We rebranded at the beginning of 2018.
We looked to our customers for feedback and shared opinions internally—asking questions like:
• Who are we?
• How many people use our product?
• What makes us better?
To create a brand identity that represented who we are and resonated with our audience, we had to do some qualitative and quantitative research. But it can be used for so much more. It can be used to save a company.
Let’s look at Lego.
Just 14 years ago, the company was nearly bankrupt. Today, the brand is the world’s leading toy manufacturer with more than $5 billion US in revenue.
So how did they turn it around?
Qualitative and quantitative research
Through focus groups, interviews, and surveys, they learned what customers perceived as Lego’s unique selling point. By observing how children play, they learned how to cater to them.
This allowed Lego to refocus their efforts, lead the charge into new markets, and give their audience what they really wanted.
Want to know how you can use qualitative and quantitative research to boost insights and make smarter business decisions? Read on.
Qualitative and quantitative research differ in their objectives, the manner and flexibility of data collection, and the type of data they produce.
The rigor of quantitative data collection is what allows for meaningful or reliable comparisons of responses across participants. Qualitative methods, on the other hand, are less formal and more flexible.
But don’t be fooled into thinking that quantitative methods are better or more rigorous—both require systematically applied research methods and analysis.
Qualitative research is a descriptive data collection technique used to discover details that help explain behavior. It conveys the richness of people’s thoughts and experiences. In short, qualitative research helps us understand the why, how, or in what way behind a particular action or behavior.
Qualitative data is anything that describes or explains—from observations of an interaction, to quotes from people about their experiences, attitudes, beliefs, and thoughts. It can also be represented in words, images, video, audio, transcripts, and so on.
Quantitative research seeks to quantify a phenomenon. It’s more structured, objective, and helps reduce researcher biases. It gets at the what of a person’s behavior by answering questions like how many, how often, and to what extent?
Quantitative data is numerical. Think measurable quantities like length, size, amount, price, and duration. The data can be used to confirm or disconfirm a hypothesis or predict relationships. Quantitative data is analyzed using statistical methods and presented in tables, graphs, percentages, or other statistical representations.
It’s a trick question. We’re not pitting qualitative and quantitative research against each other. These types of research work better together to give you the most insightful information you get.
“The advantage of a mixed methodology is you get both projectable or predictive data and explanatory or contextual insights,” says Christine Shimoda, a Market Research Strategist with 20 years of experience.
“Using a quantitative methodology, a company could confidently learn that among its target audience, 85% are likely to buy X product within the next year, and that men are more likely than women to buy said product. A qualitative methodology would allow that company to understand why men are more likely than women to buy the product.”
Quantitative research provides evidence and predictions. Qualitative research provides context and explanations. So which one is best for you? That depends on the questions you need answering.
Quantitative and qualitative research methods are systematic ways of collecting data and testing hypotheses. And guess what? It’s something you already do all the time.
We constantly take in information from our surroundings to figure out how to interact with the people around us.
The same goes for market research. A company tries to learn more about their customers and the market. Why? To develop an effective marketing plan, or tweak one they already have. The method you use to do this depends on the data that will answer your key questions.
Here are some of the most common qualitative research methods:
In-depth interviews. Known as IDI in market research circles, in-depth interviews are ideal for digging into people’s attitudes and experiences. There are two main types:
• Unstructured: broad and open-ended interviews—like a conversation about chosen topics where you allow the respondent to lead.
• Semi-structured: interviews that involve pre-arranged questions, but with the flexibility to ask follow-up questions.
Focus groups. These are effective for getting several opinions in a conversational format. Participants lead the discussion, while a facilitator guides the conversation through a list of topics, questions, or projective exercises.
Ethnography. Ethnography has its roots in anthropology, where it was used to learn about human societies starting the late 19th century. In market research, much like anthropology, ethnography involves observing or spending time with participants in their natural context. It’s open-ended and exploratory. You can see how people use products and services, rather than asking them to self-report.
