Challenges and opportunities in buying and selling personal data
During summer 2019, I set out to find out what do companies think about the idea that individuals could sell their personal data. For my study, I contacted 23 experts working with data to understand the current challenges companies face with data and whether the personal data trade could help companies in answering those challenges.
Altogether, I interviewed 23 experts based in Belgium, France, the United Kingdom, the Netherlands, Singapore, and Vietnam. The experts worked in different positions such as data engineers, head of data analytics departments or data monetisation directors.
Experts interviewed per sector:
1. Insurance (5)
2. Telecommunications (2)
3. Consumer goods (8)
4. Other (8)
The interviews showed how the question of personal data sales divided opinions. Where some experts saw individuals as potential new data sources, others warned against serious privacy threats and falsified data. The key findings of the interviews are presented here in three parts. The first part gives general remarks about the value of data and how companies measure the value of data. The second part introduces the main challenges companies face when talked about data. Finally, the third part presents the expert opinions on selling and buying data. The findings are presented as general statements to maintain the anonymity of the interviewees.
1 The value of data
It is no surprise that there is unanimous agreement among the interviewees that data is valuable. Customer data, for example, enables business optimisation, data-driven decision making, and personalised marketing. For an insurance company, data allows calculating the probabilities of accidents and product pricing with for example credit score data. Likewise, for telecommunications companies’ data is at the heart of product building, it enables knowing the ‘real customer needs’. In retail, customer data helps in knowing what the customer is interested in and personalised marketing including personalised offers send to individual’s emails.
Examples of how data creates value:
1. Insurance: Data helps insurance companies to detect a moment of life or to detect the wealth of people to know who to sell services. In the case of health insurance, the activity data from the consumers is needed to understand where and how the prevention of adverse health impact can take place.
2. Telecommunications: Customer data is important in ‘knowing your customer’ and to build products on real needs. Thus, data drives customer acquisition and retention and prevents churn.
3. Consumer goods: The customer data helps to know what the customer is interested in and to send personalised newsletters. For example, if someone is known to shop six times a year, and this year has not visited the online shop in about six months, a personalised offer can be sent to her.
Where it is easy to say that data is valuable, it becomes more difficult to say how much that data is worth, in other words, how to measure the value of data. The answer to this question usually starts with ‘it depends’. It depends on the field or it depends on the business unit within a company. A person working in marketing will answer differently than a person in production. A common approach according to the interviewed experts is nevertheless to use the key performance indicators (KPI) to keep track of the benefits derived from data. In this case, the calculated value is derived from what is built on top of data analytics. Alternatively, experts working with consumer goods believe that the value of data can be calculated from how much customer numbers have increased the profits of the company. For example, the RFM (Recency, Frequency, Monetary) analysis used in marketing, is also considered a measurement for the value of data.
As a concluding remark, the interviewees showed that there are not yet established practices on how to data should be valued and companies would like to hear from each other on the best practices on how to improve their methods.
2 Main challenges
The interviews revealed that the main challenges companies currently face are (1) privacy and data protection, (2) data quality and (3) change management.
First, the experts repeatedly named privacy and data Protection as their biggest challenge. The European Union introduced a new standard for data protection with the General Data Protection Regulation (GDPR), in force since 2018. The regulation changed significantly the rules for data processing and limited lawful grounds when personal data can be collected. The GDPR is, however, not a simple rule book that provides answers to how the legal requirements can technically be met. For example, if a customer invokes their right to be forgotten, it is not an easy task for a company to ensure that all personal data is deleted, when the data is spread across different datasets.
Also, the GDPR has raised the awareness of consumers when it comes to their right to data protection. For a company, the raised awareness can mean conflicting requests from the customers. A customer may request that less of his or her data is processed, while also demanding more personalisation of services and advertising. There is lack of understanding from the customer that, the personalisation of service is only possible with a good amount of personal data. What a company can do then, is to enforce the trust relationship between the company and its customers, and ensure that the data the company collects is used in a transparent manner.
Several interviewees mentioned also data quality as their top challenge. Data quality is impacted by several factors, but a few frequently cited were large volume of data, data silos, and data relevancy. The volume of data has grown for example due to increased use of mobile phones, which allows more touchpoints to collect information about the users. Therefore, it is crucial to have good data governance in place to be able to benefit from all the data. Thus, companies struggle to utilise the data as it exists in company silos. To achieve data quality, companies first aim to achieve data integration.
