Advertising Budget Allocation Research Articles

Corporate Cash Holdings and Advertising Spending After the Financial Crises

This paper examines the link between a firm’s characteristics and advertising budget allocation. It also shows the firm’s behavior after the Asian financial crisis and the American subprime mortgage crisis. Results show that crises can significantly impact management decisions. They can influence how multinational companies plan for their future. The study also identifies the macroeconomic factors that can influence a firm’s decision to allocate funds.

A multi-channel advertising budget allocation using reinforcement learning and an improved differential evolution algorithm

A multi-channel advertising budget allocation using reinforcement learning and an improved differential evolution algorithm

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

<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>

An Experimental Investigation of the Impact of Different Advertising Formats on Children’s Attitudes’ Towards Sports Brands

The study aims to investigate children’s attitude towards different advertising formats including TV ads, in-movie product placement and in-game advertisements, using a web-based between subjects’ experimental design among a sample of Egyptian children aged between 6 to 13 years. Children’s attitude development by advertising efforts is considered under investigated in literature particularly in Arab cultures and as they are considered as powerful influencers to parents’ purchase decisions, the study contributes to a better understanding of children’s attitudes that will guide mangers in making wiser advertising budget allocation. The results showed that there is a significant different impact of the investigated advertising formats on children’s attitude towards sports brands with higher cognitive, affective and behavioral responses resulted from the exposure to traditional video advertising compared to in- movie product placement and in-game advertisements.

Optimal Advertising Budget Allocation across Markets with Different Goals and Various Constraints

Advertising budget allocation across multiple markets has drawn considerable attention in recent years. To expand previous research and fill a gap in the current literature, this study proposes two decision models for optimal budget allocation decisions across multimarkets with different goals and various constraints. In addition to the market parameters proposed by the Vidale–Wolfe model, the present study incorporates market goals and advertising objectives into budget allocation decisions. Different types of markets are defined in terms of the goal set for market share or profit. Given the characteristics of different markets, two separate decision models are developed. Model I aims to maximize sales volume given a fixed advertising budget, while model II seeks to minimize the advertising budget given a total of targeted sales volume for all the markets. Solutions to the two models are discussed, and a numerical example is provided to demonstrate how to apply the models in making budget allocation decision.

Nonparametric advertising budget allocation with inventory constraint

In this paper, we study the optimization problem of the advertising budget allocation for revenue management faced by a marketer. Besides the advertising budget, the marketer is subject to an inventory constraint during the promotion season. The marketer can affect sales by spending on advertising but does not initially know the relationship between the advertising expense and consequent sales. We propose a nonparametric learning-while-doing budget allocation policy for the problem. Specifically, we first conduct a sequence of advertising experiments to learn (predict) the market sales response through observing realized sales (exploration), then based on the learned sales function determine the following budget allocation planning (exploitation). In particular, during the exploration and exploitation phases, we need to balance the advertising and inventory budgets simultaneously. We show that our policy is asymptotically optimal as the size of the market increases. By constructing a worst-case example, we show that our policy achieves near-best asymptotic performance. We also provide numerical illustrations to show how our policy works, and discuss how its performance changes as the system parameters vary. We also glen some managerial implications of our model and policy from the numerical results.

Advertising allocation and impact of advertising on event ticket sales: Which product, where, and when

When planning for advertising, an accurate assessment of the impact of advertising is important so that managers can allocate their budgets to maximize the return on each advertising dollar. In particular, the managers need to understand the impact of advertising on different products, media types, and time periods. This study develops a simultaneous model to measure the impact of advertising on product sales using weekly ticket sales data and to capture managers’ decision using the data on advertising budget allocation at a performing arts center. This article provides managerial recommendations for advertising decision in terms of the product, media, and time dimensions.

Optimal Advertising Budget Allocation in Luxury Fashion Markets with Social Influences: A Mean‐Variance Analysis

Optimizing advertising budget allocation in the luxury fashion industry is an important problem. In this study, motivated by real‐world practices, we consider a luxury fashion firm serving a conspicuous market consisting of two groups of consumers who influence one another. We investigate the optimal customer portfolios and budget allocation problem using the mean‐variance (MV) framework. Under the basic model in which all budget must be spent, we identify different scenarios and propose an algorithm to construct the MV efficient frontier for each scenario. Interestingly, different from the classic investment portfolio problem, we reveal that: (i) not all budget allocations between the two groups of consumers are MV efficient, which means that the efficient frontier is not continuous; (ii) in the presence of social influence, diversification of customer portfolio does not always lead to a smaller variance, which counterintuitively means that focusing on a single consumer group can reduce risk. We also prove that to maximize expected profit, the optimal strategy is to allocate all advertising budget to one group of consumers only (i.e., a polarized strategy). We illustrate analytically, the importance of identifying the right scenario for budget planning. Finally, we examine the budget saving strategy in the extended model and uncover that the respective range of efficient solutions is smaller than the one under the all budget spending strategy. This shows that the budget saving strategy offers less flexibility for MV efficient budget allocation than the all budget spending strategy.

