LUBS2840 Marketing Research Notes

Course Software and Technical Resources

  • SPSS Analysis Software (v. 30):

    • Deployment Options: Can be run locally on personal hardware via the AppsAnywhere portal or executed remotely using the Windows Virtual Desktop environment.

    • Login Gateway: Requires prior authentication via the institutional IT service portal (https://leeds.service-now.com/it/?id=kb_article&sys_id=bf58a03e9730ad903d52720f2153af66).

    • Platform Compatibility: Browsing and downloading via AppsAnywhere is supported on both Windows and Mac operating systems (https://appsanywhere.leeds.ac.uk/).

    • Licensing & Activation: Local installations require activation using an institutional license code accessible through logged-in IT accounts (https://leeds.service-now.com/it/?id=kb_article&sysparm_article=KB0015765).

  • Jamovi:

    • An open-source, free desktop alternative to SPSS for statistical computing.

    • Download Location: Accessible at https://www.jamovi.org/download.html.

Module Schedule and Structure

  • Week 1 (28th September):

    • Lecture Topic: Importance of marketing research; Marketing research process (1).

    • Tutorial Topic: Exercise on problem definition.

    • Required Reading: McDaniel & Gates (McD&G), Chapters 1 and 2.

  • Week 2 (5th October):

    • Lecture Topic: Secondary Data and Artificial Intelligence.

    • Tutorial Topic: Exercise on Buyer Personas.

    • Required Reading: McD&G, Chapter 2.

  • Week 3 (12th October):

    • Lecture Topic: Qualitative research methods (focus groups and interviews).

    • Tutorial Topic: Exercise on Qualitative Research.

    • Required Reading: McD&G, Chapter 7.

  • Week 4 (19th October):

    • Lecture Topic: Measurement and Questionnaire design; Validity & reliability of measurements; Sampling.

    • Tutorial Topic: Exercise on Survey Research.

    • Required Reading: McD&G, Chapters 3, 4, 5, and 6.

  • Week 5 (26th October):

    • Lecture Topic: Experimental research.

    • Tutorial Topic: Exercise on experimental research.

    • Required Reading: McD&G, Chapter 11.

  • Week 6 (2nd November):

    • Mid-Module Reading and Self-Assessment: No lectures; no seminars.

  • Week 7 (13th November):

    • Lecture Topic: Introduction to SPSS Analysis; Testing Reliabilities; Descriptive statistics.

    • Required Reading: Pallant, Chapters 1–3 and 6.

  • Week 8 (20th November):

    • Lecture Topic: Chi-Square Test.

    • Tutorial Topic: Exercise to familiarise with SPSS; Choice Study.

    • Required Reading: Pallant, Chapters 9 and 16.

  • Week 9 (27th November):

    • Lecture Topic: Mean Comparisons (t-tests, ANOVAs).

    • Tutorial Topic: Exercise on testing reliability and Chi-Square.

    • Required Reading: Pallant, Chapters 17 and 18.

  • Week 10 (4th December):

    • Lecture Topic: Correlations and Regression.

    • Tutorial Topic: Mean differences testing.

    • Required Reading: Pallant, Chapters 11 and 13.

  • Week 11 (11th December):

    • Lecture Topic: Mock Exam.

    • Tutorial Topic: Exercise on Regressions.

The Marketing Research Process Framework

Marketing Research Process Flowchart

  • Step 1: Identification of the Problem and Statement of Research Objectives:

    • Defining the core issue facing decision-makers and outlining clear, operationalized research goals.

  • Step 2: Creation of the Research Design:

    • Formulating the detailed master framework to guide data collection and analytical procedures.

  • Step 3: Choice of Method of Research:

    • Selecting specific data collection modes (e.g., surveys, experiments, observational studies).

  • Step 4: Selection of the Sampling Procedure:

    • Establishing the target population, sampling frame, sample size, and probability or non-probability sampling techniques.

  • Step 5: Collection of the Data:

    • Executing field operations to gather empirical information from targeted units.

  • Step 6: Analysis of the Data:

    • Cleaning, coding, transforming, and analyzing empirical data using appropriate statistical or qualitative techniques.

  • Step 7: Writing and Presentation of the Report:

    • Synthesizing findings into actionable managerial insights and presenting results to stakeholders.

  • Step 8: Follow-up:

    • Evaluating whether recommendations were implemented and assessing if additional research is needed.

Fundamentals of Research Design

Un dimanche après-midi à l'Île de la Grande Jatte by Georges Seurat (1886)

  • Definition of Research Design:

    • The structural plan or framework followed to answer research aims and objectives.

    • Serves as the blueprint for resolving specific managerial or theoretical problems.

