Marketing Mix Modeling for the Tourism Industry: A Best Practices ...

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International Journal of Tourism Sciences, Volume 11, Number 1, pp. 1-15, 2011 Tourism Sciences Society of Korea. All rights reserved.

Marketing Mix Modeling for the Tourism Industry: A Best Practices Approach

Michael J. Wolfe, Sr.∗ BBDO Inc.

John C. Crotts† College of Charleston

ABSTRACT : The intent of this paper is to introduce marketing managers and tourism researchers to an analytic tool used to optimize the allocation of a firm’s marketing budget in such a way that provides the greatest likelihood of producing the strongest return-on-investment. Marketing mix modeling combines advanced econometrics with marketing science to objectively measure the relative productivity of a complete set of marketing programs or initiatives to produce transient tourism sales. Discussed and demonstrated in this paper are the steps in performing such an analysis, how to avoid its major pitfalls, and the benefits that can be derived from the analysis. Extensions of the marketing mix method to include the effects of the social media and product driven strategies are briefly discussed. Keywords: Marketing Mix Modeling, Advertising ROI

** Michael J. Wolfe, Sr. is a Director Marketing Science, BBDO Inc., Atlanta, GA, USA. E-mail : [email protected] † John C. Crotts is a corresponding author and professor of Department of Hospitality & Tourism Management, School of Business, College of Charleston, Charleston, SC, USA. E-mail : [email protected];

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Introduction The marketing mix is the most fundamental concept of marketing. Coined in the late 1940’s, the term conceptualizes the marketer as a mixer of ingredients, who sometimes follows a recipe as (s)he goes along, adapting a recipe to the ingredients immediately available, and experimenting with or inventing ingredients no one else has tried (Culliton 1948). McCarthy (1960), was first to suggest the four P's representing price, promotion, product and place distribution as the primary ingredients of a marketing strategy. An expansion of marketing mix classifies marketing variables into two categories: the offering which consists of the product, service, packaging, brand, and price; and the process or method which includes such variables as advertising, promotion, sales, publicity, distribution channels, and new product development (Frey 1961).All of these ingredients are specifically designed to generate demand for the product or service in question. Bitner (1990) later added people (employees) and the servicescape to the model recognizing the importance of relationship development skills and the shopping environment in marketing. Marketing mix modeling is an analytical approach that uses historic sales data to quantify the impact of specific marketing activities on sales over time. The method accomplishes this by setting up a predictive model where sales volume – such as transient sales - becomes the dependent variable and the independent variables are derived from measures of the intensity of various marketing efforts. Once the dataset is assembled, multiple iterations are carried out to create a model that best accounts for the changes in the sales volume over time. Once the final model is deemed acceptable, the model can be used to simulate marketing scenarios for a ‘What-if’ analysis. In these scenarios, the marketing manager can experiment with reallocating the marketing budget across measured efforts and see the direct impact on forecasted sales; allowing one to optimize spends on specific marketing efforts that provide the greatest potential return on investment. Once adopted, the results provide two necessary ingredients of all formalized marketing plans, sales volume goals and the specific marketing tactics and investments required to reach these sales goals. Harrah’s Entertainment (Caesars) , Hyatt, Wyndham, Proctor and Gamble, Kraft, Starbucks, Coca-Cola, Pepsi, and various US state lotteries are examples of firms that today make marketing mix modeling a fundamental part of their marketing planning. Though model building or decision calculus is a common

