Data Collection: Methods, Definition, Types, and Tools
Access to Big Data and having enough computing facilities to effectively process and analyze it usually have a more significant market share and generate more income.
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Access to information gives a unique opportunity to understand the interests and demands of the customers much better and even exceed their expectations. So naturally, this makes data collection urgent, the first and most crucial step of any decision-making process. But before diving deeper into the topic, let’s get started with some essential definitions.
What Is Data Collection?
Data collection is gathering data; it's further measuring, processing, assessing, and analyzing for research purposes. It’s conducted with the help of established, validated techniques, which make it possible to answer research questions, test hypotheses, and evaluate final results. The main goal of data collection is to get access to reliable sources of information that will provide data for further analysis and make data-driven decisions possible.
Types of Data Collected
There are two main types of collected data.
- Qualitative data deals with descriptive information that cannot be counted and is not expressed through numerical values
- Quantitative data which represents information that can be counted
- The chosen data collection method depends on the subject matter and what information it is necessary to assess.
Qualitative Data Collection Methods
Qualitative data collection methods come into the limelight when it is necessary to answer the question “Why?” instead of “How many/much?” This type of data is less concrete and much more difficult to measure than quantitative data, as it usually contains descriptions and opinions on a particular topic. The methods that grant access to qualitative data include interviews, observations, product reviews, answers to open-ended questions, and others.
Quantitative Data Collection Methods
Quantitative data is built on numbers, values, and quantities. It is much more concrete than qualitative data and can be easily measured. This type of data can be gathered with the help of different algorithms and data management platforms (DMP), which count such measurable parameters as the number of users that have bought a particular product or left the cart page, how much time users spent on a website, how far they scrolled, and many others. Quantitative data, being numeric, is a perfect basis for analysis, as it is objective and reliable. Numbers don’t lie — they lead to insights for a better understanding of your audience.
Importance of Data Collection
The modern world is gradually moving to the digital space, and data surround us. Those who learn how to use it find themselves in a more favorable position than those who build their hypotheses on some other notions. Besides, businesses invest even more resources to benefit from data collection and analysis in the post-pandemic environment. COVID-19 emphasized that unexplored data improve resilience in the digital era. The initiative will remain a priority for businesses that rethink cultural and technological aspects in 2022 and beyond. Understanding the importance of data collection is beneficial because of the following reasons.
- Data-driven decisions are much more effective for corporate strategy development.
- Access to data allows us to identify problems at earlier stages.
- Data helps to prove hypotheses right or wrong before implementing them.
- Arguments supported by data are much more accurate.
- Fast access to organized data helps to save time.
These are just the main advantages of effective data management. There are many more areas that can be improved by data collection.
How to Collect Data
Data can be collected with the help of different methods. Each of them has its peculiarities. But data collectors follow five fundamental stages, no matter which method they choose.
Determine What Information to Collect
Any data collection process starts with understanding what type of data should be gathered, what topics it covers, what sources will be used, and what volumes of information are necessary. The answers to these questions are given based on the goals set before. For example, it may be necessary to collect data about the most popular content on a particular website among its visitors of a specific age who made a specific action online during the last week.
Establish a Timeframe for the Process of Collecting Data
The second stage of data collection is setting its timeframe, as different research goals need different assessment periods. For example, customers’ financial behavior usually demands more time for data collection and assessment. At the same time, some specific tasks may be more limited in time — especially in situations when data becomes outdated quickly.
Determine Which Method of Data Collection Will Be Used
The data collection method is the core of the whole process. To make the right choice, you have to consider what you need to accomplish by the end of the research, what data will be collected and assessed, and how long the timeframe will be. In addition, the number of parameters can be huge, making their configuration time-consuming.
Collecting the Relevant Data
When all the data collection process aspects are determined, you can start implementing the strategy. DMP is quite a useful tool for storing and organizing the collected data. However, to succeed, you have to have a clear understanding of what you are doing and follow the plan while making corrections whenever it is necessary.
Initiating Data Analysis and Drawing Conclusions
Once the data is collected, the data analysis and organization stage begins. This is a crucial step, turning raw information into useful and valuable insights — which businesses can use to enhance their operational processes, marketing strategies, and corporate decisions. A great number of web analytics tools can help you with this stage. The role of data has increased during the last decade when businesses have been undergoing digital transformation. The pace of digitalization isn’t going to slow down, urging companies to keep track of recent digital landscape trends. Here are three key trends to consider in the forthcoming year:
The pandemic revealed numerous weaknesses of both national and international disrupted supply chains. It resulted in supply delays and production slowdowns. As a result, supply chain analytics becomes an essential element of any non-self-sufficient business process. The whole situation will lead to diversification of suppliers and the emergence of multilevel supply chains.
