Quantitative vs Qualitative

I shouldn't use the expression "vs" in the title of this post. Simply because both terms are complementary each other. When we are collecting metrics as important as numbers (quantitative) are the reason behind (qualitative). So, the right title for this post should be "Quantitative & Qualitative".

How many of you have followed this customer journey when booking a hotel reservation? 

  • First, start with the selection of the place where you want to visit and look for the hotels around that area. 
  • Sort the list of hotels based on the rating of all the entries in the list and choose the ones with the highest rating. 
  • Then review the top positions on the list and see what other customers have said about each hotel. 
  • Finally, according to the evaluation and the comments, you have chosen which is the option that best suits you (without forgetting the prices, of course :-))
At the end, you have taken into account quantitative and qualitative data to choose the hotel you book. The price is another quantitative data.

Quantitative metrics are the numbers that we understand and we measure. For instance, rating of the Apps the we download from the markets. We can know that one specific App in the market has a 4.3 as rating. But we don't know why.

When we want to understand the reason, we usually move below looking for the comments that users that already downloaded and ran the App left in the store. This is the qualitative metric.

Quantitative metrics is the measurement of quantities. It allows us to know the use and performance of something (our business). They represent all the numbers, statistics and data about what is being used, how much, when and from where. In Digital Analytics this is known as Clickstream.

Quantitative metrics are structured, scientists, could be aggregated and extrapolated. We can put them in a spreadsheet. In Web Analytics we can use tools as Google Analytics or Adobe Analytics to gather all these metrics.

In the other hand, qualitative data is a non-structured information. It provides valuable inputs. This is because these metrics give us the reason why something is working or not. For instance, we can understand which problem have our users when they try to fill in a form in our website.

They are imprecise and subjective data. There are many ways to gather this information: usability analysis, user testing with real users, eye tracking (where the user looks), surveys, comments, opinions, questions, complaints, video recording, mouse tracking, scroll tracking...

Finishing this post, let's say that Digital Analytics requires a solid qualitative and quantitative analysis with a main goal: to understand the customer experience explicitly in order to influence and constantly improve the customer behavior on your business.

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