Data-driven Development: Correlate Feature Releases With MRR Using Controlled Rollouts

Trevor Stuart on August 23, 2016

For any company to be successful the entire team needs to work together and push towards a set of unified of goals, which often evolve into a series of vital business KPIs. For SaaS businesses, these goals may align with metrics like the number of logos, number of seats, account utilization, and total MRR. Marching towards these goals can be challenging when trying to understand how team and individual contributions impact movement in these key metrics.

ChartMogul recently published a compelling piece, highlighting how each team across a company can draw the connection between individual contributions and one such metric - MRR. By understanding this connection, each employee can understand their tangible impact on your company’s goals.

However, the ability to understand the impact of contributions is easier for some teams than others. Traditional tools make it simple for sales and customer success teams to segment key metrics like seats, MRR and churn by sales rep or account manager, but how do engineers and product teams measure the direct impact of their work on the bottom line?

As the team at ChartMogul points out, new feature releases and the overall performance of the application can have a significant impact on MRR:

New business can be tied back to new product features…[and]...churn could be tied to performance issues within the product, and thus the Product team’s ability to launch features that run smoothly and release timely.

If launches and feature releases can be correlated to customer satisfaction or transaction success, product and engineering teams can understand how purely ‘product’ decisions are impacting the business, for better or worse. 

We’re moving to a world where ideas are iterated quickly, decisions are measured, and teams are motivated by their impact on the business. Imagine if you could easily segment customers exposed to new features and tangibly measure the impact on:

What if a similar tool allowed engineering and product teams to understand their impact on seats, MRR and churn? 

At Split, we’re helping customers answer these questions through a data-driven approach to software development: controlled rollouts. By slowly rolling out features to a segment of users grouped by attribute or percentage, teams can deploy new features while monitoring success - whether that’s machine performance or conversions - through integrations with the tools they already use, like Segment for analytics, Datadog for monitoring, or Slack for communication. And when a feature doesn’t perform well, they can roll it back with the click of a button, saving the engineering time it would traditionally take to redeploy and the business impact typically associated with remediation.

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