Marketing Decision - Making

Today's marketing managers are expected to make complex decisions amid an excess of information and a lack of skills, experience, time and patience. Each marketing decision varies in terms of the problem that has to be solved, the environment in which the problem is solved, and the decision maker who has to solve the problem. To help marketing decision makers make the best possible decisions, marketing science has developed various decision aids, termed Marketing Management Support Systems.

Keywords Decision Aids; Marketing; Marketing Decision-Making; Marketing Management Support Systems (MMSS); Programmability

Marketing > Marketing Decision-Making

Overview

Marketing is the process of planning and executing the conception, pricing, promotion and distribution of ideas, goods and services to create exchange and satisfy individual and organizational objectives. The academic field of marketing formally began shortly after the turn of twentieth century and is now some 100 years old. Due to the sheer volume of products and brands, the ever increasing amount of market segments, the intensity of competition and the overall acceleration of change, marketing decision situations are often complex as decisions need to be made under increasing time pressure.

Decision making, also referred to as problem solving, is the process of recognizing a problem or opportunity and finding a solution to it. Many decisions are relatively simple and routine, but managers are also faced with decisions that can drastically affect the future outcomes of the business. The term Marketing Decision Making refers to the way marketing managers go about making decisions, as well as the decision aids that support marketing managers in the preparation, execution, and evaluation of marketing activities.

It is important that good business decisions are made as wrong decisions can easily lead to the failure of an entire organization. However, decision makers may not have all of the requisite information, skills, experience, time and even patience, to make the best decisions all the time. Even if marketing decision makers do have right type of information, it is likely that they will have too much information, since they are confronted with a constant stream of information about the market and the position of products — with formal data and informal cues about customers, distributors, competitors, and so forth (Wierenga & van Bruggen, 1997).

Faced with these challenges and more, marketers have moved from a reliance on intuition in decision-making, to the use of tools made possible by developments in statistics, model building, knowledge engineering, and information technology. Marketing decision aids, also called Marketing Management Support Systems (MMSSs), were first developed by marketing scientists in the early 1960s. Initial efforts centered on using computers to build complex models that searched for the optimal solution to a problem. The first concept introduced was a simple but robust marketing model that usually required judgmental input from the manager. This was called a decision calculus model. The next concepts were marketing information systems, marketing decision support systems, marketing expert systems, and marketing case-based reasoning systems, among others.

Further Insights

Each marketing decision situation or problem is characterized by three basic factors. These are: (i) the problem that has to be solved; (ii) the environment in which the problem is solved; and (iii) the decision maker who has to solve the problem (Wierenga, van Bruggen & Staelin, 1999).

The Problem that Has to be Solved

The most important problem characteristics are structuredness and programmability; depth of knowledge; and availability of data. The first problem characteristic, structuredness and programmability, involves the extent to which relevant elements of a problem and the relationships among those elements are known. It is the extent to which a decision can be made by using relatively routine procedures instead of more general problem-solving techniques (Perkins & Rao, 1991).

Programmed decisions are routine and structured with a well-defined starting point, a clear goal, and standardized rules for reaching the goal. They are repetitive enough to allow for the establishment of definite procedures to process them, and the decision maker usually knows from the beginning what the solution and outcome will be.

Non-programmed decisions, on the other hand, are ill-structured and have few guidelines. They involve novel problems that cannot be processed by a pre-specified method and require the decision maker to rely on general problem-solving abilities. Neither the appropriate solution nor the potential outcome is known. When making unprogrammed decisions, managers must exercise judgment, which depends on their experience, insight, and intuition.

Examples of relatively programmable and structured marketing problems are sales management and sales-force decisions, and media planning for advertising. Less-structured problems include designing marketing communication, developing a marketing strategy, and introducing new products.

The second problem characteristic, depth of knowledge, refers to generalized knowledge gleaned from scientific research regarding a problem or issue. The third problem characteristic, data, helps decision makers to form an impression of the mechanisms in a market.

The Environment in which the Problem is Solved

Decision environment characteristics, and in particular, time constraints, market dynamics and organizational culture, affect marketing decision-making. When time is short, the quickest way to solve a problem is to consult one's memory and search for similar cases experienced before (Wierenga & van Bruggen, 1997). When the market dynamics feature turbulent market conditions, marketers will find it difficult to understand and interpret what is happening. Stable markets, on the other hand, are more structured and thus easier to understand and interpret.

