Data Collection Methods in Research

This blog focuses on data collection methods in research - including various types of data collection methods to leverage, why they’re important, and how quantilope can help.

What are data collection methods?

Data collection methods are the techniques and processes used to gather information and consumer data. The gathered information and data insights are then used to advise business strategy or support any other data-backed business objective. Throughout this article, we refer to data collection methods as the means of gathering insights - not to be confused with our suite of advanced research methodologies.

There are many types of data collection methods, which we’ll explore in the next section, and researchers will often use a combination of them to gather varying insights for one cohesive story. The choice of collection method(s) should align with research goals and ethical considerations, always ensuring participant privacy and informed consent.
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Types of data collection methods

In market research studies, researchers will collect consumer feedback that’s typically anonymously aggregated for analysis purposes; this becomes your ‘data set’.

As for the selection of a data collection method, it will depend on your research goals, the nature of your research question(s), the type of data needed, your research provider, and the available resources. The first choice you’ll make is whether you want to use primary data collection, secondary data collection, or a combination of both.

Below we’ll explore the process of gathering primary and secondary insights, including the several ways to go about each type:

Primary data collection methods

Primary data collection methods refer to any process where you as the researcher or research team are the ones going out to collect data for unique insights that are specific to your business objectives. Primary research is thus highly tailored and customized - its flexibility making it a great tool for businesses with specific research needs.

Primary data collection can be done quantitatively (through the collection of numerical data), or qualitatively (through the collection of non-numerical insights from an interviewee). Beyond that, there are many formats to collect quantitative and qualitative data and below we explore some of the most commonly used formats for each in market research:

Quantitative primary data collection

Primary quantitative data collection methods capture numerical data, often with statistical testing, for the use of data charting, data comparison, and data reporting. Most quantitative data collection today is done through online surveys, which are digital questionnaires sent to a vetted panel of respondents that represent a business’ desired target audience (i.e. key demographics, a certain frequency of buyers, the general population, etc.).

Respondents will provide their feedback on quantitative questions which are then viewed in aggregate in the form of data tables, crosstab files, charts, or reports.

Some of the standard question types included in quantitative studies are:

- Rating scale questions:
  Participants provide a rating to indicate their level of agreement, satisfaction, or preference for a statement or item, and each rating is associated with a certain numerical value.
  
  For example, a rating scale question might look something like:
  
  “How much do you agree or disagree with the following statement: Online shopping is better than shopping in person in a store.”
  
  - Strongly agree (5)
  - Somewhat agree (4)
  - Neither agree nor disagree (3)
  - Somewhat disagree (2)
  - Strongly disagree (1)
  
  There are then several ways a brand could review the data from this question. They could view the data based on an overall average score (summing up the attached numerical values for each rating and dividing by the total); they could view the percentage who selected each individual rating, or, they could sum up the ‘top’ and ‘bottom’ answer choices to summarize agreement/disagreement metrics (i.e. strongly/somewhat agree = top two box (T2B); somewhat/strongly disagree = bottom two box (B2B).
  
  Rating scale questions are great for understanding how consumers feel about a certain statement, concept, product, or other element. It goes beyond simply asking respondents if they like/dislike something or a yes/no question - which can be limiting. For this reason, 5-point scales are very common, as understanding the proportion of consumers that “somewhat like” your product compared to those that “strongly like” your product is key in identifying an opportunity for improvement.
- Ranking scale questions:
  Participants order a list of items based on their preference, importance, or other criteria to determine their relative ranking.
  
  Ranking scale questions force participants to prioritize what’s more or less important, relevant, etc. to them. Researchers can choose to limit the number of rankings to just the top 2,3,5, etc., or have respondents rank all items in a given list. Brands can also choose if they want respondents to rank ‘up to’ a certain number of elements or if they must rank ‘all’ of a certain amount of elements.
  
  For example, a ranking scale question might ask:
  
  Please rank up to 3 musical genres from the list below, ranking ‘1’ as your top genre.
  - Rock
  - Pop
  - Country
  - Rap
  - Classical
  - Folk
  - Indie
  - Blues
  - Jazz
  
  or... a ranking scale question might ask:
  
  Please rank a total of 3 musical genres from the list below, ranking ‘1’ as your top genre.
  - Rock
  - Pop
  - Country
  - Rap
  - Classical
  - Folk
  - Indie
  - Blues
  - Jazz
  
  In this first question example, respondents are given the option to select just one genre, or at most, 3 genres. In the second question example, respondents are asked to rank a total of 3 genres, meaning they must select a top, second, and third favorite musical genre. The choice between these two ranking approaches depends if you want to force a set number of rankings or leave it up to consumers to decide.
  