Here are some of the most common quantitative research methods:
Surveys. An efficient way of collecting views from lots of people. Surveys can be conducted online, over the phone, and even in-person with structured interview questionnaires. They can have very targeted questions or be sweeping in their content.
Structured Observation. This is a structured form of ethnography to measure certain actions or behaviors. For example, you might measure how many boxes of cereal people pick up before choosing one to purchase. These observations can be analyzed later to understand trends or areas for improvement.
Experiment. This is the way to really identify cause and effect. Market researchers conduct controlled, manipulated, or randomized experiments to understand how specific variables influence outcomes. One of the simplest forms of experimentation is A/B testing. An example: a candy bar company makes two types of packaging and delivers them to different stores with the same sales and demographics. By measuring each stores’ sales, the company can be confident that the difference is a result of the packaging.
User Testing. You’ve heard the phrase “Show, don’t tell” So rather than asking people to explain their experience, why not get then to show you? User testing is somewhat like digital ethnography. It can tell you why you aren’t getting results and what you need to explore further.
Help Transcripts. Live chat or call transcripts can yield both qualitative and quantitative data. Reading and coding them can help you understand people’s pain points and challenges throughout your conversion funnel.
Customer Reviews. Look beyond your own surveys and check sites like Yelp or G2 Crowd. What are people saying about you? What do they like and dislike? The things people say and how often they say it can yield robust qualitative and quantitative data.
Let’s hear from Christine Shimoda again:
“Without analysis, data is just numbers or anecdotes,” says Shimoda.
“The analysis is what brings the meaning of the data to the surface. It’s what identifies the trends, story, and insights. It translates data from something that is merely interested to something that is useful and actionable.”
In short, raw data is useless until it has been analyzed.
Knowledge is power but data ≠ knowledge. It’s only through analysis and interpretation that information becomes powerful.
Dr. H. Russell Bernard, Professor of Anthropology at the University of Florida, calls the phrases ‘qualitative data analysis’ and ‘quantitative data analysis’ “delightfully ambiguous.”
“You never know if the phrase means ‘the qualitative analysis of data’ or the ‘analysis of qualitative data.’” Confusing, much?!
To simplify, data analysis is the search for patterns in data, followed by the interpretation of that information to help explain why those patterns are there.
It’s important to keep in mind that quantitative and qualitative data are not mutually exclusive.
Qualitative data can be translated into quantitative data. For example, you could count the number of times interviewees used a particular word to describe your product to yield quantitative data.
Similarly, quantitative methods of analysis require you to explain what the patterns mean and connect them to other parts of your business—a qualitative exercise!
Coding has nothing to do with computer programming. It’s a technique for organizing substantial amounts of qualitative data into bite-sized chunks.
There are two ways to start coding:
1) Make a list of what ideas you’re interested in and searching for them in the data set
2) Let the data guide you towards what is important
When it comes to coding, you are looking for repeated themes, concepts, words, and challenges.
Once you have a list of umbrella topics, they become your code label. Go through the data—transcriptions from focus groups and interviews, notes from your observations—and mark it each time it occurs.
Group the responses under their respective umbrella topics and assess what the data is telling you.
Quantitative data analysis involves turning raw numbers into meaningful information. It can involve presenting data models such as graphs, charts, tables, probabilities, and more.
Frequency tables are an excellent way to present categorical data. For example, you can demonstrate how many purchases are made from different countries. These numerical representations can also be divided into averages and medians.
Proportions, or percentages, demonstrate the relative importance of a certain category. For example, 500 sales come from Spain, but they only make up 10% of your total sales volume.
Any quantitative data analysis should answer your questions regarding what and how many.
Congrats—you’ve learned all about the differences.
Now, the key to successful qualitative and quantitative research is iteration.
That doesn’t mean doing the same thing again and again.
It means continually returning to your questions, methods, and data to spark new ideas and insights that will transform your approach to your research—and your business.