8.2 Market Research, Market Opportunity Recognition, and Target Market

Define the Research Goal

You should start by defining the goal of your research project. What are you trying to find out? Do you want to know more about your target market, their preferences, lifestyle choices, and culture, or do you want to know more about your competitors and why your target market buys from them? What are your criteria for validation of the research goal or goals?

The more time you take to clarify your research questions, the more likely you will be to achieve your research goals. If you can’t figure out exactly what you’re looking for, that’s okay. Exploratory research using a focus group can help you decide what kind of research questions to ask. A focus group is a gathering of people, typically six to twelve participants, who come together to discuss a topic presented by a moderator, who usually poses questions and collects qualitative data that can be used to answer questions or define research further.

For example, a manufacturer of water bottles might know there is a problem with their products as sales have been declining over time. The company doesn’t know exactly why or where to start, so it would use a focus group to better define the research goal or problem. Using focus groups, they might find out that they are targeting the wrong segment or that there is a need for better water bottle designs. Talking to the focus group can uncover possible research questions to undertake.

Merkadoteknia Research and Consulting is a Hispanic-owned company that conducts research for different industries that cater to Hispanic consumers. This company helps fast-food companies, retailers, and pharmaceutical companies, among others, conduct focus groups and gather insights about specific groups of people that may have different cultural backgrounds. These companies may not know exactly how to start researching their target market, so they use Merkadoteknia Research and Consulting to learn more through the use of focus groups. This approach helps better define the research goal or problem.

Determine 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. Simply observing potential customers, whether at a store or in daily activities, and noting their behaviors are a simple, effective form of ethnographic research that can help you better understand the lifestyles and habits of potential customers. Ethnographic research includes the personal observations of the subject by being immersed in the subject’s environment. Research like this helps companies see how people use their product over the course of a day.

For example, if you manufactured a water bottle and wanted to design a “better” water bottle, you could watch how people use their water bottles while working, exercising, during their commute, and so on to better understand their needs and habits. This “ethnographic” research can often uncover latent, or unstated, needs that you can use to build your concept. Unstated needs refer to those that are expected from a company, such as a certain level of quality or good customer service. These are default expectations that a customer has based on their experience with products in general.

Employing a mystery shopper, or pretending to be one yourself, is another way to learn how customers and employees act within a specific retail environment. A mystery shopper is a person who has been hired by the company or a third party to pose as a real shopper and has gone undercover with a specific goal of testing certain aspects of a business. This can take the form of purchasing a product, browsing and asking questions of an employee, interacting with other customers, or simply observing what is happening inside the store. After the experience, this person will provide feedback to the company.

Focus groups and one-on-one interviews can be useful to obtain more thoughtful answers and explore debatable topics. Both are good methods for digging deeper into the specific motivations and concerns of people, especially as they pertain to personal, privately held beliefs. This research is useful when trying to develop a more novel product, but you must do your best to eliminate “support bias” in that the panelists might seem favorable to new concepts to please or accommodate the moderator. For one-on-one interviews, it is important to develop a thorough interview or discussion guide, so other team members can also conduct the interviews. By doing this, you will ensure a consistent approach to asking key questions and documenting responses to help guide the next step of the development of your product.

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. While these surveys do not allow for much free expression on the part of the participant, the focused nature of the answers means marketers can identify trends, such as which social media platforms are preferred by customers or potential customers. For example, the researcher may very well find that Instagram is the most used social media app because of its ease of use and its ability to incite emotional responses to pictures.

Sample survey questions can be anything from “How many times have you gone to the corner store this week?” to “Which age range do you belong in?” This kind of approach is an efficient way to collect a lot of data because once the survey is written, it can be distributed in person or online to as many participants as you like. Typically, the more people you survey, the more accurately your data will reflect the demographic you are examining.

Another way to generate quantitative results is through causal research and test marketing. In both cases, you present participants with a cause and record the effect. An example of this might be a taste test in which people’s reactions to and preferences for different juice flavors are recorded. Given the limited resources of many start-ups, test marketing is a great way to make sure your product works before you start investing in its distribution.

An even more sophisticated—and expensive—method is conjoint analysis, in which respondents must rank, choose, or a rate a number of “conjoint,” or linked, features or benefits. This real-life scenario approach will give you very useful results from potential buyers, often with features or benefits ranked relative to perceived value or importance.

