DATA ANALYTICS

Data Analytics for SaaS Companies: 7  Important Metrics

Julian Alvarado

Sr. Content Marketing Manager, Sigma

SaaS companies, driven by rapidly developing technology and constantly evolving markets, must be agile to survive. Moving quickly is essential, but without accurate, up-to-date SaaS data analytics that deliver data-driven insights, software companies are operating blind.

SaaS business leaders must identify the best path to finding and maintaining product-market fit. Business intelligence for SaaS can help with this task. This post looks at the seven most important metrics for SaaS companies to track, why being data-driven is crucial but challenging, and how a modern BI tool can make your metrics actionable.

7 Essential SaaS Metrics to Track

While there are over 100 different key performance indicators (KPIs) you could track, focusing on these seven for your SaaS data analytics will provide the essential information to identify opportunities and spot problems that are holding you back.

1. Monthly Recurring Revenue

Because SaaS companies depend on recurring revenue rather than a large, initial purchase, they must look not only at overall revenue but also at secured monthly revenue. Monthly Recurring Revenue (MRR) measures how much revenue your customers are generating each month. This metric ensures cash flow health. The formula for calculating MRR is easy: for a given month, sum the recurring revenue generated by that month’s customers.

2. Annual Growth Rate

Annual Recurring Revenue (ARR) is calculated simply as 12 x MRR. Annual Growth Rate (AGR) takes ARR and looks at it from a big-picture perspective over time. This metric allows you to track growth from a window in time and compare it with the current window. For example, you might take ARR from January 2020 and compare it against ARR from January 2021. The growth rate between these numbers is your Annual Growth Rate.

3. Churn Rate

For SaaS companies, maintaining existing customers is just as important as acquiring new ones. Churn rate measures how much business was lost within a defined period. Churn can help you identify weaknesses in your customer retention strategy when you look at churn by persona, industry, and other identifiers.

4. Customer Lifetime Value

Customer lifetime value (CLV) is the average dollar amount that a customer pays during their entire lifecycle with your company. CLV tells you what your average customer is worth. You can gain additional insight by looking at CLV by vertical, company size, etc. to see which types of customers are most valuable. Find your CLV with the following formula: (Monthly revenue generated by customer x Number of years active) – Cost of acquisition and maintenance for customer.

5. Customer Acquisition Cost

Customer acquisition cost (CAC) tells you how much it costs to acquire a new customer. To calculate CAC, divide your total sales and marketing spend (including associated staff salaries) by the total number of new customers added in a given month or year. For example, if you spend $100,000 on sales and marketing in January and add 200 new customers in January, your CAC would be $500. CAC rates can help you see if growth is sustainable and if your acquisition strategy needs to be adjusted.

6. Months to Recover CAC

This metric tells you how long a customer must stay active before you break even and start seeing ROI. To calculate this number, use the following formula: CAC / (gross revenue – cost of sales).

7. Customer Health Score

A customer health score predicts the likelihood of retaining the customer. This metric helps you identify accounts that are at risk so you can take action to prevent a loss. Companies calculate this metric differently. To create your own customer health score, identify a list of inputs that predict a customer’s happiness. This list might include engagement milestones, community involvement, and willingness to refer, for example. Next, assign a value to each item on your list based on its correlation with customer happiness. Each customer’s health score will be the total of the scores for each item. For example, if the customer gets 4/5 for engagement, 2/3 for community involvement, and 3/4 for willingness to refer, the score would be 9 out of 12 possible points. By looking at the scores common to customers who churn, you can more easily determine which customers need extra attention before it’s too late.

Why Being Truly Data-Driven is Challenging

According to a recent survey, data-driven companies are 162% more likely to surpass their revenue goals compared with companies that aren’t. They also see significant spikes in customer trust and have an easier time with compliance.

But actually being data-driven is a challenge for many organizations, including SaaS companies. A NewVantage survey found that just 24% of respondents said their organization was data-driven in 2020. There are three primary reasons for this discrepancy.

162%

more likely to surpass their revenue goals compared with companies that aren’t

Disjointed Data Sources

A Market Pulse survey revealed that companies are drawing from an average of over 400 different data sources to feed their BI and analytics. To overcome this challenge, you need an ELT or ETL data pipeline to connect to your cloud data platform or warehouse. ETL (Extract, Transform, Load) is the process of quickly moving data from its source into a data platform or warehouse, which serves as a central source of information and gives your team the ability to conduct analyses more easily. ELT is a more modern process that allows you to transform data before loading it into the warehouse. Examples of these SaaS tools include Fivetran and Matillion.

Outdated Data

Real-time data is only helpful if you can also analyze it in real time. A cloud data platform or warehouse serves as a centralized repository of data from all your sources — databases, SaaS tools, mobile devices, etc. A CDW also keeps data “fresh” by ensuring it’s real-time. By using a BI tool that connects directly to the cloud data platform or warehouse rather than relying on data extracts, you have confidence that the data you’re working with is relevant and up-to-date. Users relying on data extracts also create security risks.

Hard-to-Use Analytics Tools that Choke on Data

If business teams are going to make decisions based on data, they must be able to find answers quickly and easily — without having to go to the data team every time they have a question. This requires a modern SaaS BI tool that doesn’t necessitate SQL skills or knowledge of code. A SaaS data analytics tool like Sigma that allows users to explore data in a spreadsheet-like interface will encourage adoption while ensuring security compliance. Additionally, your tool must remain simple to use even when users are dealing with datasets that are hundreds of billions of rows: Cloud data needs lightning fast analytics.

SaaS Companies Need an Ad Hoc Solution

A primary reason that more SaaS companies aren’t making data-driven decisions is that business intelligence isn’t truly self-service. Business teams must be able to do ad hoc analysis for themselves as questions arise since they must act quickly to capture opportunities and mitigate problems. Modern SaaS BI tools are designed to deliver insights when decision-makers need them.

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