Quantitative vs. Qualitative: Which Data Should You Be Focusing On?

Having the ability to choose sets us free. Freedom of choice is one of the mantras we repeat in virtually every important aspect of our lives. But what would you say if we told you that being able to choose can sometimes overwhelm us? And we're not just talking about choosing a type of milk at the supermarket.

It's paradoxical, but it's real. The paradox of choice is a theory that argues the following: having multiple options makes it harder to find the one that best suits us and pushes us to feel dissatisfied with the decision we make.

Either way, choosing isn't easy. With or without the paradox, making the right decision is a real pressure in our daily lives, especially when what's at stake are your business results and your website's conversion. You're probably familiar with the eternal question: "What should I focus on to get results?"

In this article we're going to clear up a couple of things about the great dilemma of web analytics experts (and no, it's not "To be or not to be"). The eternal indecision about which data to prioritise: qualitative or quantitative. Because it's not easy to decide which analysis to start with and what decisions to take to improve the results of your web experience.

But don't worry — at Boost we're here to make things a little easier for you. In this article we'll tell you when and why each type of data is more useful and how to combine them to master your web analytics.

Data, Data and More Data: How to Differentiate Qualitative from Quantitative

Yes, data can be overwhelming. Nowadays we have so many sources of information and types of analysis that it's very difficult to choose between them. And in many cases it's even hard to know how to tell them apart. But that's what we're here for: to help you have a clear understanding of the difference between qualitative and quantitative data.

And what better way to understand their differences and characteristics than by comparing them directly:

| | Quantitative Data | Qualitative Data | |---|---|---| | Definition | Quantitative data is numerical and completely objective data or metrics that help you measure your website's performance and results. | Qualitative data is more descriptive and subjective. It helps you understand in depth the whys and the perceptions of your users. | | Data characteristics | Governed by objective criteria and represented through numerical and statistical logic. | Governed by subjective criteria and reflect an interpretation of facts based on observation. | | Most common formats | Represented through metrics, percentages, rates or charts. | Represented in a more abstract and broad way, such as texts, opinions or counted behaviours. | | Some examples | You know these well: your website traffic, your bounce rate or your advertising campaign results (among others). | You'll recognise these too: heat maps of your website, friction points in your UX and your customers' NPS rating (among others). | | Useful tools | Any tool that provides numerical and objective data is useful. The best-known: Google Analytics, SEMrush or the analytics panel of your social networks. | You have many options, but you've surely heard of Hotjar, SurveyMonkey or the well-known focus groups. |

Understanding the differences between qualitative and quantitative data is the first step to choosing the option that suits you best. Knowing exactly what information you'll get from each one can help you rule out the less interesting data source.

What Data You Need Most at Each Moment

Let's start by clarifying something: all data is useful if you know how to use it. Both numerical data and in-depth interviews can help you identify a problem on your website and, possibly, its solution. You just need to know how to play your cards right.

Shall we revisit the differences of each type of data in a table? That way we can clearly review when, how and why they can help you improve your results:

| | Quantitative Data | Qualitative Data | |---|---|---| | What question they answer | This type of data helps you understand from the outside with an objective view: "What's happening on my website?" | This data helps you delve into details and reasons by answering: "Why is that happening on my website?" | | Main objective | Quantitative data aims to identify or reflect trends or problems at scale. | Qualitative data digs into the causes and possible improvements for problems. | | Vision and scope | Thanks to this data you can build an overall picture of the situation through a macro view. | This data lets you investigate and break down reality with a micro view. | | Main advantages | Quantitative data is quick to obtain and analyse, allowing you to react immediately. It's also easy to compare over time (if metrics remain consistent) and lets you automate analysis. | Qualitative data lets you understand your UX from a more human perspective, making it "real". Feedback and comments are indispensable for improving your results and understanding your weak points. | | Common limitations | This data never explains the "why" — it only tells you what's happening. If analysed independently it can hide part of the picture as it lacks context. | This data is subjective and cannot be analysed as easily (or as quickly). It's hard to apply in different contexts or accept as valid without ongoing analysis. |

So When Should I Use Each?

Good question. The advantages of each type of data are clear. But now the key is understanding how and when they can help you in your day-to-day. Here are some tips to get it right:

| | Quantitative Data | Qualitative Data | |---|---|---| | When to use them | The best time to rely on them is when you want to understand the state of your website and its results. They can also help you identify problems in your conversion funnel and the areas where something is failing and where to focus. | When you want to understand what's causing a problem on your website. Is it the design? The information? The fact that the purchase process doesn't work well? All these situations call for qualitative data. | | How to apply them to your conversion and sales | This data helps you better understand the effectiveness of changes to your website or campaigns, as well as comparing results across different audiences, times of year or A/B test variants. | This data helps you better understand what your web experience is not addressing. You can understand everything from your customers' needs and preferences to what works and what doesn't at a deeper level. | | Value for your digital strategy | This data is essential for making quick, concrete data-based decisions. | This data is necessary for continuously optimising your web experience, adapting it to your users. |

The Ideal Approach: Start With Quantitative and Move on to Qualitative

As with everything in life: everything in its own time. At Boost we believe it's not necessary to choose just one of these information sources; rather, their power lies in the combination of both. Why settle for one when you can have both?

But that doesn't mean you need to turn to both at all times. You need to know when to look at each one and at what point to let one or the other guide you. And for that, we have a tip: a data analysis logic that never fails.

• Start with quantitative data — The first thing is to understand what's happening and in which part of the process there are real problems. Quantitative data can help you understand where to focus your efforts and which areas to dig into further.

• Delve into the reasons behind that data with qualitative data — Once you've identified the problems and elements that need your attention, it's time to understand why that's happening on your website. Take your time (and effort) to explore the reasons and motives so that it becomes something to focus on.

• Implement changes and return to quantitative data — Take advantage of the simplicity and objectivity of quantitative data to compare before and after making the necessary changes. This way you'll be able to see whether it's really working or whether you're still not hitting the mark.

• Never set aside qualitative data — To make sure you never fall behind and lose your users to a poor experience, you always need to stop and listen to them. With qualitative data you can carry out checkpoints and understand how their experience and priorities are evolving.

At Boost, We Show You How to Get the Most From Your Data Combination

Data, whatever the type, is worth its weight in gold. That's something we know for certain at Boost. All kinds of information are useful for improving your conversion and your company's results, but the challenge is knowing how to interpret it correctly and draw the right conclusions.

It all starts here. With a contact form so we can start understanding your weak points together through quantitative data and delve into the reasons through qualitative ones. Sounds good, doesn't it?