Conducting Market Research

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Introduction

Market research is the practice of evaluating the viability of a new service or product by interviewing prospective customers directly. It allows you to communicate directly to your target market and get feed-back from your interested customers. The goal of market research is to examine the market for a specific product or service and to determine how the target market will respond to it. This can involve gathering data for market segmentation and product differentiation which can be used to target advertising campaigns or figure out which characteristics customer’s value most. Market research can be used by companies to communicate with their customers and also get information about the variability of their new products from their customers through feed-back and so on. Market Research consists of the combination of both Primary and Secondary Information

Primary Information: Primary information is the data that has been directly gathered or that has been gathered by a person or company that has been paid to do the research. Exploratory and focused research are the two main subcategories of this information kind. Exploratory research is a type of research that is less structured and uses more open-ended inquiries, produces problems or concerns that the business may need to address. Exploratory research frequently draws attention to problems that specific research addresses by providing solutions.

Secondary Information: Secondary data is the information that has already been obtained by an outside party. This can include demographic data from official census records, research studies published by trade associations, or presented findings from another company engaged in the same industry.

Market research are conducted because it helps you to meet your buyers where they are, understand their problems and pains and also provide a desired solution to help them solve it. It also provide insight to variety of things such as;

• The location your target audience and current customers conduct their product or service research
• Which of your competitors your target audience looks to for information, options, or purchases
• What’s trending in your industry and in the eyes of your buyer
• Who makes up your market and what their challenges are
• What influences purchases and conversions among your target audience
• Consumer attitudes about a particular topic, pain, product, or brand
• Whether there’s demand for the business initiatives you’re investing in
• Unaddressed or underserved customer needs that can be flipped into selling opportunity
• Attitudes about pricing for a particular product or service

Market research helps you to acquire information from a larger audience in order to get to the heart of your consumer attitudes so that you can make a better business decision. 

Analyzing Questionnaires on a Market Research

Obtaining survey from your customers is usually very difficult. You need a good survey question to be able to get the insight of the customers. After you have collected the questionnaires you need to begin the process of calculating the survey results you have gotten back. A good survey data analysis is key to getting the information and insights you need to make better business decisions. Too many free-form questions can be time-consuming and complex to analyze, as they give qualitative results rather than numerical ones. Closed-end questions, on the other hand, produce results that are easy to analyze. Analysis can also be hampered by asking instructive questions, biased questions, confusing or overly complex questions. With the right tools and know-how, research and analysis will be easy and effective. There are some steps to take while analyzing your market research which are; Take a look at your top survey questions, Determine sample size, Use cross tabulation to filter your results, Benchmarking, trending, and comparative data, Crunch the numbers and Draw conclusion.

Calculating result using your top survey questions

Assuming you had an educational conference and gave feedback surveys to attendees after the event, one of the key survey questions is: How did attendees evaluate the entire meeting? This is a look on the answers gotten from the survey

Do you plan on attending the next conference?

In tis response, we have gotten our answers in percentage. The total number of the attendees were 1500.  This shows that 61% (922 of 1500) of the attendees plan on attending the next conference while 12% ( 176 of 1500) of the attendees do not plan on attending the next conference and 27% (402 of 1500) of the attendees are not sure if they would attend the next conference.

Determine the Sample Size

A good understanding of sample size is also important for accurate and effective analysis of findings. The sample size indicates the number of people who need to complete a survey to complete a survey in order to be statistically valid.

Cross-tabulation and filtering results

For example, if you want to compare the survey of how many Doctors, Accountants and Lawyers that wants to attend the next conference. To figure this out, you want to dive into response rates by means of cross tabulation, or use cross tab reports, where you show the results of the conference question by subgroup:

This shows that 45% of Doctors are attending the next conference while 29% of Accountants are attending the conference and 26% of Lawyers are attending the next conference. 23% of Doctors are not attending the next conference while 35% of Accountants are not attending the next conference and 42% of Lawyers are not attending the next conference. 31% of Doctors are not sure if they are attending the next conference while 36% of Accountants ate not sure if they are attending the next conference and 33% of Lawyers are not sure if they are attending the next conference.

