thing to do would be to refrain from making any positive or negative changes in the claims profile within the insurance company as the source of the patterns is unethical.
The grocery chain should cease engaging in data brokerage because this could trigger a backlash from customers who discover it. Furthermore, the patterns may be inaccurate thus leading to unfair consequences.
References
Andon, P., Baxter, J., & Bradley, G. 2001, ‘Calculating the economic value of customers to an organisation’, Australian Accounting Review, vol. 11 no.1, pp. 62–72.
Cooper, B., Watson, H., Wixom, B., & Goodhue, D. 2000, ‘Data warehousing supports corporate strategy at First American Corporation’, MIS Quarterly, vol. 24 no.4, pp. 547–567.
Davenport, T. & Harris, J. 2007, ‘The dark side of customer analytics’, The Harvard Business Review, May, p. 1-9.
Ericson, R., Barry, D. & Doyle, A. 2000, ‘The moral hazards of neo-liberalism: Lessons from the private insurance industry’, Economy and Society, vol. 29 no.4, pp.532–558.
Evangelista, B. 2012, ‘Privacy concerns growing, poll finds’, The San Francisco Chronicle, 10 March, p. D1.
Lloyd, S. 2000, ‘Customers: The culling game’, Business Review Weekly, vol. 22 no.13, pp. 51-79.
Mitchell, R. 2009, ‘What the Web knows about you: How much private information is available about you in cyberspace? Social Security numbers are just the beginning’, Computerworld, 27 January 27, p.14-18.
Omer, T. 2008, ‘What Google knows: Privacy and internet search engines’, Utah Law Review, August, p. 1433-54.
Prentice 2010, Ethical and social issues in information systems. Web.