Qwirkle, Big Data, and first degree price discrimination

To me, a Qwirkle, besides being a board game, is also the definition of something that provides high satisfaction for the price. It is what I call ‘a good deal’. These would be goods that are somewhat ‘mispriced’ for us since we would be willing to pay more for them. They arise from the fact that consumers’ preferences vary but prices are usually fixed. In economic theory, utility represents satisfaction experienced by the consumer of a good. It can be seen as a measure of satisfaction or pleasure. I gave my mother a gift some time ago: a board game called Qwirkle. It sells for about 25 dollars. My mother has played the game almost every day since I bought it, about 2-3 years ago. We can safely say that she has derived a great deal of utility or pleasure from it. And at $25, it certainly has an amazing utility-price ratio. But this Qwirkle game got me thinking about other things that have for me large utility-price ratios. A good that I found to be high on this list is magazine subscriptions. Their cost is usually very low (2-8$ per issue) and they provide me with many hours of utility. Another example would be a good latte on a nice terrace (5$).

Now for simplicity, I was referring to a single good’s total utility. In microeconomics there is also the concept of diminishing marginal utility. This means that the additional utility obtained from consuming an additional unit of good is decreasing. A good latte on a nice terrace gives me great pleasure but after four, not so much (but maybe palpitations). So the utility-price ratio for the lattes will decrease if they are consumed too quickly in time but it should remain relatively constant if consumption is interspaced properly and my preferences do not change. The other thing is, it makes sense to buy more than one latte, but it does not to buy more than one game of Qwirkle for one person (zero marginal utility). The utility of playing it can decrease over time, but you still bought it only once. Total utility would be the sum of all the utilities of playing instances.

In the utility-price ratio, the denominator (price) has, at least for me, an incidence on the perceived utility: higher prices tend to affect utility negatively. This might be due to the fact that there is some guilt involved when spending larger sums of money. Thus at the top of my highest utility-price list I will usually find things that are relatively low cost. Maybe this is why some say that happiness lies in the small things (and why we love free stuff, assuming they have utility, since they have infinite utility-price ratios)! At the bottom of the list would be ‘anti-Qwirkles’, something like a Damien Hirst’s $12 million stuffed shark. Maybe there is high utility for a rich person paying $12 million for a stuffed shark, just because they can. I guess that the effect of the price element on utility is dependent on the budget constraint. Now consider the highest pleasure that you can derive from say, something at 10$. Now, to get the same ratio for something costing $1M, you would need 100,000 times more satisfaction. It seems to me that pleasure is bounded, or that it does not increase linearly with price. That might explain why rich people are not always happier, at least beyond some threshold of worth.

These Qwirkles are the results of varying preferences among individuals and unique, single pricing policies. Non-unique pricing is called price discrimination and there are multiple degrees of price discrimination. Third degree price discrimination relies on putting customers into groups and charging different rates based on willingness to pay within those groups (student or senior discount for example). Second degree price discrimination does not charge based on customer characteristics, but based on the amount of the good purchased (quantity discount). First degree price discrimination involves charging every individual customer a price based on their individual willingness to pay. With e-commerce and Big Data, first degree price discrimination becomes much easier to implement. It may mean that we will see individual-specific pricing more and more in the future, allowing companies to charge exactly what the consumer is willing to pay. This is somewhat already happening with plane tickets and hotel rooms’ prices increasing based on your search history. Discount codes, grocery rewards card, or newspaper paywalls are other examples. Orbitz last year was accused of charging Mac users more for plane tickets than non-Mac users. A Wall Street Journal investigation found that some businesses were charging customers more based on their location. During a recent New York snowstorm, Uber’s rides cost 8.25 times the standard price. Does Netflix have a high utility-price ratio for you? Then it could use Big Data to make a lot more money off you… Similarly, from my browsing habits and past purchases, Amazon can figure out that I am willing to pay more for board games.

Now, I am curious, what would be a ‘Qwirkle’ for you?

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