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BRANDAID: A Marketing-Mix Model, Part 2: Implementation, Calibration, and Case Study
Abstract
Model implementation starts with introductory steps that include orienting management, forming a team, selecting and formulating a problem, calibrating the model, and initial use. Then on-going steps take over with firefighting, tracking and diagnosis, updating and evolution, and re-use. Calibration of the model is approached eclectically in stages that include judgment, analysis of historical data, tracking, field measurement, and adaptive control. A three-year case study shows that unexpected events intersperse a planned implementation. The model emerges with multiple roles in the marketing management process.
... Advertising is a complex phenomenon that affects consumer response in myriad ways. Many analytical models have been proposed in marketing (see Blattberg and Jeuland 1981;Bultez and Naert 1975;Kalish 1985;Little 1979b;Lodish 1986;Mahajan, Muller, and Sharma 1984;Parsons 1975;Sasieni 1971;Simon 1982;Teng and Thompson 1983) and in economics (Dorfman and Steiner 1954;Gould 1970;Milgrom and Roberts 1986;Nelson 1970;Nerlove and Arrow 1962;Schmalensee 1978;Telser 1962;Vidale and Wolfe 1957). These models are often dynamic, concerned with the speed of response, decay, or carryover from one period to the next. ...
... For example, when spending is constant, the previous period's carryover does not vary. See Little ( 1979b) for discussion and illustrations with a variety of analytical models. For a general treatment, see Feinberg (1988) and Sasieni (1971). 1 In this steady state of constant advertising spending we examine the relationship, the response function, between the steady-state sales and the stabilized advertising spending levels. ...
... Technically, this means that A/k) is nondecreasing and strictly concave for both firms. Though this assumption may seem restrictive, as Little (1979a) and Lodish (1986) report S-shaped sales response functions, profit maximization implies that a firm should either operate on the concave portion of the response curve or not advertise at all. Our assumption implies simply that rational firms operate on the concave portion of the response curve. ...
Consumers choose from a relevant set of brands. Advertising encourages consumers to consider a brand as relevant. Price and other variables influence consumer choice among relevant brands. The authors examine how the explicit consideration of competitive response affects managerial recommendations and explore the interaction between price and advertising decisions. They consider cases in which managers do and in which they do not anticipate competitive actions.
... Hysteresis is a phenomenon of temporary marketing action that causes sustained sales change. Little (1979) first uses the term in marketing, and Simon (1997) describes various phenomena, including loyalty after brand switching and organizational inertia, that can lead to hysteresis. Simon also uses empirical examples of how temporary actions involving price and marketing communications created sustained change in the competitive positions of pharmaceutical, cigarette, dessert, and liquor brands. ...
... One of the early published marketing mix models focused on the relative effectiveness of pricing, advertising, and promotion to stimulate the sales of a well-known packagedfood product in a competitive environment (Little 1975). The BRANDAID project provided management with econometric estimates of these instruments' relative effectiveness and offered recommendations for improved resource allocation. ...
... In conclusion, our reanalysis of the BRANDAID data reveals that observed differences in effectiveness between advertising and promotion, as reported by Little (1975), did not have long-term sales or profit consequences. Conversely, gradual price increases offered a sizeable long-run profit opportunity for the target brand in this mature category. ...
An intuitively appealing decision rule is to allocate a company's scarce marketing resources to where they have the greatest long-term benefit. This principle, however, is easier to accept than it is to execute, because long-run effects of marketing spending are difficult to estimate. The authors address this problem by examining the behavior of market response and marketing spending over time and identify four common strategic scenarios: business as usual, hysteresis in response, escalation, and evolving business practice. The authors explain and illustrate why each scenario can occur in practice and describe its positive and negative consequences for long-term profitability. The authors propose to use multivariate persistence measures to identify which of the four strategic scenarios is taking place and illustrate this approach in the pharmaceutical and packaged-food industries. The results substantiate the authors’ proposition that the strategic scenario is a major determinant of marketing effectiveness and long-term profitability. This conclusion sets up a substantial agenda for further research.
... sponse is consistent with the economic and behavioral assumptions of the theory underlying many advertising response models in the literature (Kotler 1980, Little 1979). ...
