Data is a vital component of any good business strategy. And most companies have a good understanding of how important tools like Google Analytics are to providing them with quantitative data for their digital strategy.

However, a surprising number of businesses completely neglect qualitative data.

Trying to understand what is happening without using both qualitative and quantitative data is like using only four of your senses or closing an eye.

By obtaining and properly analysing qualitative data through the use of user testing projects, companies can ensure they have the depth of data perception needed to truly understand their business environment.

Quantitative Data vs. Qualitative Data

Quantitative data is incredibly important, but without using qualitative data in tandem a lot of the nuance in quantitative data can be lost. So, let’s be clear on what we mean by both terms.

Quantitative data is information that can be quantified. By that we mean that it can be measured, counted and given a value. Things like, profit, revenue, height and distance are quantitative.

Qualitative data is typically written, descriptive and potentially unstructured and open to interpretation. For instance, the colour of someone's hair, or a teacher’s assessment of someone’s fluency in a language.

Looking only at one, without an appreciation of the other, is a surefire way to miss some of your most valuable insights. Looking at both provides a detailed understanding of your data landscape.

Why Is Qualitative Data Overlooked?

Unfortunately, qualitative data is often overlooked in favour of the known (quantitative data). There are a few myths when it comes to qualitative data that tend to lead to it being undervalued.

The Results Confirm What We Already Know

Getting insights from real users is one of the only ways of getting past the blinkers that can be present when working so closely with or within a company.

Businesses also tend not to consist of homogeneous people who all think the same way. Often there’s disagreement and uncertainty in the decision-making process. A user testing project can often provide an element of resolution to these debates.

Qualitative Data Is Not Scientific

Qualitative data done well is scientific. Testing is carefully designed considering both internal and external validity of testing.

At Adapt, our analysis is both methodical and rigorous. We gather our test results from the original testing data and our detailed analysis. This means a client can see exactly how and why we reached our conclusions.

This Nielsen Norman Group article is a great place to start when looking for the scientific process behind qualitative user testing.

Qualitative Data Is Too Expensive

Like any other project, obtaining and analysing qualitative data costs money and takes time. However, the cost of not having data depth perception can be far greater.

Failing to understand users or making changes based on misunderstood quantitative data alone can be extremely costly. Brand perceptions can be damaged along with the performance of other investments, such as paid media, being undermined.

An investment in qualitative gives you the data depth perception needed to minimise these risks as well as provide positive benefits to your business. Testing itself can often be significantly lower than these other investments.

For usability testing, only 5 testers are needed to find 85% of usability issues, which results in a project that is not prohibitively expensive to most firms.

What Are the Benefits of Qualitative Data?

On top of everything discussed above there are several common benefits to using qualitative data.

1. Improved Conversion Rate On-Site

    By properly understanding users through extensive qualitative analysis, pain points in the user journey can be identified and alleviated.

    Opportunities for improvement can also be identified, such as finding points in the journey to provide certain reassurance, or which reassurance is the most effective, and so on.

    In combination, this can have a significant impact on conversion rate, and ultimately have a significant impact on a business's bottom line.

    2. Offsite Performance

      The understanding provided by user testing and session recordings are not just important for optimising a website, these results can also provide critical information for other elements of your strategy.

      For example, are users scouring your site for a particular piece of information? If so, consider including it in your paid media strategy to provide information crucial to your potential customers early on.

      Are users responding particularly poorly to the way certain content and/or imagery is presented on-site? Don’t just correct this presentation on site! Use it to inform the design of your Facebook creative when you next refresh the ads or the design of the cover of your sales brochure.

      3. Avoiding Quantification Bias

        Quantification bias is seen when people overvalue the importance of a certain factor, all because it can be tracked, quantified, and presented in a numeric form.

        Qualitative data can help identify and highlight the importance of certain areas that can’t be captured by quantitative, numerical data.

        By highlighting important factors that may be hard to numerically measure, quantification bias can be reduced. This can ultimately improve your decision-making and business outcomes.

        How to Get the Most Out of Qualitative Data

        The best place to start is to use the quantitative data you have already collected. Use Google Analytics (GA) and other quantitative data sources to identify typical user behaviour and areas that require a deeper level of understanding.

        Recently we had a paid media client who had an unexpectedly poor-performing launch to a campaign. Through GA, we were able to identify a key point in the user journey that was causing significant drop-off and were then able to investigate that area specifically.

        Microsoft Clarity is an easy and cost-effective way of collecting some initial qualitative data, as observing session recordings can help you gain a better understanding of some of the areas you identified in GA.

        Be careful though, there are probably more session recordings than you need and without representative samples to work from, you could superimpose your own preconceptions on the project.

        Clarity should be used in conjunction with GA as this will help you gain a deeper understanding of the numbers, as well as identify areas to look at for further qualitative data research. It should not be used to find all the answers on its own.

        From the initial quantitative and qualitative data you’ve collected, you should have a good impression of typical user journeys, areas for further research, and an idea of the best way to continue your investigation.

        Are exit-intent surveys the best way to proceed? If so, what questions need to be asked? Perhaps user testing is appropriate? What should be included in the testing script and how can you ensure you don’t influence the results of the test?

        Final Thoughts

        A truly data-driven approach requires both qualitative and quantitative data. And using both together can yield some serious benefits to a business.

        Starting the process of effective collection and analysis of qualitative data can be a challenging proposition, but by following a few simple steps you will have plenty of qualitative data at your disposal.