Quantitative Data
Definition: Quantitative data is numerical information used for statistical analysis, allowing businesses to measure and quantify variables. This type of data is essential for identifying patterns, making predictions, and making informed decisions based on measurable metrics. Examples include sales figures, market share percentages, and customer demographics, all of which provide a concrete basis for evaluating business performance and market trends.
Advantages of Quantitative Data:
- Allows for statistical analysis (who, what, when)
- Can collect data from a larger number of respondents
- Easier to analyse and compare against other data
- Facilitates objective decision-making
Disadvantages of Quantitative Data:
- Limited depth (does not capture qualitative insights)
- Limits open-ended exploration
- Lack of understanding of ‘how’ and ‘why’
- Reliability depends on the sample size
Qualitative Data
Definition: Qualitative research focuses on non-numerical and descriptive data, offering a deeper understanding of customer perceptions and behaviors. By exploring attitudes, motivations, and experiences, it provides rich, detailed insights that numerical data cannot capture. This type of research is invaluable for uncovering the underlying reasons behind customer decisions and identifying emerging trends, ultimately helping businesses to develop more effective strategies and improve customer satisfaction.
Advantages of Qualitative Data:
- Offers in-depth insights into customer perceptions and behaviours
- Provides a deeper understanding of ‘why’ and ‘how’
- Allows for open-ended questions to explore
- Often leads to generating new ideas
- Participants can express their thoughts and feeling
Disadvantages of Qualitative Data:
- Smaller sample size (may not represent the population)
- Findings can be influenced by researcher bias
- Data collection and analysis can be more time-consuming and difficult
- Difficult to make decisions from