Marketing Research for Brands Series
Corporate market research often reaches the stage where quantitative and qualitative data must be unified to draw actionable insights and conclusions.
This step of the market research process is often defined as the Data Analysis stage, and it is the modern-day equivalent of gold mining your product’s target market.
The insights and knowledge derived from a brand’s concise and organized analysis can improve the understanding of a brand’s products or services.
This in turn gives brands and marketing managers the power to identify more closely with their customer’s needs and improve on their marketing strategies and tactics.
By bringing the data together, at scale, in a way that small and precise changes can be illustrated in a communicable means, managers can take action and share their insights with all levels of the company.
Data analysis can be crucial in determining the extent to which real and observable differences exist in your tested population.
These observations are significant for marketing managers as they reflect real differences that exist not only in your data, but inherently reflect your target customer.
Conjoint Analysis is one of the most common tests used within data analysis to determine preferential products and services among surveyed populations.
The method is used to detect observable reactions in regards to different marketing offers. For example, a brand may be trying to decide between separate e-marketing campaign templates.
These insights and customer preferences can be used to make alternative marketing decisions at the highest level, in real-time. It is also a powerful means to build improved customer profiles for businesses and brands.
Aggregating big data provides marketing managers with a complete customer view. Another incremental tool in driving product growth. Obtaining key information about your product, your customer, and your competitive advantage can help highlight gaps and pose quick solutions to outstanding incompetencies.
Typical procedures in the data analysis stage are T-Tests or Anova Testing. These are strategies involving both comparing independent samples and paired means from your surveys while drawing insights from your data.
A T-Test is commonly used for testing the difference between two means, while Anova Testing involves the testing of three different means.
When researching qualitative methods, one can still comply through data analysis as long as the results are coded as if through quantitative results.
Arguably important, and just as dynamic a step in the market research process, data analysis can often be over complicated and muddled.
With the proper execution, it can define actionable insights over a large set of data.
Oftentimes used as the final, or advanced analysis stage of the research, it is rarely heralded as a pivotal point in the process.
With enough practice and an organized research process, this stage can yield crucial and pioneering answers to the brand’s biggest questions.
For an interesting explanation of the difference between running market surveys and experiments, click here.