How does the market perceive the brand? How is it different? And what compels our target customers to choose our brand? Simple questions may not always render the desired response. But if you really want to know where your brand stands, relative to the field of others, this is a definitive step. Just ask.
Need proof in numbers? Quantitative research is statistical, mathematical or numerical analysis collected from polls, questionnaires, surveys (and myriad other methods). Quantitative research (often referred to as ‘quant’) is often rigid, and designed to numerically measure the responses derived from a research question. In brand and marketing research, attitudinal measurement is indeed, well served by quantitative research—as is consumer opinion and behavior and functionality. It looks at possible changes with varying orders of frequency, direction and magnitude. Quantitative research may be an informative precursor to qualitative research or it may be used to follow up on the qualitative questions that were left unanswered.
The value is in understanding, with the relatively large sample size to support that understanding. This is big data. Quick and cost-efficient.
When properly deployed, quantitative research will assist in providing a realistic picture of the brand’s position in the marketplace, and further, what the brand means in the minds of its target audiences. This highlights quant’s function in gaining insight into their decision-making criteria; awareness and preferences, loyalty, perceptions, strengths and weaknesses of each competitive brand, awareness of the affiliation of the brand, and the perception of the affiliation.
The advantages or quantitative research are found in speed and precision—providing organizations the ability to make data-based decisions, informative analytics and therefore, more informed projections.
Quantitative research process and deliverables:
- Survey development and distribution
- List management
- Survey programming and deployment
- Survey collection
- Cleaning, compiling and coding of data
- Data analysis
- Interview transcript analysis
- Language translation, if necessary
- Formal reporting and recommendations