Corporate management acknowledges the opportunities to commercialize data. Those who manage to succeed are more likely to attract investors. As a result, corporate databases are becoming a valuable digital asset that plays a very important role in M&A processes.
Companies sell data to generate income and integrate it into their products and services and use it internally to create value streams and externally to provide customers with more relevant information.
Environmental, social, and governance (ESG) issues have been rising lately, and the trend is likely to gain pace among the corporate leadership. In addition, customers start to pay more attention to how much money the company generates and what means it is earned: no greenhouse emissions, waste-free production processes, and zero loss of feedstock are some factors for efficiency estimation.
Sampling Methods in Data Collection
The company’s target audience may consist of many customers representing different groups. The task of addressing each of them seems impossible. Sampling helps to identify particular subgroups of the audience that reflect all the main features of the whole group. The process may be complex, but some statistical methods ensure a subgroup represents the whole group in the most accurate way possible.
These sampling methods include the following.
Random sampling is the process of picking respondents without any pattern or system. At first glance, this method may seem unscientific, but it can be rather valuable, as it just gets rid of the elements that can decrease the validity of the research. Still, randomness requires some system. For example, if we consider a popular city and start to pick people who walk on the main street on Saturday afternoon, we will get a diverse cross-section of tourists. But the local population will not be present, as they usually tend to avoid such areas, especially during the weekends. Computer randomizers can be a solution to this problem.
Systematic sampling is built based on some rules designed to create regularity. For example, observing every fifth customer will give the order to the process. However, systematic sampling can be a relatively rigid method in some cases, as the data may be irrelevant, for example, when every fifth customer turns out to be a teenager under 16 years old.
Convenience sampling is considered the easiest method but is the least reliable at the same time. Therefore, it is applied to data collected from the easiest to reach. An example can be a questionnaire sent to the employees of one department instead of the whole company, which may be effort-consuming. But there are some cases when this type of sampling may be effective — for example; it is necessary to get data on the first product impression where respondents and their diversity are of no importance.
Clustered sampling is aimed at subgroups and not at individuals. The clusters are usually defined beforehand; for example, they can include areas or regions that participate in some research. Clustered sampling can be of two types: single-stage (when all the representatives of the cluster are included) and two-stage (when only particular representatives are chosen). The main advantage of this method is that the cluster is already clearly defined, and you don’t have to define it yourself. The only problem may occur if the cluster doesn’t represent the whole community accurately.
Stratified sampling is applied to subgroups of a population that have similar characteristics. For example, the respondents may be divided by gender, age, education, and other parameters. When they are determined, the risk of bias decreases. But when the characteristics are not clear, the collected data may not be very accurate. The main difference between cluster and stratified sampling is that only cluster sampling includes specific clusters in the research process. In contrast, stratified sampling considers only the individuals from each group on a random basis.
Data Collection Tool: What is It?
A data collection tool is a software, algorithm, or some other instrument for gathering data. The success of the process is defined by choosing the most suitable tool. These tools are based on different methods of data collection. For example, some include direct questions concerning customers’ preferences, while others monitor what customers do on the webpage or how they interact with different online or offline elements.
The Best Data Collection Tools for Academic, Opinion, and Product Research
The following part of the article describes the best data collection methods for academic, opinion, and product research. Read further to learn more about them.
An interview is a personal conversation between two people that collects information for research purposes. Interviews can be structured, semi-structured, or unstructured.
- Structured interviews are just verbal questionnaires, which makes them a little superficial and not very time-consuming.
- Semi-structured interviews contain several key questions that cover the research topic in a deeper way.
- Unstructured interviews are in-depth and designed to collect as much information on the subject as possible.
- Interviews offer flexibility when it comes to the level of immersion and time consumption, but they may be rather expensive to organize.
All recorders, including dictaphones and digital cameras, can be used for interview organization.
A questionnaire is a data-collecting tool that consists of a series of questions that can cover different topics and are answered by the target audience. All the questions can be divided into three big groups: fixed-alternative, scale, and open-ended. The choice of questions is defined by the purpose and scope of the research. Questionnaires are an easy and cost-effective way to collect data sets with various options for data representation. At the same time, respondents may lose interest in the middle of the questionnaire or leave some questions unanswered.
