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Three reasons why manual event tracking fails

Oskari on February 25th, 2021

Summary

  • Collecting data on user interactions is traditionally built around manual event tracking
  • Manual event tracking has three clear shortcomings: 1) you need to define what to track in advance, 2) setting the trackers up is a laborious task, and 3) maintaining them in a working order when your app changes increases the burden of upkeep
  • Kirnu collects interaction data automatically, so you do not have to worry about setting and maintaining manual trackers. Furthermore, our algorithm’s resilience to change makes it a powerful tool. When you application changes, our algorithm is able to connect the dots on its own without your assistance, freeing you from the maintenance

The old approach

The worlds of churn prediction and its close neighbor product analytics are populated by manual event tracking. The most prominent event tracking software today is Google Analytics 4 (GA4), which enables users to track page views, user demographics and some very basic user interactions with a website. GA4 is very handy for that sort of thing, and should be an entry-level staple in every web developers toolbox. 

While event tracking in GA4 is limited, its capabilities can be extended with Google Tag Manager (GTM), which allows users to manually define events which then can be tracked across a web site or web application. The majority of the most prominent churn prediction and product analytics platforms work in a similar fashion, where events are manually defined and tracked through embedding JavaScript.

At Kirnu, we have taken the manual out of the equation and replaced it with automatic and care-free. Read below what I mean by this and why automatic & care-free beats manual!

Shortcoming #1: no place for hindsight in manual tracking

Manual tracking requires you to define the events to track in advance. Forgot to define a tracker for capturing the number of people clicking on "Contact support" in your app? Too bad, that information is lost. Kirnu's autocollect for user interactions collects all essential data for you, no questions asked. With no questions asked, there are no wrong answers and thus no possibility for error in capturing the required events.

Is there a thing as too much data? No, not really, as long as the data is adequately processed. While today's machine learning models can easily handle huge amounts of data, the data still needs to be processed. Ensuring a consistent and meaningful data processing pipeline is where the magic happens.

Shortcoming #2: manual tracking is a laborious task

Manual tracking is a resource intensive task and requires the attention and co-operation of developers and product analysts / managers. Perfecting the what to collect (see shortcoming #1) might take several iterations and by then, the results of the analysis could become obsolete. To make matters worse, setting up manual tracking is only the beginning of the ride. Manual tracking requires maintenance and if you make changes to your product (app or website) the trackers need to be updated accordingly, which gets us to my third point.

Communication between client, business analyst and developer

From idea to execution, c. 2020

Shortcoming #3: manual tracking does not adapt to changes

The word manual is engraved in manual tracking and by definition, the context for manual trackers is defined in advance. What if the context changes? The trackers will have to go as well, as they stop functioning as originally intended. Back to the beginning it is. This means that if you alter the structure / contents of your application, you will have to adjust the trackers accordingly, making manual tracking non-adaptive. Trackers that do not adapt make gaps into the data, potentially endangering prediction accuracy.

Kirnu's algorithm is built to endure change and evolves with the underlying context, so you do not have to worry about change. How? That is a story for another time.

Conclusion

It is true that the level of detail in data collected by automated event tracking is higher, possibly to the extent that any correlations are lost for a human interpreter (is my clicking the picture of a cat meme 3 times in a single session really increasing my retention?). The good news is, that for a computer, making the distinction between tomatoes and the tomatos is a task they were designed for. Having seen our machine learning algorithm at work, I am confident that our autocollection provides an answer to the shortcomings of manual event tracking.

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P.S. we expect to be beta test ready during Spring 2021 and are looking for test users. Drop us a line at hello@kirnu.io if you are interested in reducing churn in your SaaS application and getting exclusive access to Kirnu.