What is the best BI tool for calculating KPIs like churn, LTV, and MRR for SaaS?

Looker, Domo, Sisense, Tableau, and Power BI are tools are good for enterprise. If you are looking for SMB (Small Medium Business), where you have 5-10 users who use BI for portfolio, then you should look at small Startup (ConverSight.ai, ToughtSpot etc.) for a per-user license, which is comparatively low in the market.

This is a common question for many businesses that have gone down the SaaS route. SaaS Applications provide a critical application in the cloud based on a subscription model, which is great for companies looking to add software or shift capital expenses to operational expenses. However, the problem is that most of these applications are getting your data out of the SaaS and into a reporting data warehouse or another third party application.

The most common means of accessing your data is normally via an ODBC/OLE connection that is purchased at an additional monthly cost from the SaaS. This is normally a read-only connection but does provide a limited degree of access to your data. Don’t expect the SaaS Company to provide you with ER diagrams or table structures, nor should you assume the data model or schemas will follow any of the best practices or common design methods. They won’t. You must remember that SaaS-based applications try to fit everyone’s needs into a standardized set of tables. So expect odd columns and table joins throughout the application.

Once you figure out where your data is, you need to decide if you're just going to query the data from the SaaS database when the reports are run or if you're going to extract your data into a data warehouse nightly and then run your reports. This also means you now have to have a database server either on-premise or in the cloud.

I prefer the latter of the two options however it does introduce a gap in the data since the extracted data won’t be real-time. The benefit is that you can query the data as often as you want without impacting the live application. The most common tools normally used for this are MS SSIS and SAP Data Services. Both applications perform similar functions however there are many other ETL tools on the market.

Once you have the data extracted, your reporting software can run it reports. Gartner provides in-depth studies on the various applications however it really comes down to the following tools:
Microsoft Power BI
SAP Business Intelligence
Qlikview/Qliksense

These are the top reporting applications. Each has their pros and cons when compared however each can do basically the same thing as the others.

Here’s what makes them stand out from each other:

Microsoft Power Bi - Easy for end-users to create Adhoc reports from almost any dataset out there.
SAP Business Intelligence- Global standard for reporting, established tools and community
Qlikview/Qliksense- Top-rated dashboard and visualization tool.

Oracle EPM is more robust and provides comprehensive coverage for calculating KPIs. The products follow a global standardized pattern which has ease of adoption and implementation.

Power BI, Tableau, and Qlik have the capability to do calculations for KPIs, are easy to create visualizations, and also have the capability to get the source from any data sources. Qlik and Tableau have more advantage than Power BI. Qlik and Power BI provide the desktop for personal use. Tableau only provides trials. For the Enterprise version, it has a feature to share and collaborate with the team. Now, only Tableau can deploy the tools on Linux environments (Qlik and Power BI only for windows).

TIBCO Jaspersoft

I think Qlik is strong at data processing and has a high performance within a large scale of data with its unique in-memory database.

I have been doing data science by Python, now looking for a tools that can easily scale up the projects at low cost. KNIME seems to be a good one so I had tried it for a few months, but find difficult to copy my concepts in Python to KNIME.

Take 07_Customer_prediction_with_H2O as an example, I can code it in Python and well understand the concepts and codes, but when it comes to "nodes" in KNIME, I can't easily understand the relationship between nodes. I copied the sample process and modify a bit according to my data and understand the propose and concept, but I still don't understand why "Parameter Optimization Loop Start --> H2O Cross Validation Loop Start --> ......" in the Random Forest Metanode for instance, now I just copy to make it work but don't understand what reasons to put the nodes in that way.

Having said that I like the concept to make data science in nodes, as I am quite experience in playing around with Node-Red.

Domo or Pentaho and the differences between the startup version and enterprise version are the tools that you can use to pull out or get in the data and it is important to have the flexibility to get the information in different ways.

Question A: Looker
Question B: Google Data Studio
Question C: It depends on the company

Check out Tableau for this BI work.

Power BI is free per user (but you pay for Premium, which enables some important features), and it gets better every month with new updates. I have used Tableau and Qlik, both of which are very good. But I think Power BI is the better bargain, and if it's not as fully capable as Tableau right now, it will be within a year.

I have an excellent suggestion: Microsoft Power BI. With all DAX/MDX functions, it's possible to calculate KPIs, LTV, MRR and a lot of others needs with an amazing cost x benefit ratio for all groups (from small start-ups to large enterprises). It's possible to utilize the Desktop version to create analysis dashboards for free and also create a free account using an enterprise/business email account to publish and share your analysis. After, you can choose between PRO or Premium Edition (with the option to deploy on-premise).

PowerBI

Power BI for good calculations .