Introduction

Procter & Gamble was initially founded in the United States in 1837 by two men named as William Procter and James Gamble. It is a leading consumer product company that provides the consumer goods in several segments including beauty and grooming, household care, snacks and coffee, health and wellbeing, pet care and fabric care (Procter & Gambel, 2015). P&G entered into the Australian and New Zealand markets in 1985 when Richardson-Vicks Inc. acquired P&G (Procter & Gamble, 2015). The company has remained market leader in the laundry detergents, diapers, shampoos, moisturizers, fabric softeners and skin care products.

With 300 brands of consumer goods and sales in over 140 countries, P&G has successfully changed its name to Procter & Gamble Pty Ltd. on 1^st January 1989. P&G has the target of knowing its customers and retailers in Australia and New Zealand for integrating the innovations that flow from the global business units into local business plans. P&G is currently operating in roughly 180 countries around the world with its unique organizational structure that offers the global scale benefits of an international company with local focus which is much relevant to the consumers. The local operations keep P&G in touch with the local communities while strong governance practices ensure that P&G conducts its operations in line with highest standards and integrity (Procter & Gamble, 2015).

Problem Identification:

P&G had launched a revolutionary scent named “player” in 2004 that looked like a CD and emitted scents. The scent was designed in such a way that the users could insert a fragranced disc into the player and it would release the scent every 30 minutes (Schneider & Hall, 2011). The themed discs had the scents named as wandering barefoot on the shore, relaxing in the hammock, strolling through the garden, exploring a mountain trail and shades of vanilla. The scent discs were to be switched at any time for creating a very different mood or atmosphere. However the disc was reusable for 50 hours for 20 plays. The problem spiraled out when P&G endorsed Shania Twain as a brand ambassador for promoting “player scent” during its launch commercials (Chief Marketer, 2008). The advertising campaign fired back because of several reasons. The company had opted Shania Twain that confused the consumers as they though that the device involved both music and scents. This ambiguity and failure of marketing campaign led to huge loss for P&G and its media partners. The main reasons were the hiring of celebrity singer, the advertisement campaign, the channel partner’s cost and wrong media carrier. A revolutionary product like “player” needed to be advertised on different platforms for educating the consumers about its value preposition (Chief Marketer, 2008). P&G clearly failed to deliver a strong educational campaign as to how the product can be used and what does it offer. It needs to know what makes the consumer buy the product. And how the problems of the consumers can be solved? By understanding the perceptions of the consumers, P&G could redesign its product and market it in a proper way so that success can be ensured (Schneider & Hall, 2011).

In the above case P&G has faced the problem where the budget for advertisements and planning is short and the product still needs to reach out the maximum number of customers.

Aim of Study:

This aim of the report is to design the better promotional campaign for “player” scent.

Research Question:

At what level should P&G advertise “Player” scent in each of the three media option available?

 

Literature Review

Advertisement has been the most important function of the enterprises as it is the vehicle of firms to approach the specific market segment and expose their products/services to the customers. The sole purpose of the advertising campaign is to maximize the impact of products on customers’ minds while staying within the frames of available budget. The constraints that firms face could be of qualitative or quantitative nature. Linear programming technique has been contributing towards the effective allocation of advertising expenses while using the MS Excel Solver facilitates the resolution of the resulting mathematical models.

According to Zangwill (1976), the issue of using the linear models for the optimal allocation of advertising expenses has been widely debated. LP technique has been widely used to solve the typical resource allocation problem. LP has been used for many years by big businesses, government agencies as well as organizations (Caine & Parker, 1996). Management culture has been changed with the advent of personal computers and the availability of spreadsheet software like Microsoft Excel. It has been used by number of firms to handle the linear and non-linear optimization problems impressively (Anderson, Sweeney, Williams, Camm, & Cochran, 2012).