... The preceding discussion has shown that nonlinear equilibrium response functions such as (3) are theoretically plausible representations of the relationship between share of market and share of marketing investments. There is also considerable empirical support for such formulations (Kotler 1980, Little 1981, Naert and Weverbergh 1981. ...
... Both econometric and judgmental methods remain available for this purpose. While each of these approaches has its own pitfalls and limitations (Chakravarti, Mitchell, and Staelin 1981;Little 1970Little , 1975Little and Lodish 1981), these traditional procedures may be expected to provide better and more meaningful parameterization than calibrations based on analogies to warfare theory. ...
This article critiques the “new paradigm of marketing strategies” proposed by Cook (JM, Spring 1983). It is shown that his formulation of the strategic marketing ambition and differential advantage concepts does not provide general or actionable links between a firm's market performance goals and its marketing mix decisions. The consequent limitations of Cook's framework are discussed, and alternative formulations that may overcome these limitations are suggested.
... Advertising response analysis has been a key component of marketing decision support systems for more than 30 years (Benjamin and Maitland 1958;Benjamin, Jully, and Maitland 1960;Little 1966;Lodish 1986). Senior managers at General Electric (Lillis and McIvor 1985), Kodak and General Motors (Barabba 1985), Carnation (Struse 1985), and Matte1 (Hatch 1980) describe how such analyses have improved advertising decisions. ...
... Senior managers at General Electric (Lillis and McIvor 1985), Kodak and General Motors (Barabba 1985), Carnation (Struse 1985), and Matte1 (Hatch 1980) describe how such analyses have improved advertising decisions. Ten years ago, Little (1979a) reported that more than $400 million was being spent annually on data and experiments for market response analysis. We expect that figure has more than doubled today with the advent of automated data collection by supermarket scanners and split-cable television advertising tests. ...
... We expect that figure has more than doubled today with the advent of automated data collection by supermarket scanners and split-cable television advertising tests. Academic interest is high as a variety of researchers investigate how to estimate response curves accurately (see, among others, Assmus, Farley, and Lehmann 1984;Chakravarti, Mitchell, and Staelin 198 1 ;Little 1975;Mahajan, Muller and Sharma 1984;Rao and Sabavala 1986). ...
Consumers choose from a relevant set of brands. Advertising encourages consumers to consider a brand as relevant. Price and other variables influence consumer choice among relevant brands. The authors examine how the explicit consideration of competitive response affects managerial recommendations and explore the interaction between price and advertising decisions. They consider cases in which managers do and in which they do not anticipate competitive actions.
... In this way, the model can become more complex in each step, while is still remains easy to communicate. Often, the evolutionary model building process is added to the other aforementioned implementation criteria (Little 1975a, and1975b). ...
... The development over time of the present study gives an illustrative example of the intuition behind the model building process that was developed by Little (1970Little ( , 1975aLittle ( , 1975b. The ultimate goal (and ideal point) of the research was to come up with an easy-to-implement tool for expert combination, which would deal with all possible biases in the (subjective) judgments of experts. ...
In this study, focus is on a systematic way to detect future changes in trends that may affect the dynamics in the agro-food sector, and on the combination of opinions of experts. For the combination of expert opinions, the usefulness of multilevel models is investigated. Bayesian data analysis is used to obtain parameter estimates. The approach is illustrated by two case studies. The results are promising, but the procedures are just a first step into an appropriate combination of expert combination, which has to be completed on important issues, such as the identification of some well-known biases.
... Throughout the late 1960s and early-to-mid 1970s, a variety of decision support applications were developed (Ferguson and Jones 1969;Little 1975). Many academicians with backgrounds in management science/operations research were attracted to the field because of the practical value of these new kinds of applications. ...
... According to Mallach (1994), a DSS architecture should define and specify the following components: ...
