Grew individuals accessing data by 10x over a year, shifting the company away from a legacy culture focused on monthly reporting data into a curious, question-asking culture.

Gained instant access to how patients, clinicians, and operations teams are behaving in a very complex kidney care clinical service.

Saved 2-4 hours per week by generating weekly auto reports for the advertising, operations, and data teams.

Helped product managers grow key products metrics by 14% on average.

Grew quarterly active users in Amplitude by 700% over the last 2 years.

With a deeper understanding of user behavior, improved report quality that is a key input for business decisions (conversion, time to convert, A/B Experiment).

Validated that feature creation aligned with user needs and enabled non-analysts to analyze user behavior.

Shortened time-to-insight from several weeks to a few days and found nuances that led to large marketing budget optimizations.

Grew data team members by 50% in 2021.

Saved ~40 hours of team work weekly in analytics.

Increased user time-on-platform by 60% from analyzing and optimizing individual lessons.

Improved retention by 51% by using Amplitude to track and experiment main activation and engagement areas.

Doubled down on insights built via heavy experimentation and achieved 300% growth in north star metric year-over-year.

Made fast data-informed decisions to not only grow existent games but also decide whether to close or keep investing in a new title.

More than doubled growth over the last 12 months after implementing Amplitude into the data stack.

Improved cross-functional visibility into product performance. 75% of the consumer-facing team using Amplitude to track app metrics, troubleshoot, and improve the product experience.

Grew active users by more than 100% year-over-year by improving product and retention strategies based on data.

Increased activation into core features by more than 5x — from sub 10% to over 50%.

Increased revenue driven by key features 3.7% through experiments.

Reduced the manpower by 3-4 hours daily in reporting.

Improved the customer experience through rapid A/B testing analysis, monitored all conversion and engagement metrics, and democratized data access to all business functions.

Monitored performance of A/B tests in real-time and conduct more flexible analyses without Data Science support.

Grew marketplace vendor monthly active users (MAUs) by 300% year-over-year and doubled GMV quarter-over-quarter.