Share your experience using Google Analytics 360

I am using Microsoft Clarity for its heat map functionality to understand general user habits and where our users and customers are using the application most across different pages. When there is a bug reported by our sales team from a customer complaining about an issue, I check Microsoft Clarity to see if any other users have faced the same issue.

On a weekly basis, I watch Clarity sessions. When watching these sessions, I can see that there are some issues on mobile view or other areas because these are real users. Whatever they are facing with is important because it represents the end user experience.

I use Microsoft Clarity to watch these videos on a weekly basis to understand what users are experiencing.

The heat map functionality is very valuable. For example, in our application there is a search box where users write an address, and it searches at the state or city level. If there is a bug in our input box, it has very high critical severity. These are the things I use it for.

I can also see in which state our application is used the most. We have 16 or 18 projects in our Meshvisor, and I can see which applications are used most by users. If an application is not valued by people, there is no need to dedicate much time to it.

Sometimes when I watch the videos, I can see that because of CSS issues, Microsoft Clarity is also showing some UI elements in incorrect places. I captured a bug when I watched the video in Clarity, but in real testing I saw everything was working as expected. What I understand is that when Microsoft Clarity loads inside the application, it may not show some CSS elements as expected.

I am a QA engineer with more than six years of experience as an automation engineer using programming languages to automate testing, and more than ten years of experience in manual testing. My review rating for Microsoft Clarity is seven out of ten.

Snowflake Analytics' data sharing feature has been instrumental for us because we were working with huge data sizes. Our workflow involved dumping data initially into an AWS S3 bucket, then sharing it with Snowflake Analytics, and then exporting it back into S3. The setup is very quick and easy with Snowflake Analytics.

For machine learning models, we require a lot of initial analytics on data, and Snowflake Analytics has had a significant impact in this area. When we preprocess data for machine learning models by splitting data into multiple parts, we were dealing with data in the billions. Snowflake Analytics helped us filter that data and check all aspects of it, such as data purity and identifying where we were missing values. In these scenarios, Snowflake Analytics has helped us gain insights about our data and prepare it for machine learning models.

My company is a partner of Snowflake Analytics through a tie-up that provides us with credits. There is a credit system in Snowflake Analytics that we use for our work.

End-to-end encryption provided by Snowflake Analytics is very important. The feature encrypts data in SSH format, and you can also decrypt that data if you have the key, which provides a very high amount of encryption security.

Snowflake Analytics has played a major role in improving our data model transformations. As I mentioned, we had very large data volumes, and it increased our productivity by allowing us to do things very quickly and receive results rapidly. Earlier, we used to do a lot of preprocessing using traditional methods and Python, but with Snowflake Analytics, we reduced the time and effort required for coding.

In my opinion, Snowflake Analytics can be improved by introducing more features, such as additional integration options. I remember using Snowflake Pro, which allows exporting direct data into the S3 bucket, but there were a couple of things that were lacking at that time, particularly data transformation during exports, and I would suggest adding some minor features.

I would like to see more integration features, specifically with Amazon Web Services, in the next release of Snowflake Analytics. Most companies are using these cloud technologies, so additional features and some transformation capabilities while exporting to different data buckets would be very helpful.

I have five years of total IT experience, and in that time, I have used Snowflake Analytics for more than one year for data processing, data manipulation, and data exporting with AWS.

The initial setup for Snowflake Analytics is very easy, but there are some difficulties concerning security permissions that you need to go through, since privacy is a concern. Apart from that, I don't see any hurdles with the initial setup.

I have not practically used Snowflake Analytics' integration with BI tools, but I know about it and understand that we can integrate Snowflake Analytics with BI tools. My review rating for this product is 8.