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The Ultimate Cheat Sheet to Data Analysis Techniques for Market Research

Posted by Communications for Research on 02/5/2019

data analysis techniques for market researchIn today’s business world, getting data isn’t hard. The ubiquity of the Internet, and especially mobile phones, has made it easier than ever for companies of all sizes and across all industries to monitor consumer engagement and collect transactional data for their customers.

It’s “Big Data” in every sense: not only is there big volume, there’s also big potential. Thus, the trick isn’t so much the gathering of facts anymore; it’s the piecing together of insight from amongst a maelstrom of often seemingly insignificant data points. Indeed, good market research has always been a lot more than data collection.

Here’s a quick look at the four types of data analysis techniques that market researchers use to make real meaning from their data stores:

Descriptive Data Analysis Techniques

Market researchers use descriptive data analysis techniques to describe historical data sets, essentially organizing raw information into groups so that any present patterns are easily discerned. Descriptive analytics do not, however, shed light on causation or allow researchers to make any reliable assumptions. Simple arithmetic tabulations (like the number of sales, website visits, complaints and items made over a certain period), as well as data aggregation and mining systems that use straightforward mathematical measures (such as mean, medium, mode and range), are all descriptive data analysis techniques that can be used to inform businesses of important facts and figures related to past business practices.  Most businesses combine rudimentary descriptive analysis with additional analysis techniques in order to fully inform their decisions.

Diagnostic Data Analysis Techniques

Diagnostic analytics use more sophisticated formulas to reveal the reasons behind the outcomes. For instance, retailers might compare recent marketing efforts with sales numbers or a price adjustment with website traffic. By comparing and contrasting data sets, researchers can often determine correlation and/or causal relationships. Conjoint and regression analyses and probability measures are examples of diagnostic data analysis techniques. Because manual manipulation of big data is becoming less feasible, researchers are utilizing computer-generated algorithms (i.e., machine learning) for a lot of their diagnostic analysis needs. 

Predictive Data Analysis Techniques

When businesses want to forecast possible outcomes, they use predictive data analysis techniques that build upon descriptive and diagnostic figures. Market researchers create complex statistical models based on existing data to help them predict the probability of certain events happening in the future. Thus, analysts must be trained in advanced machine learning programming and know how to apply algorithms to existing data sets in ways that allow for the representation of interactions between multiple variables. Regression models, including linear regression and discrete choice analysis, are two of the most popular ways researchers can help companies optimize their future business practices.

Prescriptive Data Analysis Techniques

Prescriptive data analysis techniques are considered the most advanced of all the data analysis techniques. Companies use them to help determine the steps they need to take in order to either avoid future issues or capitalize on future opportunities. Much like predictive analytics in that they seek to forecast the likelihood of certain events coming to pass, prescriptive data analysis techniques take the insight further, attempting to predict possible outcomes based on particular courses of action, as well as possible actions to take based on one or more targeted outcomes. Whereas machine learning and other computer programs might be considered value-adds for other analytical computations, they are required tools for prescriptive data analysis techniques.

Ready to Learn More?

Choosing how you analyze data is a critical part of getting results that are both meaningful and actionable. If you need help deciding how to collect and apply information in ways that ensure the healthiest ROI, contact our team at Communications for Research (CFR). Whether it’s simple coding and tabulations or more complicated regression modeling or anything in between, we can help you!

Or if you're more interested in how you can use data analysis to communicate the value of market research to your clients, check out our free eBook below:

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