Customer Research

That research seeks to describe the current status of an identified variable. The common uses for customer research are to indicate or validate a problem, market, channel or business model.

Customer Researches, as a type of descriptive researches, are designed to provide systematic information about a phenomenon. The researcher does not usually begin with a hypothesis, but is likely to develop one after collecting data. The analysis and synthesis of the data provide the test of the hypothesis. Systematic collection of information requires careful selection of the units studied and careful measurement of each variable.


• Specification Error. Population specification errors occur when the researcher does not understand who they should survey. This can be tricky because there are multiple people who might consume the product, but only one who purchases it, or they may miss a segment looking to purchase in the future. Understand who purchases your product and why they buy it. It’s important to survey the one making the buying decision so you know how to better reach them.

• Sampling Frame Error. The sample frame errors occur when the wrong subpopulation is used to select a sample, or because of variation in the number or representativeness of the sample that responds, but the resulting sample is not representative of the population concern.

• Statistical Significance Error. It usually occurs when too small (non statistically significant) sample is taken. When a finding is significant, it simply means you can feel confident that’s it real, not that you just got lucky (or unlucky) in choosing the sample. To evade the issue use the sample size calculator I coded below.

Sample Size Calculator
(customer research):

* Population - the total number of people whose opinion or behavior your sample will represent.

** Confidence - confidence level (1-α) is the percentage probability that your sample accurately reflects the attitudes of your population. The industry standard is 95%.

*** Margin - margin of error is the percentage rage (+/- relative, not absolute) that your population’s responses may deviate from your sample’s. The industry standard is 5%.

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