How to sell an app: pay-per-play or buy-it-now?

TL;DR

This paper analyzes pricing strategies for products with evolving consumer value, comparing buy-it-now and pay-per-play models, and finds that pay-per-play can enhance revenue and buyer participation under certain stochastic value evolution processes.

Contribution

It introduces a framework for analyzing dynamic pricing with stochastic value evolution, highlighting the effectiveness of pay-per-play and free trials.

Findings

Pay-per-play can increase seller revenue through effective price discrimination.

Offering free trials benefits both sellers and buyers by expanding the buyer pool.

Different stochastic models of value evolution influence optimal pricing strategies.

Abstract

We consider pricing in settings where a consumer discovers his value for a good only as he uses it, and the value evolves with each use. We explore simple and natural pricing strategies for a seller in this setting, under the assumption that the seller knows the distribution from which the consumer's initial value is drawn, as well as the stochastic process that governs the evolution of the value with each use. We consider the differences between up-front or "buy-it-now" pricing (BIN), and "pay-per-play" (PPP) pricing, where the consumer is charged per use. Our results show that PPP pricing can be a very effective mechanism for price discrimination, and thereby can increase seller revenue. But it can also be advantageous to the buyers, as a way of mitigating risk. Indeed, this mitigation of risk can yield a larger pool of buyers. We also show that the practice of offering free trials is largely beneficial. We consider two different stochastic processes for how the buyer's value evolves: In the first, the key random variable is how long the consumer remains interested in the product. In the second process, the consumer's value evolves according to a random walk or Brownian motion with reflection at 1, and absorption at 0.