The Role of Quantitative & Qualitative Research in Marketing

This presentation explores the roles of quantitative and qualitative research in modern marketing. It discusses the importance of questionnaire design and interview discussion guides, providing examples of good practice. The presentation covers correlation, regression, and time series analysis, explaining how and why they are used with examples, while also critiquing the issues surrounding these analysis techniques, particularly focusing on Big Data and its use in business decision-making. It highlights the limitations of correlation and regression and suggests ways to improve data collection for informed business decisions. The presentation concludes by emphasizing the significance of well-structured research designs and measurable goals for effective decision-making.

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Quantitative market research generally concentrates on consumer that involves the surveys,
these questions helps in examining the views of target audience. As question that being asked
helps in Improving services for better business performance.
Thus overall it helps in improving the consumer satisfaction within the organisational business.
Quantitative tool emphasise the statistical analysis across the data that obtained from polls,
surveys.

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Utilize the verbal labels: IT is efficient for using the verbal label for every response, this helps in
increasing the respondent’s attention and decrease the chances for measurement errors.

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Qualitative market research is less expensive approach to develop understanding about the
critical factors as what the individual thinks and approach to specific topic. In such the methods
are for conducting research is focus group, in depth interviews etc.
With this data it also helps in gaining the perspective as how the particular products can
efficiently fits in consumer lifestyle. This type of marketing research mainly concern with the
observational examination that governs the customer behaviour.

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Examples-
Structured interview: According to you what is the biggest challenge for the marketing
manager’s role?
Unstructured: what is your accomplishment which makes you more proud of and state why?
Semi structured: What is your reason for applying for this marketing role?

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Reason for using the correlation is that it helps in providing the quick delivery as well as simple
and concise summary about the direction and the relationship among the numeric variables.
While regression can be used to make the prediction and to examine the responses between
the variables as how a affects the b
for regression it is used for predicting the uninterrupted dependent variable through the
independent variable, as if the dependent variable is bilateral than logistic regression is
generally used. Regression equation form is Y= a+bX
Example is relation between use of ICT and student performance.

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There are some other type of time series is also present which are seasonal variation, random
and trend variations.

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It is also possible that if business decision is based on entirely on regression analysis then extrapolation may not be
beneficial for the organisation.
The most considerable issue observed in such analysis is that businesses often misinterpret the correlation factor and
wrong decisions are often taken. Strong correlation is not always meant to depict the effect and cause relationship.
However, the sole dependence of business on this type of data analysis can result in this misinterpretation and
decision making may not remain effective.

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For big data analysis time series is mainly used when organisations have to make strong and
quick decisions related to forecasting of the business patterns and trends.
Also when decision making involve analysis of single even then in time series it may create a
bigger issue if multiple events are occurring at same time. Thus with this type of data analysis it
can emerge as greater challenge to identify that which is known to be the intractable event.

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In order to make the informed decisions it is required that research design must be well
structured so that while collecting data no bias or error is observed and reliability of data can be
maintained to make effective business decisions.
Investment in correct decision making tool as well as measureable decision making goals can
help to enhance the decision making process and quality.
Clear organisation of data can also be effective and helpful in better analysis and opportunities
to revaluate the data.

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It finally discussed the issues that surrounding the analysis techniques that generally focused on
the Big data techniques and how collected data is helpful for making efficient business
decisions.