Save time and money by using the best data collection method for you.
Whether for the boardroom or classroom, you can leverage a successful strategy with the right data. But first, you need to know how to get the data. And that’s a big first step because you need to know what you’re looking for and why. Otherwise, you can be overwhelmed with irrelevant data. SurveyMonkey solutions can help you achieve the survey data collection that matters most to your business.
This article will show you how to collect data by explaining the types of data collection methods available. You’ll also learn uses for data collection and common challenges to expect. Finally, we’ll show you how SurveyMonkey methodologies can help you build and manage your feedback and insights.
Data collection is the process of accumulating quantitative and qualitative information to make informed decisions regarding business, research, and other principles. Data collection is useful for answering questions, analyzing business performances, actions, and predicting trends.
The practice of collecting data is something often used in many industries. Businesses, educational institutions, and even sporting organizations rely on statistical information to make informed decisions to drive their organization forward. But they had to go through the process of collecting that data to achieve the quantitative and qualitative results they were looking for.
Data collection can help you make better-informed decisions when it’s time to strategize your next marketing plan. It’s especially useful feedback for employee and customer satisfaction—and even events. When done right, data collection can provide useful information to help you increase brand awareness.
Let’s take a closer look at how these advantages can help your business:
Collecting data requires focusing on what problem you want to solve. But it's time well spent if you know how to collect data correctly. Even with the proper experience management methodologies, you can still face challenges when collecting data.
First, deciding what data to collect can take a considerable amount of time if you don't know what type of data you want to study. Not defining your problem can lead to time spent finding relevant data. This issue can snowball into data quality issues and a low response rate. Lastly, big data is another method of data collection that can loop you into the same challenge of searching for relevant data. Let's break all of this down:
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It's important to know all methods of data collection available to you. Otherwise, you could miss out on a method that could better serve your research efforts. Researchers' primary types of data collection are in-person interviews, mail surveys, phone surveys, online surveys, observations, focus groups, and documents. Learn ways to improve survey data solutions.
Here's a quick list of some of the advantages and limitations of each of these methods:
In-person interviews are question-and-answer conversations that researchers conduct to gain quantitative information about a service, product, or idea. This data collection method provides a personable approach that helps establish trust. However, not every person is comfortable with face-to-face interviews. Also, it's possible to collect qualitative data, but it will be time-consuming.
Mail surveys remedy any anxieties respondents may have about in-person interviews, giving them the comfort of answering your questions on their time and possibly giving you a more honest response. It's also easier to collect qualitative and quantitative data. The limitations mail surveys provide is the time it takes to collect insights since it's by mail. Furthermore, that feedback may take longer to process and analyze since it's on paper.
Phone surveys provide the formality of in-person interviews while offering respondents the comfort of answering questions at home. The challenge with this method is getting the respondents' time. Technology affords us the convenience of screening calls. Time is also a challenge because people may not want to take the time to engage in an unscheduled interview. It'll also be challenging to collect quantitative data using this method.
Online surveys allow you to collect and assess quantitative and qualitative data. Using the right experience management framework, you can increase your reach to respondents by distributing online surveys through different digital channels like email, social media, and websites. Here are 4 methods for distributing an online survey.
Observation data is when you monitor customers' reactions to your product from a distance. For example, if you're selling a new type of beauty product, you would observe how shoppers engage with your product. This data collection can help you test price points and determine if your product packaging works. Collect data from remote locations.
Focus groups are a controlled method of observation. This approach allows you to create a sample audience representing people who'll most likely buy your product to test your product, service, or idea. Focus groups can use up a lot of your time and resources for insights you could get through an online survey. However, focus groups offer researchers an immediate physical reaction.
Documents and records can provide factual information to help drive your research forward. However, finding relevant data will take time and possibly extra resources because the documents you're researching may not always be directly related to the subject matter in question.
So, what's the best approach to collecting the best data for your research?
The most cost-efficient way to quickly collect and analyze data is through surveys. Other methods can supplement your research, but online surveys are the way to go because the benefits outweigh the limitations for the quick execution of qualitative and quantitative data.
Always keep your goals in mind when collecting data. What you want to achieve needs to be the driving force of building your survey. Otherwise, the questions you pose will provide irrelevant data. Other useful tips for efficient data collection for surveys include: be direct in your questioning, and avoid loaded or leading questions. You also want to keep the survey short and always allow respondents to opt-out.
Let's review this checklist to get a better understanding of these best practices:
Remember, the purpose of asking questions is to analyze the data, and there will be a lot of it. So, try to avoid composing questions that require long answers. Try asking questions that use a yes or no response—or the Likert Scale. You can even use the Likert Scale to get a Net Promoter Score®.
Always give respondents the option to decline answering questions. It's better to have respondents skip a question instead of exiting the survey altogether. This approach allows you to collect qualitative insights. However, your quantitative data might be skewed, so keep that in mind.
Avoid asking too many questions because you'll risk the respondent exiting the survey. With online surveys, try presenting one question per page, so the respondent isn't overwhelmed with the amount of questions. You can also put page numbers so respondents can see how far along they are in the survey.
Make sure your questions are clear and concise. Avoid asking multiple questions at once. You're likely to receive only one answer, affecting your data. Learn how to write effective research questions for your next survey.
Once the surveys are distributed, and the feedback comes in, the real work begins. You'll have loads of data to analyze, and staying organized is the best way to keep from being overwhelmed by it all. SurveyMonkey solutions can help you gain visibility into survey data collected across your organization with multiple users at a user-friendly cost.
SurveyMonkey agile experience management solutions are made to work universally for decision makers, C-suite, and your most senior research experts. Access methodologies to better assess feedback and insights.
Net Promoter, Net Promoter Score, and NPS are trademarks of Satmetrix Systems, Inc., Bain & Company, Inc., and Fred Reichheld.