The filter is another method of data analysis when modeling data. Filtering means focusing on a particular subset and excluding other subsets. So instead of comparing the subgroups to each other, let’s see how one subgroup answered the question. Combined with filters, you can get an accurate picture of the accuracy of your data. For example, you can focus on women only or men only and rerun crosstabs by participant type to compare female managers, female teachers, and female students. One thing to keep in mind when slicing and dicing results is that each time you apply a filter or crosstab, the sample size decreases. A sample size calculator is useful to ensure that the results are statistically significant.

Formulating and Testing Research Hypothesis

Hypothesis testing is a formal method for testing our ideas about the world using statistics. It is most commonly used by scientists to test specific predictions called hypotheses that emerge from theory. There are 5 main steps in hypothesis testing which are; State your research hypothesis as a null hypothesis and alternate hypothesis (Ho) and (Ha or H1), Collect data in a way designed to test the hypothesis, Perform an appropriate statistical test, Decide whether to reject or fail to reject your null hypothesis, Present the findings in your results.

State your null and alternative Hypothesis

Once you have created your first research hypothesis (the prediction you want to study), it is important to reformulate it as the null hypothesis (Ho) and the alternative hypothesis (Ha) so that you can test it mathematically. The alternative hypothesis is usually the first hypothesis to predict the relationships between variables. The null hypothesis is the prediction that there is no relationship between the variables of interest. I would like to test if there is a relationship between gender and height. Based on our knowledge of human physiology, we hypothesize that men are on average larger than women. To test this hypothesis, reformulate it as follows:

Ho: On average, men are not taller than women.

Ha: Men are on average taller than women.

Collect data

To enable statistical testing, it is important to sample and collect data in a way that tests hypotheses. If the data are not representative, it is not possible to make statistical inferences about the population of interest. To test the difference in average height between men and women, the sample male and female ratios are equal, different people, socioeconomic classes, and other control variables that may affect average height. Must be covered. The range should also be considered (whole world? For countries?) Because the possible data sources in this case include data from a wide range of regions and social classes and are available in many countries around the world. It may be census data.

Perform a Statistical test

There are various statistical tests, all based on comparing intragroup variance (how much the data is distributed within a category) and intergroup variance (how different the categories are from each other). If the variance between the groups is large enough and there is little or no group overlap, the statistical test reflects this by showing a low p-value. Therefore, it is unlikely that the differences between these groups happened by accident. Or, if the intragroup variance is high and the intergroup variance is low, the statistical test reflects this with a high p-value. This means that the differences measured between groups are likely to be accidental. The choice of statistical test is based on the type of data collected. Based on the type of data collected, perform a one-sided t-test to test whether men are actually taller than women. This test reveals the following: Estimated difference in average height between the two groups. A p-value that indicates the likelihood of a difference if the “no difference” null hypothesis is true. Her t-test shows that the average height of men is 175.4 cm and that of women is 161.7 cm, with estimated true differences ranging from 10.2 cm to infinity. The p-value is 0.002.

Decide whether to reject or fail to reject your null hypothesis

Based on the results of the statistical test, you need to decide whether to reject the null hypothesis. In most cases, the p-value generated by the statistical test is used to guide the decision. Also, in most cases, the default significance level for rejecting the null hypothesis is 0.05. That is, if the null hypothesis is true, then the probability of seeing these results is less than 5%. In some cases, researchers choose a more conservative level of significance. 0.01 (1%) This minimizes the risk of falsely rejecting the null hypothesis (Type I error).

Present your findings

The results of the hypothesis test are shown in the Results and Discussion sections of the research paper.  In the Results section, you need to provide a brief summary of the data and a summary of the results of the statistical test (for example, the estimated difference between the group mean and the associated p-value). In the discussion, you can discuss whether the first hypothesis is supported by the results. Formal language of hypothesis testing Talks about whether to reject the null hypothesis. A comparison of the average heights of men and women found that they had a mean difference of 13.7 cm and a p-value of 0.002. Therefore, we can reject the null hypothesis that men are not taller than women and conclude that there is likely a size difference between men and women. If the null hypothesis is rejected, this result is interpreted as consistent with the alternative hypothesis. 

