Statistical Analysis Methods for Market Research

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:

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:

Benefits of Using Statistical Analysis in Market Research

Statistical analysis methods can provide worthwhile benefits to facilitate market research processes by:

1. 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.
2. 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.
3. 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

.