Models for Marketing Strategy

Predicting buyer behavior is often a complicated process that must take into account a number of variables affecting buyer behavior. Models can help marketers make decisions and focus their strategic marketing efforts. Although the advent of the personal computer has made model-building for marketing strategy a more attainable goal for many organizations, it is important that marketing managers and other key players within the organization contribute the expertise of their experience to the development of the model. In addition, the organization can use secondary data or collect new data through surveys, interviews, or other data collection techniques to acquire the data needed for the model. Because the real world is so complex, the model also needs to be complex in most cases. One of the essential aspects of model building is to determine which of the innumerable variables that could be potentially built into the model are important and which are not.

Arguably, done correctly, marketing is both an art and a science. The art part of the equation tends to be obvious. Businesses need to develop corporate logos, design schemes, and other branding art in order to catch the buyer's eye, encourage a purchase, and help ensure brand loyalty. The artwork associated with various media advertisements also needs to be both contemporary to demonstrate to prospective buyers that the organization and its products or services are on the cutting edge yet also distinctively target the tastes and expectations of different market segments. Jingles need to attract attention and be memorable without being obnoxious. However, no matter how stylish, contemporary, or eye-catching one's artwork is or how memorable one's slogan or jingle is, if one's marketing efforts are targeted toward the wrong segment of the market, if an appropriate marketing mix is not determined, or if integrated marketing communications are not designed to maximize the return on investment for one's marketing dollar, the marketing efforts will be less than successful.

Buyer Behavior

On its own, buyer behavior is a complex thing. It is often difficult to try to predict what goods and services a buyer might need or want or to design a marketing campaign that will help sway the buying decision. The situation in which a purchasing decision is made can also impact the probability that a potential buyer will actually purchase one's offering. For example, in a bad economy, there is less money available for spending on non-essential goods and services whereas in a good economy, consumers have more discretionary funds and are more able and willing to spend money on discretionary items and luxuries. These two factors are further complicated by the buying situation: Those factors influencing buying behavior that cannot be predicted from knowledge of either the buyer or the situation alone. For example, Dr. Pepper's advertising campaign a number of years ago that encouraged consumers to "Be a Pepper" seemed to meet all the criteria for advertising success including upbeat music, positive image, and high advertising awareness. Despite the advertisements, however, Dr. Pepper's share of the soft drink market steadily declined (Smith & Swinyard, 1999).

Variables Affecting Buyer Behavior

Predicting buyer behavior is often a complicated process that must take into account any number of variables affecting buyer behavior. Smith and Swinyard list a number of factors that contribute to the complexity of marketing decisions. As mentioned above, determining the right market segment as a focus for one's marketing efforts is an important part of the marketing process. For example, with the right marketing mix and advertising campaign, one might be able to sell the proverbial ice to Eskimos. However, in practice, it would should be much easier to focus on a different market segment that actually needs the ice.

Another consideration that needs to be taken into account in developing a marketing strategy is the multiplicity of products that are available to potential customers. One typically needs to take a different marketing task when entering a market in which similar products or services already exist than in a market in which one's offering is innovative. Similarly, if a product or service is part of a line of products or services already offered by the business, a different marketing effort may be required than if the product or service is new to the organization.

Another consideration when determining how best to structure one's marketing efforts is the possibility of the existence of conflicting objectives. For example, if a cereal manufacturer desired to introduce a new cereal to the marketplace with the objective of carving out a share of the market, it would need to be careful that the market share did not come from the share already held by its existing cereal products.

Another factor affecting the complexity of marketing decisions occurs when different functional areas within the organization have objectives that do not easily support each other. For example, the goal of the marketing department may be to sell as many widgets as possible. However, if the manufacturing department is trying the cut down on warehousing costs and does not have sufficient widgets available for potential customers or does not have in place an effective just-in-time manufacturing system, no matter how many widgets the marketing department sells, the manufacturing department will not be able to fulfill the orders. The effectiveness of one's marketing efforts is also dependent on numerous factors related to the competition. If the competition sells comparable widgets at a lower price and has an established customer base with high brand loyalty, for example, it will be more difficult for another business to gain a share of the market. Similarly, although one can design one's own marketing campaign based on the way the competition is currently marketing their product or service, one cannot necessarily predict how the competition's marketing efforts, goods, or services will change in the future and how this will affect the requirements for their own campaign.

The complexity of determining the appropriate approach for one's marketing efforts must also take into account the fact that the effects of a marketing campaign may last for substantial periods of time even when other market factors change. For example, if one's competition has great brand loyalty for their widget, even if a business introduces a better widget, it may take years to overcome the effects of brand loyalty and win a larger share of the market.

