LECTURE 3_MRF_SECONDARY DATA_2025-2026.pdf

# Overview of the marketing research process and secondary data This section provides an overview of the marketing research process and defines secondary data, contrasting it with primary data, while highlighting its importance and uses [2](#page=2) [4](#page=4) [5](#page=5). ### 1.1 The marketing research process steps The marketing research process is a systematic sequence of eleven steps designed to gather and analyze information to solve marketing problems [2](#page=2). #### 1.1.1 Key steps in the process 1. **Establish the need for marketing research:** Recognizing that research is required to address a marketing issue [2](#page=2). 2. **Define the problem:** Clearly articulating the specific marketing problem to be investigated [2](#page=2). 3. **Establish research objectives & questions:** Formulating specific goals and questions that the research aims to answer [2](#page=2). 4. **Determine research design:** Planning the overall approach and methodology for conducting the research [2](#page=2). 5. **Identify information types and sources:** Deciding what kind of information is needed and where it can be found [2](#page=2). 6. **Determine methods of accessing data:** Choosing how to obtain the identified information [2](#page=2). 7. **Design data collection forms:** Creating the tools (e.g., questionnaires, interview guides) for gathering data [2](#page=2). 8. **Determine the sample plan and size:** Deciding on the population to study and the number of participants [2](#page=2). 9. **Collect data:** Executing the data collection plan [2](#page=2). 10. **Analyze data:** Processing and interpreting the collected data [2](#page=2). 11. **Communicate the insights:** Presenting the findings and their implications to relevant stakeholders [2](#page=2). > **Tip:** Within step 5, "Identify information types and sources," a crucial element to consider is secondary data, which can often be explored before committing to primary data collection [2](#page=2). ### 1.2 Primary versus secondary data A fundamental distinction in marketing research is between primary and secondary data [4](#page=4). #### 1.2.1 Primary data Primary data are collected by a researcher specifically for the immediate purpose of addressing the problem at hand [4](#page=4). * They do not exist before the data collection process begins [4](#page=4). * Their collection requires the design and execution of a specific research study [4](#page=4). * Primary data are typically gathered through field research [4](#page=4). #### 1.2.2 Secondary data Secondary data, in contrast, are data that have already been collected for purposes other than the specific problem currently being addressed [4](#page=4). * These data already exist and were collected for a different objective [4](#page=4). * Using secondary data involves giving them a "second" use [4](#page=4). * The process of finding and utilizing secondary data is known as desk research [4](#page=4). * A significant advantage of secondary data is that they can often be located quickly and at a lower cost compared to primary data [4](#page=4). > **Example:** A company wants to understand consumer attitudes towards a new product. Primary data would involve conducting surveys or focus groups with potential customers. Secondary data could include existing market reports on consumer trends, competitor analysis, or demographic data from government agencies that provide relevant background information. ### 1.3 Importance and uses of secondary data Secondary data play a vital role in the marketing research process, offering several key benefits [5](#page=5). #### 1.3.1 Applications of secondary data * **Better problem definition:** Secondary data can help refine and clarify the marketing problem by providing initial insights and context [5](#page=5). * **Improved research objectives and questions:** The information gleaned from secondary sources can aid in formulating more precise research objectives, questions, and hypotheses [5](#page=5). * **Answering specific research questions:** In some cases, secondary data may be sufficient to answer certain research questions directly, saving the need for primary data collection [5](#page=5). * **Developing appropriate research design:** Understanding existing data can inform the choice of the most effective research design for subsequent primary data collection [5](#page=5). * **Interpreting primary data:** Secondary data can provide a valuable frame of reference for interpreting the results of primary data analysis, leading to more insightful conclusions [5](#page=5). > **Tip:** Always explore secondary data sources thoroughly before embarking on costly and time-consuming primary data collection. It can save resources and provide a stronger foundation for your research [4](#page=4) [5](#page=5). --- # Types and sources of secondary data This section outlines the classification of secondary data into internal and external types, detailing the various sources for external data. ### 2.1 Types of secondary data Secondary data can be broadly categorized into two main types: internal and external [8](#page=8). #### 2.1.1 Internal secondary data Internal secondary data refers to information collected and stored by a company for its own use. Companies leverage internal consumer or customer databases to answer critical business questions, such as identifying which products consumers buy, which consumers are the most valuable or repeat purchasers, and understanding the geographic location and profitability of different consumer segments. It also helps in analyzing seasonal purchasing patterns by product and consumer types, and in determining which consumers only engage during special offers [7](#page=7). #### 2.1.2 External secondary data External secondary data and databases are obtained from organizations outside of the firm. These sources are diverse and include government publications, non-government published data, syndicated services, and various forms of digital data [8](#page=8). ### 2.2 Sources of external secondary data External secondary data can be sourced from a variety of organizations and platforms. #### 2.2.1 Non-government published data Producers of non-government published data include academic institutions like universities, research institutes, foundations, and think tanks. Regulatory bodies and trade and professional associations also contribute to this data pool [9](#page=9). HEG (Haute école de gestion) provides access to several sources for secondary data [10](#page=10): * **e-books:** ScholarVox [11](#page=11). * **Databases:** ABI/Inform and Business Source Premier [11](#page=11). * **Statistics:** STATISTA, Eurostat, and the Swiss Federal Statistical Office [11](#page=11). * **Strategic Information on Businesses:** Orbis and Passport - Euromonitor [11](#page=11). * **Press Archives:** Swissdox, Factiva, and Nexis Uni [11](#page=11). #### 2.2.2 Government data Government bodies at national and international levels are significant sources of secondary data. **Swiss Government Sources:** * The Federal Statistical Office of Switzerland is a primary source for official statistics [16](#page=16). **U.S. Government Sources:** * US Census Bureau [17](#page=17). * Department of Commerce [17](#page=17). * Agency for International Development (USAID) [17](#page=17). * Small Business Administration [17](#page=17). * Export–Import Bank of the United States [17](#page=17). * Department of Agriculture [17](#page=17). * Department of State [17](#page=17). * Department of Labor [17](#page=17). **Other Government and Related Bodies Sources:** * European Union (Eurostat) [18](#page=18). * United Nations (including UNIDO) [18](#page=18). * World Bank [18](#page=18). * International Monetary Fund (IMF) [18](#page=18). * World Trade Organization (WTO) [18](#page=18). * Organization for Economic Cooperation and Development (OECD) [18](#page=18). * World Health Organization (WHO) [18](#page=18). * World Economic Forum (WEF) [18](#page=18). * International Chambers of Commerce (ICC) [18](#page=18). * National statistical offices of other countries, including Australia, France, Japan, Norway, and the U.K. [18](#page=18). #### 2.2.3 Syndicated services data Syndicated services are organizations that collect and sell data to multiple clients. This data is typically gathered on an ongoing basis to track consumer behavior and attitudes [19](#page=19). **Example: Quick-Track®** Quick-Track® is a syndicated market research project conducted quarterly by Sandelman to monitor key consumer behavioral and attitudinal measures for fast-food and pizza chains in various markets. The survey involves approximately 400 respondents across over 100 markets, using a combination of telephone and internet interviews. Respondents are asked about their past visits and provide ratings on restaurant experience and specific attributes like food, service, and cleanliness, on a scale of 1 (Poor) to 5 (Excellent). To ensure reliability, only chains with at least 150 responses are included in the analysis. Recent findings indicated that cleanliness (77 percent), food taste and flavor (74 percent), and order accuracy (66 percent) were the most important attributes for respondents. There is also a growing importance placed on healthy and nutritious food options, with 40 percent rating it as extremely important. Fast-food chains are responding to these demands by offering healthier menu items, such as fresh fruit bowls or salads [19](#page=19). Other examples of syndicated data providers include GfK (for in-store shelf monitoring) and Mintel [20](#page=20) [21](#page=21). #### 2.2.4 Digital