For instance, let’s say your company is trying to create a reusable water bottle to compete in a crowded marketplace. If you were to use conjoint analysis, you would create a survey that lists the water bottle attributes and prices to show consumers different choices. For example, customers could be given a set of very similar water bottles, each with a set of features the company would like to test. Features can include a locking lid so the water doesn’t leak, different bottle shapes for easier hold and water retrieval, and different colors and sizes. Each of the choices could have its own price point and the customer can choose, rank, or rate each of the choices based on those attributes and prices. Each of the choices could be described on a survey with words or pictures, and it would ask customers to choose their favorite or rank all of the choices. After collecting information and data from many consumers, researchers would use statistical analysis by applying linear regression (a predictive tool that measures the causality of one marketing variable on another) or other techniques to determine which attributes of a water bottle are more important to the customer and what price they would be willing to pay. Based on this knowledge, the company would create a product that could possibly beat the competition.

Choose a Sample

Next, researchers need to determine the sampling method. In terms of research, 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, especially if you are trying to figure out your ideal customer’s interests and how to get them to buy your product. Untargeted samples can be useful when trying to figure out who your target market might be, but they aren’t an efficient way to get to know your ideal customer.

Generally speaking, larger samples provide more accurate data, although researchers should not feel like they need to survey everyone in a market. For example, if you have a list of 5,000 contacts, you might choose to contact 500 people who would be representative of the total group of contacts. Surveying all of your population could be time consuming and expensive, so choosing a subset of the right people can yield good results. The sample must be large enough to be statistically significant, meaning that the relationship between variables is not a result of chance. The sample can then show the researcher an accurate depiction of a specific phenomenon.

For example, you can test whether men and women actually have different or similar tastes in water bottle attributes. In this case, the significance rests on the gender of the potential customer. If your sample is large enough (say 500 out of 5,000), and you find that men look for different attributes than women do, it is unlikely that the differences found were by chance. The cause of the differences would be the gender.

Therefore, keep in mind that errors of reliability and validity can arise if your sample is not thoughtfully selected. Statistical significance is then important in measuring reliability because it means that there is an actual difference in what is being measured. If there is a difference between men’s and women’s responses in our water bottle example, checking the significance will determine whether the differences really matter or if they are due to chance. Significance is measured by looking at the probability value, or P value, of the result. If it is 5 percent or less, it is usually considered a significant difference that is not due to chance, and the researcher can determine that the answers are indeed different between the two genders. Validity, on the other hand, means that the item that is being measured is relevant to the research. This means that the questions that are being asked will actually answer your research objective.

While random samples can be useful for some types of research projects, many researchers intentionally choose their sample participants based on economic and ethnographic factors to make sure they accurately reflect the nature of the demographic they seek to understand.

Collect Data

Once you’ve identified your research goals, chosen your design, and determined your sample, you are ready to start collecting data. The data may be collected through observation and taking notes. For example, if you are trying to figure out if your store layout is yielding the most productive sales, you can observe traffic flow and note what people look at and what they actually purchase. Then, you may change the merchandise layout, or the aisle size or space, to see whether people peruse other merchandise that they hadn’t seen before. Based on your observations and sales data, you can determine the best flow for your floor.

Interviews can also help secure open-ended answers. You might interview potential customers on what they think is the best way to communicate with them and what they like about certain competitors’ products. This can help you obtain additional options that you may not have thought to add on a survey, which is another way to get aggregate data.

Surveys can be done by hand or through online tools such as Survey Monkey or Qualtrics. Surveys are very helpful because you can ask question 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.

Regardless of what research technique you use, be on the lookout for data collection errors. Recording the wrong answers, failing to convey the right instructions to participants, or having to translate on the fly can all create biases that skew the answers and give you inaccurate results.

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. You can find experts within research marketing firms that specialize in collecting and analyzing data for businesses, such as Merkadoteknia Research and Consulting. These can be found online or through local business organizations, such as chambers of commerce.

Results and Next Steps

At this stage, the entrepreneur seeks to reconcile the results of their examination with the goals 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. Similarly, if the goal was to figure out what customers like about competing products, now would be the time to list those results and determine whether they are worth incorporating into your product. Regardless, it is important to be open to what the data say, even if they indicates results that are contrary to what you were hoping for. Research should be an opportunity for growth and a roadmap for the refinement of your idea.