The finance department relies on Stripe's churn statistics, while the product team utilizes Mixpanel for retention data; meanwhile, the CEO looks at a custom-built dashboard that has data discrepancies which make it impossible for them to get accurate info. Many times the main reason for all this confusion is because each organisation considers all analytics jobs to be identical in nature.
However, the SaaS analytics stack's differences are based on its actual functions—that is, cross-source reporting, product behavioural data, and revenue (subscriptions)—and allow for one to create a more accurate system to obtain reliable information.
What to Know About SaaS BI Tools
The situation surrounding SaaS analytics stacks is that most organisations begin looking for SaaS BI tools based on which tools have a 'magic bullet' that will do everything they need.
Simply put, that tool doesn't exist.
According to industry research, 73% of organisations have identified the distribution of data over multiple systems as the biggest obstacle to reporting, while 64% will use one to three days to prepare an answer to a business question.
It's impossible to solve this problem with a more attractive dashboard; the solution lies in matching the tool to the job.
For example, if you need to calculate the customer acquisition cost (CAC), pipeline, and revenue (in one place) you should be looking for cross-source BI; if you are trying to determine why a customer dropped off during step 3 of their onboarding experience you require product analytics; and, if you are simply needing order-level, clean MRR and churn stats you should buy subscription analytics.
Purchasing the wrong category of product will not only mean that you are wasting time and money, but also that you will waste the development team's time. To further clarify this point let us take a look at some of the more established players in the industry who currently offer SaaS BI tools.
Cross-Source Reporting and General SaaS BI Tools
Companies that are trying to create a single story across all company areas, like finance and marketing, will be using a combination of SaaS BI tools, Salesforce, HubSpot, Stripe and a central data warehouse to do so.
1. Microsoft Power BI
Microsoft's Power BI product has a significant integration within the Microsoft ecosystem. The tool has a powerful semantic modelling functionality and superior data governance.
Power BI works seamlessly when your organisation already utilises Microsoft's software. Microsoft offers serious enterprise scale with the product. For example, large enterprises may grow from 50 users to over 4,500 in one year when using this product or solutions from Microsoft.
The pricing for Microsoft Power BI Pro is currently $14 per user per month and for Power BI Premium Per User, $24 per user per month, subject to the terms of the contract. The pricing is based on your team’s ability to maintain the data model in the background.
2. Tableau
Tableau continues to be a leading product for visualising and creating executive storytelling with interactive dashboards.
Tableau is designed for mid-market and enterprise data teams and can manage and analyse large amounts of data. For instance, in one of Tableau's published case studies, an embedded Tableau environment was able to manage an embedded analytic system with data from 1.3 million sensors.
The embedded Tableau system produced 47 million pieces of data per day, saving the finance team two weeks on reporting tasks each month. However, to run Tableau smoothly you will require dedicated analysts, strict data governance and maintenance of the application on an ongoing basis.
3. Looker
Looker has become widely known for its semantic modelling capabilities.
Looker is a reporting tool that sits on top of your organisation's cloud data warehouse, such as Google BigQuery. This makes engineering-led SaaS companies able to establish a single unified governance model for their entire organization. As a result, everyone sees the same data as measured by the same definition.
Looker cannot help you if your data warehouse is not well structured since it only works when your data team does a good job of writing and supporting the modeling code.
4. Qlik Sense
Associative data exploration is Qlik Sense's main focus.
Qlik Sense enables enterprise teams to visually map out and navigate complex relationships between disparate datasets in a manner that does not provide fixed answers to a question, but rather allows users to search for, click on and explore their own connections across multiple data points to identify new insights.
This level of complexity is far too much for a startup looking only to monitor MRR, but it fits in well with enterprise-level implementations.
5. Domo
Domo is designed for speed and to assist business users in the development of reports.
Domo allows you to quickly create reports through hundreds of data connectors that have been pre-configured for quick implementation with your marketing, sales, and finance systems. With Domo, business users can create their own reporting views without having to rely on engineering for help.
However, you should keep a watchful eye on the cost of using Domo as the total will increase as you add users and data connections.
6. ThoughtSpot
ThoughtSpot is heavily focused on search-driven analytics with natural language processing.
Users of ThoughtSpot simply type in questions into a search bar, and charts of data will appear to answer their questions. With the integration of AI-powered business intelligence (BI) where anomalies and trends will be automatically flagged, approximately 86% of users of ThoughtSpot now validate AI analytics against original source data; this is prudent since AI makes inaccurate mathematical calculations.
You should confirm their natural language capabilities on your biggest business inquiries before purchasing.
7. Sisense
Sisense is a leading provider of embedded analytics.
SaaS vendors who wish to embed a compelling, AI-driven dashboard directly into their product offering for customers to view should use Sisense. Sisense takes care of the complex task of securely displaying external data.
You need to closely examine Sisense's tenant isolation as well as its white-labeling and API limitations. Creating customer-facing analytics has substantial security and performance requirements.
