Revenue Analytics Q&A with Mixpanel Product Manager Neha Nathan

Revenue analytics is comprehensive data analysis that helps inform decisions related to revenue growth, profitability, pricing, investments, and market strategies. Businesses and executives need to know where their sales are coming from and going, and this new functionality makes it easier to measure, monitor, and analyze a multitude of revenue metrics. It’s all made possible through our Warehouse Connectors.

This is critical for customers because revenue metrics are the most critical business metrics. Most initiatives ultimately have the goal to increase revenue or reduce cost, (e.g., launching a feature) so everyone wants to be able to measure this impact to know when and where to start, end, or modify an initiative.

Many companies may already have a BI tool like Looker. If that’s the case, we highly recommend using Mixpanel in combination with it. In this scenario, you could use Looker for reporting on key metrics in which all teams can leverage like finance, customer success, sales, etc. Then, you’d use Mixpanel to drive decisions and move faster by:

I’d also say if you don’t already have Looker or another BI tool, and are in the early phases of setting up robust revenue reporting, you could leverage Mixpanel exclusively and answer all of the above.

Separately, other product analytics tools have revenue metrics but they’re limited in scope. For example, subscription businesses must be able to ingest subscription-type data like recurring revenue and build the necessary metrics, yet many tools simply lack the ability to do either one. 

Meanwhile, transactional businesses need to track revenue through the funnel and lifetime value (ARPU x Avg lifetime). Here again, those other tools fall short because they can’t multiply by average time due to their lack of multi-metric insights. The end result being customers can’t trust their numbers, and that’s a big problem.

If there’s one universal truth about revenue data most people would agree on, it’s that it needs to be 100% correct for users to trust it. So, it’s imperative that Mixpanel get the data directly from the source of truth. 

As standard practice, we generally advise our customers to send revenue from their source of truth, which in most cases is their warehouse. Mirror mode allows customers to do that because the data you use in Mixpanel stays in perfect sync and is as reliable as your warehouse data. Without a data warehouse, transactional business customers can still access revenue analytics. But the data is only likely to be 90-95% accurate because there’s no easy way to correct for manual financial reconciliations, refunds, etc. Unfortunately, subscription businesses without a data warehouse cannot use revenue analytics in Mixpanel.

Any industry can benefit. Where we see the most usage are with transactional companies, like ecommerce and gaming, and subscription-based businesses in the media, entertainment, and B2B space. Retail and gaming commonly use the following metrics: revenue growth rate, average revenue per user (ARPU), lifetime value (LTV), return on ad spend (ROAS), and customer acquisition cost (CAC). Subscription businesses have ARR growth rate, ARR churn rate, ARR (by new, upsell, and downgrade), average deal size (ADS), net revenue retention (NRR), and expansion rate.

One example that comes to mind is Zalora in the ecommerce space. They essentially use Mixpanel Revenue Analytics to reduce customer churn, a revenue detriment metric they prioritize. They categorize users as “likely to churn” or “deal hunters” and store these definitions in their data warehouse. 

Until recently, they weren’t able to enrich that segmentation within Mixpanel very easily because they ended up manually exporting monthly snapshots from their warehouse and reuploading to Mixpanel. But as you can imagine, that placed a heavy burden on their data engineering team. Using Mixpanel's Profile History feature, part of Warehouse Connectors, they can now access accurate segmentation data from the warehouse without any manual workarounds. Fast forward to present day and they’re effectively combating churn by analyzing the behavior of churn-risk users over the previous year.

Another example is a B2B company we've been working with that was rethinking feature gating. They had a lot of features and wanted to understand which were common among startup customers versus enterprise. Prior to them having access to Revenue Metrics, getting to a V1 initially took them many months as they joined two sets of data and spent too much time going back and forth with the data team. Now they can do this in a matter of days by looking at percentage revenue adoption metrics inside Mixpanel.

We have Ecommerce and B2B temples, but keep in mind these are just a starting point. Customers can add or change anything in the template to suit their needs.

As a general rule, we believe everyone in the company should have access to revenue metrics, so product and marketing teams can all be revenue focused and measure their impact. That said, we understand for some companies or situations that might not be possible or the most useful. For customers looking for extra safeguards on their revenue data, we offer Data Views where you can filter specific data to only specific users with access, including revenue numbers.