As mobile app technology evolves, it seems logical that our mobile analytics capabilities should evolve proportionally. Yet for the most part, any evolution in the mobile analytics realm is happening at a much more glacial pace. Now that’s not to discount improvements in areas such as data visualisation, product integrations, and real-time capabilities, which have helped product managers gather and dissect their data better than ever before. These advancements are valuable, but do not supersede the underlying disproportion between mobile app technology and our capability to analyse mobile app usage that exists today.
Interestingly, this disparity is due to the data itself – you’ve all heard the quote “the devil is in the data”. But what if I was to tell you that the quantitative data you have been gathering is actually functioning more like a prologue to an important story than the story itself – in this case, your users’ story. This quantitative data gives you a powerful introduction into what users are doing in your mobile app, but it doesn’t allow you to explore their specific experiences. Mobile product managers need data that provides them with the ability to actually see and understand specific user behaviour instead of having to define it by aggregate, numerical data.
However, a few mobile analytics companies, including Appsee, have recognised this need and brought a new type of analytics to market – qualitative analytics.
And as you probably guessed, once you combine qualitative analytics with your quantitative data, you are able to obtain that epic, complete story on your mobile users. But how exactly?
The shortcomings of quantitative analytics
In order to understand the potency of this union, we first need to understand why relying solely on analytics that provides quantitative data (traditional analytics) simply does not cut it.
Let’s just review the definition of quantitative for a moment. Merriam Webster notes the definition as follows:
1: of, relating to, or expressible in terms of quantity
2: of, relating to, or involving the measurement of quantity or amount
3: based on quantity; specifically of classical verse: based on temporal quantity or duration of sounds
Numbers, numbers, numbers – that is the core of the definition. So when it comes to quantitative analytics, basically all of the data and information it collects can be measured with numbers.
This is no bad thing, in fact it’s extremely important. Quantitative data can help you gather insights on overall user actions and usage trends, such as the length of the average user session or how many users completed a certain conversion funnel. But these numbers don’t answer the pivotal question of “why?”. Quantitative analytics can only answer your number based inquiries. Numbers have an extremely important story to tell, but how do you figure out and communicate that story?
Enter qualitative analytics.
What is qualitative analytics and why is it needed?
While quantitative analytics focuses on aspects of your app that can be measured by numbers, qualitative analytics zones in on the one essential element of your mobile app that cannot be delineated by numbers. That element is the user experience; your user’s unique story within your app.
At the moment, how do you know whether your users are frustrated with a certain unresponsive button or confused by a particular feature? To put it simply, no number on a dashboard can effectively describe those specific in-app experiences. In order to fully understand and assess your users’ stories, you need data that enables you to see what your users are experiencing and how they behave. This is the essence of qualitative analytics.
With features such as user session recordings and touch heatmaps, qualitative analytics allows you to actually step into the shoes of your real users (not beta testers) and examine how they truly interact with your app. This is the best way to analyse a KPI as subjective and nuanced as user experience.
Yet the value of qualitative analytics is not limited to inspecting user experience. It also serves as an extremely powerful compliment to your quantitative data.
How quantitative and qualitative make the perfect couple
Quantitative analytics allows you to identify on a numerical basis important trends, issues, and actions within your mobile app. Then, qualitative analytics (such as unique user session recordings) augments this data by supplying the crucial “whys” behind those numbers.
Let’s look at some compelling use cases of this power couple in action.
Your quantitative analytics tells you that your daily app crash rate has increased by 50%. This is very important, but now you need to understand why this is happening. To obtain valuable visual context behind your crashes, you turn to your qualitative analytics and watch session recordings of crashed sessions from that specific day. This allows you to accurately reproduce a crash and discern the sequence of user actions that led to a crash.
You have an ecommerce app with a conversion funnel in place for purchase completion. Your quantitative data tells you that over a seven-day period, 74.4% of your users that visited the “My Cart” screen, dropped out of the funnel and did not trigger the event “Purchase Complete”. These stats alert you to the fact that your users might be encountering a potential issue or multiple issues within the “My Cart” screen. What are the issues exactly? By drilling down to specific session recordings of users that dropped out of the funnel, you can see exactly what might have caused friction within their experience.
In a nutshell, this combination of quantitative data and qualitative information allows you to streamline the process of turning data into information, and information into insights – actionable insights. No more scenarios of drowning in copious amounts of quantitative data and guesswork.
To top it off, by using qualitative analytics to distill quantitative data, you can save valuable time and resources – which product managers often are low on. At the end of the day, this quantitative and qualitative union should empower you to separate the “wheat from the chaff” within your data and make key decisions regarding your product with more confidence. We can’t wait to hear what insights you obtain.