Module 3 – Market Research Flashcards
What is quantitative data, and how is it characterized?
Numerical information (e.g., numbers, statistics, percentages).
Objective, easily presented in tables and graphs.
Used for comparisons and assessing goal success
List some advantages of using quantitative data.
Summarizes vast information.
Provides objectivity and accuracy.
Supports generalizations.
Prescribed analysis techniques for validity.
What are some limitations of quantitative data?
Limited narrative beyond numbers.
Pre-set answers may not reflect feelings.
Challenges in analysing human behaviour.
Provide examples of when quantitative data techniques are used in digital marketing.
Sales performance figures
Social media analytics
Website analytics
Keyword research
Secondary data
What is qualitative data, and how does it differ from quantitative data?
Language-based, subjective.
Descriptive (statements, opinions).
Used to contextualize quantitative data.
List some advantages of using qualitative data.
Provides rich, detailed insights.
Encourages comprehensive descriptions.
Explains individual experiences.
What are some challenges of using qualitative data?
Time-consuming.
Harder to generalize.
Subjective responses.
Provide examples of primary and secondary research in a business scenario.
Primary:Customer surveys
Secondary: Company house data
What is Primary and secondry data
Primary: Gathered for specific objectives.
Secondary: Already researched, public domain.
What is the difference between primary and secondary research data?
Primary: Customer surveys.
Secondary: Data from public sources.
What does significant mean in statistics, and how is it relevant?
Supports likelihood of non-random relationships.
Why is selecting a sample representing the entire population critical in statistics?
Determines significant occurrences.
Produces results similar to the population.
Why is it easier to obtain trends and insights in digital data?
Advanced analytics, artificial intelligence, and machine learning are enabling businesses to extract actionable insights from vast datasets.
Provides insights over time.
What is A/B testing, and where is it commonly used?
Identifies effective versions.
Used in web, email, and content marketing.
How does noise affect data, and what is relevant information in marketing?
Noise distorts data; relevance evaluates marketing initiatives.