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practice in contemporary management science, its application in the marketing sciences among academic researchers is relatively rare (Mazanec 1986). A search of Business Source Primer’s database using several variations of the keywords (marketing/media/consumer, mix, modeling/model), revealed only nine peer reviewed articles on the method (most recent examples are Ataman,Van Heerde and Mela, 2010, Collins, Dixon, Reggimenti, Sheffman, Soukhareva, Volgel and Ware 2010, Rubinson 2009, Hallward 2008), with only one in a tourism context (Mazanec 1986). This disconnect between academic research and marketing practice is due, in part, to the method’s unusual combination of marketing science and econometric modeling. In addition, the method’s use has generally been confined to the private research and consulting industry, where results are proprietary in nature, and whose methods and results are not often offered to peer reviewed journals (Kormanik 2010). The purpose of this research is threefold. First, it is to provide researchers a clear description of how to employ the marketing mix optimizing method in a hospitality or tourism setting. This industry is unusual, when compared to other goods and services, where demand varies considerably across seasons, holidays, and weather conditions; and not accounting for these variations can bias the results. Second, communicating the results to marketing practitioners can be problematic given the advanced econometrics, neural network, or path analysis techniques employed. The illustration provided in this study, we believe, communicates efficiently and effectively one type of model and its results into actionable terms most marketing executives can grasp. Lastly, we will briefly discuss ways we believe the assessment can be expanded to include line item expenses into the model that to our knowledge have not been employed. Most firms will invest annually considerable sums to both improve its product and to manage its presence in the social media, all in an effort to maximize sales in the most efficient way as possible. We contend that these expenditures can be measured and their impact can be assessed, injecting a higher degree of accountability in the marketing planning process, and providing marketers with a means of objectively measuring the relative productivity of a more complete set of marketing programs or initiatives. Often these budget allocation decisions are made via managerial judgment. Marketing-mix modeling is a unique approach, which applies science to this decision, with a full and comprehensive understanding of which marketing programs work better than others, supported with a clear factual return-oninvestment context. It is our hope that this study will invite interest of other academic

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researchers who will consider replicating and extending these tools and methods. The net effect of such efforts will be to provide marketing managers a new analytic tool they can use to optimize the allocation of their marketing budgets in such a way that provides the greatest likelihood of producing the strongest return-on-investment. This, in turn, will make their firms more economically viable serving better all their stakeholders.

Methodology The Marketing mix is the most fundamental concept of marketing. Each effort requires a financial investment that has an implementation date and costs that can be allocated across a given timeframe. The first step in a marketingmix model is to collect data and then “transform that data into an appropriate form that takes into full account the time-effects of marketing activities. Take for example a firm’s allocation in a cable television advertisement. To illustrate, the advertisement will be scheduled to air March 5 through May 21, with a cost per day of $200 across the 77-day period. In order to account for the lagged effect of advertising and other marketing stimuli, each variable is transformed to account for carryover effects into future time periods. Various decay patterns are tested to determine the highest fit or correlation with the dependent sales variable and these decayed data become inputs into the model (See Figure 1). In addition, the data inputs will contain zeros (e.g., $0) during those dates in the study period that precede the scheduled launch of each advertising campaign as well as for those dates in which the carryover effects are deemed to have fully decayed. The marketing-mix model is a statistical approach where quantified marketing activities over time are mathematically linked to a dependent variable, such as sales or revenues. Researchers have two basic model forms to choose from in the design of their marketing mix models. Once the model architecture has been agreed upon, the database can be assembled across the time period. As a rule, there should be no more than 10 data points for each independent variable. If daily sales data is available, generally two years of data is collected. If only weekly sales data is available, three years of data is generally required. If monthly sales data is all that is available, four to five years of historical data is generally sufficient. If there are fewer data points available than required, the research should consider collapsing or clustering variables together after running each response curve.

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Advertising Decay Rates 250 200 Ad Weight

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Fe b Fe 12 b M a 26 r M ar 9 Ap 23 r Ap 7 r M a 21 y M ay 4 Ju 18 n Ju n 2 Ju 16 l Ju l

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Figure 1. Illustrations of Decay Patterns of Advertising Over Time

In the Additive Model: ∑ for t = 1 to n St = x1At + x2Rt + x3Pt + x4Dt + x5Pt + x6Zt + x7Et + et St Sales in time t At Advertising spend in time t Rt Radio spend in time t Dt Product Distribution in time t Pt Promotion spend in time t Zt Seasonal Factor in time t Et Macro-economic factor in time t et Error term in time t A key part of the marketing-mix modeling exercise is validating the model. This involves plotting the model predicted results against actual data and determining how well the model fits relative to actual data. Typically, this fit is evaluated in terms of the R2 or R-Square statistic. In the current study, the fit is 98.5% out of a possible 100% (See Figure 2). Another key task is to test the ability of the model to predict across unknown periods. In order to do this, a certain number of periods are withheld from the model and model parameters are applied to determine how well the model predicts across unknown data points (see pink shaded area). Finally, we look at the MAPE statistic, or mean absolute percent error, which measures the size of the average period-to-period