For MIS specialists and nonspecialists alike, teacher and consultant Dan Power provides a readable, comprehensive, understandable guide to the concepts and applications of decision support systems. Power defines DSS broadly: interactive computer-based systems and subsystems that help people use computer communications, data, documents, knowledge, and models to solve problems and make decisions. This book covers an expanded framework for categorizing Decision Support Systems (DSS), a general managerial and technical perspective on building DSS, details and examples of the general types of DSS, and tools and issues associated with assessing proposals for DSS projects. A glossary and DSS readiness audit questions give special, ongoing value to all readers. Free eBook at https://scholarworks.uni.edu/facbook/67/
... DSS was launched in portfolio management [10]. Brandaid DSS was implemented for decision support of marketing [11]. Keen and Scott Morton book titled "Decision support systems: an organizational perspective" was one of the first architectural, models and methods summery on the field of DSS [12]. ...
Mankind has always pursued knowledge. Over the philosophy questions by Paul Gauguin (“where do we come from, what are we, where are we going”), the science may answer these questions. In every scientific field, empirical and theoretical researches are making to describe natural processes, to better understand the universe. The questions are modified by the scientists: are these two variables correlated, do two or more independent dataset show some connection to each other, does a selected parameter have effect on the second parameter, which prediction can we state from independent variables for the dependent variable. But the effort for seeking the knowledge is the same. In this paper, our Universal Decision Support System concept is presented to help to answer questions related to different scientific field.
... To furnish optimal recommendations, marketing analytics companies use models and methods often invented by academic researchers from various scientific disciplines. For instance, marketing mix models find their roots in the pioneering work of Little (1975), while methods for estimating dynamic models rest on the foundations laid by Fisher's (1922) likelihood principle and Kalman's (1960) filter. In summary, brand managers make the decisions on marketing budgets and allocations; marketing analytics companies conduct the analyses using existing models and methods to facilitate marketing decisions; and academic researchers invent new models and methods that enable informed decision making and thus improve the practice of marketing. ...
Managing marketing resources over time requires dynamic model estimation, which necessitates specifying some parametric or nonparametric probability distribution. When the data generating process differs from the assumed distribution, the resulting model is misspecified. To hedge against such a misspecification risk, the extant theory recommends using the sandwich estimator. This approach, however, only corrects the variance of estimated parameters, but not their values. Consequently, the sandwich estimator does not affect any managerial outcomes such as marketing budgeting and allocation decisions. To overcome this drawback, we present the minimax framework that does not necessitate distributional assumptions to estimate dynamic models. Applying minimax control theory, we derive an optimal robust filter, illustrate its application to a unique advertising data set from the Canadian Blood Services, and contribute several novel findings. We discover the compensatory effect: Advertising effectiveness increases and the carryover effect decreases as robustness increases. We also find that the robust filter uniformly outperforms the Kalman filter on the out-of-sample predictions. Furthermore, we uncover the existence of a profit-volatility trade-off, similar to the returns-risk trade-off in finance, whereby the volatility of profit stream decreases at the expense of reduced total profit as robustness increases. Finally, we prove that, unlike for-profit companies, managers of nonprofit organizations should optimally allocate budgets opposite the advertising-to-sales ratio heuristic; that is, advertise more (less) when sales are low (high).
Data and the web appendix are available at https://doi.org/10.1287/mksc.2016.1010 .
... The preceding discussicKi has shown that nonlinear equilibrium response fiinctions such as (3) are theoretically plausible rejsesentations of the relationship between share of nutrket and share of nrmrketing investments. There is also considerable empincal support for such formulations (Kotier 1980, Little 1981, Naert and Weverbergh 1981. ...
This article critiques the "new paradigm of marketing strategies" proposed by Cook {JM, Spring 1983). It is shown that his formulation of the strategic marketing ambition and differential advantage concepts does not provide general or actionable links between a firm's market performance goals and its marketing mix decisions. The consequent limitations of Cook's framework are discussed, and alternative formula- tions that may overcome these limitations are suggested.
... Envision six independent domain experts meeting in a conference room with a blackboard. Five of these experts (E1-E5) are specialists in different but related domains and the sixth expert (E6) has the ability to synthesize the most brilliant Product sales forecasting Product/price Silk andUrban, 1978 Urban andHauser, 1980 Advertising design Promotion Burke et al., 1990 Market segmentation Place/promotion Borch and Hartvigsen, 1991 Sales-call planning Place Lodish, 1971;Zoltners and Sinha, 1980 Media mix Promotion Little and Lodish, 1969 Marketing mix Price/product Little, 1975;Arinze, 1990 [ 26 ] David G. Schwartz Concurrent marketing analysis: a multi-agent model for product, price, place and promotion Marketing Intelligence & Planning 18/1 [2000] 24±29 statements of the other five and final solution to a problem. E6 begins the session by taking the single piece of chalk available and writing a question on the blackboard. ...