The organizational culture of a company or department will influence the prevailing attitudes and the approach to doing things, including the approach to decision-making. This will influence the way in which marketing managers go about problem solving in their domain.

The Marketing Decision Maker

The third factor that characterizes the marketing decision situation is the decision maker: His or her cognitive style, experience, education, and skills. Decision makers select, evaluate, and combine information that is available either internally or externally. Cognitive style refers to the process through which a marketing decision maker perceives and processes information. It is the organization of information in memory and the repertoire of rules for using that information. A low-analytical decision maker is more likely to use a decision aid than a high-analytical decision maker.

A decision maker with a high degree of professional experience (like a marketing decision maker) will have dealt with a large number of practical marketing problems and their solutions. Studies comparing experts and novices suggest that experts have more highly developed cognitive structures, which allow for effective problem structuring and successful problem solution.

The effects of experience are more pronounced in less programmed, unstructured decisions than in the more programmed decisions. Experts are likely to understand the uncertainties and consequences of their decisions better than their inexperienced counterparts. Novices, on the other hand, are more likely to use decision aids (Perkins & Rao, 1991). Experts and knowledgeable decision makers are likely to search for more information than normal, selecting information that is relevant and important. They are also better able to acquire information in a less-structured environment, and are more flexible in the manner in which they search for information. They will also agree more than novices regarding what information is important.

On the downside, experts are more likely to focus on rare events, but often at the expense of undervaluing base-rate information. When tasks are extremely unstructured, though, even experts cannot apply known solution strategies. Instead, they must employ heuristics (a method of problem solving that uses trial and error as well as rules of thumb to take shortcuts to a solution), and their judgments are subject to all the biases associated with human judgment processes.

The more relevant the education and skills of a decision maker, the more capable he or she will be in handling decisions. The combination of cognitive style, experience, education and skills of a decision maker results in his or her choice of Marketing Problem-Solving Modes (MPSMs). Different marketing decision makers may use different MPSMs, and the same decision maker may use different modes at different times.

There are four different MPSMs. They are: Optimizing, reasoning, analogizing, and creating. When optimizing, the decision maker has clear insight into the way processes work. This is represented by a mathematical model, which describes the relationships between the relevant variables in a quantitative way. When reasoning, decision makers also translate external events into internal models, which they manipulate.

Analogizing refers to a decision maker's natural inclination to bring to bear the experience gained from solving similar problems. Unlike analogizing, during the creating mode, a marketing decision maker searches for concepts, solutions, or ideas that are novel in order to respond to a situation that has not occurred before (Wierenga & van Bruggen, 1997). The decision maker combines known but previously unrelated facts and ideas in such a way that new ones emerge.

Creating can refer to all aspects of marketing management, including the generation of ideas for new products or services, innovative advertising or sales-promotion campaigns, new forms of distribution, and ingenious pricing. Creativity is often the means for firm survival and growth (Wierenga & van Bruggen, 1997).

When confronted with a single unique decision and no extensive past history, marketing decision makers often rely on the judgments of several individuals. Since it is unlikely that several individuals would agree on the same decision, the marketing manager must process their assessments according to his or her own judgment and the degree of belief assigned to each assessment.

In general, group decision-making allows for the generation of more input and more possible solutions to a situation. There is shared responsibility for the decision and its outcome, so that no single person has total responsibility. The disadvantages are that it often takes a long time to reach a group consensus, and group members may have to compromise in order to reach a consensus.

An interesting group decision-making model is the Delphi estimation process, where anonymous judgments are collected through questionnaires. The median responses are summarized as the group consensus, and this summary is fed back along with a second questionnaire for reassessment. This process retains the advantage of several judges while removing the biasing effects which might occur during face-to-face interaction (Best, 1974).

Marketing Management Support Systems (MMSS)

An MMSS can support a decision maker in different ways: It can help the decision maker carry out calculations (for instance, to find an "optimal" value); it can support the analysis and diagnosis of a specific situation; or it can make suggestions for users that stimulate the generation of new solutions. For some, the greatest benefit of MMSSs is that they can help frame the important issues and uncertainties associated with the problem at hand, and in the process help the decision maker come to an acceptable decision.

