Customer-Based Corporate Valuation : Modeling with Missing, Aggregated Data Summaries / Daniel M. McCarthy.

Format:

Book

Manuscript

Thesis/Dissertation

Author/Creator:

McCarthy, Daniel M., author.

Contributor:

Bradlow, Eric T., degree supervisor.

Jensen, Shane T., degree supervisor.

Brown, Lawrence D., degree committee member.

Hardie, Bruce GH, degree committee member.

University of Pennsylvania. Department of Statistics, degree granting institution.

Language:

English

Subjects (All):

Penn dissertations--Statistics.

Statistics--Penn dissertations.

Local Subjects:

Penn dissertations--Statistics.

Statistics--Penn dissertations.

Physical Description:

viii, 134 leaves : illustrations ; 29 cm

Production:

[Philadelphia, Pennsylvania] : University of Pennsylvania, 2017.

Summary:

There is growing interest in "customer-based corporate valuation," explicitly tying the value of a firm's customer base to its financial valuation. This dissertation studies the theory and application of customer-based corporate valuation. The dissertation is comprised of three essays, each of which studies a different aspect of the topic. In the first essay, we develop a general customer-based corporate valuation framework. In doing so, we enumerate the determinants of corporate value and how predictions of customer base activity can be used to inform these determinants. In the second essay, we develop a customer-based corporate valuation model that is specifically suited to contractual (or subscription-based) businesses. We apply this model to publicly-disclosed data from two companies, DISH Network and Sirius XM Holdings. In the third essay, we develop a customer-based corporate valuation model for non-contractual (or non-subscription-based) businesses. This is a more challenging problem, because non-contractual businesses have more complex transactional patterns--they are characterized by latent attrition instead of observable churn behavior, and often have irregular purchase timing and spend amounts. We apply this methodology to data from a large business unit of an e-commerce retailer, valuing the business unit as a whole, decomposing this valuation into existing and yet-to-be-acquired customers, and analyzing customer profitability. In both essays two and three, we assume that the modeler is an external stakeholder, and thus only has the ability to observe a very limited, possibly incomplete, periodically disclosed collection of customer data summaries, unlike a situation in which the granular data is observed. We conclude with a short chapter which describes areas for future research.

Notes:

Ph. D. University of Pennsylvania 2017.

Department: Statistics.

Supervisor: Eric T. Bradlow; Shane T. Jensen.

Includes bibliographical references.

OCLC:

1312247489