Gregory Mesaros of eWinWin recently won a patent to provide consumers with variable pricing via wireless devices. This simple concept makes perfect sense when you consider how inefficient pricing normally is. Imagine, for instance, Sally and Sam are walking down a street. Sally is very hungry, and Sam isn’t. A hamburger stand nearby offers burgers for $5. Sally would be willing to pay $7 … and Sam only $4, based on their current state of mind. But because the stand charges everyone the same $5, only Sally buys a burger.
Now, if a sensor could pick up their smartphone IDs and understand somehow their recent behavior and desires, the hamburger stand might be able to beam Sally and Sam different prices for the same meal. $7 for Sally, $4 for Sam, would entice both to buy — and the outlet would make $11 for two sales, doubling sales volume (two) and boosting margins (since the average burger price is now $5.50). Everyone is happy.
Variable pricing is not new, of course: hard negotiators end up paying less for cars that softies; grocery stores use coupons to entice price-conscious shoppers to pay a little less for cans of soup; consumers rushing to the mall on Black Friday may pay less for a new TV. But real-time, instantaneous variable pricing has eluded marketers before now. Mesaros’ patent would match behavioral tracking of consumers with pre-set options for prices and offers, and seek to find the best instant match.
Does this seem unfair? Perhaps. Uber, the new crowd-driven taxi service, has received bad press for jacking up rates during periods of peak consumer demand such as snowstorms in major cities. Subscription companies such as cable and utilities often face customer churn when one customer realizes she is paying more than her neighbor for equivalent service. But one-price-fits-all strategy is a holdover from the early 1900s when there was no scientific method to efficiently match the cost of a good with the variable desire of an individual consumer. Regarding fairness, one could also argue it is unfair to charge more for a good than many are willing to bear, pushing you away from potential purchases. Making one person pay $7 so another could pay $4 for a sandwich would optimize the utility of a transaction for everyone.
With mobile technology now being mapped to personal datasets, soon, we may all pay different prices for everything.