Much of the decision making problems occur in the real world within an environment where the constraints, goals and consequences of the action plan followed are not known (Bellman & Zadeh, 1999). Due to randomness in a decision making process, human capability to analyze and solve the problem might fail. Situations where money is involved is very complication and seems much more suited to mathematical modeling. Bass and Lonsdale (1966) explored the usage of linear programming at first for selecting the media. The report examined the overall influence of various methods and techniques that could be used by the firm for exposing their product. Mihiotis and Tsakiris (2004) also reviewed the advertising planning literature and discussed the possible best combinations of placements and promotion through commercial (channel, frequency and time) with the overall goal of highest rating subject to the constrained advertising budgets. Bhattayacharya (2009) also presented a model that was designed for deciding the number of advertisements in different advertising media and the overall optimal allocation of available budget that is assigned to the different media.

The linear programming was emerged during 1960s with Miller and Starr (1960) and Day (1962) developed a criteria to successfully apply linear programming principles in order to select the media for solving the place and timing of advertisement campaigning with respect to budgetary constraints. Ching et al. (2006) also developed a linear programming model for optimizing the number of advertisements of a given time length in order to ensure the required level of awareness and exposure to customers. Whereas Starsch (1965) extended the model by including the type of markets where the products can be advertised.

Contribution to Research

Present literature have been focused on optimal media selection and the allocation problem, however no study has been focused towards maximization of number of exposures given the budget constraints. This study will optimize the expected number of exposures through linear programming model applied in Excel.

Methodology

In order to solve the advertising mix problem, linear programming is used. The linear programming model will be formulated for the “player” scent. This problem is much related to resource-allocation problem where three activities will be considered for advertising the product successfully. These three media selection options are TV commercials, Social Media and Print Media. However the decision needs to be made on the level of these activities i.e. how many number of TV commercials, Social Media Flash Advertisements and Newspaper Advertisements are needed to be run for “player” scent of P&G. So the three resources would be Advertising budget (approximately $10 million), Planning Budget (approximately $1 million) and TV spots available for running the advertisement campaign are 6.

Components and Assumptions of Linear Programming Model:

Linear programming model was chosen because of its easy applicability and efficiency in solving the solutions. The optimal solutions found through linear programming model is guaranteed to be found. It also generates a useful sensitivity analysis which helps the managers in analyzing the problem and finding its optimum solution easily (Dantzig & Thapa, 2006). Since much of the problems being faced by the organizations are linear in nature, so using Linear Programming Model will be beneficial. There are three main components of linear programming model i.e. Decision Variables, Objective Function and the Constraints. The four main assumptions of the linear programming model are its linearity, divisibility, certainty and non-negativity (Luenberger & Ye, 2008).

Model Formulation:

Based on the approximate data above, the problem for “player” scent has three resource constraints:

Advertising Budget Used ≤ $10 million

Planning Budget Used ≤ $1 million

TV Spots Used ≤ 6

While the objective function is to maximize expected number of exposures. The assumed data is outlined below. The mathematical model will help in measuring the objective in terms of the expected number of exposures.

For advertisements the budget of P&G is $10 million (constraint) whereas in order to advertise through TV adverts the cost will be $500,000, for Social Media Flash Adverts the cost will be $500,000 and for Print Media Newspaper Adverts the cost will be $100,000.

For planning, the budget available to P&G is $1 million, whereas for planning the TV Advertisements, $90,000 will be required, for planning Social Media advertisement strategy, $30,000 will be needed and for planning out the newspaper advertisements $40,000 will be needed. The expected number of exposures are 1,300,000 for TV advertisements, 600,000 for Flash advertisements on Social Media and 500,000 for Newspaper advertisements through print media. There are no more than 6 TV commercials allowed.

Model Characteristics:

A mathematical model is intended to provide the approximate solution to the problem. Approximations and simplifying assumption does not generally require a workable model, however there is a need of high correlation between the prediction of the model and what would actually happen if the real problem got a solution (Luenberger & Ye, 2008). The overall contribution of each activity to the overall measure of performance is proportional to the level of the activity. The measure of performance is the total profit from the activities. The case above has resource constraint for each resource and the amounts of the resources used will depend on the level of activities (Luenberger & Ye, 2008).