Marketing management presents challenging ground for the integrated use of intelligent agents. The design of a strategic marketing plan presents a natural division of four distinct yet independent decision problems. Concurrent execution of these four functions can yield a significant decrease in time-to-market for new products, and turnaround time for modified products. Electronic information is a two-edged sword. On the one hand, it results in information overload, taxing the decision- making process. On the other hand, the electronic nature of these vast information flows makes them accessible to intelligent agents who can improve the decision-making process. This paper presents the problem of information overload and decision-making complexity in marketing management. In it, we present a blackboard-based agent architecture that allows individual agents to proceed toward their given goals yet react opportunistically to developments reported by other marketing agents. We present the blackboard model of problem solving, and discuss why it is well suited to support multiple marketing agents. CMA, a multi-agent model for concurrent marketing analysis, is discussed and the agent architecture required for creating a cooperating society of marketing agents is presented.
According to the New European Bauhaus, sustainability, aesthetics and inclusion should guide the renewal of our consumption and production lifestyles, based on a place-based and human-centred approach at different scales: territorial, urban, neighbourhood or small municipality. On the other hand, both the ecological transition and the resource circularity should support the revolution of project processes, methods and tools in a life-cycle perspective. This will take at least 25 years, but immediate action is urgently needed to redefine the boundaries of the future balance between humankind, environment and resources that are at risk.
In this scenario, the architectural heritage could become a resource—cultural, social and economic, but also environmental—as a stock of materials and energy embedded in the built environment. The historic centres (HC), a particular part of this heritage, are threatened by increasing depopulation and abandonment phenomenon as a paradigm of the linear approach to the built environment: HC embody values and resources that should be preserved and kept in use with a view to resources circularity. However, possible valorisation and regeneration strategies presuppose interventions that put at risk the values as well as the embedded resources.
The paper presents an ongoing methodology for the analysis and representation of small historic centres, based on a multidimensional model mapping that aggregates complex data on state of conservation and use, material consistency, residual performances, material and immaterial values. The intersection of new uses, performance improvement and associated environmental impacts describes the small historic centre’s margins of adaptability and resilience to potential risk of loss and alteration, by providing future design scenarios for regenerating and adapting.
A customized, stepwise, log-linear, distributed lag, restricted market response model is proposed to estimate the effects of various elements of promotion expenditures on sales in the presence of potentially significant effects due to trend and/or seasonality when using time-series data. As distinct from standardized software packages, the customization offers management several benefits: (a) an (optional) imposition of prior restrictions on the directions of the coefficient variables; (b) an empirical determination of the lag structure for selected variables; (c) the detrending of the data to allow for the assessment of incremental marketing mix effects above trend; and (d) a simplified sensitivity analysis. The model is empirically tested and validated using sales data for a brand where the impact of several marketing mix variables is estimated and investigated via policy simulations. A comparison of these results with those obtained from a corresponding unrestricted model illustrates the advantages of this approach. Finally, the limitations of this procedure and directions for future research are discussed.
A decision-making approach integrating information from managerial experience with that provided by marketing models is proposed. The approach suggests that managers should learn to value more those marketing control variable levels that have been linked to favorable changes in performance in order to make more effective and profitable decisions.
This paper presents a comparative analysis of the findings of two field studies and three recent laboratory experiments that assessed the efficacy of judgment based models in aiding marketing decision making. This analysis indicates factors that may affect the effectiveness of these models. The implications of the findings for users of judgment based marketing decision models as well as model builders are discussed, and suggestions are made for future research to improve the models’ effectiveness.