Modeling & Statistics

Obviously, accounting for all the factors influencing the probability of success of a marketing effort can be an overwhelming task. One set of tools that can be used to help marketers make such decisions and focus strategic marketing efforts is statistics in general and regression analysis in particular. Statistics and regression analysis allow for the development of various models and decision support systems that help them better understand the intricacies of the real world processes and how best to target their marketing efforts. At one time, the use of such tools was laborious and time consuming, and readily available only to large corporations with the funds to pay for such models. However, with the advent of the personal computer and its relatively faster and less expensive computing power, the ability to model market and buyer behavior is available to most businesses.

In general, a model is a representation of a situation, system, or subsystem. Conceptual models are mental images that describe the situation or system. Mathematical or computer models are mathematical representations of the system or situation being studied. Models can be used in marketing to either represent the current situation of the market or what the market might look like in the future. For example, a marketing model might be developed to represent such factors as the existing distribution system for a product, the value structure of potential customers, or how various types of advertising affect the attitudes or behavior of the target market segment. These variables can then be manipulated to estimate the effects on buyer behavior if one or more of the variables is changed.

There are a number of resources for model building. It is important that marketing managers and other key players within the organization contribute the expertise of their experience to the development of the model. Such inputs are invaluable for developing a model that accurately represents the real world situation. Data to aid in building a marketing model can come from a variety of sources. The organization can use existing (i.e., secondary) data that were collected for another purpose by the organization, industry, or other source as inputs into the model. Or, the organization could engage in the collection of new data through surveys, interviews, or other data collection techniques to acquire the data needed for the model. Examples of the types of data that can be useful in marketing models include the attitudes, perceptions, and preferences of potential consumers, the product usage of various market segments, or how other factors (e.g., the economy, income, gender, educational status) affect one's buying habits.

Because the real world is so complex, in most cases the model also needs to be complex. However, one of the essential aspects of model building is to determine which of the innumerable variables that could be potentially built into the model are important and which are not. As a result, model building is often an iterative process in which the modeler repeatedly tries to develop a marketing model that adequately and accurately models the real world without including extraneous variables that do not account for a significant proportion of the variation. The difficulty of this task is one of the reasons that mathematical modeling is not used more often by businesses to help shape their marketing strategy. On the other hand, if a model is too mathematically complex, it can become useless from a practical point of view. Decision makers will also have to accept that there is some degree of uncertainty even when using the model. Part of the art of using marketing models is to determine what degree of uncertainty is acceptable so that a model that is complex -- but not too complex -- can be developed. To do this, a model should be simple to use and understand, adaptable to other situations or products, and be complete on the salient factors of the situation. Unfortunately, this often seems a daunting task, particularly for managers who are knowledgeable in the area of modeling or are not willing to spend the time and effort necessary to build (or have built) a marketing model. Another problem in building a model to inform one's marketing strategy is that sometimes the data necessary to build the model simply are not available. This is often true when attempting to build a model to be used in the marketing of an innovative product or service.

A dramatic example of innovation, in terms of both products and services, is Internet commerce. As a new space for buying, selling, merchandising, advertising and providing customer service, the Internet has entered into the marketing models of every industry and retailer as either variable or strategy. The low cost of entering the marketplace for entrepreneurs, the cost-efficiency of Web based retailing, as opposed to maintaining real stores with large inventories, the dynamics of social media and data-informed, consumer targeted, pay-on-click advertising, pose formiddible challenges to brick-and-mortar establishments that must rely on customer loyalty and experience to counterbalance the convenience and discounted pricing of Internet shopping (Azadi & Rahimzadeh, 2012). Advertising outlets expanded well beyond signage, periodical ads, and 30 second commercials on traditional broadcasting and allowed brands to develop more comprehensive marketing strategies, including digital videos ranging from short animated ads to commercials that employ cinematic effects, plot, character, and recognizable actors. The holy grail of advertising became the viral video, which would spread a brand's message through enthusiastic social media (Hof, 2013; Rothenberg, 2013).

Applications

Coupon Distribution

Most of us receive many coupons every week both in the newspaper and other publications as well as through the mail. Both manufacturers and local businesses advertise their products this way in an attempt to attract potential customers' attention and get them to try a product or visit their store and hopefully gain brand loyalty. However, from a customer's point of view, not all coupons are created equal. For example, the envelope of bundled coupons that many local businesses send out may be viewed and utilized differently than the Saturday coupon sections of the regional newspaper by different people.