A Multi-attribute Online Advertising Budget Allocation Under Uncertain Preferences

Introduction: The present research was conducted at the University of Delhi in 2017.
 Method: Websites were ranked on the basis of feedback from unbiased experts. Later, we proposed an integrated approach by combining ordered weighted averaging (owa) operator with fuzzy analytic hierarchy process (fahp) for budget allocation.
 Results: A numerical example related to a company, which deals with consumer goods and wants to advertise on few e-commerce websites is discussed at the end of the paper. Budget distribution is decided by solving multi-objective maximum-dispersion-minimum-variance (mdmv) owa and fahp method.
 Conclusions: The proposed methodology aids managerial decisions made by handling multiple attributes simultaneously through industry experts’ opinion, and using a simple proportional rule for allocating budget.
 Originality: The conventional methods based on reach maximization, exposure or profit cannot meet the budget allocation needs of the modern advertising planning. Firstly, they do not take into consideration multiple attributes of media. Secondly, they do not incorporate the expert opinion and their preferences. To address these problems, we propose a multi-attribute method based on the advertising budget allocation method to divide the budget into individual websites. The attributes under consideration are: system quality, content quality, usage, trust, customer support, online customer feedback, and personalization.
 Limitations: In this study, we used a mdmv-owa operator in fuzzy environment but in future occasions, it may be extended to intuitionistic fuzzy domain.

Total Exposure Effects of TV/Online Video Advertising and Optimizing Advertising Budget Allocation : Using 3-Screen (TV, PC, Mobile) Single Source Panel Data

N-스크린 시대의 소비자는 방송 등 동영상을 TV로만 시청하는 것이 아니라 PC, 태블릿, 모바일 기기 등 여러 스크린을 동시에 이용하고 있다. 이러한 소비자의 매체 이용 행태를 반영하여 광고 또한 TV뿐 아니라 다른 스크린을 통해 노출되고 있다. 하지만 여러 매체를 통해 노출되는 광고 캠페인의 통합적 효과에 대한 연구는 미비한 실정이다. 따라서 이 연구는 TV, PC, 모바일을 모두 이용하는 소비자들로 구성된 3ᐨ스크린 싱글소스 패널을 이용해 세 개의 매체를 통한 방송, 동영상 광고의 통합 노출량, 중복 노출, 중복이 제거된 통합 노출량을 확인하고 최적화 알고리즘(Optimization Algorithm)을 통해 통합도달률을 극대화 할 수 있는 광고비 최적 배분 모델을 구축하고자 했다. 이를 위해, 먼저 TV, PC, 모바일의 광고비와 통합도달률 간 관계를 탐색하고 매체의 조합 간 중복노출비 발생량을 규명했다. 매체 간 중복비를 바탕으로 매체 간 이용이 독립적인지, 중복 노출 성향을 보이는지, 상호배타 성향을 지녔는지 등 이용자의 매체별 시청 행태 속성에 대해 추론했다. 마지막으로, 광고 예산의 규모에 따라 통합도달률 최적화값을 획득할 수 있는 광고비 최적 배분화를 예측하였다. 매체의 다양화가 빠르게 진행되는 매체환경에서 효과적인 크로스 미디어 광고캠페인의 중요성이 증대되고 있는 것을 고려할 때, 이 논문이 제시하는 3ᐨ스크린 싱글소스 패널 데이터를 활용한 실증적인 통합 광고 노출 효과 규명과 광고비 최적 배분 추정은 중요한 의의를 가지며 유용한 학술적, 실무적인 함의를 제공하리라 기대된다.

The effectiveness of different forms of online advertising for purchase conversion in a multiple-channel attribution framework

The Internet has given rise to many new forms of advertising. Scientific studies have focused on individual reactions to specific advertising forms in isolation and have offered little guidance for aggregate-level budget allocation decisions, which are typically based on simple rules. This article compares the long-term effectiveness of nine forms of advertising—seven online and two offline—by means of a structural vector autoregressive model and restricted impulse responses. For five product categories, we investigate how these forms of advertising generate traffic, affect conversion, and contribute to revenue. We find that content-integrated advertising is the most effective form, followed by content-separated advertising and firm-initiated advertising. Although online advertising forms have similar power to drive traffic, content integration dominates content separation in the area of progression toward purchase. Last-click attribution underestimates content-integrated activities and suggests online advertising budget allocations that yield 10%–12% less revenue than the status quo, whereas the model's proposed online advertising budget allocation yields a 21% revenue increase over the status quo. These results highlight the payoffs for companies that integrate content into online media.

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

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 regression method 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.

Optimal allocation of digital marketing budget: The empirical Bayes approach

The article outlines a framework for online advertising budget allocation. First, it explores the empirical Bayes methodology for learning the effectiveness of different online ad placements – from historical data of varying quality. Second, it describes an analytical procedure for optimal budget allocation, which builds on risk management and reinforcement learning techniques.

Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship

Models of advertising response implicitly assume that the entire advertising budget is spent on disseminating one message. In practice, managers use different themes of advertising (for example, price advertisements versus product advertisements) and within each theme they employ different versions of an advertisement. In this study, we evaluate the dynamic effects of different themes of advertising that have been employed in a campaign. We develop a model that jointly considers the effects of wearout as well as that of forgetting in the context of an advertising campaign that employs five different advertising themes. We quantify the differential wearout effects across the different themes of advertising and examine the interaction effects between the different themes using a Bayesian dynamic linear model (DLM). Such a response model can help managers decide on the optimal allocation of resources across the portfolio of ads as well as better manage their scheduling. We develop a model to show how our response model parameters can be used to improve the effectiveness of advertising budget allocation across different themes. We find that a reallocation of resources across different themes according to our model results in a significant improvement in demand.

ADVERTISING IN A COMPETITIVE PRODUCT LINE

In many markets, competing but differentiated firms offer a product line in order to target each product to a different type of customer. Under competition, firms, while trying to target different products to different segments, are also interested in "stealing" customers from the competitors and, in particular, customers from the most profitable segments. How should a profit-maximising firm allocate the advertising budget along a competitive product line? Devel oping a closed-loop time-variant Nash equilibrium strategy for a multi-player multi-product advertising game, we find the determinants of the optimal advertising budget allocation in a product line in an oligopoly growing market. Moreover, for a symmetric competition in a fixed market, we derive an analytic feedback time-variant Nash equilibrium strategy. Marketing implications regarding the firm's strategic orientation in product preference advertising spending, as well as comparison to rivals' spending, are discussed. Finally, we show that using a single-product advertising game instead of a multi-product advertising game, leads to over-advertising.

Optimal advertising levies with application to the Norway-EU salmon agreement

This paper focuses on optimal advertising policy for a competitive industry that produces large tradable surpluses and raises funds for promotion through per-unit levies on production or exports. Results indicate that an export levy biases the advertising budget allocation in favour of the export market, but provides no clear incentive to intensify advertising effort. Applying the model to the Norway-EU Salmon Agreement, results suggest that the current 3 per cent levy may be welfare-increasing from the Norwegian producer perspective. Whether the current budget allocation of 67 per cent to the EU is efficient depends critically on whether advertising elasticities across markets are uniform, a hypothesis that needs to be tested. Copyright 2000 by Oxford University Press.

An empirical investigation of the advertising spending decisions of a multiproduct retailer

In a multiproduct retailing context, an approach for testing marketing mix variables is presented that should be of interest to retailers and researchers alike. Traditional model building for decision evaluation purposes suffers from a serious drawback in that the underlying model is assumed to be known. The present approach incorporates the search for the “true” model as an intrinsic part of the method. Based on up to date modeling perspectives, the approach can be divided into three steps: (1)the use of multiple time series analysis to assist in model specification; (2) the choice of functional form, model estimation and testing; and (3) the use of statistical and simulation methods to test decision making hypotheses. The approach is applied to three years of a western wear retailer's weekly sales and advertising data; multiple time series analysis is used to specify a simultaneous equation model that captures demand interdependences. After subjecting the model to several stability tests, it is then used as a basis for testing several interesting hypotheses concerning the retailer 's advertising decision making behavior. The key insight for multiproduct retailers is that advertising budget allocation decisions across products and media are probably more important than decisions affecting the level of the total budget since the former have a greater impact on profit contribution.

ADSPLIT: A Multi-Brand Advertising Budget Allocation Model

Abstract ADSPLIT is a computer-based, interactive, marketing model which optimally allocates a specified corporate promotional budget among individual brands competing for limited resources. The model requires either (a) regression-based response function coefficients based on historical data, or (b) judgments on what sales should be for different values of price and advertising expenditure for the brands, which the program then uses to estimate the function parameters. The advertising-sales response function is modeled through a flexible form that allows both concave and S-shaped relationships, while sales are related to price through a constant elasticity (Cobb-Douglas) function. The model uses the estimated or input functions to compute the optimal budget allocation for the brands, given an upper bound for the total promotional expenditures and upper- and lower-budget bounds for every brand, using nonlinear optimization heuristics. Optimal prices for the brands are also computed.

Advertising Budget Allocation under Uncertainty

This article presents a model for the allocation of an advertising budget to geographic market segments, or territories, when the sales response to advertising in each segment is characterized by a probability distribution. It is shown that allocation decisions that are based on the expected sales response may be associated with a relatively large degree of risk and, therefore, non-optimal to a risk-averse manager. The model derives an “efficient frontier” in terms of the expected profit and its variance resulting from alternative budget allocations. The manager then chooses the optimal allocation based on his/her preference function.

A Taxonomic Evaluation of Advertising Budgeting Models

Abstract The advertising budget allocation decision has been recognized as a critical decision that should receive logical, scientific determination. However, actual decision-making practice indicates that marketers typically use qualitative, non-scientific methods when setting advertising budgets. This article discusses the reasons for this practice by focusing on the properties of qualitative models which make them attractive to practitioners and the properties of quantitative models which make them unattractive. To assist in this analysis, a taxonomy for the classification of various qualitative and quantitative budget models is constructed, the emphasis of which is on the perceived usefulness of a budget model to practitioners.