Why Conduct Market Research

Market research is essential to keep up with current market trends and stay competitive. Whether you’re starting a new business, expanding, or developing a new product, market research is essential. It helps you understand your target market, increase sales and drive business growth. Market Research helps to;

• Identify new Customers

To identify potential new customers, you first need to understand who they are. You also need to know the main demographics. When looking at a product or service, it is important to consider the following questions: Who uses your product or service? How old is your customer group? What are their income levels, marriage status and geographic location? By using market research to understand these factors, you can target your customers more effectively.

• Get to know your existing customers

You need to spend some time understanding who your existing customers are. Here are some questions to ask yourself: Why do your customers choose your product over that of your competitors? How do your customers use your products?  How does your product solve the problem? Who or what influences their purchase decision? What do your customers like to do, see and read? Understanding how existing customers use their products and what challenges they solve can help businesses improve their products and identify upsell opportunities with existing customers.

• Set realistic targets for your business

Now that you have information about your target customers and existing customers, you can use this data to set achievable and realistic goals for continuous improvement and business growth. In today’s business environment, a customer-centric approach is essential. The most common approach in market research is to use the STP model: segmentation-targeting-positioning.

Segmentation: Who does your product appeal to in terms of demographics, geography, or other factors?

 Targeting: How can you target and reach the sector in which your product appeals?

 Positioning: How can you position yourself as the first choice in your target market than your competitors?

• Develop new and effective strategies

Market research data helps you make more informed decisions. For example, in relation to pricing, sales channels, marketing media, or to identify opportunities to launch new products or services. These results also help you make more informed decisions about your existing operations and activities. Should I expand or collapse? Is there room for diversification in your current business? Are you working on the right target group in your marketing efforts?

• Solve your biggest business challenges

If you have already identified a business problem, conducting market research can help you identify the root cause of the problem. For example, you can determine if a new competitor has entered the market or if brand awareness has declined and sales have declined. There are different types of market research that can help identify different defects. Brand research, consumer research, product development and usability testing, consumer research, and many other areas.

• Investigate expansion opportunities

Market research helps identify potential areas of business expansion. It provides an opportunity to test the market to see if there is room for new products and services. You can also do market research to find the best place to open a new business.

• Identify how to expand your offering

Market research helps you discover new markets with inadequate or demanding services. For example, you can identify market trends that change due to new housing, higher education levels, or other changes that open new opportunities for your company. Overall, market research isn’t just for start-ups. Companies of all forms, sizes, sectors, industries and experience levels can benefit from market research. Market research helps you learn more about new and existing customers, identify and solve problems, and explore new opportunities that pave the way for business growth.

Statistical Tools in Market Research

Statistical analysis is a quantitative data analysis method that uses numbers to assign measurable coefficients that are easy to compare and interpret. Statistical analysis collects and analyzes raw data to identify patterns and trends that can be used to make informed decisions. The process of using statistics for market research includes: Defines the type of data extracted from the target population, Examine the relationship between data and population size, Summarize insights and develop models that define all visible connections between datasets and populations, Testing the model to determine the validity of the model and Predict future trends and integrate results into your business strategy. There are two main statistical analysis methods commonly used for market research purposes: descriptive and inferential statistics. Both methods have different goals and applications, making them suitable for evaluating different data sets.

Descriptive Statistics

Descriptive statistics provide insight into the data collected, but do not infer the larger population from which the data sample was extracted. This method basically describes the sample by summarizing and graphing the data. Performing market research using descriptive statistics helps organizations understand the basic characteristics of quantifiable sets of data by grouping data and identifying patterns and trends. This method is relatively simple because it involves basic mathematical calculations and data aggregation, and generates key figures to evaluate past business practices and their effectiveness. Some common descriptive statistical analysis methods are:

Measure of Frequency: These are mathematical functions such as counts, percentages, and frequency occurrences. The frequency measurement is primarily used to count the number of occurrences of a particular variable, event, or number in a dataset. Used to determine how often the reaction occurs in samples.

