In this post, we’ll go over online business metrics for beginners and wrap up with a mention of 2 other stats mid to advanced business care about.
The goal is to shed some light before you engage in optimizing Google Analytics.
Table of Contents
Basic Online Business Stats
Let’s say you had a product or service you’re ready to sell and you decide to market this product or service through paid ads.
Let’s see what the numbers would look like based on these sample inputs:
- Earnings: $1000
- Ad Spend: $250
- Email List Signups: 500
- Customers: 50
Cost Per Lead (CPL)
Ad Spend/Email List Signups = 250/500 = $0.50
Average Lead Value (ALV)
Earnings/Email List Signups = 1,000/500 = $2.00
Cost Per Acquisition (CPA)
Ad Spend/Customers = 250/50 = $5.00
Average Customer Value (ACV)
Earnings/Customers = 1,000/50 = $20
Use as a Predictive Tool
Woohoo, looks like this is profitable. So, assuming all other things are equal, what will it take to get to $5,000 a month in earnings?
The goal for $5,000 a month of earnings:
- Customers Needed = 5,000/ACV = 5,000/20 = 250
- Email Subscribers Needed = 5,000/ALV = 5,000/2 = 2,500
- Email Subscribers Per Day = Email Subscribers Needed/30 = 2,500/30 = 84 (83.33 repeating, can’t have a third of a subscriber)
- Daily Ad Budget = Email Subscribers Per Day x CPL = 84 x 0.50 = $42
Other Business Metrics
You will most likely not have to worry about these business metrics in the beginning, but we wanted to briefly go over them, so you know what they are when you see them out in the wild.
Churn Rate
Churn rate is a metric used to calculate the number of existing customers that stop doing business with you in a subscription-based business model. They could be labeled as customers or subscribers.
Churn Rate = Customers at Beginning of Month – Customers at End of Month) / Customers at Beginning of the Month
Example = (100 – 90)/100 = 10%
You can calculate churn over a monthly, quarterly, or annual time frame. Based on how frequently customers pay and based on your own assumptions, this calculation can get tricky or in the least, paint an inaccurate picture.
Shopify’s Engineering Blog tackles this problem in a post titled Defining Churn Rate by offering a more accurate version. In case the math here makes your head spin, Recurly has a nice summary blog post about putting this recommended churn rate calculation into practice. It’s pretty neat seeing this in action.
Customer churn can also be tracked on account closures, non-renewals of a contract or service agreement, or a decision for a customer to use another provider.
Cohort Analysis
A cohort is a group of people who have a common characteristic during a period of time. So, a cohort analysis would look at groups of people who have taken a common action during a select period of time.
Businesses are interested in this data because this type of analysis identifies trends in user behavior.
Google Analytics can do this! Besides getting groups of traffic by device, you can also measure the impact of different marketing activities on a specific group of recipients.
Patrick Han has a good primer on what cohort analysis is and why it’s an important part of Google Analytics.
You’ll most likely use this metric before the churn rate.