CONSUMER RESEARCH PROCESS Chapter 6 CONSUMER RESEARCH PROCESS • Before moving into the consumer research process, let us first understand a very key issue in consumer research, i.e., the difference between market research and marketing research. • Marketing Research vs. Market Research You will find that these terms often are used inter changeably, but technically there is a difference. Market research deals specifically with the gathering of information about a market’s size and trends. Marketing research covers a wider range of activities. While it may involve market research, marketing research is a more general systematic process that can be applied to a variety of marketing problems. • THE EIGHT-STEP RESEARCH PROCESS • We are depicting the consumer research process in eight steps: 1. 1a. Problem/opportunity identification, 1b. Problem/opportunity formulation 2. Create the research design 3. Choosing a basic method of research 4. Selecting the sampling procedure 5. Data collection 6. Data analysis 7. Preparing and writing the report 8. Follow-up STEP ONE A: PROBLEM/OPPORTUNITY IDENTIFICATION • 1. The research process begins with the recognition of a business problem or opportunity. 2. Problem/opportunity emerges when: Environment change. 3. Examples: Technological breakthrough, new legal policy, social change, high unemployment rate 4. How to know there is an environmental change? Continuous information collection/search, internal data, managerial experience, or even gut feeling may help. 5. Two fundamental questions that is asked: (1) should we alter our marketing mix in order to perform better? (2) If so, what should we do? 6. Another important question could be: Can we predict possible environmental change? This can be done by using historical data and trying to find trend and factors that affect the emergence of such trend. STEP TWO: CREATING THE RESEARCH DESIGN • 1. A plan that researchers follow to answer the research objectives and/or test the hypotheses. 2. Whether the design will be Descriptive and/or Causal (diagnostic and predictive)? 3. Descriptive design: Answer the questions who, what, when, and how. 4. In quantitative research, we may calculate the mean, median, mode or S.D. of the data collected. For example: 35% of the respondents said they like classical music. 5. Causal design: Examine whether one variable causes or determine the value of another variable (two variables at least). 6. Independent variable (The cause, example demographic variables) and dependent variable (the outcome, musical preference). 7. In quantitative research, you may use regression analysis to analyze the association between two (or more) variables. For example, the older the respondent, the more he likes classical music. STEP THREE: CHOOSING A BASIC METHOD OF RESEARCH • 1. Analysis of secondary data. 2. Survey. Obtain factual (e.g., age) and attitudinal (e.g., musical preference) data. 3. Observation. Obtain behavioral data, researchers and subjects do not have direct interaction. 4. Experiment. The researchers deliberately change the independent variable(s) and record the effects of that (those) variable(s) on other dependent variable(s). Experiments are frequently used in testing causality. STEP FOUR: SELECTING THE SAMPLING PROCEDURE • 1. Sample is a subset of the whole population. 2. Why sampling? May be…Population is too big, population unknown, insufficient resources to conduct a census. 3. Sample should be “representative” – should help the researchers to make inference about the population. STEP FIVE: DATA COLLECTION • 1. Under a natural or controlled environment? Especially important for experimental designs. 2. Survey: Mall intercept, telephone, mail, Internet…each method has different advantages and disadvantages. For example, response time, response rate, structure of questions, costs, etc. STEP SIX: DATA ANALYSIS • 1. It’s a process that interprets the observed data into meaningful information. 2. In this module, I will teach you how to use ttest, analysis of variance (ANOVA) and bivariate regression analysis to analyze the data.