Allocation of marketing budgets to maximize customer equity

Koosha, Hamidreza; Albadvi, Amir

doi:10.1007/s12351-017-0356-z

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…In related literature, customer lifetime value has been considered as an important factor in allocating the promotion budgets using various approaches ( [26,19]). The experience Fischer et al [14] of marketing budget allocation showing that profit increase from improving allocation across products or regions is more effective than expanding the total budget.…”

Section: Media Planningmentioning

confidence: 99%

An application of approximate dynamic programming in multi-period multi-product advertising budgeting

Khalilzadeh

Neghabi

Ahadi

2023

JIMO

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<p style='text-indent:20px;'>Advertising has always been considered a key part of marketing strategy and played a prominent role in the success or failure of products. This paper investigates a multi-product and multi-period advertising budget allocation, determining the amount of advertising budget for each product through the time horizon. Imperative factors including life cycle stage, <inline-formula><tex-math id="M1">\begin{document}$ BCG $\end{document}</tex-math></inline-formula> matrix class, competitors' reactions, and budget constraints affect the joint chain of decisions for all products to maximize the total profits. To do so, we define a stochastic sequential resource allocation problem and use an approximate dynamic programming (<inline-formula><tex-math id="M2">\begin{document}$ ADP $\end{document}</tex-math></inline-formula>) algorithm to alleviate the huge size of the problem and multi-dimensional uncertainties of the environment. These uncertainties are the reactions of competitors based on the current status of the market and our decisions, as well as the stochastic effectiveness (rewards) of the taken action. We apply an approximate value iteration (<inline-formula><tex-math id="M3">\begin{document}$ AVI $\end{document}</tex-math></inline-formula>) algorithm on a numerical example and compare the results with four different policies to highlight our managerial contributions. In the end, the validity of our proposed approach is assessed against a genetic algorithm. To do so, we simplify the environment by fixing the competitor's reaction and considering a deterministic environment.</p>

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Section: Media Planningmentioning

confidence: 99%

An application of approximate dynamic programming in multi-period multi-product advertising budgeting

Khalilzadeh

Neghabi

Ahadi

2023

JIMO

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“…The primary goal of affinity marketing is to promote cross-selling by taking advantage of the other party's customer membership. Other aspects of partnering programs include collaborative marketing efforts and inventory management or joint logistics planning (Koosha & Albadvi, 2020). These partnering programs offer multiple benefits, including improved brand reputation, the ability to enter a new market, and boosted brand awareness to enhance a company's performance and productivity.…”

Section: Partnering Programsmentioning

confidence: 99%

Relationship Marketing and Customer Retention - A Systematic Literature Review

Rosário,

Casaca

2023

Studies in Business and Economics

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Relationship marketing is the process of establishing, maintaining, and improving solid relationships with a company’s customers and other stakeholders. Relationship marketing can significantly impact customer satisfaction, trust, commitment, and communication, which are critical in customer retention and increasing the probability of using a company’s products or services in the future. With globalization and technological advancements, competition and consumer demands have become more complicated, prompting firms to employ strategies that strengthen customer loyalty and trust for long-term relationships. This paper aims to identify research trends in the field through a systematic bibliometric literature review of research on relationship marketing and customer retention. The review includes 61 articles published in the Scopus® database, presenting up-to-date knowledge on the topic. The bibliographic search included peer-reviewed articles published up to 2022. The findings indicate that relationship marketing provides a win-win solution: customers benefit from special and unique treatment under relationship marketing, and companies benefit from stronger customer relationships that eventually translate to higher profitability and performance in target markets’ marketing initiatives. Relationship marketing produces multiple opportunities and benefits that facilitate the creation, maintenance, and improvement of customer retention programs.

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“…Previous studies focused on marketing resources related to budget decisions indicated that profit improvement from better allocation across products or regions is much higher than that from improving the overall budget [24]. Koosha and Albadvi [25] provided a multi-period process model to allocate marketing budget to customer segments in a long-term view and a dynamic process. Genetic algorithm and simulated annealing approaches were adopted to find the near optimal solution.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Marketing Resource Allocation with Two-Stage Decisions

Zhang

Liao

et al. 2022

JTAER

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In the precision marketing of a new product, it is a challenge to allocate limited resources to the target customer groups with different characteristics. We presented a framework using the distance-based algorithm, K-nearest neighbors, and support vector machine to capture customers’ preferences toward promotion channels. Additionally, online learning programming was combined with machine learning strategies to fit a dynamic environment, evaluating its performance through a parsimonious model of minimum regret. A resource optimization model was proposed using classification results as input. In particular, we collected data from an institution that provides financial credit products to capital-constrained small businesses. Our sample contained 525,919 customers who will be introduced to a new product. By simulating different scenarios between resources and demand, we showed an up to 22.42% increase in the number of expected borrowers when KNN was performed with an optimal resource allocation strategy. Our results also show that KNN is the most stable method to perform classification and that the distance-based algorithm has the most efficient adoption with online learning.

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Allocation of marketing budgets to maximize customer equity

Cited by 6 publications

References 27 publications

An application of approximate dynamic programming in multi-period multi-product advertising budgeting

An application of approximate dynamic programming in multi-period multi-product advertising budgeting

Relationship Marketing and Customer Retention - A Systematic Literature Review

Dynamic Marketing Resource Allocation with Two-Stage Decisions

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