Case studies
How the Mailtrap team used data to multiply its revenue by 4.66x in only 3 years
Industry
Software & Technology
What we did
Data visualization, product dashboard
Outcomes
466% MRR growth
Industry
Software & Technology
What we did
Data visualization, product dashboard
Outcomes
466% MRR growth
Problem
Mailtrap is a Railsware product that was first developed for its own use but eventually made available to the broader development community. From a small internal script, it has grown into an Email Delivery Platform that covers all email-related needs in one place: testing, sending, and in-depth tracking with hundreds of thousands of users and a 6-digit MRR. But, as is often the case, with scale new challenges emerged.
As user numbers grew, so did the number of analytical tools in use and the sheer amount of data points to analyze. Financial data would flow from Braintree, Stripe, and Profitwell. App usage stats would fill BigQuery and PostgreSQL tables. Website and adoption details would reside in Google Analytics.
Another issue the team wanted to address was of a financial nature. Mailtrap has enjoyed steady user growth for many years, and the team knew the product was excellent. However, the existing freemium model allowed many to use Mailtrap extensively without ever needing to upgrade to a paid plan.
There was a lot of hidden revenue to be unlocked here, but it had to be approached smartly. Rather than relying on intuition or simply copying competitors’ pricing, they chose to take a data-driven approach.
“Some of our projects, such as conversions, would be nearly impossible to run without the data integrations Coupler.io set up for us. Many decisions would have relied more on gut feelings than data, and this would have made our growth path much more unpredictable.
Solution
Our team of data experts started working with Mailtrap in 2017. We analyzed where data is generated and set for all of it to flow into respective BigQuery tables. For that, we used several Coupler.io importers and built plenty of custom data integrations. This enabled the Mailtrap team to have data flowing automatically at the chosen frequency, with little to no effort on their side.
Next, our analysts built an extensive Data Studio dashboard and plugged in the BigQuery tables with data. This allowed calculating and displaying all the vital metrics in a single destination — revenues by plan, product funnels, conversions to paid plans split by cohorts, churn breakdowns, and much more. The dashboard has grown into hundreds of charts and has been an integral part of any decision-making at Mailtrap ever since. To top things off, we set for a daily stats digest to be sent into Mailtrap’s Slack channels to easily spot any fluctuations.
The dashboard was also the central place for analysis during the pricing redesign in 2019. We dug deep into how paid and free users use Mailtrap and which features they utilise most. We checked the usage stats for different cohorts of users and figured out where the plan limits should be.
We aimed to continue offering Mailtrap for free to casual users but push those using the tool more extensively to paid plans. We drew several scenarios and analyzed how users would be affected. We gauged how the churn rate may spike and what the outcome would be for Mailtrap’s bottom line. Finally, we decided on the optimal scenario, and the team shaped up Mailtrap’s new pricing.
When the pricing went live, approximately 20% of free users upgraded to a paid plan. The downgrade rate was negligible. The data collected over the first week after the change proved that we were heading in the right direction. The MRR from the new signups has doubled, and it has not dropped below that level ever since. The overall MRR has grown by 466% in the three years since the pricing change.
Tools & technologies
Data sources
