In today's world, it is no exaggeration to say that the effective use of data and the realization of data-driven marketing determine the success of business. However, to effectively use data, it is not enough to simply collect and analyze information.

In this article, we will explain research design and statistical analysis, which are important for making marketing a science (researching marketing) in order to realize data-driven marketing. When you hear the word "research," you might think of an academic setting, but here we are referring to a scientific approach used in business. We will unravel the different types of research and clarify how to make scientifically based decisions in business.

1) The role of research design in marketing research

1.1) What is research design?

For those unfamiliar with research, the term research design may be unfamiliar.Research design, in a nutshell, is the "type" of research..

In research, a hypothesis is formulated and interventions and surveys are carried out to prove the hypothesis. Then, objective facts, research results, and other data are collected and examined.

In this research process, the "type" of how the research will be conducted, including the research subjects, intervention method, measurement method for evaluation items, and evaluation period, is called the research design.

1.2) Research process

The marketing research process typically involves the following steps to clearly define the research objectives and develop a plan to achieve those objectives:

1. Research purposes:Set the objective of what you want to clarify through your research. For example, it is a good idea to set a specific objective, such as the structure or mechanism of results, such as increased sales, improved customer satisfaction, or increased brand awareness.
2. Research topic:Identify specific questions or problems that need to be investigated and analyzed to achieve your research objectives.
3. Research methods and types:Determine the appropriate research method and type to answer your research question.
4. Data collection:Based on the research methodology, define the type, quantity, quality criteria, etc. of data that needs to be collected.
5. Data Analysis:Select the appropriate analytical method or model to answer your research question.
6. Interpretation of results and stakeholder engagement:The analysis results are interpreted to derive solutions to research questions. In order to more smoothly apply the results to business, it is important that all parties involved are on the same page regarding the interpretation method and the implications obtained.

In data-driven marketing, the research process and design are crucial as a foundation for obtaining useful insights. By designing optimal marketing research, you can obtain useful information that will enable you to develop products that meet market needs and formulate brand strategies to establish a competitive advantage.

 1.3) Factors for ensuring research quality

In a proper research design, neutrality, reliability, validity, and generalizability are fundamental principles for ensuring the quality of research.

• Neutrality:This means that the study is free from personal bias. It uses methods that are not dependent on personal feelings so that your (or the researcher's) opinions do not influence the results of the study, keeping the results fair and unbiased.
• Reliability:It's about the consistency and reproducibility of research. Ideally, you can run an experiment many times, or have different people run the same experiment, and they all get the same results. It shows that your research methods are stable and can deliver the same results every time.
• Validity:It's all about being able to accurately measure what you're actually trying to evaluate. If you don't measure it properly, the measurements are meaningless.
• Generalizability:This refers to whether the findings can be applied not only to the particular people (sample) or conditions studied, but also to other situations. Generalizability means that the findings can be expected to apply broadly.
  

These factors should be carefully considered during the design of a study and are essential to increase the reliability and validity of the study. If a study design meets these criteria, the findings are more likely to be accepted as a strong scientific basis.

2) Categories and classifications of marketing research

2.1) Classification by nature of data

Qualitative Research

Qualitative research aims to gather non-quantifiable data to understand consumer behavior, emotions and motivations. It uses methods such as interviews, focus groups and observations to delve into consumer psychology and cultural contexts. This approach is particularly useful for gaining exploratory insights into new markets and products.

Specific application examples:

• Understanding potential consumer needs for new product development
• Consumer sentiment analysis regarding brand image
• Exploring the lifestyles and values ​​of target customers

Quantitative Research

Quantitative research uses numerical data to test specific hypotheses and arrive at statistically significant results. It typically involves surveys or experiments, and uses large samples to obtain generalizable results. Quantitative research is particularly useful for measuring the effectiveness of specific marketing strategies.

Specific application examples:

• Measuring the effectiveness of advertising campaigns
• Determine the direction of service improvement through customer satisfaction surveys
• Numerical data analysis of consumer behavior for segmentation

Qualitative and quantitative research complement each other to provide a solid foundation for making data-driven decisions when formulating your marketing strategy. The deeper understanding gained from qualitative research provides the basis for improving the design of quantitative research, and conversely, the statistical evidence gained from quantitative research reinforces the insights of qualitative research. Thus, by combining both approaches, you can develop a more comprehensive and reliable marketing strategy.

