Changelog

We’ve added Behavioral Segments to Bucket. Now, you can filter companies and create segments based on feature usage. 

You can create company-based segments by STARS state, usage frequency, customer satisfaction, event counts, and first or last usage date. You can combine them with company attribute filters to become even more granular and powerful.

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This addition makes identifying power users and upsell opportunities, amongst many other use cases, simple. Since you’re working with easily understandable STARS states instead of messy event counts, you can:

• Identify power customers by filtering for companies that are frequent users of your key features.
• Find upsell opportunities by finding companies that have not yet started using valuable features.
• See which sub-features are popular in the context of companies that use their parent feature.
• Filter companies by usage frequency to see adopters that aren't using a feature often to understand feature stickiness.
• Filter companies by satisfaction ratings to, for example, find enterprise customers that have left a dissatisfied score on a key feature.
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They also let you roll out feature flags to the most relevant companies. 

For example, you’re about to roll out a feature flag called “Huddle Polls” and want your initial beta segment to contain only “Huddle” power users. These filters allow you to create a segment with companies in the Retained STARS stage who use “Huddle” daily. 

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We’ve made it easier to stay on top of your feature releases’ goal progression. Now, you can get more details of goal progression directly in Releases. The new interface gives you at-a-glance insights into feature engagement and satisfaction, including target values, feature names, and goal subsegments.

We’ve started displaying goal progression in absolute values. This makes goals, particularly percentage-based goals, clearer and easier to communicate.

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When you dive into a specific feature, you’ll see visualizations showing goal progression since release, letting you follow its evolution and understand the impact of iterations. 

If you’re setting up a new goal, we’ve improved metric dropdown to make it simple to quickly select the most frequently used goal metrics.

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We’ve made it simple to visualize overall feature satisfaction over time for companies in any STARS state. The visualization gives you a simple way to ensure you’re making iterations that improve feature satisfaction scores. 

You can now see a rolling average of satisfaction scores for each feature alongside the qualitative feedback from Live Satisfaction's no-code surveys that you know and love. 

The distribution chart surfaces dissatisfied companies, making it simple to act on their feedback and convert them into satisfied customers.

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You can select different evaluation periods and rolling average time windows that can be as granular as you need.

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We’ve added new core functionalities to Bucket: Releases and feature flags. With the release of these new features, we’ve cleaned up the sidebar.

Now, it only contains top-level items: Features, Releases, Flags, Companies, Tracking, and Settings.

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To jump between feature views and segments, use the new switcher interface found next to the title as shown in the video above.

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We’ve made it easier to find specific companies and features in features and segments. You can now search by company or feature name to find key customers and see how they’re using your features.

Search for a feature, go to the “Companies” tab, and search for the company. After selecting the company, you’ll see its usage metrics and feedback for each feature you’ve set up in Bucket. 

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You’re also able to search for specific companies in segments. Simply select a segment and use the search box to find the customer you’re looking for.

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We got this request from a lot of you since we launched customizable table columns. Now, you can (finally) save your customized feature view and segment tables so they are there waiting for you on your next login.

Customize your columns and simply click the “Save” button to create or update them for yourself and all Bucket users in your organization.

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Updates are also automatically reflected in your Slack reporting, ensuring the entire team gets the same insights.

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We’ve made it even easier to get Bucket up and running without involving the engineering team with the new Bucket Tracking SDK on Segment. Setting up browser tracking can be done in as little as three steps. 

Getting started is simple: 

1. Set up the Bucket Web (Actions) destination, which loads the Bucket Tracking SDK, in Segment to receive data from your product. 
2. Copy your tracking key from the Bucket tracking page and add it to the "Tracking Key"  field in the Segment.
3. Enable the destination. That’s it, just wait for the data to start flowing. 

You’ll have Live Satisfaction enabled for immediate customer feedback with no additional implementation and you’ll automatically be kept up-to-date with the latest version of the Bucket Tracking SDK.

Everything you track with Segment will be tracked in Bucket. You can even change mappings directly in Segment without engineering help. Here’s an example of setting up page tracking as a Bucket event.

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It’s also possible to implement configuration overrides, like translations or theming for Live Satisfaction popups, but you’ll need to have your engineering team reach out to us for support.

We’re continuing to work on making Bucket more powerful and easier to implement so you can keep building features that drive business impact. 

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Today, we’ve released our most impactful feature yet: Releases. Releases lets you monitor feature engagement and satisfaction goal progression for each release in real-time from ideation to iteration.  

Releases combines goal setting, the STARS framework, and automation to give you the feature insights you need to address issues and increase adoption and satisfaction, automatically.

Goal setting lets you establish clear objectives and performance expectations for key releases. STARS makes it simple to set goals, for example, reach 50% in Adopted and receive high customer satisfaction for a new feature. Since features are tied to segments, you can decide if the goal applies to all accounts or only to customers on a specific pricing plan.

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And, if you’re unsure what goal to set, you can go step by step with one of the templates. Start with smaller objectives and adjust as time goes on. For example, focus on reaching 25 companies in Adopted and collect 15 qualitative feedback responses with Live Satisfaction to get some initial feedback then bump up your goal once you reach it.

We’ve also integrated Releases with Slack to make your release evaluation insights chase you. Goal tracking and feature reporting data are shared automatically, becoming instantly accessible and actionable for the entire team.

When the evaluation period is finished, the consistent data lets you analyze and benchmark release performance so you can make more informed decisions on whether the release was successful or needs another iteration.

We’re hosting a webinar this Friday to show you exactly how it works or you can check out our new website to learn more.

We’re excited about the impact this can have making sure product and engineering teams are focusing on the right features.

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Features usually consist of many smaller pieces, interactions, and settings. We tackled multiple event tracking, letting you associate multiple events with a single feature a few weeks ago. Today, we’re introducing feature hierarchies to make managing features in Bucket more intuitive!

When combined with the multiple event tracking OR operator, you can create parent features that encompass all sub-features by simply dragging and dropping. 

These tree-like hierarchies make it easier to manage and structure features. Both top-level and nested features can be sorted within the hierarchy and Feature views.

The data will remain the same. Feedback is still associated with a single feature and you get STARS segments based on the event(s) you’ve specified for each feature.

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Previously, each feature on Bucket was tied to a single event, limiting its adaptability. We’ve introduced a significant change that lets you associate multiple events with a single feature, making Bucket more versatile!

For instance, let’s say you’ve shipped a new feature, “Huddles,” that has audio and video interactions. In this example, the granularity of audio vs video isn’t important for you, overall feature use is. Both of these events indicate high-level feature usage. Until now, you had to track these events as separate features or utilize event attributes.

Now, you can consolidate multiple events in a single feature. Using our example, you can create a single feature called “Huddles”. You can attribute audio OR video events to the “Huddles” feature alongside the target segment, giving you greater flexibility in grouping similar events.

You can also use this update to:

• Track larger features or product sections by adding multiple sub-features to the tracking criteria  
• Accommodate tracking data changes, like fixing a typo in the event name. If you accidentally named an event “Audi huddle” and then changed it to “Audio huddle”, you can add both events to the criteria to consolidate new and historical data in a single feature. 

This update lets you create features that capture a broader range of interactions without being constrained by the previous 1-to-1 mapping. This new approach simplifies the process and allows for more efficient feature organization on Bucket. 

Last week, we made the STARS criteria more flexible. This week, our focus was on making the tracking criteria more powerful. More to come.

Happy shipping!