  Ranking questions are good for researchers who want to know the relationship between items rather than just which ones are preferred (i.e. which are more preferred than others).
- Single/Multi-Select Questions:
  In single-select questions, participants choose just one option from a list, while in multi-select questions, they can choose multiple options that apply to them.
  
  For example, a survey question could be:
  
  (Single select) Which of the following fast-food restaurants do you most frequently visit? (select one answer):
  
  - McDonald’s
  - Wendy’s
  - Burger King
  - Chick-fil-A
  - Subway
  - Taco Bell
  - Chipotle
  - KFC
  - Panera Bread
  - Arby’s
  - Jimmy John’s
  - Carl’s Jr.
  - Jack in the Box
  - Sonic
  
  (Multi-select) Which of the following fast-food restaurants do you frequently visit? (select all that apply):
  
  - McDonald’s
  - Wendy’s
  - Burger King
  - Chick-fil-A
  - Subway
  - Taco Bell
  - Chipotle
  - KFC
  - Panera Bread
  - Arby’s
  - Jimmy John’s
  - Carl’s Jr.
  - Jack in the Box
  - Sonic
  
  Using a multi-select question can sometimes lead to the ‘everything is important’ bias, where respondents select every item in a list even if it’s not really all that important to them. For this reason, brands might prefer to use a single select, ranking question, or even an advanced method like a MaxDiff.
- Matrix Questions:
  Participants provide responses to a set of related questions organized in a matrix format, using the same set of response options for each row or column.
  
  For example, the below question asks respondents to clarify how often they visit each of the fast-food restaurants discussed in the above multi-select question. The frequency of their visit would be shown across the top of the matrix chart, and each fast-food chain would be listed down the side. Using this approach, brands can still capture insights for a variety of brands or elements, but get more specific into actual consumer behavior.
  
  For each of the fast-food chains below, please select how frequently you visit a location in a typical month: Daily, several times a week, once a week, every other week, once a month:
  
  - McDonald’s
  - Wendy’s
  - Burger King
  - Chick-fil-A
  - Subway
  - Taco Bell
  - Chipotle
  - KFC
  - Panera Bread
  - Arby’s
  - Jimmy John’s
  - Carl’s Jr.
  - Jack in the Box
  - Sonic

Qualitative primary data collection

Just like quantitative data, qualitative data can be collected through primary means. As the research team, you’ll go out and collect your own qualitative feedback from a group of people - be it online videos, in-person focus groups, ethnographic observations, or some other form of qualitative data collection. Though generally more time-consuming than quantitative research, qualitative findings can provide super valuable context for brands.

To collect qualitative data, a moderator will pose questions to respondents that go beyond standard numeric feedback (i.e. quantitative data). Using this type of research, respondents can explain their thought process, shopping habits, or other feedback in their own words rather than being confined to a rating scale or multiple choice list. For this reason, qualitative research makes a great partner for quantitative research studies - with the latter getting at the ‘what’ and the qual getting into the ‘why’.

Qualitative data collection might also be used prior to a quantitative study to confirm preliminary hypotheses or to guide the types of questions researchers want to ask in their quantitative questionnaire.

As briefly mentioned above, here are some of the commonly-used formats for qualitative data collection:

- In-person face-to-face interviews or focus groups
- Online videos (individual or focus groups)
- Online community boards
- In-store shopping observations (i.e. ethnography)

A few examples of qualitative research questions include:

- What do you love or hate about [x]? (Showing respondent a product, brand, concept, etc.)
- Please explain your standard process to prepare for a vacation - from booking your trip, to packing, to transportation.
- You mentioned you find the healthcare industry confusing - why is that?

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Secondary data collection methods

Unlike primary data collection methods, secondary data collection methods do not involve the unique collection of data findings. Rather, researchers will use already-existent research materials from different sources such as reports, syndicated studies, sales metrics, websites, statistics, or trade publications (to name a few examples).

Secondary methods of data collection are great for initial brainstorming or as supporting insights to primary data collection. Many secondary sources are free, though some may require payment to access full reports or data files. Even so, secondary data is generally cheaper than running your own primary research study - making it a great resource for teams with limited budgets.