2.2) Classification by research purpose

Exploratory Research

Exploratory research is stillGain new insights and ideas about poorly understood problems or phenomenaThis research design helps to identify hypotheses and new problems, allowing you to answer questions such as:

• What untapped opportunities exist in the market?
• What new products and services are consumers looking for?
• Are there trends that your competitors are overlooking?

Descriptive Research

Descriptive research isDescribe and understand specific aspects of a phenomenon or market in detailOur goal is to provide a detailed snapshot of the current market situation and consumer behavior (observations at the time of research) so that you can answer questions such as:

• How is our product positioned in the market?
• How do customers perceive our brand?
• What are the demographic characteristics of my buyers?

Causal Research

Causal studies areClarifying cause and effect relationshipsThe purpose of this research design is to test for causal relationships between specific variables. It can answer questions such as:

• How did a particular advertising campaign affect sales?
• What effect will price changes have on consumer purchasing behavior?
• How will the introduction of new sales channels change customer engagement?

Correlational Research

 Correlation studies areExamine the relationship between two or more variablesResearch design. This study helps us understand how certain variables relate to other variables. However, correlation does not imply causation. It can answer questions like:

• Do high engagement campaigns on social media actually lead to increased sales?
• Do brands with high customer satisfaction tend to have higher repeat purchases and customer loyalty?
• How much will competitors' price changes affect the sales volume of your products?

2.3) Classification by observation method

Experimental Research

Experimental research plays an important role, especially in causal research.Manipulating certain variables and measuring the results in a controlled environmentBy analyzing the results, we can clarify the relationship between cause and effect. This approach helps us understand how a particular marketing strategy or product change affects consumer behavior. For example, A/B testing is a type of experimental research that is widely used to compare the effectiveness of different marketing messages.

Observational Research

Observational studiesObserve behaviors and phenomena as they occur in the natural environmentThis is often used in descriptive and exploratory research. This method is useful for understanding broader patterns of behavior and trends that cannot be captured through experimental studies. It provides information that reflects real-world conditions, such as market trends, customer lifestyles, and competitor activities.

2.4) Classification by research period

Cross-sectional Studies

Cross-sectional studies areMeasuring multiple variables at a timeA research design that allows you to quickly understand relationships and patterns between different variables. It allows you to answer questions such as:

• What are the differences in product perceptions and preferences among customers of different age groups?
• How high or low is brand awareness in certain locations compared to the national average?
• Do limited-time promotions have an immediate impact on customer purchasing behavior?

Longitudinal Studies

Longitudinal studies areMeasuring the same variables repeatedly over timeIt is a research design, which allows us to track changing patterns and trends over time.

• How will customer purchase frequency and average purchase size change after implementing new marketing strategies?
• How do customers' evaluations of a product change as it transitions from new release to mature stage?
• How will changes in consumer lifestyles and values ​​affect demand for specific product categories in the long term?

2.5) Classification by approach

Inductive Approach

Induction isIt starts with observations and derives general theories and laws from facts and data.This method is particularly used in qualitative research, where an inductive approach develops broad theories through individual case studies and detailed data analysis. Inductive methods are useful for discovering new phenomena and providing new perspectives on existing theories.

Specific application examples:

• Identify new market trends from patterns of consumer behavior
• Understand customer sentiment towards your brand through social media data analysis
• Conduct a competitive analysis to clarify your positioning

Deductive Approach

The deductive method isIt starts with an existing theory or hypothesis, derives specific predictions from that hypothesis, and then validates them.It is an approach to research that examines a range of factors, including the nature of the product, the type of product, and the type of product that is being sold. It is often used in quantitative research and uses statistical methods to test hypotheses. Deductive methods are particularly important when testing the validity of a theory or measuring the effectiveness of a particular marketing strategy.

Specific application examples:

• Measure the effectiveness of your advertising campaigns and calculate your ROI
• Analyze the relationship between customer satisfaction and brand loyalty
• Evaluate the impact of specific pricing strategies on market share

Each of these research types has different characteristics and may be used in different combinations depending on the research objectives and questions. For example, you may start with exploratory research to understand a new market and follow it with descriptive research to quantify certain elements of that market. Or you may use correlational research to find patterns in customer behavior and then apply experimental research to test different marketing strategies.

