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

Format:

Book

Thesis/Dissertation

Author/Creator:

McCarthy, Daniel M., author.

Contributor:

Jensen, Shane T., degree supervisor.

Bradlow, Eric T., degree supervisor.

Hardie, Bruce GH, degree committee member.

Brown, Lawrence D., degree committee member.

University of Pennsylvania. Statistics, degree granting institution.

Language:

English

Subjects (All):

Statistics--Penn dissertations.

Penn dissertations--Statistics.

Local Subjects:

Statistics--Penn dissertations.

Penn dissertations--Statistics.

Genre:

Academic theses.

Physical Description:

1 online resource (142 pages)

Contained In:

Dissertation Abstracts International 78-12B(E).

Place of Publication:

[Philadelphia, Pennsylvania]: University of Pennsylvania ; Ann Arbor : ProQuest Dissertations & Theses, 2017.

Language Note:

English

System Details:

Mode of access: World Wide Web.

text file

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:

Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.

Advisors: Eric T. Bradlow; Shane T. Jensen; Committee members: Lawrence D. Brown; Bruce GH Hardie.

Department: Statistics.

Ph.D. University of Pennsylvania 2017.

Local Notes:

School code: 0175

ISBN:

9780355110142

Access Restriction:

Restricted for use by site license.