Marketing Objectives Marketing objectives are the targets that the marketing department wishes to achieve. These objectives should be compatible with the firm’s overall objectives. E.g. if growth was a firm’s objective then the marketing department may consider entering an overseas market or launching new products. Marketing objectives should be SMART Create customer satisfaction in a profitable way Source : IB Business & Management Marketing Objectives may Include Maintain/ increase market share Market leadership Product positioning Consumer satisfaction Diversification Market development New product development Product innovation High market standing Constraints on achieving marketing objectives Finance Costs of production The size and status of the firm Social issues Time lags (liquidity problems) Activities and reaction of competitors The state of the economy The political and legal environment Market Research is ... “Process of systematically gathering, recording and analyzing data and information about customers, competitors and the market so as to create a business plan, launch a new product or service, fine tune existing products and services and expand into new markets” (Source : The National Women’s Business Center (NWBC)) Market research serves to identify the wants and needs of customers. Market research involves collecting primary and secondary data to gain some insight into the structure of a market. (Source : IB Marketing and Management) Why engage in Market Research Develop a detailed profile of the "ideal" consumer in the market segment a business is trying to reach. Be competitive, productive and profitable Determine consumer interest and therefore create a business plan or launch a new product or service tailored to the customers. Fine tune existing products and services or expand into new markets. Provide an understanding of how or why things are as they are. Determine which portion of the population will purchase a product/services based on variables such as age, gender, location and income levels Why engage in Market Research To identify opportunities in the market To minimizes the risk of doing business. Uncover and identify potential problems Evaluate business and its success Why engage in Market Research Gives businesses up-to-date and accurate information. Allows a business to see whether current products meet the needs of customers. Customers tastes and preferences change over time. Allows businesses to improve their marketing by using a distinct marketing mix for each customer target group (marker segment). Assesses potential customer reactions to a new product. Gives businesses an understanding of the activities and strengths used by their competitors Helps businesses predict what is likely to happen in the future Drawbacks of Market Research Information and findings are only as good as the research methodology used. Directly asking customers whether they are willing to pay higher prices will generate obvious responses. (Garbage In Garbage Out, GIGO) Data and information can be inaccurate due to bias The cost of good market research is often very high. The Research Process The market research process: Problem definition More details of this process on pg. 102 of book, Fig 4.1 Research design Data collection Data analysis and interpretation Presentation of results Copyright ©2005 by South-Western, a division of Thomson Learning. All rights reserved. Explanatory vs. design science research Design science Explanatory research research The phenomenon ”out there” to be created Data collected and analyzed created and analyzed Reasoning/research hypothetico-deductive, abductive, design inductive practical syllogism explanatory theory, artifact (e.g., technology) End product prediction Disciplinary basis natural and social science engineering Knowledge interest cognitive pragmatic (Mikko Ketokivi) Exploratory Research To uncover insights, to understand. Definitions are loose, processes are flexible. Samples are small and non-representative. Uses qualitative data. Your findings are provisional, and usually leave the door open for additional research. Methods used are: Discussions with experts. Pilot surveys. Case studies. Analysis of secondary data. Qualitative research. Causal Research Analysing the Cause and Effect of a problem. What is causing something and what is the effect? E.g. Did an increase in price cause a decrease in sales? Independent variable = cause Dependent variable = effect The effect may be a decrease in sales, but the cause may be an increase in price, increased in competitors’ advertising, decrease in competitors’ price, promotional activity in the market, economic factors etc …. Method used is: Experimentation – You see if one variable effects another variable. Comparative Performance of Data Collection Methods Interview Focus group Telephone Mail Cost/response High Fairly high Low Very low Speed Fast Fast Very fast Slow Large Large Moderate Moderate Sample dispersion Low Low High High Response rate High Very high Fairly high Low Probing High High Fairly high None Quantity of data Brassington and Pettitt (2006) Principles of Marketing Why using SEM ? What is SEM ? A comprehensive statistical approach to testing hypotheses about relations among observed and latent variables. Standard statistical Approaches: Ex. Correlation, multiple regression, factor analysis, ANOVA, MANOVA... A general methodology for specifying, estimating, fitting, and evaluating models of relationships among variables. What are Structural Equation Models? Also known as: Covariance structure models Latent variable models “LISREL” models Structural Equations with Latent Variables Special cases: ANOVA Multiple regression Path analysis Confirmatory Factor Analysis Recursive and Nonrecursive systems What are Structural Equation Models? SEM associated with path diagrams intelligence test 1 test 2 test 3 test 4 test 5 δ1 δ2 δ3 δ4 δ5 What are Structural Equation Models? Latent variables, factors, constructs Observed variables, measures, indicators, manifest variables Direction of influence, relationship from one variable to another Association not explained within the model What are Structural Equation Models? Depress 1 Family support Self rated closeness δ1 Spousal rating δ2 Depress 2 Depress 3 depression ζ1 Physical health ζ2 Kids rating δ3 Self rating ε4 MD rating ε5 # visits to MD ε6 Notation η Latent Endogenous Variable ξ Latent Exogenous Variable ζ Unexplained Error in Model x & y Observed Variables δ & ε Measurement Errors λ, β, & γ Coefficients Notation Two components to a SEM Latent variable model Relationship between the latent variables ηΒη Γξ ζ Measurement model • Relationship between the latent and observed variables xΛxξδ yΛyηε Example of SEM questions Control Factor in Broadband Research is a latent variable with knowledge, facilitating conditions (on TAM) Control Factor and Customer Behaviour may be affected by each other. Others... The impacts of economic development on democracy may be indirect. Characteristics SEM requires formal specification of a model to be estimated and tested. Confirmatory rather than exploratory (eg. multiple regression) approach inferential purpose assess directional and non-directional influences simultaneously capability of assessing measurement error Advantages of SEM reduces measurement error by having multiple indicators of a latent variable ability to test overall models and individual parameters ability to statistically compare nested and non-nested models ability to test models with multiple Dvs ability to model mediator variables (processes) ability to model error terms ability to model relations across groups, across time SEM Approaches 2 major approaches, each based on the researcher’s goals confirmatory factor analysis (CFA) structural equation modeling (SEM) For each major approach, one can take 1 of 3 additional methodological approaches strictly confirmatory alternative models model development SEM Variables observed = measured, manifest, indicators latent = theoretical constructs variables that are defined by the observed variables goal is to model the commonality in the observed variables can be items, subscales, or scales sound like factor analysis? and then look at relations between latent variables sound like multiple regression? How do data relate to learning? multivariate descriptive statistics multivariate data modeling SEM univariate descriptive statistics univariate data modeling Data exploration, methodology and theory development abstract models Understanding of Processes modified from Starfield and Bleloch (1991) realistic predictive models more detailed theoretical models
