Data Insights for Business Decisions

This presentation explores the roles of quantitative and qualitative research in marketing research and data analysis. It covers the importance of relevant questionnaire design, discussion guides, and data analysis methods. It also discusses the use of correlation, regression, and time series analysis in business decision making, as well as the limitations and critiques of these techniques. Finally, it provides insights on how to collect and use data for informed business decisions.

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Introduction
• Roles of quantitative market research in business
• Importance of relevant questionnaire design
• Role of Qualitative research in Business
• Importance of discussion guide and use
• Correlation. Regression. And Time Series Analysis
• Limitations of Correlation, regression
• How to improve collecting Data (when the data is required to make
business decisions)

Introduction
The upcoming report looks into the importance of quantitative and
qualitative research in the marketing research and data analysis.
Additionally, it also looks into the role of correlation and regression in
the business firms with examples of each. Furthermore, it also contains
the statements on the advantages and disadvantages of correlation and
regression analysis in the business decision making. Moreover, it also
comprises the data analysis method that a business should adopt for
the effective decision making.

The role of quantitative research in modern
marketing research and data analysis
In marketing research
• Allows the businesses to perform research at scale.
• Helps business in providing insights about the large groups of customers or
population altogether.
• Assists the companies to make comparisons between the different groups by
age, gender, etc. to gain knowledge of the differences or resemblances.
In data analysis:
• It helps the business price to get the meaningful data from the raw numbers
with the applicability of rational and critical thinking.
• Quantitative research involves the evaluation of frequencies of variables and
distinction between the variables.

The role of quantitative research: Questionnaire design with
examples of good practice
Tools of Quantitative research:
• Probability Sampling
• Interview
• Surveys/ Questionnaire
• Observations
• Importance of relevance questionnaire design in the quantitative research:
• Practicality: The questionnaire helps the business to target the customers and allow
the researchers to manage strategically. It also helps in managing the questions and
format by collecting large data quantities.
• Cost- efficiency: The business owners can easily place the surveys on the company’s
website and avail the response from the customers. There are no surveyors involved.
So, no extra cost is charged.

The role of qualitative research in modern marketing research
and data analysis
Qualitative research is the method of inquiry that interprets the data
conveyed via language and behaviour in natural settings. It is the
mechanism that provides the expressive information which is not
conveyed by the quantitative data. These expressive data can be values,
feelings, motivations and beliefs.
Role of Qualitative research in modern marketing research:
• Helps in understanding the behavioural pattern and wants of the
customers.
• Assists in getting an understanding of what a consumer think about
the product and what is his perception towards it.

The role of qualitative research. The Interview Discussion guide:
design & use (with examples)
Role of quantitative data in data analysis is that it is used to gain the
understanding of people feelings and thoughts, and these insights
delivers the ground for the future qualitative study. It also helps the
company to find out the survey instruments for conducting the
quantitative research.
Discussion Guide: It holds the set of topics and questions that a
surveyor would like to talk about to the customers in the interview.

Correlation & Regression:
How & why it is used - with examples
Correlation is the statistical measure which describes the degree or
extent to which two variables can move together i.e., in coordination
with each other. If the two variables tend to move in the same
direction, then it is considered as the positive correlation and if they do
not move together or if they move in an opposite direction then they
have negative correlation. Example for the positive and negative
correlation in an organisation.
• If the company hire more people for sales, then the sales will also be
increases.

Time series:
What it is & how it is used - with examples
• A time series is the series of data points that are listed or graphed in a
timely manner or it can also be defined as the collection of
observations of well -defined data items which is obtained by
repeated measurements over time. Time series is used to evaluate a
model that can be useful for projections of the business tools such as
prices of the stock, sales, turnover, profit, etc. It is used in an
organisation for demand forecasting for retail, procurement and
dynamic pricing: To make predictions about the customer demand for
a product is very important because the procurement and supply of
the goods depends on this factor only.

Critique of issues surrounding the analysis techniques
Correlation is the term which is defined as the relationship between data in a
business and is most commonly use in the analysis of financial reports and it
support decision making of the business. Regression term used define the
relationship between data which are recorded in sets but also it used to define
as that if one data set changes, it will make all further causes to the
corresponding change in the other data set. Regression is often used in sales
forecasting, product, and service development, predicting future market trends,
and other data causes. Correlation and regression analysis gives business
leaders in making more impactful predictions based on patterns of data used in
the sets.

How data can be collected & used to
make informed business decisions.
Time series focusing on Big Data & its use in business decision making (focusing on Big
Data & its use in business decision making)
Data driven decision are the decisions that are grounded on the data. In this method the
data is gathered and business decisions are framed from the procedure that are based
on the goals or KPIs. Interpreting and observing and also processing the data so that it
gives the good and reliable information. It is very essential for the business to collect the
data which will affect the company’s goals and objectives in a positive manner. The data
collected shall relate to the aim and goals of the business organization. For this purpose,
the data management, data governance and data visualization are very important that
delivers the procedure of informed data decisions and support the management in their
operation. For effective data collection the company must make the decisions that rely
on the data quality and insights. By this the company will be able to make the decisions
that ensures the continuity and profitability of the business organisation.

Conclusion
• From the above report it can be concluded that the quantitative and
qualitative research plays a crucial role in a business enterprise for
gaining knowledge on the terms of figures and emotions. Along with
this, it is also clear that how the questionnaire and telephonic
interviews can help the business organizations in making informed
decisions on the basis of data. Furthermore, it also gives a clear
representation that how discussion guide helps in the structure of
interview and it has its huge significance in the company.

List of References
• Crippa, M. and et.al., 2019. A circular economy for plastics: Insights from
research and innovation to inform policy and funding decisions.
• Newton, J.E., Nettle, R. and Pryce, J.E., 2020. Farming smarter with big data:
Insights from the case of Australia's national dairy herd milk recording
scheme. Agricultural Systems, 181. p.102811.
• Nestor, D.M.J. and Ogudo, K.A., 2018, December. Geo and Graph Analytics
for Dynamic Cellular Transactions Insights, Improving Quality and Business
Decisions:" Quality X Map". In 2018 International Conference on Intelligent
and Innovative Computing Applications (ICONIC) (pp. 1-6). IEEE.
• John, R.J.L., 2021. From Data to Insights through Conversation. The
University of Wisconsin-Madison