A customized, stepwise, log-linear, distributed lag, restricted market response model is proposed to estimate the effects of various elements of promotion expenditures on sales in the presence of potentially significant effects due to trend and/or seasonality when using time-series data. As distinct from standardized software packages, the customization offers management several benefits: (a) an (optional) imposition of prior restrictions on the directions of the coefficient variables; (b) an empirical determination of the lag structure for selected variables; (c) the detrending of the data to allow for the assessment of incremental marketing mix effects above trend; and (d) a simplified sensitivity analysis. The model is empirically tested and validated using sales data for a brand where the impact of several marketing mix variables is estimated and investigated via policy simulations. A comparison of these results with those obtained from a corresponding unrestricted model illustrates the advantages of this approach. Finally, the limitations of this procedure and directions for future research are discussed.
Specification is an important step in the model building process. As discussed in Chap. 1, the goal of this step is to express the most important elements of a real-world system in one or more mathematical equations. In other words: the outcome of this step is a formula that summarizes the most important relationships of the phenomenon that we are studying.
In this chapter we consider methods and procedures for the estimation of model parameters in cases where the basic assumptions of the general linear model are violated. When this is the case we need either other specifications and/or other estimation methods: “re-estimation
”. In Sect. 6.2 we introduce Generalized Least Squares (GLS) estimation methods.
Product line selection has been a complex problem for marketers. From consumer product group companies to airlines industry, marketers have had to make decisions considering a range of market- and firm-specific factors leading to product line profitability or market share or revenue. While a large product mix in terms of breadth (number of product lines offered), length (vertical differentiation in the line through features), and depth (horizontal differentiation, i.e. color, flavor, pack size) offers the firm various advantages, they may not be sustainable in the long run. Hence, marketers need to have a robust decision model in a dynamic business environment characterized by uncertainty and lack of information while making product line selection decisions. This chapter examines the interplay of multiple conflicting decision objectives and bounded rationality of the decision maker. It recommends a methodology for marketers to arrive at a satisficing product line selection strategy, which is both satisfying and sufficing the constraints and desired objectives. From a theoretical perspective, the proposed model considers a set of intra-firm, inter-firm, and multi-market competition variables. Given the marketer’s bounded rationality and hence the inability to define the goals of the decision objectives precisely, there is a need for firms to transform product line selection strategy for sustained profitability. The methodology suggested in this chapter is multi-objective mixed-integer fuzzy-goal programming. The proposed methodology is a significant departure and transforms the traditional product line selection decision models. However, empirical validation of the model is beyond the scope of this chapter.
Supporting decision making is an important and potentially transformative research topic that is challenging for academic researchers to study. Anecdotal evidence suggests that computerized decision support systems (DSS) can improve decision quality and change the structure and functioning of organizations. To make progress in our understanding of this phenomenon there is an ongoing need for more decision support case study field research that includes documenting decision support impacts. Research case studies help understand the use and consequences associated with building and using computerized decision support. More descriptive and technical information about specific DSS will be helpful in explaining the variability of these technology artifacts. Current theory related to computerized decision support is inadequate and research case studies can potentially assist in theory building. The possibilities for improving and increasing decision support continue to evolve rapidly and research case studies can help define this expanding, changing field of study. More “good” case studies and more details about each specific case is useful, helpful, and a significant contribution to understanding how computing technologies can improve human decision making.
The transformation from classic monolithic systems to distributed, more intelligent and autonomous e-Work networks has resulted in the development of many tools for supporting e-Systems over the last decade. In Chapter 4, we discussed agents and agent-based systems, as a basic cornerstone of collaborative e-Work along with the principal techniques for the optimal design and control of e-Systems based on them. This chapter introduces decision support systems (DSS) and communication protocols, as the other essential supporting tools for collaborative e-Work, along with more details on various types of software agents (Fig. 5.1). The human-/computer-integrated tools, i.e., computer integrated manufacturing (CIM) and human-computer interaction (HCI), will be discussed in the next chapters. Decision support systems refer to an interactive computerized system that helps people make decisions based on information that is culled from a wide range of sources. Protocols provide an efficient and effective platform for autonomous entities to interact and be productive. Software agents enable the automation and integration of flexible, adaptive and heterogeneous activities, components, and tasks in a collaborative network.
In travel and tourism, like elsewhere, application of standard optimization routines to marketing decision making is straightforward as soon as the relationship linking market response to input has been modeled adequately. A tailor-made decision calculus procedure eliciting managerial judgements on the pairwise relative importance of market share influence factors provides the weights otherwise inaccessible by objective parameter estimation. Though the procedure has been in practical use successfully for two years, focus in this paper must be on theoretical foundations due to space limitations.
