How Mixpanel Repackaged Its Pricing To Fit Its Users Better

Ajit Ghuman -

Packaging your offerings by focusing on the value created for the customer can help you get the price you deserve. According to an EY article, product packaging is the ‘secret weapon’ to increase that value for priority customer segments, further enhancing their willingness to pay. It is important to identify opportunities to improve revenue performance by preparing a case for package-market fit.

So how are companies actually making the switch to new pricing models while revisiting their packaging approach? Group Product Manager and Head of Pricing at Mixpanel, Pranav Kashyap, illustrates how. The following case study is an excerpt from author Ajit Ghuman’s book, Price To Scale.

----------------

Mixpanel had an events-based pricing model initially (since 2009) until the recent change in 2019. Under the events-based model, each item that you tracked on the website was an event. This tracked very well with our costs — the more events they sent us, the more expensive it was.

The issue was that this did not work well for a lot of our customers. For one, it was really difficult for them to predict how many events they would need. Lots of people were new to the concept of tracking.

The solution involved everything from packaging to changing the core price metric to also setting the price levels.

We first figured out which were the big customer segments and what use cases they had. We found a mix of start-ups and large customers. Within them, there were:

  1. Engineering, Product and Design (EPD) teams: At start-ups, all that the EPD teams really wanted to know was whether they were achieving product-market fit and whether they had the users that they needed. Large enterprises have lots of different teams, so they wanted to control who had access to data, who was using what, whether they had Single Sign-On (SSO), and how they were doing as a company.
  2. Data teams: Data engineering teams were very heavily involved. They wanted to connect their stack and have their data flow into Mixpanel, which was clean; or have Mixpanel data flow back into their stack.
  3. Marketing teams: They wanted to run lots of A/B tests, do email marketing, push notifications, and so on.

These were the different groups, and each had different use cases. But certain things were consistent. Everyone wanted to know how they are doing as a company, and whether their features were being used. But then, each group had some specific nuance around that need, too.

Using all this, we repackaged Mixpanel.

Earlier, we used to sell an events plan and a people plan. Then, we split it out into Mixpanel Analytics, after which you could add things on for the data team in the ‘Data Pipelines Package’.

For marketers, there was a ‘Messaging Package’. Separately, we also had a ‘Groups Package’ specifically targeted at B2B companies, where we actually incurred more costs in storing data because we had to store multiple copies of it.