Understanding the differences and relationships between different types of research can help guide you in how to best answer your business or marketing questions. Also, these are not mutually exclusive categories and different methods can often be combined to arrive at a comprehensive understanding of your research question.

4) Statistical analysis: Improving the reliability of marketing research

The role of statistical analysis in marketing research is much more than simply compiling data. It is an essential specialized field for extracting useful information from all marketing data and formulating strategies to help companies succeed. Here we will focus on some of its roles and introduce the importance of statistical analysis.

• Understanding the data:Statistical analysis is a way of understanding the fundamental characteristics of data. Descriptive statistics such as the mean, median, and standard deviation are used to understand trends and distributions in the data.
• Discovering patterns:It allows you to discover hidden patterns and relationships in your data.Cross-tabulation analysisThrough this, we clarify the relationship between variables.
• Testing the hypothesis:Statistical analysis provides the means to develop and verify hypotheses to verify the effectiveness of marketing strategies.regression analysisetc. to determine whether the hypothesis is statistically significant.
• Decision support:Statistical analysis reduces uncertainty in interpreting the results of an analysis, allowing you to make more confident choices.Predictive Modelcan be used to improve decision making accuracy.
• segmentation:Use multivariate analysis to classify customers into different segments and develop targeted marketing strategies.Cluster analysisand principal component analysis are often used for this purpose.
• Tracking trends:Time Series AnalysisYou can track trends and seasonality and plan future marketing efforts through this.
  

Accurate statistical analysis makes it possible to assess whether differences or correlations between data are due to chance and to predict the future. Statistical analysis is a powerful tool for reducing uncertainty in marketing research and increasing the reliability of data-based decisions.

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5) Issues and solutions in marketing research

Marketing research plays a key role in data-driven decision-making, but it also comes with many challenges. Understanding these challenges and addressing them appropriately can improve the reliability and validity of your research. Here are some examples:

Issue 1: Data quality and consistency

If data quality is poor or data from different sources is inconsistent, the analysis becomes less reliable.

approach

• Establish clear data collection criteria before collecting data.
• Use multiple data sources to ensure data diversity.
• Cleanse and preprocess data to improve quality.

Task 2: Selection and manipulation of variables

Selecting the wrong variables and analyzing the data without manipulating them can lead to erroneous conclusions.

approach

• Identify important variables based on the research objectives and clarify how to operationalize them.
• Consider experimental designs to test causal relationships.

Issue 3: Sample bias

Using a sample that is biased towards a particular population will prevent you from obtaining generalizable results.

approach

• Random or stratified sampling will be used to ensure the representativeness of the sample.
• Clarify sample selection criteria to reduce the possibility of bias.

Task 4: Selection and application of statistical methods

Application of inappropriate statistical methods may lead to misinterpretation of data.

approach

• Select appropriate statistical methods based on the research objectives and data characteristics.
• Verify results using multiple methods to ensure consistency.

Implementing these strategies properly can help you overcome challenges in marketing research and produce more accurate and reliable research results that contribute to the success of data-driven marketing.

Summary

Data is the foundation of modern marketing, and its effective use is directly linked to the success of a company. Sophistication of research design and accuracy of statistical analysis are essential to extract true value from data and build a competitive advantage in the market. In this article, we have introduced how these elements function in marketing research.

From qualitative research to quantitative research and causal relationship analysis, various research methods provide scientific evidence for business decision-making. However, issues such as data quality, variable selection, sample bias, and application of statistical methods must always be addressed. Addressing these issues and using appropriate research design and statistical analysis can provide more reliable results for formulating marketing strategies.

Ultimately, the success of data-driven marketing depends on a company's ability to understand and use data appropriately. I hope this article will provide guidance for marketers and business leaders on this journey. By believing in the power of data and adopting a scientific approach, the future of marketing will be even more revolutionary.

XICA has been providing services in the field of data science in marketing for over 10 years, and has a track record of supporting over 250 companies, mainly domestic enterprise companies. Our analysts and consultants have extensive and deep expertise in a wide range of industries, and they use data science to help clients make better decisions.

If you have any questions regarding specific marketing research projects aimed at realizing data-driven marketing,Contact us.

Author information

Marketing team of XICA Co., Ltd.