So what does it mean to be ‘proactive’, as opposed to ‘reactive’, and how does that apply to your app analytics? Better yet, how proactive can you actually be with your analytics?

In today’s post we’ll explore these key questions and help you solidify a proactive approach that will enable you to become more data-driven than ever before.


Reactive App Analytics

In order to understand what it means to have a reactive approach to your analytics, let’s first examine the underlying definition of ‘reactive’ (courtesy of good ol’ Google dictionary).

  1. showing a response to a stimulus.
    “pupils are reactive to light”
    1. acting in response to a situation rather than creating or controlling it.
      “a proactive rather than a reactive approach”
    2. having a tendency to react chemically.
      “nitrogen dioxide is a highly reactive gas”


Essentially, the word ‘reactive’ eludes that the subject does not have the initiative. The events trigger the agenda and the subject must constantly adjust themselves to the new events thrown at them. The act of being ‘reactive’ is typically devoid of consistency and stability, since it is determined by ever-changing stimuli.

Thus, when it comes to reactive analytics, the stimulus/situation- a certain metric or statistic- determines the response. Although reactive analytics is great at helping you understand what happened in a particular situation, it does not enable you to plan in advance or anticipate certain results.

To help drive home the point, let’s look at a common example of a reactive analytics scenario for mobile apps:

When comparing your retention stats from the past week with the week before, you notice a 22% increase in users that never returned to the app after one use. Alarmed, you pour over your data from that specific week for hours trying to figure out just why there has been an increase in app abandonment.

Now this is not to say that this is the ‘wrong’ way to get to the answer- this is actually the typical approach with most analytics systems. But being ‘typical’ in the mobile world is boring, especially when there is a more efficient way to approach your app analytics.

Enter- proactive analytics.


Proactive App Analytics

Let’s again go back to the basics and take a look at the underlying definition of ‘proactive’.

  1. (of a person, policy, or action) creating or controlling a situation by causing something to happen rather than responding to it after it has happened.
    “be proactive in identifying and preventing potential problems”
    synonyms: enterprising, take-charge, energetic, driven, bold, dynamic, motivated, go-ahead

With proactive, the subject has all the control and initiative. In particular, the sample sentence is worth noting:

“Be proactive in identifying and preventing potential problems.”

Associated synonyms also possess a fundamental positive connotation: ‘enterprising’, ‘take-charge’, ‘driven’, ‘bold’, ‘dynamic’ etc.

Basically, a proactive approach focuses on eliminating issues before they even appear.

Take a look below at qualities that are typically associated with reactive and proactive work, and how they fundamentally differ from each other.

reactive versus proactive app analytics
Image Source:


Thus, when it comes to app analytics, proactive analytics aims to obtain actionable insights before issues arise for your app and its users. It embraces the ‘science of anticipation’, by forecasting results from available data.

Let’s jump back to the retention scenario. Via proactive analytics, you would monitor your numeric data in real-time, and all associated metrics, and pinpoint potential issues before they have a major affect on your app. This would allow you to reach quicker and simpler solutions, improve your competitive advantage, and ultimately increase user satisfaction.

However, there’s no denying that distilling numeric data, especially in a proactive manner, takes time and a lot of skill. To become a master, proactive ‘data analyst’ on top of executing everything else on your plate, is a huge feat.

But get this, what if you could go beyond, numeric, aggregate data, zone in on single users, and obtain actionable insights without having to look at any numbers? This is only possible via qualitative analytics, a breed of proactive analytics that allows you to see how specific users are interacting with your app.


Qualitative App Analytics

What is qualitative app analytics? In a nutshell, it is a new type of app analytics that came about due to the need of mobile professionals to more accurately understand and optimize their app’s user experience. Qualitative analytics focuses on examining real behaviors and unique interactions, data that cannot be properly conveyed via numbers.

So just how does qualitative analytics allow you to observe specific user experiences with your app? Qualitative analytics possesses a robust visual data tool known as user recordings which empowers mobile professionals to visually examine users’ interactions with their app.

User recordings are an extremely powerful asset for upholding a proactive approach to analyzing your app. Again, let’s jump back to the retention scenario. Yet this time, instead of proactively monitoring variations in your aggregate, numeric data, you watch a sample of user recordings of first time sessions throughout the said week.

By watching user recordings throughout the week, you notice that some users experienced a bug when trying to login to your app via Facebook, which cause them to quit the app and never return. A few user recordings also show that some users tried to bypass your login screen by swiping at the bottom of the screen, had no reaction from your app, and subsequently quit their session. Aha! You have identified two major friction points in your app, that you can proactively address before you have to see a major drop in new user retention.

Appsee App Analytics User Recording
Example of login user recording via Appsee.

Since the user recordings allowed you to see exactly how your users are experiencing your app, you don’t need to spend hours of your valuable time trying to dissect your numeric data and understand the reasons behind certain numbers. And with that substantial amount of time saved, you can use that time to proactively conduct more optimization initiatives such as A/B or usability tests.

Proactive analytics, specifically qualitative app analytics, can enable you to become a truly data-driven mobile app professional. Via proactive analytics, you can harness the power of qualitative data to project trends, troubleshoot issues, and keep pace with your users’ preferences- ultimately ensuring the utmost positive user experience possible.

And with that, we will leave you with this final mantra:

“Act. Don’t React!”