Decision-making aids come in various forms. Some, for instance, are data-driven, while others are knowledge-driven. However, all MMSSs consist of a combination of four components that determine their capability and functionality. These components are as follows:

1. Some form of information technology: Hardware such as computers, workstations, optical scanning technology and telecommunication systems; and software such as database management programs, spreadsheets and graphical/communication software.
2. Analytical capabilities: Statistical packages for analyzing marketing data, parameter-estimation procedures, marketing models, and optimization and simulation procedures, for example.
3. Marketing data: Quantitative information about variables such as sales, market shares, prices, one's own and one's competitors' marketing-mix expenditures, distribution figures, and so on.
4. Marketing knowledge: Qualitative knowledge about such things as the structure of markets (submarkets and market segments), the suitability of specific sales-promotion campaigns, typical reactions to advertisements, heuristics for the acceptance of clients, and so on (Wierenga & van Bruggen, 1997).

Several types of MMSSs can be distinguished, each type having a different combination of the four components. The oldest type of MMSS is the marketing model, which dates back to the early 1960s, and which aims at finding the best solution to a problem. In this approach, a mathematical representation of the relevant marketing phenomena is first developed. Optimal values for the marketing-mix variables are derived through techniques such as differential calculus and operations research techniques, such as linear and integer programming and simulation. Econometric techniques are also required to handle marketing data, and when there is no data available, a manager's judgment can be used to calibrate the parameters of the model. Examples of marketing models are MEDIAC for media planning and SH.A.R.P. for shelf-space allocation in supermarkets.

Marketing Information Systems (MKISs)

Marketing Information Systems (MKISs) emerged in the second half of the 1960s, when the concept of management information systems was applied to the field of marketing. The main function of an MKIS is to provide information about what is going on in the market and to examine the causes of observed phenomena. MKISs answer the questions, "What is happening in the market?" and "Why did it happen?"

MKISs are passive systems: They provide information, but it is up to the marketing decision maker to attach conclusions to this information and to decide whether to act on those conclusions. MKISs typically provide data about marketing indicators on a regular basis. Thus, a marketing research or information systems department may send out marketing figures on a monthly basis to relevant employees in the company, sometimes sending different figures to different persons, depending on their responsibilities.

A successful MKIS must incorporate executives' information needs and decision processes, and the user must understand the system structure. Unfortunately, the advantages of many new MKISs are lost because new system developments are not integrated with the information system that already exists in a company.

Marketing Decision Support Systems (MDSSs)

Marketing Decision Support Systems (MDSSs) emerged in the 1970s. Compared with classical operations research, which was the main source of inspiration for marketing models, a decision support system takes a more practical and flexible approach to problem solving. Its purpose is to support rather than replace managerial judgment, and to improve the effectiveness of decision making rather than its efficiency. Companies use MDSSs to gather information from the environment and turn it into a basis for action. Through the data, models, analytical tools and computing power of an MDSS, marketing managers can model marketing phenomena (major marketing variables such as sales, advertising, promotion, and price) according to their own ideas.

An MDSS can be seen as an extension of an MKIS. Like an MKIS, it is a combination of information technology, marketing data and analytical capabilities, but with much more emphasis on analytical capabilities. Whereas an MKIS is particularly geared toward answering "what" and "why" questions, an MDSS is especially equipped to answer "what if" questions. Examples of MDSSs are the ADBUDG system, which predicts market shares for given advertising budgets; and ASSESSOR, which predicts the market share of a new product, given its attributes and the introduction campaign.

Marketing Expert Systems (MESs)

Marketing Expert Systems (MESs) are MMSSs that emphasize the marketing knowledge component. The expert system concept emerged in the field of artificial intelligence in the late 1970s. Its basic philosophy is to capture the knowledge from an expert in a particular field and make that knowledge available in a computer program that helps solve problems in the field. The goal of an expert system is to replicate the performance levels of a human expert in a computer model.

In an expert system, knowledge is usually represented in the form of "if-then" rules. For example, "if" you want to stimulate trial, then sampling is an appropriate type of sales promotion" (Wierenga & van Bruggen, 1997). An expert system basically searches for the "best" solution to a given problem. Examples of MESs are Dealmaker, which contains knowledge collected from grocery and drug retailers and can predict the impact of a given special offer; and ADCAD, which is an advisory system for advertising copy and execution.

