Bucket is launching publicly with fresh Seed investment
What a ride the last 1.5 years have been! Thanks to all early adopters for having shown interest in us and for helping create a better way to ship features that matter.
In this economic environment, where resources are more scarce than ever, companies need to make sure that product teams are productive and release features that delight and retain customers.
To do so, as validated on hundreds of discovery calls, feature evaluation needs a consistent framework and it needs a repeatable workflow.
Our solution is the open-source STARS framework and our new revolutionary feature evaluation workflow. STARS is a framework to measure feature engagement and satisfaction consistently in SaaS products – and it’s baked into the core of Bucket.
Read on for a quick product tour or explore our new homepage.
Track adoption and retention of features with a click of a button. Compare engagement across customer segments and audit customer impact over time. Consistently measure feature engagement and satisfaction with the STARS framework.
Go beyond the engagement metrics with qualitative feedback and satisfaction scoring to answer the “why” behind the engagement metrics. When releasing a new feature, gather in-depth feedback from customers as they interact with it the first time.
Analyze feature engagement for each feature per account.
Do you want to see new adopters of a feature? There’s a list for that.
Do you want to talk to recent churners? There’s a list for that.
Zoom out to ensure feature relevancy over time and compare the health of key features. Track trends across multiple dimensions, like adoption, frequency, retention or satisfaction. Easily determine which features should get cut.
Bucket is the missing step between feature deployment and customer satisfaction. Planned features automatically enter into an evaluation period upon release. During the evaluation, Bucket reports feature engagement and customer feedback in real-time via Slack.
At the end, you make a decision: Is the feature done for now or is a new iteration needed?