Building Revenue Metrics

Overview

Mixpanel’s Revenue Analytics gives product teams comprehensive visibility into how their decisions directly impact business outcomes, connecting product usage with financial performance. It also allows revenue teams to work under one umbrella by breaking down silos and streamlining revenue operations across the customer lifecycle.

Revenue Analytics eliminates the traditional barrier between product and financial data by seamlessly integrating with existing data warehouses. Whether your business relies on transaction-based purchases or subscription models, this powerful feature provides a unified view of how product changes translate to revenue results, without complex data engineering or lengthy wait times.

Key Benefits

• Eliminate data silos between product analytics and revenue data
• Make data-driven decisions without requiring data team involvement
• Track revenue changes over time, including refunds, charge-backs, and subscription changes
• Analyze both one-time purchases and recurring revenue streams

How It Works

Revenue Analytics leverages Mixpanel’s Warehouse Connectors, utilizing two primary data synchronization mechanisms: Mirror and Profile History.

Mirror maintains parity between your data warehouse and Mixpanel by regularly polling for changes in the data source and updating the values in Mixpanel. This ensures integrity for purchase-based revenue models by accounting for post-purchase modifications such as refunds, charge-backs, and order cancellations.

Profile History utilizes a time-series data structure to track state-based metrics like MRR/ARR at specific points in time. This provides an accurate historical analysis of subscription revenue changes and ties them to user behavior. The system maintains a record of changes to user profiles, which allows for retroactive cohort definitions.

You can look at how to build these metrics in Mixpanel by leveraging our SaaS KPIs template on either your data or our public demo data set. There is also a public dashboard to give you a view of the metrics you can build.

Common Use Cases

Mixpanel’s Revenue Analytics enables you to analyze your data in powerful ways, regardless of your team, the stage of your company, or your business revenue stream.

Transaction-based Business Models

Revenue Analytics can help answer the following example questions for transaction-based business models.

Acquisition:

• What are D7, D30, and D60 Average Revenue Per User (ARPU)? How has this changed month over month?
• Which acquisition channels bring customers with the highest Life Time Value(LTV)?

Conversion optimization:

• What is the Average Order Value (AOV) of users who went through the 5-step purchase funnel versus those who did the 3-step funnel?
• Where is revenue dropping in the 5-step purchase funnel?

Comparing spend across segments:

• What is the revenue split across the various categories? How has this changed year-over-year?
• How does the revenue generated by first-time buyers compare to that of returning customers during major sales events such as Black Friday?

ROI Analysis on Ad Spend:

• Which are our most effective marketing channels, such as those with the highest Total Revenue to Customer Acquisition Cost(CAC) ratio?
• What is the change in the AOV after the launch of a 10% off promotional discount?

Building the Metrics

Total Revenue: Calculated as the Sum of Revenue made across all purchases.

Average Order Value (AOV): Calculated as Total Revenue over the Total Number of Purchases. See this example in our demo project.

Life Time Value (LTV): Calculated as the cumulative spend from users since they landed on the platform. Often you look at spending in the first 30 or 60 days by the expected lifetime (2 years, 3 years, etc).

This report shows the cumulative revenue of new users (identified by the Sign Up event) by cohort over their lifetime (5 days, 30 days, 60 days). It leverages the Property Sum feature of the Retention report to look at retained users’ cumulative spending.

Average Revenue Per User (ARPU): Calculated as the Sum of Revenue over time divided by the number of Unique Users. See this example in our demo project.

Customer Acquisition Cost (CAC): Calculated as the Total Acquisition Costs over the Number of New Customers Acquired. In other words, how much money do we have to spend to acquire a user? A good acquisition strategy would lead to a LTV/CAC value above 1. See this example in our demo project.

The report above leverages Mixpanel’s Ad Spend data model.

Subscription-based Business Models

Revenue Analytics can help answer the following example questions for subscription-based business models.

General revenue

What is our current Annual Recurring Revenue(ARR)? How has this changed over time? What’s our current Net Revenue Retention (NRR)?

Understanding upgrades/churns

What are the top accounts that have upgraded or churned? What is the average engagement (e.g., Viewed Reports/ARR) for accounts that have upgraded versus churned? Should engagement be a leading indicator?

Feature usage by plan or revenue bucket

What is the feature usage and adoption, broken down by plan type? What is the ‘time to activation’ for accounts in various revenue buckets?

Impact of product initiatives

We improved our growth plan last year. How many accounts have come in through our basic plan in the last 12 months? They could be on other plans today. We launched a new offering to upsell customers. Is there a correlation between adoption of the feature and upsell revenue?

Building the Metrics

Revenue Analytics makes use of two new computed properties to create subscription revenue metrics. The model is built by comparing two data points:

1. Recurring Revenue Change: looks at the ‘numerical difference’ between the 2 values
2. Recurring Revenue Change Type: tells you the ‘type of change’ listed below:

• 20 → 5 = Downgrade (positive to less positive)
• 5 → 0 = Churn (positive to zero)
• 0 → 5 = New (zero to positive)
• 5 → 20 = Upgrade (positive to more positive)
• 5 → 5 = Flat (no change)

When creating a Revenue Metric (e.g. incremental MRR in the last month), there are two ways to control the calculation: Time and Change Type.

1. Time lets you analyze based on the past number of months, which allows you to determine “What should today’s value be compared versus 1 month ago?”, for example.

2. Change Type enables you to look at the incremental revenue for different types of accounts, whether New, Upsell, Downgrade, or Churn.

You can also look at the latest value as of a specific time period, or for any value during the time period. For example, you might want to answer: show me all customers who were on a free plan in the last 12 months, even if they are on a priced plan, now.

Net Revenue Retention (NRR): Calculated as the percentage change in revenue base.

• NRR > 100% → Existing customers are being retained and the user base is growing
• NRR < 100% → Existing customers are a leaky bucket and we are not able to upsell them
• Formula:
  • Current MRR from Existing Biz / Prior MRR
  • (Prior MRR from Existing Biz + (Upsell MRR - Downgrade MRR) ) / Prior MRR

MRR Churn: Calculated as the drop in Revenue for existing users (it does not consider upsells). Typically, the target ARR Churn is below 15%. What does this translate to on a monthly basis?

• Formula
  • 1 - ( (Current MRR from Existing Biz - Upsell MRR) / Prior MRR )

MRR Growth Rate: Calculated as the overall growth in MRR (New + Existing Biz)

• Current MRR / Prior MRR

MRR by Revenue Type: Calculated based on the following:

• New Revenue: you want this to go up since this is net - new money (land motion)
• Upsell Revenue: you want this to go up, too, since this is also net - new money (expand motion)
• Downgrade / Lost Revenue: you want this to be minimal
• Flat Renewal: you want this pie to keep growing as you land more logos.

Ultimately, the Goal is to have (New + Upsell) - Downgrade to be positive, which means recurring revenue is growing.

FAQ