The data reporting process is based on gathering information and submitting it for further analysis. The key factor of this method is using a reliable data source, as inaccurate information can lead to bad decisions. Despite the fact that data reporting can help with decision-making, results may be biased, as the source of information may present it in a subjective way, or respondents might not give detailed answers. The main sources of information for data reporting include non-governmental organizations, newspaper and website articles, and even hospital records.
This method introduces new information to existing data. Getting data from existing sources like archives or research journals can be a good example. The main advantage of this method is data accuracy and its easy accessibility. Still, some problems with data evaluation and understanding may occur.
As we can see from the name of this method, data is gathered through observation of the research object. The observer can be an observer, participant, or both. This method is easy to organize, it is applied practically everywhere, and the respondents don’t have to create any reports afterward. On the other hand, the drawbacks of observation include unpredictable validity and costly organization. The main tools for compliance include checklists and direct observation.
This method is applied for qualitative analysis of the object when it is necessary to collect data about the respondents’ feelings and opinions. The focus groups are offered open-ended questions in order to get feedback. This method is cost-effective, and the gathered data is usually very detailed. But bias may occur, and the outcomes are generally uncontrollable. Difficulties with assembling an inclusive group of respondents are also a common thing. The best tools to get the answers from the focus group are the following.
- Two-way. In this case, one focus group answers the questions while the second one listens to them and offers its point of view on the problem to initiate further discussions afterward.
- Dueling moderator. Two moderators with opposing opinions participate in the discussion within the focus group to facilitate new ideas and points of view.
This data collection method applies innovative methods and demands the invitation of both individuals and groups to gather relevant responses. This research is used when it is necessary to collect qualitative data on sensitive subjects through other methods. Combination research encourages participants to collaborate on a deeper level and enriches the data. But it costs more and takes more time than all the above-described methods.
The Best Templates for Data Collection
Different templates can be applied for various customer surveys. Let’s have a look at best practices.
Templates for assessment of customer satisfaction allow collecting data on such aspects as what the most popular product or service is, whether the customer will recommend it or not, and whether the level of customer service is good enough.
Templates that assess some customer demographic characteristics give access to information like gender, age range, and social group breakdowns. Respondents also sometimes are asked to share their personal information or political views.
This template is necessary to gather feedback concerning the purchased product or service details. In addition, it helps to assess the satisfaction level of the product itself and delivery, and other essential aspects.
These templates are used to gather large sets of data concerning respondents like gender, age, race, educational background, etc.
The Best Method for Collecting Qualitative Data
The best method for collecting qualitative data, which relies on the respondents' feelings, views, and beliefs, is the combination research. The main reason for this is that the method relies on interviews and focus groups, which are the most useful for sensitive data that cannot be measured in numbers.
The Best Method for Collecting Quantitative Data
The best method for collecting quantitative data is the questionnaire, as it can be cost-effective with a large number of respondents involved. The gathered data sets can be voluminous, but a unified template makes them easy to organize, visualize, and analyze. One more benefit of a questionnaire is that it allows comparing the current data with the previously gathered information.
Data Collection and Lead Generation
Data collection and lead generation are closely connected. According to the definition of lead generation, it is the process of turning customer interest in a product or service into a sale. This notion means collecting personal contact data (a “lead) through a particular web form in marketing. A lead is the foundation of a company’s activity.
Leads are generated with the help of different means, data collection being one of them. However, as more and more sales are done on the Internet, online lead generation has become one of the most efficient methods.
Companies collect personal information such as names, email addresses, and preferences by using different online forms or making visitors register on the website. Then, people are offered something valuable in exchange for that data, like a premium account or a discount. Therefore, it is necessary to provide visitors with a positive customer experience to make their further conversion into real customers smoother.
Gated content is another tool that makes access to personal information more accessible. It is demonstrated only when the visitor leaves some contact information. Without a content gate, users can surf the web page without any notifications about registration events, which would have a negative impact on sales.
With the help of online forms for downloadable content, a controllable lead generation process is an effective way to increase the quality of the leads. But in this case, the risk of getting inaccurate data emerges, as many people nowadays have separate emails for advertising and spam letters.
In order to cope with this situation, companies may use CAPTCHA for email verification or Internet Protocol limiting to restrict the number of available downloads from a particular IP address.
Companies can also introduce social media and search engine optimization (SEO) into their marketing strategies in order to generate more leads. Social media helps engage with potential and existing customers more effectively, while SEO will help promote the website in search engine results pages (SERP). Combining the above-described methods is the most effective way to generate leads.
Published at DZone with permission of Anastasiia Komendantova. See the original article here.
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