Megan Ingram
August 18, 2022
Humans have a cultural bias towards numbers, numbers carry a weight that other evidentiary support doesn’t. We want proof and validation. But, advancements like asynchronous online platforms, AI text analytics, web scraping, and coding software have paved the way for more precise quantitative research methods. What does this mean for qualitative research? Are focus groups, interviews (IDIs), and ethnographies still a valuable tool? What approach should you use when conducting marketing intelligence? In this blog, we dive into the difference between qualitative vs. quantitative research, choosing research study design, and what this means for the future of digital research.
I like to think of the difference between the two research methods as believing vs. knowing. Quantitative research leads to more directional insights by synthesizing large sets of numerical data or words, grammar, and language. Data petabytes can be social media posts, customer reviews, blogs, and forums identifying demographics, psychographics, and behaviors. Hearing from audiences when not prompted informs the stories we want to tell. Quantitative research guides the formation of a story (or theory) but still needs to be further tested and validated with qualitative research.
“If quantitative research is the outline of a picture, qualitative research colors it in.” — Global Web Index
Qualitative research tells you what people think, feel, or behave non-numerically. Often we want proof and validation that what you believe is right. This is where qualitative research comes in. Using first hand observation from focus groups, interviews (IDIs), ethnographies, and usability testing, researchers gather in-person insights and validation about a concept, idea, or key research question. You can probe deeper and gain confidence in your ideas, theories, and messages.
For validated insights and conclusions, you need a mix of both. One without the other often leads to either a poor hypothesis and asking the wrong questions, or a lack of confirmation from the right people. A mixed methodology allows you to use secondary data to guide a well-researched discussion guide for the basis of your questioning . Then, you can ask and further probe on questions to your target audience, confirming whether your hypothesis and data-driven instincts are correct. The combined research output should drive audience identification, formulating a strategy, and validation of products, concepts, and topics that answer your core research questions.
“Big data is like teenage sex: Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” — Famously stated by Dan Ariely, a professor of psychology and behavioral economics at Duke University
Many of us are aware of the latest advances in tech with machine learning, artificial intelligence, and automation. But are we actually using them to create more value and impact? Human intelligence and artificial intelligence when used together create deeper insights to answer the “why”. The more we as researchers can uncover and understand the cognitive elements in what people say and their decision-making, the more we can pinpoint insights that extend beyond marketing to operations, sales, product development, and more. This starts with continuing to understand the ever-changing capabilities of tech and is complemented by an emotional understanding of reasoning and problem-solving.
August 18, 2022