8. Hex
Hex uses a very different approach by focusing on collaborative data notebooks.
Hex integrates SQL, Python, and polished data products in one workspace. It is specifically designed for SaaS organisations with heavy data science workloads. Analysts can create complex code and publish the output as an interactive application for the business team.
Do not buy Hex if you are looking for a simple executive view of key performance indicators (KPI). Hex is an in-depth analysis tool.
9. Metabase
Metabase is the #1 choice of startups and technical teams who want inexpensive, quick access to their database.
Metabase provides a robust query maker for non-technical personnel, while still allowing engineers to write raw SQL. Both cloud-hosted and open-source versions of Metabase are available.
The tradeoff of using Metabase is you will take ownership of your own data model, user permission settings, and internal governance.
10. Amazon QuickSight
If you build all your software using Amazon Web Services (AWS), then Amazon QuickSight is the ideal tool to use.
Amazon QuickSight has standard dashboards and provides users the ability to embed analytics. The greatest benefit of Amazon QuickSight is its option of pay-per-session pricing. If you have a large number of users (hundreds) that only login to view their dashboard once a month, it would not cost you anything to have those sessions tracked.
You should compare the total number of sessions to the amount you could save by switching from named-user licenses.
Product Analytics and Event Tracking
When it comes to product analytics and event tracking, these tools do not care about your finance ledger (what you are spending).
They only look at what a user actually interacts with on your application (e.g. click), how much time that user spent in the app, and what point in time that user stopped using (or left) your app.
1. Amplitude
Amplitude is one of the most in-depth product analytical tools available today.
With Amplitude you can create funnels based on user cohorts, and can also utilize feature retention metrics. Amplitude was created primarily with the idea of creating a large product from a product led SaaS business (fast follower). Amplitude has a free tier that is very generous, allowing you to track up to 2 million events each month free.
Amplitude provides a wealth of information for your product development teams about user behaviour; however, Amplitude does not replace your current financial reporting capabilities.
2. Mixpanel
Mixpanel is a direct competitor of Amplitude, both are software products for tracking events.
Mixpanel focuses on the promptness of event tracking, session replay, and providing information through data pipelines. New businesses particularly benefit from using Mixpanel because they get their answers quickly. Mixpanel provides 1 million events and up to 10,000 session replays for free. Mixpanel also charges $0.00028 per extra event after exceeding 1 million, and enterprise plans can begin at $20,000 per year.
As with any analytics software, be cautious with tracking volume. If there is poor tracking configuration, you may have excess amounts of events flooding into your system and increasing your bill dramatically.
3. PostHog
PostHog refers to itself as a platform for engineers.
The application provides an integrated approach for analytics not only by providing analytics alone, but also providing auto-capture capabilities (user clicks on buttons), enabling the tracking of users within the context of each event, feature flags, and user surveys, as well as a data warehouse.
Reportedly, over 90% of its customers have used the free version of the application, which has 1 million events for free followed by a pay per use (0.00005 USD per event) pricing structure. The app is focused towards technical founders; finance and sales teams may have a difficult time understanding how it works.
4. Heap
The game was changed for Heap when they introduced the concept of the auto-capture.
Rather than requiring every developer to write an event code for each click, Heap captures everything automatically. The product teams can record their events retroactively. If you want to review the number of people who clicked a specific link last month, the information is available to you.
On the negative side, auto-capture is going to create a large amount of data (junk). Therefore, it will be necessary to have strict internal protocols in place to maintain user privacy and keep the data free of clutter.
Subscription and Revenue Analytics
As much as possible don't try to build a MRR or churn dashboard in generic SaaS BI tools. The subscription analytics tools built specifically for this type of analytics calculate these types of metrics out of the box.
1. ChartMogul
ChartMogul integrates with billing engines such as Stripe, Chargebee, or Apple (the latter due to their App Store billing).
ChartMogul automatically calculates SaaS metrics (i.e., MRR, churn, LTV, and movement revenue) perfectly. It takes care of the "absolute nightmare" which is upgrades, downgrades, and refunds, and you won't have to write one line of SQL code.
ChartMogul is the best source of truth regarding revenue. Just remember, ChartMogul does not provide product click tracking or marketing expenditure tracking.
How to Choose the Right SaaS BI Tools for Your Data
At some point, every SaaS company hits the wall wherein they can no longer trust the accuracy of their own metrics.
The reason for this is that if you just buy another license, this will not resolve your data culture problem at your organization. Only 9% of organizations have a single data layer where they can accurately query it.
If you simply purchase another dashboard tool and connect it to your messy database, you will get incorrect results much more quickly. You need to first define the issue you are trying to resolve.
If you are a product team that doesn't know what to do, then start implementing event tracking. If your MRR calculations are incorrect, connect to a billing analytics tool. If you are an enterprise with 15 different departments and are looking to consolidate into one source of truth, then invest heavily in both a central data warehouse and a strong semantic layer.
Select the SaaS BI tools that fit your specific needs today. Define all your metric definitions, and trust the system.