The Finnish academic marketing research and the marketing research made in practice can be clearly distinguished. However, there are many efforts to narrow the gap and marketing information systems are emerging which constitute a positive factor in the use of analytical marketing models and methodology in business decisions. The use of marketing concept in extending to the areas of of industrial marketing, marketing of services and nonprofit and government marketing. There is also a growing interest in strategic marketing planning.
This paper presents to a large extent recent efforts in the field of academic marketing research in Finland. The natural starting point is the distribution research, which has strong traditions in all Nordic countries. In Finland the competitive, open market has been gradually replaced by four vertical marketing systems or blocks, which account for about 90 per cent of the grocery trade at the retail level. There is also a tendency towards integration in the specialty goods and industrial goods trade. The result has been increased competition between systems. There is the problem how to manage a whole interorganizational system, which consists of numerous enterprises of varying sizes. These problems have been recognized by distribution channel managers, government agencies and academicians. It is suggested that the behavioral and economic aspects are both important in the research of interorganizational coordination. The performance of the entire vertical marketing system is dependent on each of its subunits. The productive capacities of subunits constitute the most challenging areas in the strategic planning of marketing systems.
Among the decision areas of marketing mix pricing is perhaps the least analysed with behavioral concepts and methodology. Cost-based pricing strategies are still dominant in Finnish business life. Different ways to measure consumer price perception and price sensitivity are discussed in a brand choice situation. The problems in applying simulation models in the decision making of marketing management is next considered by a representative of managers. Finally, the possibilities of descriptive modeling 1n organ1zat1onal buying analysis are illustrated and the results of a Finnish intensive study on the buying process of industrial components are reported.
The presentation of the state of the art of marketing decision models can be performed in, at least, the two following ways:
(1)
Presentation of the outcomes of the model building process, that is, a presentation of models which have been built.
(2)
Presentation of the research needs and opportunities resulting from a discussion of the steps of the model building process.
Since the early 1950’s the scope of mathematical techniques in marketing has expanded far beyond ‘classical statistics’ and we have seen enormous productivity of model builders in marketing. Pioneering work in this area, among others, has been performed by Verdoorn [1956] and Ferber and Verdoorn [1962]. Yet the number of marketing models that are actually used in private industry or in the public sector remains rather low. Since the early 1970’s the problem of implementation has been of concern to many practitioners and academicians. This means that we are now coming into an era of model building in marketing, where emphasis is on models that are good representations of reality but that are also easy to use: implementable marketing models.3
The purpose of this study is to give a survey on deterministic dynamic advertisingsales-response models. First some empirical founded advertising-sales-effects are used to formulate modeling requirements which have to be fulfilled by empirical relevant sales-response models. Several alternative ways of modeling sales-advertising relationships are then discussed and critically compared whether they fulfil the derived modeling requirements for empirical relevance or not. Directions for potential generalizations are finally discussed.
One of the objectives of the HECTOR (HEterogeneous Computers TOgetheR) project of IBM and the University of Karlsruhe was to encourage scientists from different disciplines to strengthen the use of hard- and software in their fields of research and teaching. This paper reports on first attempts to develop and implement marketing decision support software which — in further steps — will be integrated into marketing courses at the Faculty of Economics.
The rapid development of Web technology has opened a new approach to Decision Support System (DSS) development. For instance, Social Media is one of the Web 2.0 digital platforms that allow the creation and exchanges of user-generate content through an interactive interface, high user control and mass participation. The concept and characteristics of Web 2.0 such as remote, platform-independent, context-rich and easy to use, which is fulfill the concept and purpose of DSS. This paper outlines some of the elementary concepts of Web 2.0 and social media technology which can be potentially integrated within DSS to enhance the decision-making process. Our initial investigation indicates that there is limited study attempt to embed Web 2.0 into DSS. Thus, this paper highlights the importance of Web 2.0 technology in order to foster the betterment of DSS development and its usability.
