Nowadays, having access to powerful analytics is key to empowering business applications. Check out how we built AppLevel to be a strong contender in this field.
We love a good challenge. And many times, that means having the opportunity to create something from scratch!
This is the story of AppLevel, a business-data analytics tool that helps any app owner track the metrics that are most valuable to them, from user acquisition to monetization, and much more.
It allows businesses that use enterprise applications to run their companies to gain so much more out of what they already have, by leveraging the high volumes of stored data from inside existing software within organisations - and enabling powerful analytics and data processing tools on top of them.
When we started to discuss with AppLevel's founders, we quickly understood how knowledgeable and experienced they were about analytics and business tracking. This helped a lot and influenced the great results of our collaboration, as we love working with clients who know their field so well. This way, they are able to spot patterns and express their expectations clearly and be upfront about them.
There were already some high fidelity wireframes on the table, and a clear view of what’s expected of the base components of the app and also the design.
The challenge was mainly around big data, data collection, optimization, and last but not least, data aggregation. A second challenge was the report generation. With big data, comes big responsibility – a lot of processes that are rolling in the background.
Basically, what the app does, it emits events that are captured by an SDK and that stocks up this data. Then, data is sent and stored in the cloud to later be processed through some pipelines and aggregated into our operational database. After the collection, data is aggregated and prepared to be served in the UI.
When it comes to big data, we’ve created scripts that run periodically, based on formulas dictated by the app owners. They generate periodical reports along with data filtering. The data in place is huge, so the efficiency of display was a top priority to the Quickleaf team. The end users need to see smooth transitions between their press of a button and what they see in the UI. Big data makes it difficult to implement such smooth processes, but evidently, not impossible.
When we started to discuss with AppLevel's founders, we quickly understood how knowledgeable and experienced they were about analytics and business tracking. This helped a lot and influenced the great results of our collaboration, as we love working with clients who know their field so well. This way, they are able to spot patterns and express their expectations clearly and be upfront about them.
There were already some high fidelity wireframes on the table, and a clear view of what’s expected of the base components of the app and also the design.
The challenge was mainly around big data, data collection, optimization, and last but not least, data aggregation. A second challenge was the report generation. With big data, comes big responsibility – a lot of processes that are rolling in the background.
Basically, what the app does, it emits events that are captured by an SDK and that stocks up this data. Then, data is sent and stored in the cloud to later be processed through some pipelines and aggregated into our operational database. After the collection, data is aggregated and prepared to be served in the UI.
When it comes to big data, we’ve created scripts that run periodically, based on formulas dictated by the app owners. They generate periodical reports along with data filtering. The data in place is huge, so the efficiency of display was a top priority to the Quickleaf team. The end users need to see smooth transitions between their press of a button and what they see in the UI. Big data makes it difficult to implement such smooth processes, but evidently, not impossible.
When we started to discuss with AppLevel's founders, we quickly understood how knowledgeable and experienced they were about analytics and business tracking. This helped a lot and influenced the great results of our collaboration, as we love working with clients who know their field so well. This way, they are able to spot patterns and express their expectations clearly and be upfront about them.
There were already some high fidelity wireframes on the table, and a clear view of what’s expected of the base components of the app and also the design.
The challenge was mainly around big data, data collection, optimization, and last but not least, data aggregation. A second challenge was the report generation. With big data, comes big responsibility – a lot of processes that are rolling in the background.
Basically, what the app does, it emits events that are captured by an SDK and that stocks up this data. Then, data is sent and stored in the cloud to later be processed through some pipelines and aggregated into our operational database. After the collection, data is aggregated and prepared to be served in the UI.
When it comes to big data, we’ve created scripts that run periodically, based on formulas dictated by the app owners. They generate periodical reports along with data filtering. The data in place is huge, so the efficiency of display was a top priority to the Quickleaf team. The end users need to see smooth transitions between their press of a button and what they see in the UI. Big data makes it difficult to implement such smooth processes, but evidently, not impossible.
When we started to discuss with AppLevel's founders, we quickly understood how knowledgeable and experienced they were about analytics and business tracking. This helped a lot and influenced the great results of our collaboration, as we love working with clients who know their field so well. This way, they are able to spot patterns and express their expectations clearly and be upfront about them.
There were already some high fidelity wireframes on the table, and a clear view of what’s expected of the base components of the app and also the design.
The challenge was mainly around big data, data collection, optimization, and last but not least, data aggregation. A second challenge was the report generation. With big data, comes big responsibility – a lot of processes that are rolling in the background.
Basically, what the app does, it emits events that are captured by an SDK and that stocks up this data. Then, data is sent and stored in the cloud to later be processed through some pipelines and aggregated into our operational database. After the collection, data is aggregated and prepared to be served in the UI.
When it comes to big data, we’ve created scripts that run periodically, based on formulas dictated by the app owners. They generate periodical reports along with data filtering. The data in place is huge, so the efficiency of display was a top priority to the Quickleaf team. The end users need to see smooth transitions between their press of a button and what they see in the UI. Big data makes it difficult to implement such smooth processes, but evidently, not impossible.
The thing about AppLevel, that makes it stand out, it’s that you can pull out data as granular as you need. You can monitor your daily active users, the average time spent by each of them, user acquisition data, engagement, monetization, and everything that can be tracked in real time.
Another great feature of this app is the ability to generate custom reports. Based on the data that the app collects, the app owner can create the reports that interest them the most. That way, the experience is personalized and they can customize the UI in dashboards that are of most interest to them.
The thing about AppLevel, that makes it stand out, it’s that you can pull out data as granular as you need. You can monitor your daily active users, the average time spent by each of them, user acquisition data, engagement, monetization, and everything that can be tracked in real time.
Another great feature of this app is the ability to generate custom reports. Based on the data that the app collects, the app owner can create the reports that interest them the most. That way, the experience is personalized and they can customize the UI in dashboards that are of most interest to them.
The thing about AppLevel, that makes it stand out, it’s that you can pull out data as granular as you need. You can monitor your daily active users, the average time spent by each of them, user acquisition data, engagement, monetization, and everything that can be tracked in real time.
Another great feature of this app is the ability to generate custom reports. Based on the data that the app collects, the app owner can create the reports that interest them the most. That way, the experience is personalized and they can customize the UI in dashboards that are of most interest to them.
This was (and still is ongoingly) one of the most exciting and rewarding projects we built. It never fails to deliver on the ever rising challenges of a complex technical product, while allowing us to enjoy the benefits of great communication with a very professional, business oriented client.
All this makes the process of working on this project a lovely experience for us, but let's hear what our client had to say about our collaboration.
