VEGETACIÓN

Game theory is the study of mathematical models of conflict and co-operation between rational decision-makers (Myerson, 1991). Newer studies regarding game theory support this

multiple decision-makers and how the choices these decision-makers make influence the outcome of the inter-related concepts, and how that, in turn, affects the concepts themselves as well as future decision-making processes. These concepts or probabilities exist in an interdependent and non-linear environment that determines the outcomes or expected usefulness of the concept being studied (Gintis, 2005).

Game theory is applied in economic science and was developed by John von Neumann in the 1940s, but has since developed to be a widely used methodology to solve and predict complex problems and answers (Hu, 2009). It allows all the elements being studied to be ranked, which enables the participant or decision-maker to plot all possible future outcomes.

The basic underlying premise is that every player who uses the model will have specific preferences that will affect the weighting of the elements in the “game”, thereby plotting a singular, unique path to a possible outcome. This is relevant to the exploratory model that is the centrepiece of this study. Different types of campaigns are planned, executed and measured through the perspective and individualistic approach of a marketing practitioner.

The elements of the campaign affecting the eventual result and its relative weighting in importance will thereforee not necessarily change, but the application or decisions made after analysing the underperformance of certain elements and the impact that will have on future outcomes will differ from practitioner to practitioner.

In the researcher’s experience, the financial services industry with its sub-categories and, specifically, the direct-response advertising discipline can be seen as a “game” with multiple players vying for a larger market share and making decisions within the same set of rules, capabilities, tools, and even potential resources as most campaigns of this nature are business-case driven and do not form part of management expenditure.

The analytical and predictive powers of game theory have been further developed and expanded by the inclusion of descriptions of psychological decision-making and monetary judgement processes instead of pure rationality (O'Doherty, 2014). This has led to behavioural game theory, which includes human phenomena such as emotions, mistakes and rule-of-thumb decisions. As the model in this study also includes subjective variables, such as advertisement recall and recognition, purchase intention and experience ratings, in addition to objective criteria such as the audience reached, return on investment ratios and profit margin (all of which can be interdependent and linked in some way), game theory is well suited to

form the statistical backbone of the proposed model as the model represents perfectly what is referred to as a pay-off matrix (Schelling, 2010). Behavioural game theory aims to determine how individuals will react or make decisions in situations of strategic interaction through the use of objective, numeric variables.

Due to the interactive and interdependent relationship that exists between marketing practitioners and their intended target audience, understanding what effect changes in expected outcomes like response rates or audience reach can have on the success of a campaign, is central to managing marketing spend optimally. Circumstances and environments change over time, as well as driving forces of decision-making within consumer communities (Ponssard & Saulpic, 2005). Being able to predict what effect these changes will have on planned activities and outcomes and having the flexibility to adapt is a practical application of game theory modelling –exactly what this study aims to achieve through the proposed direct-response metrics model.

Through the understanding of inter-related and interdependent factors that influence campaign outcomes in relation to the intended objectives, triggers can be identified that can be manipulated to achieve the intended results. An inverse or pay-off matrix that predicts the effect of changed inputs on various other inputs can be used to identify these triggers. The matrix in Appendix 1 portrays what the model could potentially reflect.

EViews version 7.2 was used to process the data following the survey. The software combines relational database and spreadsheet technology and is usually used in time-series-oriented econometric analysis. EViews interfaces with various programs for input and output purposes, including SPSS, Excel and SAS.

5.3. SUMMARY

This research study is explorative in nature and the proposed model that forms its centrepiece uses both existing and new variables, as well as objective and subjective inputs. This complexity is further underlined by the belief in the marketing industry that certain things are not measurable and that the disciplines of financial management and marketing should not converge. The research methodology used in this study is thereforee a mixed method design, incorporating both positivist and interpretivist – in the form of a constructivist approach – philosophies and designs, to cater for the duality of inputs into the model.

Both qualitative and quantitative methodologies were used. First, focus groups were used to broadly identify the themes concerning marketing strategists and practitioners within the financial services industry, as well as to confirm the validity of the variables included in the proposed model. Online surveys were also sent to respondents using LimeSurvey, which facilitated the use of pair-wise comparisons within the AHP framework to assign relative weightings to the constructs and variables contained in the model. The final step in the research process was to test the validity of the model by applying game theory modelling to historical campaign data.

CHAPTER 6

MARKETING METRICS: A QUALITATIVE UNDERSTANDING

6. INTRODUCTION

This research study aims to propose the introduction of a new measurement model for direct-response advertising in the financial services industry. This research process entailed both a qualitative and quantitative approach to incorporate both positivist and interpretivist philosophies. The qualitative research involved focus-group discussions with marketing strategists and practitioners representing the disciplines required. This strategy was used to confirm themes underlying the research objectives as well as to test the validity of the constructs and variables contained within the proposed model. Four focus groups were held and 25 participants represented the fields of direct customer communication and marketing.

They had experience in engagement, brand building, marketing and media strategies, campaign execution and distribution channels.