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Composing offer sets to maximize expected payoffs

Title	Composing offer sets to maximize expected payoffs
Author	Atahan, Pelin, Johar, M., Sarkar, S.
Publication Date:	2016
Publication Place	- Digital Commons
Subject	Data analytics, Ecommerce, Personalization, Probability theory, Target marketing
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	2-s2.0-84995783606
Record ID	a39b4c0d-f957-4607-bf52-bfccb73240b1
Library Location	Sectoral Education and Professional Development
Date	2016
Sample Text	Firms are increasingly using clickstream and transactional data to tailor product offerings to visitors at their site. Ecommerce websites have the opportunity, at each interaction, to offer multiple items (referred to as an offer set) that might be of interest to a visitor. We consider a scenario where a firm is interested in maximizing the expected payoff when composing an offer set. We develop a methodology that considers possible future offer sets based on the current choices of the user and identifies an offer set that will maximize expected payoffs for an entire session. Our framework considers both the items viewed and purchased by a visitor and models the probability of an item being viewed and purchased separately when calculating expected payoffs. The possibility of a user backtracking and viewing a previously offered item is also explicitly modelled. We show that identifying the optimal offer set is a difficult problem when the number of candidate items is large and the offer set consists of several items even for short time horizons. We develop an efficient heuristic for the one period look-ahead case and show that even by considering such a short horizon the approach is much superior to alternative benchmark approaches. Proposed methodology demonstrates how the appropriate use of information technologies can help e-commerce sites improve their profitability.

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Özyeğin University

Composing offer sets to maximize expected payoffs

Author Atahan, Pelin, Johar, M., Sarkar, S.

Publication Date 2016

Publication Place - Digital Commons

Subject Data analytics, Ecommerce, Personalization, Probability theory, Target marketing

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 2-s2.0-84995783606

Record ID a39b4c0d-f957-4607-bf52-bfccb73240b1

Library Location Sectoral Education and Professional Development

Date 2016

Sample Text Firms are increasingly using clickstream and transactional data to tailor product offerings to visitors at their site. Ecommerce websites have the opportunity, at each interaction, to offer multiple items (referred to as an offer set) that might be of interest to a visitor. We consider a scenario where a firm is interested in maximizing the expected payoff when composing an offer set. We develop a methodology that considers possible future offer sets based on the current choices of the user and identifies an offer set that will maximize expected payoffs for an entire session. Our framework considers both the items viewed and purchased by a visitor and models the probability of an item being viewed and purchased separately when calculating expected payoffs. The possibility of a user backtracking and viewing a previously offered item is also explicitly modelled. We show that identifying the optimal offer set is a difficult problem when the number of candidate items is large and the offer set consists of several items even for short time horizons. We develop an efficient heuristic for the one period look-ahead case and show that even by considering such a short horizon the approach is much superior to alternative benchmark approaches. Proposed methodology demonstrates how the appropriate use of information technologies can help e-commerce sites improve their profitability.