Algorithmic Marketing with Data-Driven Simulations
Marketing researchers and practitioners care about why and how products or services are adopted by consumers. The influential theory of innovation diffusion has been established for decades, but modeling and simulating the diffusion process remains notoriously challenging. Lately, agent-based models...
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ndltd-VANDERBILT-oai-VANDERBILTETD-etd-06202017-1018172017-07-14T05:31:39Z Algorithmic Marketing with Data-Driven Simulations Zhang, Haifeng Computer Science Marketing researchers and practitioners care about why and how products or services are adopted by consumers. The influential theory of innovation diffusion has been established for decades, but modeling and simulating the diffusion process remains notoriously challenging. Lately, agent-based models (ABMs) have dominated traditional aggregate diffusion models, due to the remarkable advantage to capture individual heterogeneity and social and spatial interactions. Our critical review of the empirically-grounded ABMs of innovation diffusion, however, reveals that few such ABMs are calibrated properly, validated rigorously, and developed explicitly for prediction. This clearly limits their use in supporting decision-making in practice. The thesis contributes a rigorous data-driven agent-based modeling (DDABM) approach that relies on state-of-the-art machine learning techniques to effectively calibrate and validate agent behavior models on massive and rich individual adoption data. The models are integrated into multi-agent simulations to precisely forecast roof-top solar adoption and efficiently explore subsidizing strategies in San Diego county, USA. Historically, ABMs were used to answer âwhat-ifâ questions and draw insights on the efficacy of different policies, however, few could provide executable and quantitative decisions. Mathematical optimization has been widely used to provide numerical solutions in many domains, but little effort has been made to couple it with ABMs. By solving marketing optimization problems in several important settings, such as, dynamic seeding of emerging technologies, route planning for door-to-door targeted marketing, and budget optimization in multi-channel marketing, the thesis also strongly demonstrates how efficient algorithms can aid the design of effective marketing policies facilitated by data-driven simulations, like ABMs, providing optimal or near-optimal actionable plans for marketers. The presented research characterized by computational modeling techniques and algorithmic methods could lead to our ultimate goal of intelligent machine-automated marketing. Yevgeniy Vorobeychik Gautam Biswas Doug Fisher Bradley Malin William Rand VANDERBILT 2017-07-13 text application/pdf http://etd.library.vanderbilt.edu/available/etd-06202017-101817/ http://etd.library.vanderbilt.edu/available/etd-06202017-101817/ en restrictsix I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Vanderbilt University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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Computer Science Zhang, Haifeng Algorithmic Marketing with Data-Driven Simulations |
description |
Marketing researchers and practitioners care about why and how products or services are adopted by consumers. The influential theory of innovation diffusion has been established for decades, but modeling and simulating the diffusion process remains notoriously challenging. Lately, agent-based models (ABMs) have dominated traditional aggregate diffusion models, due to the remarkable advantage to capture individual heterogeneity and social and spatial interactions. Our critical review of the empirically-grounded ABMs of innovation diffusion, however, reveals that few such ABMs are calibrated properly, validated rigorously, and developed explicitly for prediction. This clearly limits their use in supporting decision-making in practice. The thesis contributes a rigorous data-driven agent-based modeling (DDABM) approach that relies on state-of-the-art machine learning techniques to effectively calibrate and validate agent behavior models on massive and rich individual adoption data. The models are integrated into multi-agent simulations to precisely forecast roof-top solar adoption and efficiently explore subsidizing strategies in San Diego county, USA. Historically, ABMs were used to answer âwhat-ifâ questions and draw insights on the efficacy of different policies, however, few could provide executable and quantitative decisions. Mathematical optimization has been widely used to provide numerical solutions in many domains, but little effort has been made to couple it with ABMs. By solving marketing optimization problems in several important settings, such as, dynamic seeding of emerging technologies, route planning for door-to-door targeted marketing, and budget optimization in multi-channel marketing, the thesis also strongly demonstrates how efficient algorithms can aid the design of effective marketing policies facilitated by data-driven simulations, like ABMs, providing optimal or near-optimal actionable plans for marketers. The presented research characterized by computational modeling techniques and algorithmic methods could lead to our ultimate goal of intelligent machine-automated marketing. |
author2 |
Yevgeniy Vorobeychik |
author_facet |
Yevgeniy Vorobeychik Zhang, Haifeng |
author |
Zhang, Haifeng |
author_sort |
Zhang, Haifeng |
title |
Algorithmic Marketing with Data-Driven Simulations |
title_short |
Algorithmic Marketing with Data-Driven Simulations |
title_full |
Algorithmic Marketing with Data-Driven Simulations |
title_fullStr |
Algorithmic Marketing with Data-Driven Simulations |
title_full_unstemmed |
Algorithmic Marketing with Data-Driven Simulations |
title_sort |
algorithmic marketing with data-driven simulations |
publisher |
VANDERBILT |
publishDate |
2017 |
url |
http://etd.library.vanderbilt.edu/available/etd-06202017-101817/ |
work_keys_str_mv |
AT zhanghaifeng algorithmicmarketingwithdatadrivensimulations |
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1718496507057930240 |