Measure of Central Tendency: It describes the center position of the distribution for a given dataset. It is used to show the mean response by analyzing the frequency of sample data points and expressing them as mean, median and mode. Central Tendency measures identify the most prevalent trends or common features in sample data.

Measure of Variability: It describes the central position of the distribution of a particular dataset. Used to show average response by analyzing the frequency of sample data points and expressing them as mean, median, and mode. The central tendency measurement identifies the most common trends or characteristics of sample data.

Measure of Position: It describes the central position of the distribution of a particular dataset. Used to show average response by analyzing the frequency of sample data points and expressing them as mean, median, and mode. The central tendency measurement identifies the most common trends or characteristics of sample data.

Inferential Statistics

Inferential statistics use the insights and measurements obtained from the sample set to extrapolate the results to a larger set. This method is primarily used to draw conclusions from trial samples and generalize points to the population of interest. The basic assumption of this method is that the sample size is an accurate representation of the population. This requires some built-in safeguards to identify the population, incorporate relevant sampling techniques to extract the sample set, and explain sampling errors. This method is more complex than descriptive statistics, but provides richer numerical data for future business strategies. Here are some common statistical analysis methods for inference:

Factor Analysis: This is used to create the underlying structure for a larger set of correlated variables. The purpose of is to consolidate the information contained in multiple original valuables into a smaller set of composite dimensions while minimizing information loss. Simply put, create easy-to-manipulate variables by plotting a set of observed variables (usually a semantic differential scale) in terms of common factors that can explain the correlations that apply to a larger population. By doing so, you reduce the data.

Conjoint Analysis: This is used to distinguish how market research participants make complex purchasing decisions, including recognition and evaluation of various variables related to a product or service. In a conjoint analysis, respondents need to assess trade-offs related to various factors such as pricing, branding and identify their impact on purchasing considerations to assess customer decision-making criteria.

Cross Tabulation: This technique is used to assess patterns, trends, relationships, and probabilities by grouping variables to understand correlations between different variables in sample data. By placing variables side-by-side in a two-dimensional table, this method provides a unique perspective and perspective that is useful for identifying relationships and evaluating insights that may not be immediately apparent.

TURF Analysis: Fully unique reach and frequency analysis is used to evaluate and optimize product combinations and optimize communication strategies by analyzing communication sources and frequency reach. With TURF analysis, you can evaluate potential media and market quotes and develop optimal communication and placement strategies. Identify the number and frequency of arrivals for each communication method to better understand market sentiment.

Correlation: This method provides a detailed study of the relationship between two or more variables from a dataset and their application to the entire population pool. This helps businesses predict future behavior by establishing positive or negative causal or dependency relationships. Intensity is measured at higher numbers on a scale of -1 to +1.

Regression Analysis: This is a commonly used method for predicting the strength of relationships between two or more variables. To perform a regression analysis, you need a dependent variable whose variability depends on another variable and an independent variable whose variability is controlled by the experimenter and whose variability does not depend on another variable. The analysis evaluates the effect of the independent variable on the dependent variable to understand which variable has the greater effect.

Hypothesis Testing: The linguistic hypothesis test is another way to draw conclusions about the population by testing a representative sample set against the expected value or hypothesis defined by the experimenter. The hypothesis can establish relationships between variables and provide insights into population characteristics such as mean and variability through t-test, chi-square, and ANOVA tests. This method makes it easy to draw good conclusions when it is not possible to test the entire population. However, this method requires advanced sampling techniques to ensure that the sample is representative of the population.

Conclusion

The goal of market research is to examine the market for a specific product or service and to determine how the target market will respond to it. This can involve gathering data for market segmentation and product differentiation which can be used to target advertising campaigns or figure out which characteristics customer’s value most. Market research can be used by companies to communicate with their customers and also get information about the variability of their new products from their customers through feed-back.

Call-for-action

Tel: (+234) 802 320 0801, (+234) 807 576 5799

Email: info@mocaccountants.com

Office Address: 5, Ishola Bello Close, Iyalla Off Street, Alausa, Ikeja, Lagos, Nigeria

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