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model error. In the Multiplicative Model: ∑ for t = 1 to n St = x1At × x2Rt × x3Pt × x4Dt × x5Pt × x6Zt × x7Et + et St Sales in time t At Advertising spend in time t Rt Radio spend in time t Dt Product Distribution in time t Pt Promotion spend in time t Zt Seasonal Factor in time t Et Macro-economic factor in time t et Error in time t R2 = 98.5%    Holdout R2= 96.4%  MAPE = +/‐0.6%  16,000,000  14,000,000  12,000,000  10,000,000  8,000,000 

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Figure 2. Model Validation and Reliability

Successful marketing-mix modeling is both an art and a science. It requires a technically proficient analyst who also possesses a sound knowledge of the business domain. Building good models not only requires managing and transforming data, but also specifying a model which has neither too many (over-specification) or too few predictive variables. A properly specified model will have continuous and parsimonious response function like Figure 3a., while an over-specified model will often produce a vaguely right, in terms of face validity, irregular response curve like Figure 3b.

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Figure 3a. Characteristics of a Properly Specified Model

Figure 3b. Characteristics of a Overly Specified Model

We currently use a software package called “4Thought”. IBM through its SPSS division purchased the software from Cognos, and to our knowledge does not license new nor support early versions of the software. Nevertheless, there are other paths to the same goal, including alternative neural network software. Hence, the main goal of this paper is to give researchers a good understanding of the methods capabilities and the benefits that can be derived from the analysis. Researchers should be careful to closely check the initial results before executing the full model. Examining the relationship of each independent variable on the sales data for face validity is an important first step to insure that the results do not violate common sense. If unusual relationships are found (e.g., coefficient for price on demand is positive), check the +/− signs of each predictor coefficients to find errors in the dataset, eliminate violating variables, or find alternative measures. In addition, be careful that the data is scaled uniformly (e.g., dollars, Euros, Pesos) avoiding issues related to measurement error. Lastly, check to see if each variable in the model is significantly correlated with the sales data at the probability level of .10 or higher. Again, “4Thought software” makes this a simple next step where all predictor variables should have a t-score equal to or greater than 1.6. Including variables in the model that are not statistically significant will reduce the ability of the derived model to predict demand. Again, the recommended solution is to

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eliminate non-significant variables and test substitute variables. Once the final model is deemed acceptable, the model can be used to simulate future marketing scenarios where the marketing manager can experiment with reallocating the marketing budget and see the direct impact on forecasted sales. This is accomplished by exporting to Excel the entire dataset including model’s log linear values. It will require the development of an Excel template (See Figure 6). Once the data on the simulator sheet is built out with the data in order of model on the macro sheet, the simulator can be used to test ‘what if’ scenarios.

Demonstration of Methodology In November 2009, we applied the marketing mix optimization modeling method to daily walk up ticket sales to Patriot’s Point Naval Museum. The museum is located in Mount Pleasant, South Carolina and is principally center on the aircraft carrier the USS Yorktown and its preserved aircraft interpreted for its missions during WWII through the NASA space program. The purpose of the predictive modeling exercise was to assess the productivity of the museum’s advertising activities to affect ticket sales from free and independent visitors (e.g., no group sales), thereby identifying opportunities for optimizing and improving future admission sales through a more productive investment across the marketing mix. Daily paid attendance data was assembled across the timeline beginning January 1, 2006 through September 30, 2009. The last six weeks of data were retained as hold out data for subsequent validity and reliability checks. In cooperation with the museum’s accounting office, special events office, and advertising agency, a total of 22 predictor variables were assembled into a database representing each initiative’s beginning and ending date with the cost of the initiative divided equally among the its timeline. In designing the model, it was assumed that daily attendance was driven by marketing and advertising tactics that can be modified by macro issues in the economy (changes in personal income), weather, seasonality, special events, holidays, and days of the week effects. Changes in personal income and weather data were obtained from secondary sources for the multi-state region and destination respectively. Multiple iterations of the modeling process produced a final model that accurately fit 97.2 percent of historical sales data. This same model applied to the six weeks of holdout data accounted for 93.4 percent in changes in daily sales data, indicating the derived model produces reliable results.

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Results Results of the analysis indicated that approximately 80 percent of ticket sales were driven by marketing activities (See Figure 4). This is one of the highest proportions we have seen to date, but was supported by other survey results where the museum attracts a high proportion of visitors are first time visitors to the destination (64.2%) and the attraction as well (80.4%).Advertisements placed in two local visitor guides (e.g., the local convention and visitor bureau and Gateway visitor guide), outdoor advertisements (OOH), and the museum’s online marketing efforts (e.g., banner ads, SEM) were identified as the most productive advertising channels.

