One of the most important innovations in market research is the emergence of tools that enable beginners to analyze market research data. This is especially valuable for looking at the same data sets in ways, and attempting to glean new and interesting insights.
While these tools have various strengths and weaknesses – i.e. some are easier to use, others offer more features and functions, and so on – generally speaking, they cover all of the following statistical methods:
- Factor Analysis: This method is used to establish what are the strongest underlying dimensions of a bigger set of intercorrelated variables. For example, this factor analysis can shed light on what combination of aspects, characteristics or priorities are most important to a certain type of customer group. What’s more, this analysis can be narrowed down to a handful of variables vs. dozens, which is more practical and actionable.
- Cluster Analysis: This method is used when the goal is to group a set of data objects together into homogenous groups (i.e. a cluster). For example, a business may conduct market research to identify its various customer segments, and then conduct cluster analysis to see if any such segments share similar characteristics (e.g. objectives, pain points, perceptions, demographics, preferences, etc.) that are distinctly different from other segments.
- Conjoint Analysis: This method is used when the purpose is to distinguish how market research respondents perceive and evaluate different variables that are part of a product or service. For example, conjoint analysis can help a business understand to what extent customers make a buying decision based on price vs. quality, or service vs. brand recognition, and so on. In some cases conjoint analysis reveals insights that are not even known to respondents themselves, which can be extremely valuable (i.e. customers may believe that quality is their most important decision-making criterion, when in fact price or brand recognition may in fact be more influential based on how they behave).
- Multiple Regression: This method is used to predict the value of a variable, based on changes to two or more different variables. For example, multiple regression can shed light on how sales revenues may increase based on the amount of money spent on advertising.
- Discriminant Analysis: This method is used for predicting membership in a group (or population or cluster) based on measured characteristics of other variables. For example, a business can use discriminant analysis to glean whether a factor such as income level is useful for distinguishing customers who purchase their products vs. customers who purchase from competitors. If such a classification exists, then a marketing and advertising campaign can be designed to leverage this insight.
These are just some of the ways that today’s powerful tools can help beginners analyze market research data, and do what matters most: translate qualitative and quantitative data into business intelligence, actionable insights and substantial ROI.
To learn more, contact the Communications For Research team today. While our co-CEO Colson Steber learns more about your business, he’ll be able to provide you with market research recommendations and he can even build out a market research quote based on your timeline and budget.
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