Marketing Knowledge-Based Systems (MKBSs)

Marketing Knowledge-Based Systems (MKBSs) refer to a broader class of systems than do MESs. In MKBSs the knowledge originates from any source, not just from human experts, but also from textbooks, cases, and the like. Also, MKBSs do not stand for just one particular approach to dealing with knowledge in marketing: they cover a range of knowledge representation methods, procedures for reasoning, learning, and problem solving that can be brought to bear to support marketing decision making. An example of an MKBS is the Brand Manager's Assistant, which supports brand managers with monitoring, analyzing, and designing tasks related to their brands.

Marketing Case-Based Reasoning Systems (MCBRs)

Marketing Case-Based Reasoning Systems (MCBRs) make cases available in a case library and provide tools for retrieving and accessing them. Historical cases are stored with all the relevant data kept intact, in "raw form." This is different from storage in "compiled form," such as rules that an expert has deduced from previous experiences. Case-based reasoning systems use indexes for representing cases, search and retrieval algorithms to find the right cases, and procedures for matching, adapting and transforming cases. An example of MCBRs is ADDUCE, which infers how consumers will react to a new advertisement by searching relevant past advertising events.

Marketing Neural Nets (MNNs)

Marketing Neural Nets (MNNs) are used to model the way people recognize patterns from signals. An MNN should be able to recognize promising new product opportunities (if properly trained on the relationship between new product characteristics and success on past cases) or distinguish between successful and less successful sales-promotion campaigns. A distinctive feature of neural nets is their ability to learn. Neural nets are more suitable for prediction than for explanation. The technology has been used to predict television audiences, for market segmentation, and for database marketing.

Marketing Creativity-Enhancement Programs (MCEPs)

Marketing Creativity-Enhancement Programs (MCEPs) are computer programs that stimulate and endorse the creativity of a marketing decision maker. An example of a marketing system that has a "creativity module" is the CAAS system for advertising design, which performs a creative search of pictorial motifs for emotional advertising.

Other MMSS Systems

There are other types of MMSS, such as the Profit-Oriented Decision (PROD) system, which is used in evaluating marketing decisions in terms of their contribution to the welfare of the business firm. Another MMSS is the Analytic Hierarchy Process (AHP), which leads to the likely identification of all key components affecting decision-making, by building a customized hierarchy to represent each problem. AHP can be used to resolve many complex problems that confront marketing management, including:

1. How to reconcile a range of conflicting objectives, under scarce resources;
2. Managing a vast amount of data;
3. Dealing with subjective and objective data to make a decision; and
4. Managing to resolve conflict amongst multidisciplinary teams (referred to as multi-person problems) (Davies, 1993).

Conclusion

According to Wierenga, van Bruggen & Staelin (1999), there is substantial proof that MMSSs can increase firm profit and other measures of performance, depending on the specific characteristics of the situation in which the system is used and specific success measures one is looking at. Since managers can gather partial information, interpret information inaccurately, ignore differences in goals, and lack patience, marketing decision aids can in many cases outperform managers in repetitive tasks. Just as there are production and process activities better and more efficiently done by machine than by labor, there are decisions in marketing that may be made better and more efficiently when they are automated (Wierenga & van Bruggen, 1997).

In general, the effectiveness of an MMSS depends on three factors, namely: The extent of top management support; the cognitive style and experience of the MMSS user; and the fit of the MMSS with the decision environment. A successful MMSS should match with the thinking and reasoning processes of the manager.

Although it may appear that there is a proliferation of decision aids in existence today, there is a need for more tools to assist marketing decision makers in carrying out their tasks. It is likely that there will be more automation for decisions regarding existing products and less automation for decisions pertaining to new and innovative products. Markets that are stable are likely to see more automation of decision-making than markets that are turbulent (Wierenga & van Bruggen, 1997). While activities such as copy creation will not be automated, models can help guide the appeal to be used, and how many creative executions to produce.

As companies shift from a make-and-sell model to a sense-and-respond model of marketing, marketing decision making may shift from the short-run, the tactical, and the maintenance of the established, to the long-run, the strategic, and the launch of the innovative. A word of caution, however: Despite the advantages and attractiveness of marketing decision aids, firms would do well to focus first on the needs of their business, not merely on the enabling technology per se. Expensive software is not the key to marketing success.