Below we’ll cover some common internal and external secondary data sources:

Internal secondary data sources

Though most secondary data comes from external sources, there is some internal secondary data that might be valuable to your research efforts. These include:

- Sales metrics/reports
  Use internal sales information to understand how well a product is performing and what the current market is like
- Cross-team studies (i.e. research ran by another department)
  What better source of customer information than that gathered from your own company? Work collaboratively across departments to reuse and repurpose consumer insights instead of duplicating research efforts. Oftentimes, the data collected by one team is just as useful to another.
- Social media metrics
  Review your social channels (Instagram, Facebook, TikTok, etc.) to see what people are saying about your brand, products, or services. This is free information that comes directly from consumers themselves.
- Website traffic/performance metrics
  Look into website traffic and click-through rates to see how people are engaging with your content. Learn if current CTAs are working as intended, or if your user interface is doing its job of keeping searchers on your page.

External secondary data sources

External secondary data sources would be things like:

- Industry reports/papers
  These are reports that cover a particular industry like automotives, appliances, or consumer electronics. Reports will cover the best brands, new emerging trends, general pricing information, and more - all of which is helpful context for your brand operating in that industry.
- Syndicated studies/experiments
  These are studies conducted by a research organization and then made available (sometimes for purchase) to a wide range of clients. They typically will focus on a broad industry, market, product, or trend rather than narrowing in on one in particular.
- Webinars
  
  Webinars are a great source of information on a topic or industry, and mostly free! Join a webinar in your related field to learn about trends, consumer behavior, or any other metric that could spark ideas for your own business objectives.
- Public sales/transactional information
  
  You can learn a lot by looking up a company’s sales or revenue. This helps form an idea of the major players in your category, their current success, and how you might effectively compete.
- Interviews
  
  Just as a researcher might conduct their own interview (aka, primary research), many of these interviews are then made publically-available online (aka, secondary research). Check out sites like Tedx or YouTube for interviews from praised leaders in your industry or category.
- Case studies
  Case studies are a great way to understand how customers are leveraging research, what their needs are, and how they’re solving for them.
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Importance of data collection methods

As highlighted with the examples above, methods of data collection can vary drastically yet all are valuable in their own way for different research purposes. Data collection methods are important to consider when planning your research study as they can shape the outcome of your consumer insights and thus, your company’s decision-making.

If you leverage only qualitative data collection methods, you might get really detailed information about your audience that’s useful for certain initiatives, but later find you needed numeric data to generate an impactful report for stakeholders. Or, maybe you only gather data quantitatively, generating a great data analysis dashboard but stakeholders then ask you for deeper reasoning behind the insights.

While not all studies will require both quantitative and qualitative methods, or both primary and secondary sources, these are all elements of your study to proactively think about to guide your research questions. If you know what you might want to get out of a study, it will be easier to know what to put into the study as well. It’s equally important to mix up data collection methods within a survey to keep things interesting for respondents and increase response rates (i.e. including a variety of close-ended questions like rating scales and multi-selects along with some open-ended questions too).

Data gathering is a huge part of effective decision-making, but only if done right. Use the above data collection techniques to set up a valuable research study for high-quality insights that lead to informed decisions.
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How quantilope automates data collection

When it comes to customer data collection, researchers and research teams have many data collection tools to choose from. The best data collection tools will be those that offer a variety of methodologies, are intuitive to use hands-on (or, offer a consultative support team), and allow you to view your data in real time.

quantilope’s Consumer Intelligence Platform does all of the above through the use of automation. Within the platform, each step of the data collection process is automated - from a survey template setup to automated charting and reporting. Within a survey template, researchers can customize their data collection technique to include a variety of question types or advanced methodologies. For those that would rather build a questionnaire from scratch, quantilope’s library of pre-programmed questions and methods makes it easy to drag & drop onto a blank slate.

Even before your final data set is in, you can start to look at real-time insights to form initial hypotheses around your research objective. Real-time charting means less time overall from initial brainstorming to a final report for your stakeholders.

quantilope offers both quantitative and qualitative data collection tools, making it a true one-stop-shop for teams looking for high-quality consumer insights in a quick timeframe.

Hear it from a quantilope user themselves:

“quantilope has made the process of creating survey questions, finding sample, and reporting findings user-friendly and organized. It is truly a one-stop shop for all things involved in market research.“
- Dylan Rose, Sr. Manager Business Intelligence at PBS