T-Patterns in Business

42 Pages Posted: 13 Nov 2017

See all articles by Neeraj Arora

Neeraj Arora

University of Wisconsin - Madison - Department of Marketing

Glenn Fung

American Family Insurance

Srinivas Tunuguntla

University of Wisconsin - Madison - Department of Marketing

Date Written: November 6, 2017

Abstract

Sequence of events that occur over time may have recurrent patterns. In this paper we develop a scalable methodology to uncover such time patterns and demonstrate their value in business applications. We build upon prior work by Magnusson (2000) and identify four limitations of the existing T-pattern algorithm that preclude it from being useful for typical business problems.

The four categories of limitations are:

(i) scalability,

(ii) supervised learning,

(iii) heterogeneous individuals,

(iv) distributional assumptions, and propose a solution for each limitation.

We use simulations to exhibit the properties of our proposed algorithm and its ability to uncover true T-patterns. The simulations demonstrate the gains accrued from our proposed algorithm when compared to the original T-pattern algorithm. We use insurance claims data from a well-known insurance company to test the algorithm. We show that the algorithm successfully detects T-patterns that routinely occur in the context of insurance claims. Using each T-pattern as a binary feature in machine learning models we classify the claims into the two groups of satisfied and dissatisfied customers. This reveals T-patterns that separate dissatisfied customers from satisfied ones and identifies ouch-points that could minimize customer dissatisfaction with the claims process.

Keywords: journey map, path-to-purchase, machine learning, classification, time pattern, algorithm

Suggested Citation

Arora, Neeraj and Fung, Glenn and Tunuguntla, Srinivas, T-Patterns in Business (November 6, 2017). Available at SSRN: https://ssrn.com/abstract=3066839 or http://dx.doi.org/10.2139/ssrn.3066839

Neeraj Arora (Contact Author)

University of Wisconsin - Madison - Department of Marketing ( email )

975 University Avenue
Madison, WI 53706
United States
(608) 262-1990 (Phone)
(608) 262-0394 (Fax)

Glenn Fung

American Family Insurance ( email )

Srinivas Tunuguntla

University of Wisconsin - Madison - Department of Marketing ( email )

United States

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