Penny to penny, on how to improve the accuracy of GA4 data collection
- Bluerank & mFinanse

Strategy and implementation

Step 1 - Analysis of customer needs

The meeting with the mFinanse team proved to be very valuable. During the discussions with the marketing experts, we discussed key aspects of their activities, such as business goals and KPIs on each element of the potential customer path.

To further understand what data we should focus on, we conducted an analysis of the Universal Analytics implementation. In doing so, we wanted to identify and confirm key metrics, indicators and targets.

credit score calculator

Step 2 - Website analysis

Analysis of the website revealed the use of a security mechanism called 'Content Security Policy' (CSP). CSP allows web application developers to define the sources from which additional resources used by the application, such as external JavaScript files, can come. The main purpose of such security measures is to prevent XSS (Cross-Site Scripting) attacks, which attempt to insert malicious codes into web forms or application URLs.

Due to the number of calculators, the lack of built-in data layers (Data Layer) and the presence of the CSP mechanism, custom solutions were required.

We created a script for this purpose, which we then implemented using Google Tag Manager (GTM). It takes data from the calculators, generates data layers and then allows this data to be used to feed into Google Analytics 4 (GA4).

credit score calculator data layer

Step 3 - Implementation and deployment plan

With the tool developed and an implementation plan built, we set about implementing the relevant events on the site, including conversion events. The plan included events such as form interactions, uploads from individual calculators, user clicks, but also more micro KPIs such as time spent on the blog or page scroll depth.

Step 4 - Data verification

Verification of the implementation into GA4 is an important part of the analytical process. We checked the validity of the data collected and its quality. In this way, we were able to ensure that the analyses would be based on reliable data. The check included reviewing reports, configurations and comparing results with other data sources such as the client's CRM.

Step 5 - Documentation  

At the end of the work, we prepared detailed documentation that covers the entire implementation process, the implemented events, the rules triggering them and the parameters. This documentation is a valuable source of information for all members of the mFinanse team, facilitating both the understanding of the functioning of the implementation and possible modifications. In addition, it fosters consistency of operations for the future.

Step 6 - Workshop

During the workshop, participants had the opportunity to familiarise themselves with the key functionalities of the GA4 tool. We prepared dedicated reports that allowed a better understanding of the specifics of the business and the needs of the company, such as which calculator users use most often or a report on detecting anomalous user sessions.

 Google Analytics 4 – exploration report, calculators

Google Analytics 4 – exploration report, anomaly detection

The presentation also covered the use of documentation and data collected in GA4 to create personalised information summaries.

In addition, the information collected, such as time spent on the blog, scroll depth and user 'path sequence exploration' type reports, allowed the identification of areas that may need to be refined or modified to further develop and improve the service provided.