data Digital data, often referred to as big data, encompasses a vast amount of structured, semi-structured, and unstructured information that can be mined for insights. These data are characterized by their volume, velocity, variety, and variability [22](#page=22). ##### 2.2.4.1 Web analytics Web analytics involves building consumer profiles by tracking website events. This process helps businesses understand user behavior on their websites, such as pages visited, time spent on site, and conversion rates [23](#page=23). ##### 2.2.4.2 Social media data Social media data, also known as user-generated content (UGC), consists of any information created and shared by users of online platforms. This includes reviews, comments, tips, and discussions about new product uses. Social media monitoring, or social media listening, is the process of actively collecting, organizing, and analyzing this data to derive consumer insights, including sentiment analysis [24](#page=24). --- # Evaluation, ethical considerations, and use of secondary data This section explores the benefits and drawbacks of employing secondary data in research, detailing methods for assessing its reliability and accuracy, and examining the ethical and legal dimensions associated with its utilization, including referencing and AI-assisted research [25](#page=25) [26](#page=26) [27](#page=27) [28](#page=28) [29](#page=29) [30](#page=30) [31](#page=31) [32](#page=32) [33](#page=33) [34](#page=34). ### 3.1 Advantages and disadvantages of secondary data Secondary data offers several advantages, including rapid acquisition, lower costs, and general availability. It can also enhance primary data collection and potentially fulfill research objectives [25](#page=25). However, several drawbacks exist. Reporting units or sample targets might be incompatible with research needs, and definitions used to classify data can differ. A significant concern is timeliness, as data may be outdated. Furthermore, the credibility of secondary data can be questionable [25](#page=25). > **Tip:** While social media offers a wealth of unfiltered consumer opinions and is excellent for tracing trends, its data is often shallow, consumers are not identifiable, and reviews can be subject to manipulation [25](#page=25). ### 3.2 Evaluating the credibility and accuracy of secondary data To effectively evaluate secondary data, a systematic approach is necessary, focusing on several key questions [26](#page=26): * **Purpose of the study:** What was the original goal of the research that generated this data [26](#page=26)? * **Data collector:** Who collected the information? Understanding the source's expertise and potential biases is crucial [26](#page=26). * **Information collected:** * What population does the data represent, and how well [26](#page=26)? * What specific constructs or phenomena are being measured [26](#page=26)? * What are the key dependent and independent variables [26](#page=26)? * **Accuracy and biases:** * What potential errors and biases are present in the data [26](#page=26)? * Has the data undergone cleaning and processing [26](#page=26)? * **Data collection methodology:** How was the information originally gathered [26](#page=26)? * **Timeliness:** When was the information collected [26](#page=26)? * **Consistency:** How well does this information align with other available data sources [26](#page=26)? ### 3.3 Ethical and legal considerations in the use of secondary data The use of secondary data necessitates careful attention to ethical and legal implications, particularly concerning personal data and intellectual property rights [27](#page=27). #### 3.3.1 Personal data and privacy legislation If the secondary data contains personal information, its further processing is subject to Data Privacy legislation. Researchers must ensure full compliance with all relevant legislative requirements and regulations [27](#page=27). #### 3.3.2 Rights and permissions for data usage It is essential to ascertain whether there is a legal right to use the data. This includes checking if the data is bound by specific Terms of Use conditions or protected under Copyright law [27](#page=27). #### 3.3.3 Due diligence when purchasing data When acquiring data from third parties, rigorous due diligence is paramount. Key checks include [27](#page=27): * Verifying that the data provider has the legal permission to sell the data [27](#page=27). * Ensuring the data was gathered legally [27](#page=27). * Confirming compliance with relevant legislation and professional codes or standards by the data provider [27](#page=27). * Investigating any existing complaints filed against the provider with relevant authorities regarding their data use or handling [27](#page=27). * Assessing whether the provider has adequate processes in place to safeguard privacy, ensure