Product Metrics
Through my ongoing data science education and experience as a Data Product Manager, I possess the skills to analyze data and draw precise insights. I am passionate about making data-driven decisions for product roadmaps and closely evaluating usage to measure the success of new features. On this page, I showcase some of the metrics and analysis that I regularly track.
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Please note that exact numbers have intentionally been removed for company privacy.
Retention
Retention is an important health indicator I monitor monthly, as it can be a signal of product issues and eventual churn. While the example graph below gives me a high level view, I would use this as a starting point and then dive deeper into particular accounts that weren't returning and work with account managers to understand reasons and take action.
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One example outcome of this analysis was the implementation of in-app onboarding to better assist users setting up some of our stickiest features.
Usage of Beta Features
To avoid disrupting users with a drastic change, we opted to test a UX update to a major core feature in beta before releasing it. The feature was launched with an opt-in button, and we used the below chart to ensure that enough users tried it before deprecating the legacy version. The target amount of users was based on 25% of all users anticipated to interact with the feature in the time range after release. Additionally, we monitored the retention of the new UX and conducted user interviews to measure success of the new UX.
