Being both structured and statistical, quantitative research provides you with the ability to draw conclusions and make an educated decision about a course of action.
Most quantitative research is used to prove or disprove a predetermined hypothesis that you may have come up with while conducting qualitative research.
Quantitative questions take up the major bulk of most surveys, but they are often used inefficiently. When creating a quantitative question, make sure it will allow you to achieve one of these three goals:
Knowing these characteristics helps you understand who your respondents are, how they act and what they like or expect.
For example, let’s suppose you ask your customers to rate their satisfaction with your customer service on a scale from “very satisfied” to “very dissatisfied”, and 20 percent say they are “very satisfied”. Although it’s good to know where you currently stand, this number can also be used as a yardstick to measure your progress in the future.
Let’s suppose that after the original survey you make changes to better meet customer needs. You can now conduct the same survey again and see whether the percentage of customers who are “very satisfied” has risen or fallen. This allows you to effectively measure the progress you are making with customer satisfaction over time as well as to directly measure the effects of new initiatives and processes implemented between surveys.
Let’s go back to the example above. By adding demographic questions about your respondents’ age, gender and income, you will be able to compare questions such as: Are young men more satisfied with your service than older women?
By comparing different groups, you’ll be able to work out who to target, how to message them and when your product needs to change to fit a particular market.
Beyond the realm of surveys, you can also use quantitative research in various ways. Check out a couple of examples to show how:
Big Data can tell you a lot about what people do, but remember that it seldom tells you why they act in a certain way. You’ll need more direct qualitative and quantitative research for that!
Let suppose you plan to change the packaging for something you sell and you want to understand its potential impact on sales. You could introduce the new packaging in just a few shops and compare its sales to the old packaging. Causal experimentation is the concept behind A/B testing.
Now you have the tools to hit the ground running, but don’t forget to incorporate some qualitative research before you do. For more information about how to use both in your research design, check out this page.
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