Figure 4. Decomposing Attendance Sales

The results revealed that during the study’s timeline, the museum derived in US dollars approximately $4 for every $1 it invested in its marketing activities. While this seems high, for Patriot’s Point, virtually all of its patrons are onetime visitors and therefore much of the museum’s revenue is highly dependent on marketing efforts. Advertisements in local visitor publications provided the greatest return on investment generating a high $22.59 for every dollar invested. This was followed distantly by the museum’s investments in its online internet presence. Advertisements in a freely distributed weekly newspaper (e.g., City Paper) performed quite well relative to its minimal costs. These results were relatively consistent with the museum’s onsite survey where the vast majority of visitors either made their decision to visit the museum at home before their trip or after arriving in the destination. Though the initial analysis indicated that outdoor

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advertisement (billboards) generating greater than one in four of all ticket sales, the high costs contributed to a low return on investment measure ($1 to $2.78). Aerial advertisements, street team promotions and advertisements in the combined category of scouting and veteran magazines returned the least.

Figure 5. Marketing Revenue Returns per Dollar Invested

Paid attendance at the naval museum declined 3.8 percent in 2009 compared to the same period in 2008. Contributors to that decline were identified as a $3 parking fee coupled with a unusually wet spring and summer months. In this analysis, instigating the $3 parking price fee on July 1, 2009 cost the museum -6.5% in attendance and was the largest negative driver.In addition, weather played a factor as well. For every inch of rain experienced in a day reduced attendance by 0.8 percent or 43 paid admissions. A strong base momentum, which can be thought of as unexplained variance, countered the negative drivers. Elements that contribute to this base take the form of unmeasured influences such as word of mouth and customer loyalty. The results of the analysis was plotted on a 2X2 grid where the relative cost of the investment was plotted against its incremental return in ticket sales. In the upper right hand quadrant lies the marketing activities that are relative high contributors to sales but are also high in costs.

As noted previously,

advertisements in the two visitor guides (CVB guide and Gateway) as well as resources devoted to search engine marketing should be considered critical and expanded where practical. Investments in billboards (e.g., Adams Outdoor

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Advertising) though relatively effective, are costly are in the range of maintain or potentially scale back.

The lower right quadrant contain elements that

should be expanded in that in the dataset had a measurably impact on sales at relatively low costs. Investments in the lower left side quadrant should be thought of as investments that should either be reduced or eliminated.

Figure 6. Marketing Variance (Year 2008 vs. Year 2009)

Figure 6. Marketing Investment Matrix

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Marketing mix modeling can be thought of as a simulator which moves marketing dollars from less to more productive activities such that total attendance can be maximized without increases in the total marketing investment. In the following chart, direct comparisons can be made relative to the museum’s actual spending and the mix of revenue generated by each marketing activity. A clear picture of more and less productive investments emerges that can be compared directly with the model’s recommendations of what an optimal investment should look like. The model’s solution is estimated to increase total attendance by 19 percent, obtainable without increasing the overall marketing budget. The solution calls for relatively higher spending in visitor and group publications, online, and the City Paper. The largest relative reductions are for outdoor billboards, advertisements in scouting and veteran magazines, aerial advertising, and street team promotional activities.

Conclusion The advertising and promotional budgets in most firms are a large line item. Hence, tourism researchers have given a lot of attention to identify advertising effectiveness and return on investment. Much experimentation has been made with aided recall designs, adding tracking mechanisms, and experimental designs, all in an effort to assess each investment’s effectiveness in generating sales and profits. The pitfalls and limitations are well documented (Pratt, McCabe, Jimenez and Blake 2010. Kim, Hwang, and Fesenmaier 2005, Messmer and Johnson 1993, Perdue and Pitegoff 1990, Woodside 1990). Even the click-through and view-through methods advertisements that can track unique visits from an online display advertisements through the online point-ofsale is understood to produce inaccurate estimates of advertising effectiveness (Kormanik 2010). We contend that the marketing mix modeling method, discussed and demonstrated in this paper, represent an alternative means of assessing advertising effectiveness producing results that are clear and actionable for the marketing manager. Though the concept is relatively easy to understand, the steps in conducting such an analysis is far from simple. It is our hope that the explanation provided will invite the interest of researchers who will learn and contribute to the method’s advancement. All the data needed to conduct such an analysis a firm has in its possession. The method has also the unique ability to assess all elements of the marketing mix for its individual and collective impact on sales. In addition, it