  • Conceptual Metaphor (Pointillism in Research):

    • Illustrated through Georges Seurat's 1886 masterpiece, Un dimanche après-midi à l'Île de la Grande Jatte.

    • Just as pointillist paintings construct a complex visual image out of thousands of isolated, individual dots of color, a research design organizes individual data points into a coherent structure that resolves business issues.

  • Taxonomy of Research Design Types:

    • Exploratory Research Designs:

      • Fact-finding studies undertaken primarily in the early stages of a project.

      • Purpose: To clarify ambiguous research problems, establish research directions, or generate hypotheses.

    • Descriptive Research Designs:

      • Structured studies aimed at describing market characteristics, segment profiles, or business phenomena.

      • Purpose: To answer the core questions of who, what, when, where, and how.

    • Causal Research Designs:

      • Studies designed to determine whether one variable directly causes or dictates the value of another variable.

      • Involves strict manipulation of independent variables and control of extraneous factors (e.g., experimental research designs).

Secondary Data vs. Primary Data

  • Secondary Data:

    • Data gathered for a purpose other than addressing the specific problem currently under investigation.

    • Pre-existing data compiled by internal operations, commercial data providers, or external entities.

  • Primary Data:

    • Originated directly by a researcher specifically to address the unique problem at hand.

    • Requires executing all eight phases of the formal marketing research process.

Categories and Typologies of Secondary Data

  • Academic Literature Reviews:

    • Examines academic theories, theoretical constructs, and conceptual models relevant to the research topic.

    • Common theoretical models include:

      • Theory of Reasoned Action (TRA)

      • Theory of Planned Behaviour (TPB)

      • Institutional Theory

      • General Consumer Behaviour Frameworks

  • Industry Reviews:

    • Compiles facts, financial benchmarks, and operational figures concerning the client organization, its industry sector, key competitors, and macro-environmental forces.

  • Scanner Data:

    • Transactional information captured at retail points-of-sale (POS) via laser barcode scanning devices.

    • Provides high-precision data regarding SKU sales, real-time pricing, purchasing volume, purchase timestamps, and location details.

  • Internal Secondary Data:

    • Data generated internally during routine business operations.

    • Company Accounts: Details on customer account histories, promotional/advertising spending, sales force operational costs, and inventory turnover.

    • Customer Databases: Detailed logs including customer names, contact information, purchase histories, and unit quantities ordered.

    • Sales Reports: Operational metrics detailing sales force territories, headcount allocations, and individual sales target performance.

  • External Secondary Data:

    • Data sourced from third-party entities outside the organization.

    • Computerised External Databases: Commercial databases providing sales leads, global business metrics, and political risk assessments.

    • Directories and Published Sources: Business directories (e.g., Fortune 500 lists) and syndicated industry market intelligence (e.g., Mintel reports).

    • Trade Association Reports: Specialized whitepapers, market statistics, and industry benchmarks published by professional trade bodies.

    • Syndicated Services: Commercial entities that gather standardized pools of data and sell them to multiple client subscribers.

Buyer Personas in Marketing Strategy

  • Definition:

    • A semi-fictional model representing an ideal target customer, constructed using market research and empirical data.

  • Strategic Function:

    • Allows organizations to understand and empathize with target segments, guiding product development, sales approaches, customer service protocols, and marketing strategy.

  • Core Architectural Components:

    • Demographics: Age, income level, location, educational background, household composition.

    • Professional Status: Current job role, seniority level, decision-making capacity, industry context.

    • Psychographics: Personal values, lifestyle attributes, attitudes, interests.

    • Pain Points and Challenges: Primary bottlenecks, unmet operational needs, day-to-day frustrations.

    • Influences and Key Information Sources: Preferred communication channels, trusted industry blogs, social networks, media subscriptions.

    • Purchasing Process: Behavioral path to purchase, evaluation criteria, buying triggers, decision barriers.

  • Case Profile Example (Peter):

    • Demographics and Professional Background: 35 years old, holds a marketing degree, works as a Product Manager for a mid-sized enterprise.

    • Experience: Prior experience operating as a Product Owner for software products alongside agile teams; formal training in Scrum frameworks.

    • Challenges & Goals: Proven track record managing mature product lines; currently faces the operational challenge of creating an innovative, brand-new product using agile methodologies.

Advanced and Non-Traditional Secondary Data Methodologies

  • Geo-Marketing and Spatial Intelligence:

    • Mechanism: Utilizes spatial and location-based data (GPS tracking, satellite positioning) combined with public secondary datasets (e.g., government reports, census records) to build localized marketing campaigns.

