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Hypothesis Driven Approach to Survey Analytics
Written by Nidhi
Market research is, but seldom treated as, something beyond reporting figures and displaying good looking charts. The intent of market research should be to provide in-depth insights and answer key questions around the business problem at hand. However, more often than not, post the survey execution, researchers and analysts end up fishing in the ‘numbers’ ocean, and with great difficulty, find their way to final insights and recommendations. What they leave the table with, often, are piece-meal insights that may or may not add up to strategic recommendations.
However, consulting as a profession requires quick and effective market research, most of which is conducted with specific end objectives in mind.
At Diamond, we extend the hypothesis driven approach (HDA) to conducting market research and survey analytics. HDA is the answer to most of the woes and worries faced by a market researcher
Let us use a simplistic scenario to explain HDA. Suppose, we want to conduct a study to understand the buying behavior of people towards personal computers and one of the hypothesis we want to test is that ‘price is an important attribute in purchasing a PC’.
This approach begins with hypothesis definition. In the example considered earlier, we want to test whether price is an important driver of purchasing a PC. This is followed by laying out sample analysis that would help prove or disprove the hypothesis, e.g. % respondents rating price as an important driver or average rank of price as a driver. Next, we would need to gather data on parameters such as relative importance of drivers, allocation of points between various drivers to support the analysis. The process till here would culminate into survey and questionnaire design (e.g. the exact question to be asked, the likert scale to be followed.) followed by survey execution (e.g. online vs. offline).
The beauty of the process lies in the fact that once the survey execution is complete, the collected responses can be directly fed into the sample analysis generated in the second step. The process closes with hypothesis validation and delivery of insights and recommendations to the end user.