Quantitative marketing research is the application of quantitative research techniques to the field of marketing research. It has roots in both the positivist view of the world, and the modern marketing viewpoint that marketing is an interactive process in which both the buyer and seller reach a satisfying agreement on the "four Ps" of marketing: Product, Price, Place (location) and Promotion. As a social research method, it typically involves the construction of questionnaires and scales. People who respond (respondents) are asked to complete the survey. Marketers use the information to obtain and understand the needs of individuals in the marketplace, and to create strategies and marketing plans.
Data collection The most popular quantitative marketing research method is a survey. Surveys typically contain a combination of structured questions and open questions. Survey participants respond to the same set of questions, which allows the researcher to easily compare responses by different types of respondent. Surveys can be distributed in one of four ways: telephone, mail, in-person and online (whether by mobile or desktop). Another quantitative research method is to conduct experiments into how individuals respond to different situations or scenarios. One example of this is A/B testing of a piece of marketing communications, such as a website landing page. Website visitors are shown different versions of the landing page, and marketers track which is more effective.
Differences between consumer and B2B quantitative research Quantitative research is used in both consumer research and business-to-business (B2B) research. However, there are differences in how consumer researchers and B2B researchers distribute their surveys. Generally, surveys are distributed online more than in-person, by telephone or by mail. However, in B2B research, online research is not always possible, often because it is difficult to get hold of certain business decision-makers via email. As a result, B2B researchers still often conduct surveys via telephone.
Typical general procedure Simply put, there are five major and important steps involved in the research process:
Defining the problem. Research design. Data collection. Data analysis. Report writing & presentation. A brief discussion on these steps is:
Problem audit and problem definition - What is the problem? What are the various aspects of the problem? What information is needed? Conceptualization and operationalization - How exactly do we define the concepts involved? How do we translate these concepts into observable and measurable behaviours? Hypothesis specification - What claim(s) do we want to test? Research design specification - What type of methodology to use? - examples: questionnaire, survey Question specification - What questions to ask? In what order? Scale specification - How will preferences be rated? Sampling design specification - What is the total population? What sample size is necessary for this population? What sampling method to use?- examples: Probability Sampling:- (cluster sampling, stratified sampling, simple random sampling, multistage sampling, systematic sampling) & Nonprobability sampling:- (Convenience Sampling, Judgement Sampling, Purposive Sampling, Quota Sampling, Snowball Sampling, etc. ) Data collection - Use mail, telephone, internet, mall intercepts Codification and re-specification - Make adjustments to the raw data so it is compatible with statistical techniques and with the objectives of the research - examples: assigning numbers, consistency checks, substitutions, deletions, weighting, dummy variables, scale transformations, scale standardization Statistical analysis - Perform various descriptive and inferential techniques (see below) on the raw data. Make inferences from the sample to the whole population. Test the results for statistical significance. Interpret and integrate findings - What do the results mean? What conclusions can be drawn? How do these findings relate to similar research? Write the research report - Report usually has headings such as: 1) executive summary; 2) objectives; 3) methodology; 4) main findings; 5) detailed charts and diagrams. Present the report to the client in a 10-minute presentation. Be prepared for questions. The design step may involve a pilot study in order to discover any hidden issues. The codification and analysis steps are typically performed by computer, using statistical software. The data collection steps, can in some instances be automated, but often require significant manpower to undertake. Interpretation is a skill mastered only by experience.
Statistical analysis The data acquired for quantitative marketing research can be analysed by almost any of the range of techniques of statistical analysis, which can be broadly divided into descriptive statistics and statistical inference. An important set of techniques is that related to statistical surveys. In any instance, an appropriate type of statistical analysis should take account of the various types of error that may arise, as outlined below.
Reliability and validity Research should be tested for reliability, generalizability, and validity. Generalizability is the ability to make inferences from a sample to the population. Reliability is the extent to which a measure will produce consistent results.
Test-retest reliability checks how similar the results are if the research is repeated under similar circumstances. Stability over repeated measures is assessed with the Pearson coefficient. Alternative forms reliability checks how similar the results are if the research is repeated using different forms. Internal consistency reliability checks how well the individual measures included in the research are converted into a composite measure. Internal consistency may be assessed by correlating performance on two halves of a test (split-half reliability). The value of the Pearson product-moment correlation coefficient is adjusted with the Spearman–Brown prediction formula to correspond to the correlation between two full-length tests. A commonly used measure is Cronbach's α, which is equivalent to the mean of all possible split-half coefficients. Reliability may be improved by increasing the sample size. Validity asks whether the research measured what it intended to.
… excerpt ends here. Continue reading the full article.