Terms & Concepts

Analytic Hierarchy Process (AHP): This process leads to the likely identification of all key components affecting decision-making by building a customized hierarchy to represent each problem. AHP can be used to resolve many complex problems that confront marketing management

Cognitive Style: This is the process through which a marketing decision maker perceives and processes information; the organization of information in memory and the repertoire of rules for using that information.

Delphi Estimation Process: A group decision-making tool where anonymous judgments are collected through questionnaire. The median responses are summarized as the group consensus, and this summary is fed back along with a second questionnaire for reassessment.

Heuristics: A method of problem solving that uses trial and error as well as rules of thumb to take shortcuts to a solution.

Marketing: This is the process of planning and executing the conception, pricing, promotion and distribution of ideas, goods and services to create exchange and satisfy individual and organizational objectives.

Marketing Case-Based Reasoning Systems (MCBRs): These marketing management support systems make cases available in a case library and provide tools for retrieving and accessing them. Historical cases are stored with all the relevant data kept intact, in "raw form."

Marketing Decision-Making: This refers to the way marketing managers go about solving problems. It also refers to the range of decision aids or tools that support marketing managers in the preparation, execution, and evaluation of their marketing activities.

Marketing Decision Support Systems (MDSSs): These marketing management support systems support rather than replace managerial judgment, and they improve the effectiveness of decision making rather than its efficiency. MDSSs are used to gather information from the environment, and through them, marketing managers can model marketing phenomena according to their own ideas.

Marketing Expert Systems (MESs): These are marketing management support systems that emphasize the marketing knowledge component. The expert system concept emerged in the field of artificial intelligence in the late 1970s. Its basic philosophy is to capture the knowledge from an expert in a particular field and make that knowledge available in a computer program for solving problems in that field. The goal of an expert system is therefore to replicate the performance levels of a human expert in a computer model.

Marketing Information Systems (MKISs): These marketing management support systems provide information about what is going on in the market, to examine the causes of observed phenomena. MKISs are passive systems: They provide information, but it is up to the marketing decision maker to attach conclusions to this information and to decide whether to act on those conclusions.

Marketing Knowledge-Based Systems (MKBSs): These are marketing management support systems where the knowledge originates from any source, not just from human experts, but also from textbooks, cases, and the like. MKBSs cover a range of knowledge representation methods, procedures for reasoning, learning, and problem solving that can be brought to bear to support marketing decision making.

Marketing Management Support Systems (MMSSs): Also known as marketing decision aids, the term Marketing Management Support Systems refers to a range of tools which facilitate and support marketing decision making.

Marketing Neural Nets (MNNs): These marketing management support systems are used to model the way people recognize patterns from signals. Marketing neural nets are more suitable for prediction than for explanation.

Marketing Problem-Solving Modes (MPSMs): These are the various decision-making styles that marketing managers choose to use. MPSMs are the product of the combination of an individual decision maker's cognitive style, experience, education and skills. The four MPSMs are optimizing, reasoning, analogizing, and creating.

Profit-Oriented Decision System (PROD): This marketing management support system is used in evaluating marketing decisions in terms of their contribution to the welfare of the business firm.

Programmability: This is the extent to which a decision can be made by using relatively routine procedures instead of more general problem-solving techniques.

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Suggested Reading

Agostinho, O. (2012). Proposal of adaptability indexes to support management of engineering and marketing systems. Proceedings of the European Conference on Information Management & Evaluation, 1-8. Retrieved November 15, 2013, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=82397546&site=ehost-live

Simon, H. (1960). The new science of management decision. New York: Harper & Row Publishers, Inc.

Simon, H. (1973). The structure of ill-structured problems. Artificial Intelligence, 4, 181-201.

Spence, M., & Brucks, M. (1997). The moderating effects of problem characteristics on experts' and novices' judgements. Journal of Marketing Research (JMR), 34, 233-247. Retrieved May 23, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=9705016753&site=ehost-live

Wierenga, B., Van Bruggen, G., & Staelin, R. (1999). The success of marketing management support systems. Marketing Science, 18, 196-207. Retrieved May 23, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=2727530&site=ehost-live