You’ve finished conducting your market research and now have a veritable ocean of raw data to sift through. You must further categorize the data in order to make sense of why, exactly, this information matters to your specific study (and the greater world around you) in the first place.
Without the proper tools to analyze this kind of raw data, it not only remains of little use to market researchers, but often goes as far as becoming a distracting pitfall that ensnares many studies with otherwise great potential.
This is where statistics and statistical methods come in to save the day and make meaning out of the mess of data at hand.
Breaking Open the “Code” of Raw Data With Statistics
Let’s look at it like this: the raw data is like a long series of hieroglyphics taken from the wall the of some ancient temple you explored while gathering your market research. You are Indiana Jones in need of a way to crack the code and understand the significance of its contents so you can present your findings to the committee back at Marshall College (and earn a bonus to finance that adventure to Tibet you’ve had your sights set on).
As such, statistical methods become your means of deciphering, the technique that allows you to “break the code” in terms of analyzing, categorizing, and most of all understanding the data at hand.
Your raw data quickly goes from cryptic code to poised, presentable, and meaningful data with the implementation of statistical analysis. Let’s take a brief look at how this works.
Prepping for the Expedition: Using Statistics to Optimize your Market Research before Analysis
There are several main statistical methods you can choose to tackle your data (and win the admiration of archaeologists the world around); however, you can also use statistics to help prepare for your market research itself.
In order to yield the most accurate results possible, it is often prudent to perform a calculation to find the optimal sample size of the population of individuals your market research will be targeting. Calculating the sample size by hand can be somewhat of a lengthy process, but fortunately, there are online calculators like this one that make the process a breeze.
The Right Tools for the Dig: Choosing the Best Statistical Method for Your Data
After you have successfully found the optimal sample size for your study, conducted your market research, and gathered your raw data (all while navigating rolling boulders and other perils that only Indiana Jones would be faced with, of course), it’s time to choose the proper statistical method to analyze the data.
There are several to choose from, each with its own specific function and benefits. Here are a few of the most commonly used methods:
- Chi Square Test – A statistical method of testing the correlation between two categorical variables (Ex: How gender [M/F] correlates with hand dominance [R/L]). A Chi Square Test can be a long, fairly complicated process if done manually, but there are special online calculators that expedite and simplify the process for you.
- Multiple Regression – a statistical method that is used to estimate the equation that yields the best fit for explaining how values of the dependent variable are affected by shifts in value of a number of independent variables (Ex: How revenue for rare artifacts is affected by the price of precious metals, historical significance, current trade rates etc).
- Factor Analysis – a statistical method used to determine which relationships are strongest between a larger set of variables that are intercorrelated. When there are multiple correlations between certain variables in your data, Factor Analysis will allow you to determine which are most significant to your study.
- Cluster Analysis – this method helps identify “clusters” in your data that pool segments together with similar traits that are distinctly different from the other segments that have been collected (Ex: Experienced, elderly adventurers who care about the historical significance of your artifacts in one cluster versus younger thrill seekers who care more for the monetary value of your artifacts in the second). Typical analyses are usually segmented into 3-5 clusters, and are used for identifying target audiences for marketing purposes.
- Conjoint Analysis – a statistical method used to uncover consumer preferences in regards to different offers and options available on the market. Furthermore, this method attempts to signify why consumers hold these preferences and what market factors directly affect them. Conjoint takes into equal consideration the inferred utility of an attribute that affects consumer perception and the relative importance of the attribute to the consumer
Turn Your Data into Treasure Befitting Any Museum
Statistical Methods have proven to be an invaluable tool when conducting market research in the modern age. Statistics allows market researchers to wade through pools of raw data, sorting it into meaningful categories while ultimately gaining an understanding of its significance.
Like Indiana Jones and his iconic hat, market researchers are never in action without using the statistical methods as the tools of their trade.