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can take into account various environmental conditions that can moderate the consumer response. Seldom do we ever see other forms of advertising effectiveness studies take into account factors like weather and macroeconomic conditions in their studies. This purported method also holds the promise of accurately measuring the effects of the social media on sales. The need exists among companies to not only understand the effects of each Web2.0 medium has on its future sales, but how their public relations efforts can impact the sentiment consumers share in the eWOM environment. Lastly, we contend one of the most overlooked areas of hospitality and tourism research is the effect changes in the servicescape have on a firm’s sales and profits. The product offering is a fundamental aspect of the marketing mix that can be accurately measured and its impact assessed on its return on investment. In closing, it is our hope that this paper will not only stimulate interest among tourism researchers not only to the method’s potential, but to assist them in avoiding its many pitfalls. For those with such an interest, we encourage you to email either author requesting a copy of a model training we will gladly share. The 24 slide powerpoint presentation contains a series of screen shots of COGNOS’ Food4 Thought functions and Excel templates including descriptions, designed to increase one’s comprehension of the steps required to build and execute such a model we have described.

ACKNOWLEDGEMENT The authors wish to express their gratitude to Dick Trammell, the Executive Director of Patriots Point Naval Museum for allowing us to share the results of this study. A description of the method would be meaningless without illustration of the results.

REFERENCES Ataman, M., Van Heerde, H., & Mela, C.(2010). The Long-Term Effect of Marketing Strategy on Brand Sales. Journal of Marketing Research, 47 (5), 866-882,

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Bitner, M. (1990). Evaluating Service Encounters: The Effects of Physical Surroundings and Employee Responses. Journal of Marketing, 54(2), 69-82, Burke, J. & R. Gitelson (1990), Conversion studies: Assumptions, applications, accuracy and abuse. Journal of Travel Research, 28(3), 46-51. Collins, J., W. Eadie, M. Reggimenti, D. Sheffman, J. Soukhareva, J. Volgel, and B. Ware (2010). How precise magazine inputs can improve media mix modeling: The impact of more behavioral metrics on ROI. Journal of Advertising Research, 50(1), 10-15. Culliton, J. (1948). The management of marketing costs. Boston: Division of Research. Graduate School of Harvard School of Business Administration. Hallward, J. (2008). Make measurable what is not so: Consumer mix modeling for the evolving media world. Journal of Advertising Research, 48(3), 339-351. Kim, D., Y. Hwang & D. Fesenmaier (2005). Modeling tourism advertising effectiveness. Journal of Travel Research, 44 (1), 42-49. Kormanik, B. (2010). The ROI of marketing: Here’s how to figure out if your spend is really paying off. Hotel Interactive. November 19. http://www.hotelinteractive.com/article.aspx?articleid=18833 Mazanec, J. (1986). Allocating an advertising budget to international travel markets. Annals of Tourism Research,13(4), 609-634. McCarthy, E. (1960). Basic marketing: A managerial approach. Homewood, IL.:Irwin. Messmer, D. & R. Johnson (1993). Inquiry conversion and travel advertising effectiveness. Journal of Travel Research, 31(4), 14-21. Perdue, R. and B. Pitegoff (1990). Methods of accountability research for destination marketing. Journal of Travel Research, 28 (4), 45-49. Pratt, S., S. McCabe, I. Jimenez and A. Blake (2010). Measuring the effectiveness of destination marketing campaigns: Comparative analysis of conversion studies. Journal of Travel Research, 49(2), 179-190. Rubinson, J. (2009). Empirical evidence of TV advertising effectiveness. Journal of Advertising Research, 49(2), 220-226. Silberman, J. and M. Klock (1986). An alternative to conversion studies for measuring the impact of travel ads. Journal of Travel Research, 24(4), 12-16.

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Woodside, A. (1990). Measuring advertising effectiveness in destination marketing strategies. Journal of Travel Research, 29(2) , 3-9.

Received December 09, 2010 Revised March 18, 2011 Accepted April 16, 2011