Now there are a number of diagnostic tests, akin to marketers' rules of thumb, for certain conditions. For example, cancers that produce a large amount of a protein called HER-2 are likely to be susceptible to treatment with Herceptin. Similarly, a particular gene seems to control sensitivity to warfarin, a blood thinner, so knowing about an individual's variant of that gene can help a patient's doctor to set the right dose.
Clearly, the more we know about patients, conditions, treatments, and outcomes, the better we will be able to predict outcomes on an individual basis. Patients will often benefit from statistical analysis that shows which drugs work on which kind of people— often long before scientists figure out why. In advertising, most of the data are about people, their demographics, and their purchasing behavior. In drugs, it's mostly about their genetics and their physical conditions. But the science of discovering correlations and patterns is much the same.
This increased transparency carries promise and peril for the companies involved. It's disruptive in the short run. Marketers want to reach people who will buy, and publishers love to sell ads aimed at those people. But publishers are afraid of finding out that a large percentage of their audience may not be good customers.
Drug companies want to sell their drugs to everyone who could possibly benefit, and the idea of only a limited customer base for each drug disturbs them—even as regulators also may be slow to understand the benefits of individual drug-targeting and may not approve reimbursements for the relevant tests. By separating the high-value targets, you implicitly discover the low-value targets as well.
But low-value targets for one ad or drug could be high-value targets for another. Indeed, the long-run aim is to find the right offers for the right targets—whether ads for goods and services or drugs for illnesses—more efficiently than ever before.
This article comes from Project Syndicate.