Statistical Analysis Methods for Market Research
Statistical analysis will take market research to the next level
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Introduction
Primary market research allows organizations to collect information from target markets by employing traditional quantitative and qualitative techniques.
However, acquiring data is not the only factor for conducting effective market research as data is more widely available thanks to the power of technology and the internet and online panels.
Modern advancements make it easier for businesses across all industries to monitor customer and market sentiments while collecting large quantities of data.
The power of data is not inherent as a data set is only as good as the analysis and insights. The right analysis methods can assist your business in extracting key information and trends from a pool of random data points that analysts can use to set short and long-term growth strategies.
What is Statistical Analysis?
Statistical analysis is a quantitative data analysis method that uses numbers to assign a measurability factor that is easy to compare and interpret. Under statistical analysis, the raw data is collected and analyzed to identify any patterns and trends which can be used for informed decision making.
The process of using statistics for market research involves:
- Defining the type of data to be extracted from the target population
- Exploring the relationship of the data with the population set
- Developing a model that summarizes insights and defines any visible links between the data set and the population
- Testing the model to establish the validity of the model
- Incorporating the results into your business strategy by anticipating future trends
Statistical Analysis Methods
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 they do not draw any conclusions about the larger population the data sample is extracted from. This method essentially describes a sample by summarizing and graphing data.
Conducting market research with descriptive statistics can help organizations understand the basic features of any set of quantifiable data by grouping data and identifying any patterns or trends.
This method is relatively simple as it involves basic mathematic calculations and data aggregation to yield important figures to evaluate historical business practices and their effectiveness. Some common descriptive statistical analysis methods include:
This involves mathematical functions, including counting, percentage calculation, and frequency occurrences.
Measures of frequency are used to primarily count the number of times a specific variable, event, or number appears in a data set.
It is used to establish how often a response occurs in the sample.
This describes the central positions of a distribution for a given data set.
It is used to display the average responses by analyzing the frequency of the sample data points and expressing it using the mean, median, and mode.
The central tendency measure identifies the most common trends or shared characteristics in the sample data.
This describes the central positions of a distribution for a given data set.
It is used to display the average responses by analyzing the frequency of the sample data points and expressing it using the mean, median, and mode.
The central tendency measure identifies the most common trends or shared characteristics in the sample data.
This describes the central positions of a distribution for a given data set.
It is used to display the average responses by analyzing the frequency of the sample data points and expressing it using the mean, median, and mode.
The central tendency measure identifies the most common trends or shared characteristics in the sample data.
Inferential Statistics
Inferential statistics use insights and measurements derived from the sample set and extrapolates the results to a larger set.
This method is primarily used to draw conclusions from an experiment sample and generalize the points to a relevant population.
An underlying assumption of this method is that the sample size is an accurate representation of the population which requires us to identify the population, include relevant sampling techniques to extract the sample set, and have some built-in safeguards to account for sampling errors.
While this method is more complicated than descriptive statistics, it provides richer numerical data for future business strategies. Some common inference statistical analysis methods include:
This is used to establish the underlying structure of a larger set of correlated variables.
The purpose is to condense information contained in multiple original valuables into a smaller set with composite dimensions while ensuring there is minimum loss of information.
Simply stated we are reducing data by making one variable, which is easy to manage by representing a set of observed variables (typically semantic differential scales) in terms of common factors which can explain correlation that can be applied to a larger population.
This is used to distinguish how market research respondents make complicated purchasing decisions that include perceiving and evaluating different variables related to a product or service.
Conjoint analysis requires respondents to evaluate the tradeoffs involved with different factors such as prices, branding, etc., and identify their bearing on purchase consideration to evaluate the decision-making criteria for customers.
This technique is used to evaluate patterns, trends, relationships, and probabilities by grouping variables to understand correlations between different variables involved in the sample data.
This method identifies relationships that might not be readily apparent by placing the variables next to each other in a two-dimensional table which provides a unique perspective and outlook beneficial for gauging insights.
The Totally Unduplicated Reach and Frequency Analysis is used to rank and optimize product combinations and while fine-tuning our communication strategies by analyzing the reach of communication sources and frequency.
TURF Analysis allows you to evaluate estimates of media and market potential in devising optimal communication and placement strategies.
It identifies the number of users reached by each communication method and how often they are reached so you can have a stronger grip on market sentiments.
This method provides an in-depth study into the relationship between two or more variables from a data set and their application to the overall population pool.
This can help businesses make predictions about future behavior by establishing a causal or dependency relationship that can be positive or negative.
The intensity is measured by a higher numeric value on a scale ranging from -1 to +1.
This is a commonly used method for predicting the strength of a relationship between two or more variables.
To run a regression analysis, you need to have a dependent variable whose variation is dependent on another variable and independent variables which are controlled by the experimenter, and its variation is not dependent on any other variable.
In the analysis, the impact of independent variables on the dependent variable is evaluated to understand which variables have a greater impact.
Speaking hypothesis testing is another way to derive conclusions about a population by testing representative sample sets against experimenter-defined expectations or hypotheses.
The hypothesis can establish relationships between variables or provide insights about population properties such as mean and variation through T-Test, Chi-Square, and ANOVA tests.
This method makes it easy to draw suitable conclusions when it is impossible to test the entire population.
However, this method requires sophisticated sampling techniques to ensure the sample is representative of the population.
Benefits of Using Statistical Analysis in Market Research
Statistical analysis methods can provide worthwhile benefits to facilitate market research processes by:
- Producing theories backed by numerical evidence. The quantitative nature of statistical analysis provides a solid numeric framework to provide objective support for relationships between variables and hypotheses. These statistics make it easier for organizations to make well thought out decisions to serve customers better and provide relevant products and services to align with long-term goals and a positive impact on productivity.
- Yielding data that is easily calculated and analyzed. The precision offered by numbers and percentages can provide answers that can be analyzed by performing arithmetic functions. They do not need to be coded, unlike qualitative data, to improve understandability. This cuts down on data processing time while producing relevant results.
- Yielding a larger respondent pool. Before implementing any statistical analysis techniques, you need to have a data set. It is important to note that the data set for quantitative research is typically larger than that of qualitative research because it consists of close-ended questions, which are less time consuming, thereby encouraging more people to complete the survey or questionnaire. The larger data set allows you to have a more accurate sample representing the population which provides greater credibility to any results derived from the analysis
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Jim Whaley
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Jim Whaley is a business leader, market research expert, and writer. He posts frequently on The Standard Ovation and other industry blogs.
OvationMR is a global provider of first-party data for those seeking solutions that require information for informed business decisions.
OvationMR is a leader in delivering insights and reliable results across a variety of industry sectors around the globe consistently for market research professionals and management consultants.
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