It is important to have the datasets up to date. For example, to send personalised marketing offers, companies need to know what their customers are interested in and when those interests change. Analysing browsing behaviour gives clues on what a person might want to buy. However, it does not tell the intent of the buyer. A person may be shopping for themselves, for their daughter or grandfather, and what she is buying today may not be what she is looking for tomorrow. Likewise, if there is a change in a person’s life, how is that reflected in the dataset to avoid sending offers that are no longer relevant for the customer. These issues of relevancy and recency of data were frequently brought up by experts working with consumer goods.
The third challenge, most of the interviewees mentioned, was the topic of change management. For a company to profit from data, the company culture and especially the upper management needs to be willing and ready to base its business decision on data analytics. For example, one of the interviewees mentioned that managers may trust algorithms when they shops online but when it comes to a business decision, they rather rely on their gut feeling. Also, scandals like Cambridge Analytica have made companies more risk-averse with everything that has to do with data. An expert said that there is a long way before managers take data seriously and trust that they can make more educated decisions with it.
3 Selling and buying data
Most of the interviewees answered that the company in which they worked in does not sell data but is buying data from third-party vendors such as Experian and LexisNexis. For example, an insurance company buys an aggregated data set of credit scores from the United Kingdom. None of the companies bought data from existing data exchange platforms, which allow individuals to sell their data, such as Wibsom.
The expert opinions split around the idea to buy data directly from their customers. About half of the experts found the idea interesting, while the other half raised concerns about privacy and ethics of personal data trade.
The experts working in the insurance sector saw potential in the idea due to the lack of direct interaction between the company and their end-customers. Knowing more about their end-customer would benefit their business optimisation as well as digital marketing. However, also opposing view presented by an expert working for another insurance company. In his view, enough information is obtained through user accounts and as such, there is no need for more data purchased from the individuals themselves.
Experts working with consumer goods had the most positive response to the idea. An expert suggested that when an individual sells his or her data knowingly and willing, there is an opportunity to collect more relevant data. With more relevant data, there can be better predictions on the purchasing behaviour of the customers. Thus, compliance with the privacy and protection regulations is ensured when individuals explicitly consent to the data collection. An expert also mentioned that they have already seen an increase in profits due to personalisation of marketing and would be therefore ready to compensate individuals for their data. Although one of the interviewees advised that the compensation should not be monetary — rather company rewards.
Nevertheless, not all experts were excited about the idea. On the contrary, they saw serious privacy and ethical concerns. In their opinion, it is difficult to ensure that a consumer has fully understood what they signed up for when they sell their personal data. Consequently, raising the question, whether personal data trade is more threat than a promise to an individual’s data protection rights.
Also, another problem with the idea is that individuals can easily sell falsified data. When data is exchanged for compensation, it can incentive individuals to lie and fabricate the data. Also, when data is extracted from an individual’s social media account, the insights derived from it can be questionable. To quote an expert:
“In social media, there is a lot of noise. Others are active, some not. But it does not tell in the end that much about a person”.
4 Conclusions
The interviews showed that data is considered valuable by everyone. However, it is not clear how the value of data can be measured, and the practices in different companies vary. The companies, however, share similar challenges.
First, companies struggle with privacy and data protection issues. There is a great will on behalf of the companies to be compliant with privacy and data protection regulations, but in the current privacy-aware-climate ensuring compliance to the customers, requires companies to build a new kind of trust relationship with the customers to address their privacy concerns.
Second, almost all companies struggle to ensure data quality.
Third, the data cannot bring value to the company unless its decision-makers are ready to embrace data-driven decision making.
The goal of this study was to find out whether companies want to buy data from individuals. The results were not conclusive. Half of the interviewees showed interest in the idea, while the other half turned it down at once. The advocates saw that buying data from individuals could ensure that data is collected with their full knowledge and consent, hence is privacy compliant. The personal data trade can also be a way to build a new type of trust relationship between the company and its customers. In contrast, experts opposing the idea had valid concerns about how to ensure that individuals are not providing falsified data. Also, the experts were alarmed about the idea individuals could sell their data without fully understanding the ramifications of it, and as such, pose a greater danger to their privacy.
