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SAAS Metrics – Easy When You Know How

Digital Marketing
2 Min Read

Originally published October 31, 2017 , updated on October 17, 2023

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Published Date: The date when the blog went live on GL website. Updated Date: The latest date when the GL Content team updated this blog.
Raj Anand, Goodman Lantern
Highs and lows in business are normal aspects of everyday operations. Do you waste time by investigating every fluctuation in your metrics? If you focus, you can have far better benchmarking.

So Much Data, So Few Insights?

Natural fluctuations in your key SaaS metrics will happen. It’s vital to know what “normal” looks like for your business. Most startups struggle with this.
Knowing what figures to expect from your SAAS Metrics on a normal day can be a challenge. Analyse how you arrived at the numbers you’re benchmarking against.
Be honest – have you done the math or are you visually judging your figures?
Guesswork is no way to run a business. Especially when there’s a super-simple way to benchmark your results.

Easy Benchmarks

Take 20 minutes out of your day to do some easy calculations. It’s good to know your averages, but that’s not enough. The “normal” range of figures for your company will fall on your average line, as well as above and below it. Next, you can begin by analyzing the numbers that fall outside of your “normal” range for SAAS Metrics.

Doing the (Easy) Math

You’ll need some calculations. You need to understand your Median, and your Standard Deviation.
– Median: This is the number in the middle of your data set. To find it, arrange your data from the largest to the smallest number. If you have an even number of entries, find the average between the two middle numbers.
– Standard Deviation: This measures the range of your data. It helps to figure out if your data of SAAS Metrics is pretty consistent, or generally differs a lot. If your data is widespread, you need to have a wider “normal” zone.
A note on Averages: These don’t work for making business decisions. Outlying data can reduce accuracy, resulting in misleading results.

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