It’s Tuesday afternoon and you’ve just been handed a stack of customer feedback forms with a wealth of information and data to analyse before Friday. You look at the forms; there are numbers, scales, and paragraphs upon paragraphs of commentary.

You pause for a moment and breathe. Where do you begin? What can you even do with all that information?

But which one is better Qualitative Analysis or Quantitative Analysis?  Which will provide the best results? This post reviews where to use each approach for the best outcomes.

What is Qualitative Analysis

Qualitative analysis is data analysis where you are relying on a subjective review of the data, i.e. where you have data that cannot be objectively assessed and you must use your human judgement.

These are examples of qualitative analysis:

• Theme analysis of the free text information in a customer feedback survey
• Analysis of the management ability in a company
• Art, film and book reviews

What is Quantitative Analysis

Quantitative analysis is data analysis based on some objectively verifiable fact about the data. The most familiar versions are statistics and charts. These two approaches deal in the objective data collected.

These are examples of quantitative analysis:

• Customer feedback survey statistical analysis
• Stock market price change analysis
• Animal population changes based on human observation

Where To Use Quantitative Vs Qualitative Analysis

Qualitative analysis is used to explore and understand a topics and ideas, whereas quantitative analysis is used to confirm conclusions about topics and ideas.

Generally you will start you process with qualitative analysis and questions. From there you create quantitative survey questions and analyse them to confirm your understanding.

Let’s say you want to discover reasons why customers disliked your service, how to improve a product or why one area of your business is better than another, that’s when Qualitative Analysis becomes the more suitable selection for you.

It provides an exploration into individuals’ feelings; uncovering the motivations, desires, behaviour and needs. However, the accuracy/validity of the findings may vary due to the subjective nature in which the information and data is analysed and interpreted.

Below is an example of a question which provides Qualitative data:

Once you have an understating of the subject area you can create quantitative questions analyse them for confirmation.

Quantitative analysis enables you to interpret the data statistically and make mathematical judgements about it.

This is what you need if you are:

• Trying to identify patterns over a period of time;
• Deciding how effective a certain variable is; or
• Determining the satisfaction levels of a particular service, etc.

This approach is useful because it can provide you with objective information to understand what has occurred or what will occur.

Below is an example of a customer survey question which generates quantitative data:

You might think this would be qualitative data because it asks about a subjective topic but the data it generates consists of numbers, which are analysed quantitatively.

Validity and Reliability

Quantitative analysis is all about hard objective data i.e. non-anecdotal information. As Quantitative Analysis is a ‘numbers game’, the validity and reliability of the data gathered from the customer feedback or survey is much more concrete.

This is not to say all Quantitative analysis is reliable and valid; the validity and reliability depends on the instruments used, what it’s measuring and the skill of the analyst in statistical techniques.

Qualitative data is great to explore your respondents’ perception and to uncover hidden reasons why your customers are happy or unhappy with your products. Whatever the reason may be, you need to remind yourself of what you’re trying to achieve with the data that you have.

How Much Data Do You Need?

The notion of the ‘bigger the better’ applies to Quantitative analysis as, generally, the larger the sample size the better the insights you are able to obtain from the data.

Let’s say for example, you would like to rate your organisation’s professionalism on a Likert Scale of 1-7. You will be able to gain a more reliable perspective of your organisation’s professionalism with a larger population.

A larger sample size enables you to be more confident on how the overall population feels.

Putting It All Together

In the customer feedback process you want to have both Qualitative and Quantitative Analysis because they complement each other.

For instance in your survey you will want to have a range of questions for quantitative analysis to tell you what you are doing well and what you are doing poorly:

You should also have some questions for qualitative analysis to allow you to understand how to improve:

Qualitative Vs Quantitative Analysis FAQ

Originally Posted 12 November 2012, Last Updated 28 June 2021