data security, and prevent harm to data subjects [27](#page=27). ### 3.4 Referencing sources Properly quoting and referencing sources is fundamental to academic integrity. Resources such as `https://www.hesge.ch/heg/campus/infotheque/services/guides?citations-et-bibliographies` can provide guidance on citation styles and bibliography creation [28](#page=28). ### 3.5 Utilizing AI for research Artificial Intelligence (AI) tools can be valuable aids in research, particularly for identifying and accessing relevant information [31](#page=31) [32](#page=32) [33](#page=33) [34](#page=34). > **Tip:** When using AI for research, employ specific and relevant prompts to receive more accurate and useful responses [31](#page=31). It is crucial to remember that AI-generated articles are often not peer-reviewed. Therefore, researchers must diligently identify peer-reviewed articles and critically engage with the AI's output. The typical workflow involves asking AI a question, using relevant prompts, identifying peer-reviewed articles, reading their abstracts, and then downloading the full PDFs for deeper analysis. Search engines like Google Scholar (`https://scholar.google.com/`) are excellent resources for this purpose [29](#page=29) [30](#page=30) [31](#page=31) [32](#page=32) [33](#page=33) [34](#page=34). ### 3.6 Group assignment considerations For group assignments, students are often tasked with searching for secondary data relevant to a team problem. Each student is typically required to identify and analyze at least two relevant sources. This process should begin with defining clear research objectives and formulating research hypotheses [35](#page=35). --- ## Common mistakes to avoid - Review all topics thoroughly before exams - Pay attention to formulas and key definitions - Practice with examples provided in each section - Don't memorize without understanding the underlying concepts

| Term | Definition | |------|------------| | Secondary Data | Data that has already been collected for purposes other than the specific problem at hand; these data already exist and are repurposed for a new use, a process also known as desk research. | | Primary Data | Data that is originated by a researcher specifically for the purpose of addressing the problem they are currently investigating; these data do not exist prior to the researcher designing and conducting a study to collect them. | | Desk Research | The process of searching for and using secondary data, which is often located quickly and inexpensively, for a research project. | | Internal Secondary Data | Data that is generated and stored within a company, often found in consumer or customer databases, used to understand purchasing behavior and profitability. | | External Secondary Data | Data supplied by organizations outside of the firm, including government data, non-government publications, syndicated services, and digital data sources. | | Syndicated Services Data | Data collected by commercial research firms that systematically gather and sell information to multiple clients, often related to consumer behavior or industry trends. | | Big Data | A large volume of structured, semi-structured, and unstructured data that has the potential to be mined for information, characterized by volume, velocity, variety, and variability. | | Web Analytics | The measurement, collection, analysis, and reporting of web data for purposes of understanding and optimizing web usage, often used to build consumer profiles from website events. | | Social Media Data | User-generated content (UGC) such as reviews, comments, and tips created by users of online systems and intended for sharing, used for gaining consumer insights through social media monitoring. | | User-Generated Content (UGC) | Any information, such as reviews or comments, that is created by users of online systems and intended to be shared with others. | | Sentiment Analysis | A process within social media monitoring that involves actively gathering, organizing, and analyzing social media data to understand the opinions and emotions expressed by users. | | Data Privacy Legislation | Laws and regulations that govern the collection, processing, and storage of personal data, requiring compliance to protect individual privacy. | | Copyright Law | A legal right that grants the creator of original works of authorship exclusive rights, including the right to reproduce the work and distribute copies. | | Due Diligence | The process of investigating and verifying all pertinent facts that a reasonable person would expect to know before entering into an agreement or transaction, particularly important when buying data. | | Peer-Reviewed Articles | Scholarly articles that have undergone scrutiny by experts in the same field before publication to ensure quality, validity, and significance. |