A response model reproduces the dependence of one variable on a set of different variables. The independent variables of market response models consist of marketing instruments (like prices, advertising budgets, sales force size, distribution) and environmental variables (like GDP, weather), their dependent variables are marketing goals (like market volume, sales, market share). Market response models represent essential components of marketing decision models that serve to either assess normative consequences of given marketing policies or to determine optimal marketing policies (i.e. levels of marketing instruments) (Parsons and Schultz [1978], Lilien and Kotler [1983], Hanssens, Parsons, and Schultz [1990], Hruschka [1995]).
This chapter places decision automation in abroader context of using information technologies in decision making. Key definitions
and abrief history of computerized decision support create important boundaries. Then adescription and explanation of characteristics
of computerized
decision support systems (DSS) clarifies this application domain. After reviewing management information needs, amodern
taxonomy of decision support systems is briefly summarized. Issues associated with building DSS and various architectures
are reviewed before concluding the overview.
The launch strategies of new consumer goods have not been subject to rigorous study, although anecdotes abound in the form of news items in newspapers and specific industry journals. This contrasts with the copious research into new industrial product launch which shows that, prior to commercialization, the process of developing product advantage commensurate with the requirements of the target market is central to management thinking. In this article, we seek to answer the question 'how are new consumer products launched and with what performance outcomes?' To do this, our focus is on the details of the decision content of the launch. The paper is divided into three sections. First, we review the literature to elicit the details of what comprises a new product launch and discuss these details within the context of consumer new product launches, developing a series of specific research questions. Secondly, we describe a survey of 208 new consumer product launches in the UK. Finally, we present our findings and discuss the results.
The development of Operations Research (OR) after World War II (WWII) was greatly influenced by OR pioneers who applied their
knowledge and experiences to the problems of business and industry. In parallel, they brought OR methods and practices into
academia, and created the environment for the development and training of a new cadre of OR practitioners and researchers.
In the forefront of this new group, we find John D. C. Little. From being the first to receive a Ph.D. in OR, John went on
to leave his own lasting imprint on the field as a progenitor of the field of marketing science and its applications, the
person behind the eponymous Little’s Law for queues, an influential academic leader, and a highly successful OR researcher,
practitioner, and entrepreneur.
Today, a large number of powerful analytic methods is available to marketing managers. In many cases, though, managers solve marketing problems based only on their experience, judgements, and beliefs rather than using a quantitative model. Even when a quantitative model is applied, human judgement is necessary to decide whether the model is appropriate and to interpret the results. This paper presents how Artificial Intelligence concepts can be applied to identify the marketing managers' problem solving approaches and capture them in a computer program. Specifically, an expert system for market share analysis of a consumer brand is developed.
Application of standard optimization routines to advertising decision-making is straightforward once the relationship linking market response to input has been modeled adequately. Sometimes, however, advertising management has to cope with decision situations where econometrics fail to detect a reliable relationship. A new decision support procedure eliciting managerial judgments on the pairwise relative importance of market share influence factors provides the factor weights otherwise inaccessible by objective parameter estimation. Allocation of an advertising budget to international travel markets thus becomes susceptible to optimization through dynamic programming.
This article describes the development and testing of a forecasting system for retailers who plan periodic promotions. When the number of variables describing the promotion is large relative to the historical database of past promotions, traditional forecasting approaches cannot be applied. In such cases, retailers must rely on the expertise of their buyers to subjectively estimate promotional unit sales. This research develops a Case-Based Reasoning system that allows all buyers to forecast promotional sales as accurately as the organization's expert buyer and, by making the subjective process explicit, also provides an avenue to improve forecast performance over time. The system (1) selects the historical analogs that are most similar to the planned promotion, (2) adjusts the sales of each analog to account for any differences between the analog and the planned promotion, and (3) combines the forecasts derived from the multiple analogs to arrive at a single sales projection. The performance of the system was tested and found to compare favorably to the performance of an expert buyer in a large national retail organization.
Today managers and policy makers are confronted with an overwhelming range of choices of computer software to develop decision support systems (DSS). The authors argue that DSS language evaluation and selection should be a multi-step process involving most, if not all, of the following: 1. End User Needs Assessment and Problem Diagnosis 2. Critical Success Factor Identification 3. Feature Analysis and Capability Review 4. Demonstration Prototype Development 5. External User Surveys 6. Benchmark and Simulation Tests 7. Programmer Productivity and End User Orientation Analysis The objectives of each of these activities are described, as well as specific procedures for accomplishing them. In addition, the authors discuss the usefulness of a multi-disciplinary task force to accomplish the DSS language evaluation and selection process.
