Product analytics reside in the heart of SLASCONE. There are out of the box analytics that are relevant for every software vendor. Beyond these, you can define custom analytical fields in order to collect additional data, specific to your application. SLASCONE’s product dashboard is not only a simple (aggregated) visualization of charts. You can zoom in from almost every chart to the underlying licenses or devices.
OUT OF THE BOX ANALYTICS
LICENSE OVERVIEW
License analytics give you a quick, aggregated overview of your licenses.
EDITION ANALYSIS
This chart reveals the Master Templates and Licenses distribution.
FEATURE ANALYSIS
It gets more interesting when you zoom in from this chart to the license view where you can perform an on/off multi-feature analysis:
- Show all licenses with Feature A (on) and Feature B (on)
- Show all licenses without Feature A (off) and without Feature B (off)
LIMITATION ANALYSIS
The analysis provides the mean/average value for each limitation and a column chart visualizing its distribution.
DEVICES PER LICENSES ANALYSIS
The analysis provides the mean/average amount of devices per license and a column chart visualizing the value distribution. This helps you get an immediate idea about the (average/mean) size of your installations.
DEVICE OVERVIEW
Device analytics give you insights about activated devices.
SOFTWARE RELEASE ANALYSIS
Quickly identifying old/not supported/problematic installations is a very powerful tool for product managers and support engineers. The chart gives you a summarized (major version, minor version or revision) overview, while you can zoom in to the respective devices. If one software release has a critical bug, you can easily identify the affected devices and proactively inform them.
CUSTOM ANALYTICS
Beyond the out-of-the-box device analytics (software version, operating system), SLASCONE enables you to collect custom data using analytical fields or usage features.
ANALYTICAL FIELDS
Analytical fields should be used, when the last recorded value (not the history) is more important.
Let’s assume your product supports multiple databases, and you want to know how many installations run SQL Server, Oracle or IBM. In this case you would create an analytical field Database System. This field can now be populated through analytical heartbeats.
POST /isv/{isv_id}/data_gathering/analytical_heartbeats
LOG
By default, a new analytical heartbeat is created, only if the new value is different from the existing/last value. Instead of creating a new row in the database, the existing/last one is updated (last modified date). This ensures optimal performance, especially in bigger datasets.
However, you can enable a full log of an analytical field. This will cause a new database row regardless of the value of the analytical heartbeat.
USAGE ANALYTICS
USAGE FEATURE ANALYSIS
Usage features should be used for time-based analysis e.g., how many times a feature was used last week/month/year etc. They can be populated through usage heartbeats.
Note that while (normal) heartbeats have licensing/activation implications, analytical and usage feature heartbeats do not affect the licensing/activation status at all. They just provide an easy way to collect data.
USAGE MODULE ANALYSIS
While usage features typically represent high-level functionality, usage modules typically represent technical content, such as a code class, function or module.
A usage heartbeat is always bound to a usage feature. Usage modules is an optional field/extension of usage heartbeats (a usage heartbeat might contain a usage module or not), in order to provide more analysis granularity.
There is an m:n relationship between usage features and usage modules: A feature can call multiple modules. Similarly, a module can be called by multiple features.
You can read more about usage analytics here.
POST /isv/{isv_id}/data_gathering/usage_heartbeats
GLOBAL FILTERS
The product dashboard has multiple filters controlling every chart:
- Product Edition
- Customer Type: It is paramount to create meaningful customer types and map your customers to the right customer type. Otherwise, you are not going to be able to get the best out of your analytical data. Typically, you will have many internal/test licenses that should not influence your analysis. By filtering out the customer type ‘internal’, you can remove this noise.
- Customer Tag
- License Type
- License Tag
- License Status (Active/Inactive): The default value of this filter is active, because normally only the active licenses are analysis-relevant. Nevertheless, you can choose to analyze all licenses.
Comments
0 comments
Please sign in to leave a comment.