e-journal

Optimization of Advertising Budget Allocation Over Time Based on LS-SVMR and DE

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The advertising budget allocation problem for financial service is dealt with based on statistical learning and evolutionary computation in this paper. Taking the carry-over effects of the advertising into account, the least squares support vector machine regression (LS-SVMR) is used to construct the response model. A comparison between the proposed response model and traditional regressionmethod based market response models is implemented. The results show the effectiveness and validity of the former model. Taking the budgets allocated to every month in the planning horizon as decision variables, the budget allocation optimization model is built and an improved differential evolution algorithm is used to find the optimal solutions. Finally, the proposed budget allocation method is illustrated by a practical problem.
Note to Practitioners—In modern society, advertising is an important part in brand marketing strategy. The planning and design of an advertising campaign involve several decisions, among which is advertising budget allocation. It aims to determine the optimal advertising expenditure among individual brands over a predetermined planning horizon, competing for a limited resource or geographic market segments. However, little work deals with the allocation of a given advertising budget for propagating services over
time. In this study, a method for the optimal advertising budget allocation over the planning horizon of the advertising campaign for a financial service is provided. A response model is constructed using the LS-SVMR. Taking the budgets allocated to every month in the planning horizon as decision variables, the budget allocation optimization problem is proposed and an improved differential evolution algorithm is then applied to find the optimal solutions. The effectiveness of the proposed advertising budget allocation method is validated by a numerical example.
Index Terms—Advertising budget allocation, differential evolution algorithm, least squares support vector machine regression (LS-SVMR), optimization.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

No. Panggil

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Penerbit

New York : IEEE., 2014

Deskripsi Fisik

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 11, NO. 4, OCTOBER 2014

Bahasa

English

ISBN/ISSN

1545-5955

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

VOL. 11, NO. 4, OCTOBER 2014

Subjek

TEKNIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

Yuli/Agus

Versi lain/terkait

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