As the holiday season approaches, we have reached the end of our Trino Summit 2022 recap posts. With the last talk of the summit, Mei Long from Upsolver gave an insightful overview of how they use data to inform product decisions.
When talking about product-led growth (PLG), it helps to start by defining what it even means. The core idea is simple: see how users engage with your product, and make decisions based on how you can improve the product to better serve those users. At Upsolver, the goal of PLG is to maximize user value. The issue is that while this can be simple in some situations, when you’re delivering complicated analytics tools, it’s not always immediately clear what features would be the most valuable or useful. You need a lot of data to glean a lot of insight, and you need to make sure your insights that can lead to action. And of course, you need to be absolutely certain that your data is high-quality, accurate, and trustworthy, lest you end up accidentally giving a customer a ten million dollar discount.
Mei explores the initial pass at using analytics to drive PLG at Upsolver, letting her intern use a tool called Amplitude that worked for a time and for limited use cases. As Upsolver grew, the analytics requirements did, too, and Amplitude wasn’t powerful enough for Upsolver’s use case, nor for the more complicated queries and analysis that needed to be run.
Want to guess what query engine they swapped to using? Trino. Mei dives into a quick demo that shows how Upsolver ingests all of its streaming data and stores it for Trino to query, driving down time-to-insight to make it quick and efficient to ask questions and make decisions based on those answers. With Trino at the ready, Upsolver has never been better-equipped to work towards PLG.
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