5 SaaS Metrics That You May Be Measuring Incorrectly
5 SaaS Metrics That You May Be Measuring Incorrectly
What metrics can you gather about how your service is seen by the market; how can you make sure that you are calculating these metrics correctly and gaining the best insight from those metrics?
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Would you put a lot of money into an investment without a guaranteed return? What if I told you that you would only get your investment back through a slow trickle over an extended period? Doesn’t that sweeten the deal? No I am sure it does not. You would maybe think twice about investing, and rightly so. This is the common dilemma with SaaS business models. This is why so many companies use SaaS metrics to help move forward.
For this reason, metrics are extremely important in determining the viability of the product and the company future. This is due to the fundamental difference of SaaS companies taking all the expenses of a product up front and waiting for the long term loyalty and value of their customers to regain their investment. Once a solid set of customers is established however, profit growth grows exponentially.
But what if those metrics are being measure incorrectly? In the following article I will discuss some of the absolutely essential metrics your SaaS needs to be tracking. Any common misconceptions regarding calculations will also be covered.
Monthly Recurring Revenue (MRR)
This is one of the most important SaaS Metrics a business needs. Not to be confused with simple monthly revenue, what the MRR does is take all the varying subscription plans you have and allows you to see a normalized average of your projected income per month. This metric is very important to track income levels and also serves as monitor of financial growth and health.
Signing customers to longer term contracts up front is a powerful strategy for securing recurring revenue funds. This helps to keep your projected MRR levels up and gives stability to the business.
Ah, Churn. This one of the very well-known and important SaaS metrics for determining growth and product viability, but there are actually two common ways to calculate churn. These are simple Customer Churn and Revenue Churn.
Customer churn simply tracks the number of clients lost per tracking period, whereas revenue churn will show you the revenue lost due to churn. This will reflect much more accurately the magnitude of customers lost.
There could be the case of a single large client that provides the same revenue as three smaller clients. Customer churn would show a small increase in percentage churn, whereas Revenue churn has three times the magnitude.
Another simple way to manipulate the reported churn percentage is by choosing which users to exclude/include. Excluding fresh customers of less than 90 days, or simple vanity data users (those who have logged in once) will increase reported percentage churn as the churned customers now represent a bigger piece of the pie.
No particular churn calculation is right or wrong, rather you can yield more insight from different sets of data.
Customer Acquisition Cost (CAC) and Life Time Value (LTV)
CAC is the cost spent on acquiring new customers and is calculated by dividing the amount spent on marketing and promotion by the total number of customers.
The Life Time Value of a customer is determined by multiplying the average MRR, by the total number of months in an average customer’s lifetime.
These are the base definitions. The problem is that they are contingent on years of steady and consistently accumulated data. How does a relatively young startup, for instance, know the average lifetime of a customer? Changes in the product itself or a number of other factors can affect both the LTV and the CAC.
For this startup scenario, there is a different approach to these calculations.
Expected CAC and LTV SaaS Metrics
You start with your current cost per opportunity. To find this, Simply divide the total sales cost by total opportunities (chances at conversions). Then multiply your opportunities by your win rate to get your total expected customers. Next divide your expected marketing costs by expected customers and you have an expected CAC.
Expected LTV can be calculated with a similar predictive procedure, but it is a bit complex to go into. More importantly, I wanted to discuss the benefits of this expected data. The principle benefit is that this is forward-looking data instead of the backward-looking nature of the standard calculation. This predictive data is very important for new businesses starving for forecasting.
As with the different methods of churn data collection, there are simply different options to collect your SaaS Metrics data sets. Data and statistics can be used to tell great truths about your progress and they can also be used to dig deeper and better understand where your company is going.
Published at DZone with permission of Omri Erel , DZone MVB. See the original article here.
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