How to sell a dataset? Pricing policies for data monetization

Sameer Mehta, Milind Dawande, Ganesh Janakiraman, Vijay Mookerjee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The wide variety of pricing policies used in practice by data-sellers suggests that there are significant challenges in pricing datasets. The selling of a dataset -- arranged in a row-column format, where rows represent records and columns represent attributes of the records -- is more nuanced than that of information goods like telephone minutes and bandwidth, in the sense that, for a buyer, it is not only the amount of data that matters but also the type of the data. We develop a utility framework that is appropriate for data-buyers and the corresponding pricing of the data by the data-seller.

A buyer interested in purchasing a dataset has private valuations in two aspects -- her ideal record that she values the most, and the rate at which her valuation for the records in the dataset decays as they differ from her ideal record. The seller allows individual (and heterogeneous) buyers to filter the dataset and select the records that are of interest to them. The multi-dimensional private information of the buyers coupled with the endogenous selection of records makes the seller's problem of optimally pricing the dataset a challenging one. We formulate a tractable model and successfully exploit its special structure to examine it both analytically and numerically. A key result we establish is that, under reasonable assumptions, a price-quantity schedule is an optimal data-selling mechanism. Such a schedule has a nuanced interpretation in the data-selling context in that buyers buy different sets of records but the price for a given number of records does not depend on the identity of the records chosen by the buyer. Even when the assumptions leading to the optimality of a price-quantity schedule do not hold, we show that the optimal price-quantity schedule offers an attractive worst-case performance guarantee relative to an optimal mechanism. Further, we numerically solve for the optimal mechanism and show that the actual performance of two simple and well-known price-quantity schedules -- two-part pricing and two-block pricing -- is near-optimal. We also quantify the value to the seller from allowing buyers to filter the dataset.
Original languageEnglish (US)
Title of host publicationACM EC '19
Subtitle of host publicationProceedings of the 2019 ACM Conference on Economics and Computation
PublisherAssociation for Computing Machinery, Inc
Pages679
Number of pages1
ISBN (Electronic)9781450367929
DOIs
StatePublished - Jun 17 2019
Externally publishedYes
Event20th ACM Conference on Economics and Computation, EC 2019 - Phoenix, United States
Duration: Jun 24 2019Jun 28 2019

Publication series

NameACM EC 2019 - Proceedings of the 2019 ACM Conference on Economics and Computation

Conference

Conference20th ACM Conference on Economics and Computation, EC 2019
CountryUnited States
CityPhoenix
Period6/24/196/28/19

Keywords

  • Data monetization
  • Multi-dimensional mechanism design
  • Price-quantity schedules

ASJC Scopus subject areas

  • Marketing
  • Computer Science (miscellaneous)
  • Economics and Econometrics
  • Computational Mathematics
  • Statistics and Probability

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