Grail Beats Cancer with DNA Tests

What is it?

One of today’s most promising products might just be around the corner.. Our suffering and long battle with number one’s killing disease might finally come to an end. Jeff Huber, Grail’s CEO, lost his own wife to cancer and was determined to spin out a company that promised to detect and ultimately provide the tools to beat cancer before it could spread throughout a person’s body. With a background in massive data businesses at Google, including Google Ads, Apps, and Maps. At Grail, Huber moved from mapping the world to mapping genomes. Grail is developing blood tests that can detect many types of cancer before symptoms arise. Expectations that cancer blood tests will quickly turn into a multibillion-dollar industry has attracted growing interest from investors. Grail has raised more than $100 million from Illumina, Bill Gates, Jeff Bezos’s venture fund, Bezos Expeditions, and Arch Venture Partners.

How does it work?

The testing concept is to use high-speed DNA sequencing machines to cleanse a person’s blood for fragments of DNA released by cancer cells. If DNA with cancer-causing mutations is present, it often indicates a tumor is already forming, even if it’s too small to cause symptoms or be seen on an imaging machine. A DNA test able to pick up many kinds of cancer could be revolutionary because tumors caught early can often be cured with surgery or radiation.

Sounds promising, but is this all not too good to be true?

Any developer of a screening test for cancer faces challenging obstacles. How often will the test find cancer, and how often will it give a wrong result? Is it truly reliable? What’s more, even tests that do discover cancer early can turn into medical disasters if patients end up getting aggressive and costly treatment for cancers that won’t kill them. As Huber states: “If you look at this business, it’s littered with failures. With a few exceptions, screening tests have been invariably horrible,”. To prove early detection is possible, Grail will spend millions on organizing clinical trials involving as many as 30,000 people. It will test all of them and then see if the tests are able to catch cancer earlier than established methods. Grail is the only company currently able to implement sequencing technology at a cost that’s low enough to carry out such studies and bring an inexpensive test to market. Grail has a price advantage because it is a spin-off of Illumina, a company that makes and sells more than about $2 billion worth of DNA sequencing instruments, chemicals, and test kits annually to university scientists and other labs.

A bright future ahead?

Eventually, the DNA tests will be available in every hospital in the US and every person should be allowed to take it at a price of 600 dollar. Timing matters, and the intersection of genome sequencing and the computation that is possible now, with new technologies like machine learning, it feels like we are at the right time to make this happen. If Grail succeeds, in the future, get a cancer diagnosis to being about as eventful as having the flu, that would be a good outcome.


Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness

We have most likely all had this thought once: how does this website know these things about me? Well, basically every website collects information on its users. However, users are not always aware because not every website informs its user on this data collection process.

Personalization is concerned with delivering the right content to the right person at the right time, which makes it a customer oriented marketing strategy. Data is needed to be able to provide this content delivery to the right person. When websites make a conscious effort to inform users on data collection, they engage in overt information collection (Sundarand Marathe 2010). It is assumed that users atomically agree on data collection after they have received such an information effort. However, when users experience ads that contain personal information without being informed that their data is collected by the website, this will lead to negative reactions and can harm businesses (Milne, Bahl and Rohm, 2008). This sense of vulnerability is more like to be accepted by users when the website is marked by trust (Urban, Amyx, and Lorenzon 2009).

The result is a personalization paradox , where personalization effectiveness depends on the context. It would be beneficial for online retailers to understand how they can prevent or minimize the negative effects of personalization. Practitioners claim it is more user friendly to not inform users because informing them would result in users to take longer in performing online tasks. Moreover, when users do not know their data is collected, they might act more naturally and thereby delivering richer data (Verhoef et al. 2010). For the customers themselves it is important that they feel conformable during website visits. Therefore, retailers need to make sure customers feel safe, while still being able to provide them with optimal personalization marketing to enhance customer experience.

The research includes three studie. Study 1 and 3 had the most interesting findings. In the first study, the personalization paradox is tested by comparing situations in which websites did and did not inform users on data collection, and how more personalized advertisements affected click-through rates. Facebook is used as the setting of the research because promotions through social media are important. Participants first had to review the advertisement. Hereafter, they could fill in their click-through intention and their perceived vulnerability on a 5-item scale. The results suggest that click-through rates after personalized advertisements increase when websites overtly collect data and decrease when websites covertly collect data due to the vulnerability it causes to users, meaning that vulnerability mediates the effect of personalization and information collection on click-through intentions ( see Figure 1). In the third study, the findings suggest that negative effects caused by collecting data covertly, can be mitigated by providing information and control to users at the moment that personalized advertisements are shown to them. Probably because this enhances reliability and trustworthiness.



Figure 1

The paper convincingly addresses the personalization paradox and provides valuable ways in which negative effects of covertly data collection can be mitigated. The result is that retailers can make careful decisions as to what information should be used and how sensitive this information is to users. Furthermore, retailers need to be transparent on their data collection methods, which will benefit their business. Lastly, this will probably enhance customer satisfaction, since customers will feel like retailers act in the best interest of the customer.

Milne, George R., Shalini Bahl and Andrew Rohm (2008), “Toward a Frame-work for Assessing Covert Marketing Practices,” Journal of Public Policy& Marketing, 27 (1), 57–62.

Sundar, S. Shyam and Sampada S. Marathe (2010), “Personalization versus Cus-tomization: The Importance of Agency, Privacy, and Power Usage,” HumanCommunication Research, 36, 298–322.

Urban, Glen L., Cinda Amyx and Antonio Lorenzon (2009), “Online Trust: Stateof The Art, New Frontiers, and Research Potential,” Journal of InteractiveMarketing, 23 (2), 179–90.

Verhoef, Peter C., Rajkumar Venkatesan, Leigh McAlister, Edward C. Malt-house, Manfred Krafft and Shankar Ganesan (2010), “CRM in Data-RichMultichannel Retailing Environments: A Review and Future Research Direc-tions,” Journal of Interactive Marketing, 24, 121–37.