Because of the different approaches of different buyers to clipping and using coupons, the decision as to whether or not to advertise one's product or service with coupons can be difficult. The costs of designing, printing, and distributing coupons need to do more than pay for themselves. Coupons need to bring on a sufficient return on investment in the form of new sales in the short term and new customers in the longer term to justify not only investing in coupons but also in using part of one's limited marketing budget for coupons rather than for other marketing efforts. Further, just deciding to use a coupon promotion is only one of a number of decisions that need to be made. One must also determine the face value of the coupon, how many coupons should be distributed, when the coupons should be distributed, and to whom the coupons should be distributed, among other questions.

Modeling the Effectiveness of Coupon Promotions

Neslin and Shoemaker (1983) illustrate the use of models for marketing strategy by building a model for determining how profitable coupon promotions are. As shown in Figure 1, the decision as to whether or not to offer a coupon program for one's product is a complex task that needs to take into account a number of factors. Specifically, there are three groups that affect whether or not a coupon program will be successful: The manufacturer, the retailer, and the consumer. Each of these groups makes decisions about different factors that can negatively or positively impact the effectiveness of a coupon program.

Factors to Consider

In addition, three other factors need to be taken into account when determining whether or not to institute a coupon program.

• The first of these factors is the probability of purchase among potential customers before the promotion. If, for example, the majority of targeted customers will purchase the product at full price, it would more than likely not be to the seller's benefit to offer a discount.
• Second, one must take into account whether the effect of a coupon promotion would be to accelerate sales (i.e., buyers purchase the item earlier than they would have so that there is an increase in normally projected sales during the coupon dates with a subsequent drop in sales after the coupon expires). If a coupon only accelerates sales with a subsequent drop-off in sales, the coupon program may not have been of value.
• Third, it must be considered whether the coupon program will attract potential new buyers for a product over time and if it will be a way to gain a larger market share and increase brand loyalty.

Using the techniques of mathematical modeling, a decision-making tool was built for use by marketing managers in determining whether or not a coupon promotion program would be of value in bringing in and retaining customers. Although the model was developed for a specific situation, it shows the complexity of factors and interactions which need to be taken into account in model building for marketing strategy development.

Internet marketing is especially able to exploit the value of promotional coupons. Consumers looking for a place to eat, for example, can find instant coupons on their smartphones delivered to them as part of their restaurant search. Marketers can launch promotional campaigns with short lead times and target campaigns to particular geographic locations or during defined time periods, such as during weekday lunchhours. Products that might cause the consumer some embarrassment in a store purchase can be openly promoted with coupons for purchase from a Web site (Promotional pioneers, 2012).

Conclusion

Designing a winning marketing strategy can be a complicated and nuanced task. One must understand not only one's product and service, but also the market or segment of the market to which that product or service will most likely appeal. In addition to understanding the potential buyers, one must also take into account the situation in which one is trying to market as well as the buying situation, that ephemeral complex of factors resulting from the interaction of buyer and situation. Based on this knowledge, one must then determine the right marketing mix, the timing of the mix, and make other decisions about how best to market one's product or service. Particularly when marketing innovations or entering a new market, the multitude of possible factors on which such decisions can be made can be overwhelming. Fortunately, with the advent of inexpensive computing power offered today, marketing managers can develop models of their unique marketing situation to use as decision making tools and to optimize their marketing strategy.

Terms & Concepts

Brand: A trademark or distinctive name that is identified with a particular product, service, or organization that makes it publicly and easily distinguishable from other products, services, or concepts. A brand may include a name, logo, slogan, or design scheme associated with the product, service, or organization.

Brand Loyalty: The reluctance of a buyer to switch to another brand of product or service because s/he is familiar and comfortable with the brand s/he is currently using or has used in the past.

Buyer Behavior: The complex processes by which consumers choose, acquire, use and dispose of goods and services in order to fulfill their needs and desires.

Buying Situation: Factors influencing buying behavior that cannot be predicted from a knowledge of either the buyer or the situation alone.

Decision Support System (DSS): A computer based information system that helps managers make decisions about semi-structured and unstructured problems. Decision support systems can be used by individuals or groups and can be stand-alone or integrated systems or web-based.

Integrated Marketing Communications: An approach to marketing communications that combines and integrates multiple sources of marketing information (e.g., advertising, direct response, sales promotions, public relations) to maximize the effectiveness of a marketing campaign.

Market Segmentation: A marketing strategy in which a general population or market is subdivided into categories based on an a priori definition of potential buyers and their likeliness to purchase one's goods or services. Marketing efforts are then concentrated on the segment most likely to purchase with the objective of gaining a major share of the segment as opposed to a smaller share of the general category of potential buyers.

