Since his decision to raise the price of HIV drug Daraprim by 5000%, CEO of Turing Pharmaceuticals Martin Shkreli has been labelled, variously, a ‘scumbag’, a ‘douchebag’, and a ‘monster’.
It’s easy to have a go at Shkreli, mostly because he is now seen as a price-gouging scumbag that represents everything bad about the world. In his defence though, valuations are difficult to arrive at and with a lack of laws surrounding them, easy to inflate. Goods and services are largely worth what people will pay for them. So in the case of potentially life-saving medicine, people will pay substantial sums. As Shkreli himself said, $50,000 is actually good value for staying alive, although admittedly not as good as the $900 it cost before his price hike.
The Shkreli saga has acutely demonstrated that there needs to be a balance struck, and simply saying ‘because we can’ will not prevent public outcry and government intervention that is potentially far more damaging than a price point you feel undervalues the product.
Clearly, having a pricing structure that means only the rich can live is not compatible with anyone’s concept of a compassionate government. In light of this, could data analytics be used by governments to set prices on goods such as drugs, to ensure a middle ground that is affordable, profitable for the company, and enables further research?
McKinsey & Company estimate that up to 30% of the thousands of pricing decisions companies make every year fail to deliver the best price, amounting to substantial lost revenue. Big Data should enable companies to get a firmer idea on the correct price point.
There are numerous customer touch points that companies can look at to draw highly specific insights that would influence the price, for example, the cost of the next-best competitive product versus the value of the product to the customer. By looking at the data, analysts can find patterns that highlight opportunities for differentiated pricing at a customer-product level, based on willingness to pay.
For the pharmaceutical industry, the use of analytics to determine price is more complicated. People’s willingness to pay for their own survival is usually unlimited, and companies must factor in the morality of any pricing decisions made. For companies, this is a metric that will vary greatly from one to another.
There are numerous ways that the US government could help to keep life-saving drugs affordable for patients. Hilary Clinton has put forward a plan that would allow Medicare to negotiate for lower prices on medications and increase competition for generic versions of medications, which Medicare could do because of its substantial purchasing power. John Castellani, head of the Pharmaceutical Research and Manufacturers of America, has responded by saying that this idea ‘would turn back the clock on medical innovation and halt progress against the diseases that patients fear most.’ The idea that competition will stifle innovation is strange, but is indicative of pharma’s attitude towards change. It may be necessary that government intervenes directly on pricing, and using analytics could give this intervention the appearance of objectivity that companies may find more palatable. Metrics such as patients’ ability to pay could be considered alongside other metrics like the necessity of the drug, other options, profit margin, and willingness to pay, making investment from companies worthwhile, but not at the expense of human lives.