Although there have been many research articles about how to measure service quality, how service quality perceptions are formed, what effect service quality has on behavior, and service quality’s financial impact, there has been little discussion to date of the potential impact of service quality on competitive marketing decisions. This paper considers directly the issue of how an analysis of the impact of comparative service quality can inform tactical marketing decisions in a competitive marketplace. We propose and empirically demonstrate a simple theoretical framework of how market share changes result from changes in service quality, by the focal firm and/or by a competitor. In addition we show how price changes trade-off against changes in service quality, and how comparative customer value is affected by changes in service quality and/or price. Our framework enables us to evaluate the projected market share shifts produced by proactive changes in service quality and/or price, and also enables us to evaluate the projected effectiveness of reactions to competitors’ changes in service quality and price. For example, our framework suggests that a quickly-implemented increase in service quality (rather than a matching price cut) may sometimes be an effective tactical response to a competitor’s price cut. We illustrate the implementation of our framework on actual longitudinal industry data. We show how the market share impact of changes in service quality and/or price can be projected, and how this information can be used to drive competitive marketing decisions.
Focuses on product advantage, a major contributing factor to new product performance, by examining the launch strategies associated with high and low levels of product advantage. Views a launch strategy as integrating protocol decisions, which have steered the course of a product’s development with the tactical marketing mix decisions. Data confirm all associations between key elements of new product protocol and product advantage. Growth-related objectives guide the development of new products with high advantage, while the speedy development and early timing of the projects, the focus on growth markets, and the use of a niche targeting strategy are the hallmarks of products with high advantage. Contends that companies offering the world a better mousetrap do not believe the myth that a path to its door will be beaten; the better mousetrap requires and receives a different launch treatment from more pedestrian competitors.
The adoption of a model-building approach to marketing is today inevitable, due to improvements in hardware and software and the increased professionalisation of marketing and its techniques. Aggregate response models are focused upon, particularly the issues of which responses are realistic and should be modelled, how the response can be expressed and how a choice can be made between options available. The traditional model-building process is described, and the inclusion of correct variables found to be critical, the primary means of doing this being statistical analysis. Simple expressions perform as effectively as more complex ones, and should be used if able to give operationally meaningful results. Cross-correlation analysis and biased estimation techniques provide good guides to usable variables and their effects.
There is considerable evidence that firms and institutions that have adopted and utilized operations research and related management sciences (OR/MS) experience severe internal disruptions and changes as the new technology is introduced. In some cases the reactions have been adverse enough to result in temporary rejection of the technology and a period of three to five years has been necessary for reintroduction of OR/MS. Such lags represent lost opportunities with attendant costs that firms and institutions can ill afford. There is obvious need to manage the introduction of OR/MS into organizations to minimize the inevitable resistances to change and to expedite institutional acceptance and practice. This paper presents a normative framework for this purpose based on four case studies of Indian implementation experience. The selected firms, which represent different sectors of industry, adopted different strategies for introduction and diffusion of OR/MS. The authors have noted several points of similarity between the Indian experience and the experience of U.S. organizations which indicate a fair measure of general validity for the proposed normative framework.
An empirical analysis indicates that the order of entry of a brand into a consumer product category is inversely related to its market share. Market share is modeled as a log linear function of order of entry, time between entries, advertising, and positioning effectiveness. The coefficients of the entry, advertising, and positioning variables are significant in a regression analysis on an initial sample of 82 brands across 24 categories. These findings are confirmed by predictions on 47 not previously analyzed brands in 12 categories. Managerial implications for pioneers and later entrants are identified.
This paper develops a model and an associated estimation procedure to forecast and control the rate of sales for a new product. A repeat-purchase diffusion model is developed, incorporating the effect of marketing variables---detailing force effects in particular---as well as a word-of-mouth effect. Bayesian estimation, with priors developed from past products is used to estimate and update the parameters of the model. The procedure is used to develop marketing policies for new product introduction.
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