Marketing: According to the American Marketing Association, marketing is "an organizational function and a set of processes for creating, communicating and delivering value to customers and for managing customer relationships in ways that benefit the organization and its stakeholders" (AMA, 2009).

Marketing Mix: The combination of product, price, place, and promotion that is used to get a product into the hands of the consumer. One of the primary tasks of marketing is to optimize the mix to best position the product for success in the marketplace.

Model: A representation of a situation, system, or subsystem. Conceptual models are mental images that describe the situation or system. Mathematical or computer models are mathematical representations of the system or situation being studied.

Regression: A statistical technique used to develop a mathematical model for use in predicting one variable from the knowledge of another variable.

Return on Investment (ROI): A measure of the organization's profitability or how effectively it uses its capital to produce profit. In general terms, return on investment is the income that is produced by a financial investment within a given time period (usually a year). There are a number of formulas that can be used in calculating ROI. One frequently used formula for determining ROI is (profits -- costs) × (costs) × 100. The higher the ROI, the more profitable the organization.

Strategic Marketing: The subfunction of marketing that examines the marketplace to determine the needs of potential customers and the strategy of the competitors in the market, and attempts to develop a strategy that will enable the organization to gain or maintain a competitive advantage in the marketplace accordingly.

Strategy: In business, a strategy is a plan of action to help the organization reach its goals and objectives. A good business strategy is based on the rigorous analysis of empirical data, including market needs and trends, competitor capabilities and offerings, and the organization's resources and abilities.

Statistics: A branch of mathematics that deals with the analysis and interpretation of data. Mathematical statistics provide the theoretical underpinnings for various applied statistical disciplines, including business statistics, in which data are analyzed to find answers to quantifiable questions. Applied statistics uses these techniques to solve real world problems.

Bibliography

American Marketing Association. (2009). Marketing. AMA Resource Library: Dictionary. Retrieved February 20, 2009, from http://www.marketingpower.com/%5Flayouts/Dictionary.aspx?dLetter=M

Azadi, S., & Rahimzadeh, E. (2012). Developing marketing strategy for electronic business by using McCarthy's four marketing mix model and Porter's five competitive forces. EMAJ: Emerging Markets Journal, 2(2), 47-58. Retrieved October 31, 2013, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=90596367&site=ehost-live

Neslin, S. A. & Shoemaker, R. W. (1983). A model for evaluating the profitability of coupon promotions. Marketing Science, 2(4), 361-388. Retrieved February 14, 2009, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=4474166&site=ehost-live

Promotional pioneers keep the classics to hand. (2012). Marketing Week 36(1), 25. Retrieved October 31, 2013, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=84617579&site=ehost-live

Smith, S. M & Swinyard, (1999). Introduction to marketing models. Retrieved February 9, 2009, from http://marketing.byu.edu/htmlpages/courses/693r/modelsbook/

Suggested Reading

Aake, D. A. & Weinberg, C. B. (1975). Interactive marketing models. Journal of Marketing, 39(4), 16-23. Retrieved February 14, 2009, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=4996125&site=ehost-live

Gensch, D. H. (1968). Computer models in advertising media selection. Journal of Marketing Research, 5(4), 414424. Retrieved February 14, 2009, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=5004742&site=ehost-live

Hof, R. D. (2013). How Facebook slew the mobile monster. Technology Review, 116(3), 75. Retrieved October 31, 2013, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=87418391&site=ehost-live

Roberts, J. H., Nelson, C. J., & Morrison, P. D. (2005). A prelaunch diffusion model for evaluating market defense strategies. Marketing Science, 24(1), 150-164. Retrieved February 14, 2009, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=16384195&site=ehost-live

Rothenberg, R. (2013). Time to jump in. Adweek, 54(17), 23. Retrieved October 31, 2013, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=87485206&site=ehost-live

Sarstedt, M. (2008). Market segmentation with mixture regression models: Understanding measures that guide model selection. Journal of Targeting, Measurement and Analysis for Marketing, 16(3), 228-246. Retrieved February 14, 2009, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=33869450&site=ehost-live

Sturts, C. S. & Griffis, F. H. (2005). Pricing engineering services. Journal of Management in Engineering, 21(2), 56-62. Retrieved February 14, 2009, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=16606151&site=ehost-live

Weiss, D. L. (1964). Simulation for decision making in marketing. Journal of Marketing, 28(3), 45-50. Retrieved February 14, 2009, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=6740880&site=ehost-live