Changelog

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.

Happy shipping!

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. 

Happy shipping!

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.

Happy shipping!

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.

Happy shipping!

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!

Respecting and protecting people’s online privacy and security are key tenets at Bucket. That’s why we’ve built a product that doesn’t require any Personal Identifiable Information (PII) and keeps data safe with secure, industry-standard systems.

We’re excited to announce that we have migrated all our servers to locations within the European Union (EU), taking our commitment to privacy and security even further.

Protecting customer data is our top priority and we wanted to take all measures possible to do so. The European Union is home to some of the world’s strictest and consumer-centric privacy laws (including one of which you’re likely more than familiar with, GDPR). All of which, we are big supporters of.

This migration lets European-based customers or those with whitelist privacy obligations use Bucket without the need for any additional data protection processes. For our customers outside of Europe, you can take advantage of the many compliance benefits of having data hosted in the EU and demonstrate your commitment to user privacy.

Our migration to the EU doesn’t only keep Bucket customers compliant with current privacy laws but also protects them from future regulatory changes. 

This isn’t the only thing we’re doing to boost our commitment to security. We’re also in the process of acquiring our ISO 27001 and SOC 2 Type II certifications. You can stay up to date with our progress on our security page.

Happy shipping!

We’ve just made the STARS configuration more customizable by adding frequency-based strategy capabilities. This lets you factor in the frequency of interactions for the Adopted criteria.

But wait, what is a frequency-based strategy?

It’s where you track feature usage frequency, not event count. Let’s say you deploy a new feature. You see that a company has logged 30 events with the feature over the past four weeks. They would be considered Adopted. However, if you look closer, those 30 events all took place on a single day. Then, the feature was never used again. In reality, they didn’t adopt the feature.

A frequency-based approach requires consistent interaction with a feature over a certain time period. For example, a company would need to use a feature on 10 separate days within a 4-week period to be considered as Adopted. This allows you to get real insights into feature adoption rates.

With the previous count-based strategy, companies were deemed Adopted once they had interacted with a feature a certain number of times (e.g. 5 times). Companies could reach this threshold when several company users each interacted with the feature a few times - without any of them truly adopting it. Before this change, the solution was to bump up the event count threshold.

With the new frequency-based strategy, you can define how many days (say 5) in a given period (say 4 weeks) the company needs to have interacted with the feature for them to be counted as Adopted.

Once Adopted, if the company stops fulfilling the criteria during your set period parameters (using our previous example, the past 4 weeks), they will be deemed as Churned from the feature.

Here’s an example:

The frequency-based strategy is now the default on new features while all existing features have been automatically migrated. 

Happy shipping!

Segments allow you to group companies by login activity and custom attributes. This is useful for making a segment of monthly active companies or paying customers, for example.

However, when browsing a segment, it isn’t possible to add columns that also show feature engagement or satisfaction.

For example, let’s say you have an “Business” segment and three key features in your product. Understanding how many Business companies have adopted all three key features, how often they use them and how satisfied they are with them would take a ton of manual work.

…Until now! With our latest update, Bucket enables you to create such a list with just a few clicks.

Introducing feature columns

Bucket automatically computes various key insights for each feature in your product. With this latest update, you can now add columns to your company segments that show any of the following feature insights:

• STARS state (Never, Tried, Retained or Churned)
• Frequency (Daily, Weekly, Monthly or Quarterly)
• Satisfaction (CSAT score)
• Total events count
• First used timestamp
• Last used timestamp

See it in action

Jumping back to our previous example: If you want to view STARS engagement, Satisfaction and Frequency for each of the three key features for your Enterprise customers, here’s how to do it in just one minute:

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Two-stage release

This is the first of two major releases for feature columns. In this first release, we’ve enabled feature columns in segments.

In the next release, we’ll also allow you to filter by features on segments, just like you already do with attributes. For example, this enables you to create an “Activated” segment consisting of companies that have logged in recently and actively use your key features.

Stay tuned!

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We’ve also added attribute columns

By the way, it’s also now possible to display any company attribute as a column. When hitting the cog wheel, you’ll now get the option to choose a feature or a custom attribute.

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By the way, did you know that Bucket can also track feature usage based on attributes?

More updates soon!

Bucket is built on the STARS framework and gives you a STARS funnel out of the box, for any feature.

Provided with just a single event, the STARS funnel shows you how far your customers have reached in the “feature success funnel” for the given feature.

Should companies in Retained later churn, they regress one step. Similarly, if Satisfied companies become dissatisfied, they’ll also regress one step.

While the STARS funnel is great for showing where companies are right now, in the context of a feature, the historic movements - like churned companies - weren’t obvious to find on Bucket.

So, we wanted to improve on that!

To do so, we’ve added STARS states, which take historical STARS movements into account and allow you to filter by them in the UI and in exported data.

The STARS states are:

• Never
• Tried
• Retained
• Churned

More updates next week! 🚀

We recently introduced manual data export, which enables you to download the complete feature usage of every company for every feature you’re tracking into a CSV file. This new improvement to the data export automates this process by continuously bringing Bucket data into your data warehouse – from Snowflake to RedShift via an AWS S3 bucket.

To get started, log into Bucket, go to App Settings and select the “Data Export” tab. You can now configure the scheduled data export into an Amazon S3 bucket – head over to our documentation here to learn more about the configuration and the options, like the daily and weekly cadence.

How can I use this in my organization?

Use the automated data export to empower other departments in your organization by blending Bucket data into their workstreams. A couple of examples:

Enhance CRM with Bucket data

By integrating key feature adoption metrics and satisfaction scores from our Live Satisfaction module into your CRM (say for example Salesforce or Hubspot), you can get a comprehensive overview of each customer, including their interactions with your product. On a daily basis, this can provide helpful context to your support team during customer calls, or it can be used to identify potential churner accounts based on their product usage.

Other customer facing teams can also benefit from the enhanced Bucket data, for example:

Sales – Customize sales pitches based on commonly used features or feature groups for specific target segments for greater relatability. Or identify upselling or cross-selling opportunities based on patterns in feature usage, for example by highlighting features that have high satisfaction or usage from similar customer segments on a higher pricing plan.

Marketing – Create truly product-data-driven marketing campaigns by identifying specific user segments based on their interactions with selected features and then measure the effectiveness of marketing campaigns by tracking changes in feature engagement over time.

Product Marketing – By mapping out feature interactions over time, Product Marketing teams can align their product launches with high-usage periods to maximize the visibility of new features. And after a launch, Product Marketing can share lists of high- or low-engagement customers with the support team to streamline activation campaigns.

Increase cross-functional collaboration

In a recent episode of Lenny’s podcast, Brian Chesky, the founder of Airbnb, makes the point that the health of an organization can be measured by how close the engineering and marketing department are. Our CEO, Rasmus, echoed this and shared his insights on Twitter and talks about how Bucket can help R&D teams work more closely with other departments, specifically with the marketing department: 

Elevate data analysis with Bucket’s feature data

With this automation, you can now combine Bucket data with your relevant internal business metrics and level up your business intelligence analysis, build advanced visualizations and effectively tie business outcomes to product engagement data.

One of our customers uses the Bucket data to refine their companies churn model by adding product engagement attributes about feature retention to existing company attributes like company size, industry and more to build a more comprehensive model.

Get started with Bucket in minutes

Bucket connects to your product via Segment and through our API, so getting started is easy. Empower your product team and unlock feature value by combining product analytics and qualitative feedback – all in one place.

Click here to get started for free or click here to book an intro.