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Application of quantitative research techniques to the field of marketing
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.

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.

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.

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.

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. Content validation (also called face validity) checks how well the content of the research are related to the variables to be studied; it seeks to answer whether the research questions are representative of the variables being researched. It is a demonstration that the items of a test are drawn from the domain being measured. Criterion validation checks how meaningful the research criteria are relative to other possible criteria. When the criterion is collected later the goal is to establish predictive validity. Construct validation checks what underlying construct is being measured. There are three variants of construct validity: convergent validity (how well the research relates to other measures of the same construct), discriminant validity (how poorly the research relates to measures of opposing constructs), and nomological validity (how well the research relates to other variables as required by theory). Internal validation, used primarily in experimental research designs, checks the relation between the dependent and independent variables (i. e. Did the experimental manipulation of the independent variable actually cause the observed results?) External validation checks whether the experimental results can be generalized. Validity implies reliability: A valid measure must be reliable. Reliability does not necessarily imply validity, however: A reliable measure does not imply that it is valid.