Step 1 – Define the Research Goal

You should start by defining the goal of your research project. What are you trying to find out? The more time you take to clarify your research questions, the more likely you will be to achieve your research goals.

Step 2 – Choose Research Design

The next step is to determine which research techniques will most effectively help you answer your questions. Considering what you want to learn and determining what your budget is will help you decide if qualitative or quantitative research best suits your needs. Well-designed research projects often use some combination of both.

Qualitative research uses open-ended techniques such as observation, focus groups, and interviews to gain an understanding of customers’ basic reasons, opinions, and motivations. It can be shared through words, descriptions, and open-ended comments. Qualitative data gives context but cannot be reduced to a statistic. One-on-one interviews are an excellent way for researchers to get more in-depth responses from research participants and allow the researcher to ask specific questions that match the respondent’s unique perspective as well as follow-up questions that piggyback on responses already completed. An interview allows the researcher to have a deeper understanding of the needs of the respondent, which is another strength of this type of data collection. The downside of personal interviews it that a discussion can be very time-consuming and results in only one respondent’s answers. Therefore, in order to get a large sample of respondents, the interview method may not be the most efficient method.

Quantitative research focuses on the generation of numerical data that can be turned into usable statistics. This kind of research most often takes the form of surveys or questionnaires that pose multiple-choice questions with predefined answers. Collecting primary data is commonly done through surveys due to their versatility. A survey allows the researcher to ask the same set of questions of a large group of respondents. Response rates of surveys are calculated by dividing the number of surveys completed by the total number attempted. Surveys are flexible and can collect a variety of quantitative and qualitative data. Questions can include simplified yes or no questions, select all that apply, questions that are on a scale, or a variety of open-ended types of questions. Electronic surveys are sent or collected through digital means and have the benefit of being less time intensive and is often a more economical way to gather and input responses than more manual methods.

Step 3 – Choose Sample

Your sample refers to who you will survey and how many people you will include. In most cases, you will want a sample that reflects your target market, the population who is most likely to purchase your product.

Step 4 – Collect Data

Depending on the type of research you conduct, your data collection methods will differ. In this course, students will complete interviews and surveys.

Interviews are a great method to secure open-ended answers. You might interview potential customers on what they think is the best way to communicate with them, what they like about certain competitors’ products, and get reactions to your product.

Read about interview best practices here.

Surveys can be done by hand or through online tools such as Survey Monkey or Qualtrics. Surveys are very helpful because you can ask questions to current or potential customers about your product, competitors’ products, customer service, and any other information you may seek to create or improve your business. They are an easy way to collect large amounts of data from many customers, and they allow you to calculate responses. Online tools are particularly useful in providing repositories of data that can be later exported to other analytical tools such as Excel or SPSS.

Read about survey design best practices here.

Step 5 – Analyze Data

Once you collect your data, the next step is to make sense of it. How you analyze the data depends largely on what you want to get out of it. Typically, you will be looking for patterns and trends among the answers. Data analysis is a field unto itself, and when complex analysis is required, seeking the assistance of experts is often worth the extra cost.

First, let’s discuss some basic statistics that can be used to represent data.

The first is the mean of quantitative data. A mean is often defined as the arithmetic average of values. The formula is:

Sum of Values / Number of Values = Mean

A common use of the mean calculation is with exam scores. Say, for example, you have earned the following scores on your marketing exams: 72, 85, 68, and 77. To find the mean, you would add up the four scores for a total of 302. Then, in order to generate a mean, that number needs to be divided by the number of exam scores included, which is 4. The mean would be 302 divided by 4, for a mean test score of 75.5. Understanding the mean can help to determine, with one number, the weight of a particular value.

Another commonly used statistic is median. The median is often referred to as the middle number. To generate a median, all the numeric answers are placed in order, and the middle number is the median. Median is a common statistic when identifying the income level of a specific geographic region. For instance, the median household income for Albuquerque, New Mexico, between 2015 and 2019 was $52,911. In this case, there are just as many people with an income above the amount as there are below.

Mode is another statistic that is used to represent data of all types, as it can be used with quantitative or qualitative data and represents the most frequent answer. Eye color, hair color, and vehicle color can all be presented with a mode statistic. Additionally, some researchers expand on the concept of mode and present the frequency of all responses, not just identifying the most common response. Data such as this can easily be presented in a frequency graph, such as the one in Figure 7.2.

Additionally, researchers use other analyses to represent the data rather than to present the entirety of each response. For example, maybe the relationship between two values is important to understand. In this case, the researcher may share the data as a cross tabulation (see Figure 7.3). Below is the same data as above regarding social media use cross tabulated with gender—as you can see, the data is more descriptive when you can distinguish between the gender identifiers and how much time is spent per day on social media.

Not all data can be presented in a graphical format due to the nature of the information. Sometimes with qualitative methods of data collection, the responses cannot be distilled into a simple statistic or graph. In that case, the use of quotations, otherwise known as verbatims, can be used. These are direct statements presented by the respondents. Often you will see a verbatim statement when reading a movie or book review. The critic’s statements are used in part or in whole to represent their feelings about the newly released item. It is the responsibility of the researcher to decide which presentation method will most accurately convey the research findings.

Step 6 – Results and Next Steps

At this stage, the entrepreneur seeks to reconcile the results of their research. For example, if you were doing exploratory research about a potential product you wanted to bring to market, now would be the time to ask questions such as whether the research suggests market potential.

Primary research, gathering original data through methods like interviews and surveys, provides fresh insights tailored to entrepreneurial questions.  As valuable as these firsthand insights are, they’re most effective when combined with secondary research.