    • Data Formats:

      • Degrees, minutes, seconds format: 15∘N,30∘W15^\circ\text{N}, 30^\circ\text{W}

      • Decimal Degree System format: 15.23456,−30.6789015.23456, -30.67890

    • Survey Geolocalization: Survey software (e.g., Qualtrics) automatically logs respondent coordinate data (e.g., Latitude 53.78810119628953.788101196289, Longitude −1.6007995605469-1.6007995605469).

    • Regulatory Warning: Geographic coordinate data cannot be legally processed or analyzed without obtaining explicit, prior authorization from participants.

    • Analytical Mapping: Platforms like MapMaker (https://mapmaker.geods.ac.uk/), developed by the Consumer Data Research Centre (CDRC), enable micro-segmentation based on location data.

  • Web Scraping and Social Listening:

    • Definition: Automated extraction of structured information from websites and unstructured web resources.

    • Legal & Ethical Considerations:

      • Legitimate use cases: Powers search engine algorithms and academic indexing engines (e.g., Google Scholar).

      • Malicious use cases: Used by malicious automated bots to scrape target individuals for unauthorized tracking or political manipulation.

    • Sentiment Analysis:

      • Computational identification and classification of opinions expressed within unstructured text.

      • Categorizes textual sentiment into positive, negative, or neutral states using specialized natural language processing (NLP) dictionaries.

      • Crucial for managing Customer-to-Business (C2B) review platforms like TripAdvisor.

    • Empirical Empirical Application Case Study (Gerrath et al., 2023):

      • Study: Gerrath, M. H., Mafael, A., Ulqinaku, A., & Biraglia, A. (2023). Service failures in times of crisis: An analysis of eWOM emotionality. Journal of Business Research, 154, 113349.

      • Dataset: Analyzed 327,205327{,}205 tweets directed at commercial airline brands across the initial 12 months of the COVID-19 pandemic, comparing them against pre-crisis baselines.

      • Empirical Findings: Across crisis phases, general levels of joy declined while expressions of sadness and anger rose in tweets targeted at weaker brands. High brand strength acted as a protective buffer against negative electronic word-of-mouth (eWOM) caused by exogenous shocks.

Predictive Margins for eWOM Emotionality across COVID-19 periods

  • Artificial Intelligence and Virtual Consumer Modeling:

    • Synthetic Consumers:

      • Definition: Simulated consumer agents built using machine learning, AI, and synthetic data generation techniques to replicate real consumer behavior, preferences, and purchasing habits.

      • Key Advantages: Ensures full privacy compliance (avoids handling real personal data), lowers costs compared to primary field research, accelerates insight generation, scales across niche markets, isolates testing variables, and offers a safe virtual playground for evaluating high-risk ideas.

      • Key Disadvantages: Cannot replicate genuine human emotion or irrational actions, limited real-world realism, susceptible to data bias and algorithmic error, heavily dependent on source data quality, lacks real-world contextual awareness, and still requires validation via real-world qualitative feedback.

    • Customer Digital Twins:

      • Definition: Dynamic, real-time virtual models representing an individual buyer or micro-segment.

      • Operational Superiority: Continuously updates using live transactional, behavioral, and psychographic data streams.

      • Contrast with Static Models: Unlike static buyer personas or single-use synthetic samples, digital twins enable real-time simulation of consumer responses to adjustments in the marketing mix (e.g., testing pricing strategy changes, prospective product features, or communications).

Strategic Evaluation of Secondary Data

  • Core Strategic Advantages:

    1. Clarifies or redefines the research problem during initial exploratory phases.

    2. Can directly provide the solution to the problem, removing the need for new data collection.

    3. Suggests prospective methodological alternatives for future primary research.

    4. Identifies potential operational, legal, or analytical difficulties beforehand.

    5. Provides necessary contextual background, baseline metrics, and theoretical backing.

  • Core Strategic Disadvantages:

    • Potential lack of availability.

    • Lack of direct conceptual relevance to the specific research problem.

    • Inaccuracy or underlying data errors.

    • Insufficiency of sample size, scope, or detail.

  • Evaluation Criteria Framework:

    • Methodology Used: Evaluating data collection methods, response rates, sampling procedures, and questionnaire design to verify reliability, validity, and generalizability.

    • Accuracy: Auditing for structural errors in approach, design, sampling, data processing, and analysis by cross-referencing alternative data sources.

    • Currency: Measuring time lags between field data collection and publication to prevent using outdated information.

    • Objective Nature: Examining why the original study was conducted to ensure alignment with current research goals.

    • Nature of Data: Reviewing exact variables analyzed, operational definitions, and relationships examined to determine practical utility.

    • Dependability: Assessing the credentials, expertise, and authority of the publishing source.