Changelog

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.

Live Satisfaction enables you to automatically ask your users for feedback after they used a new feature. It’s easy to set up and an effective way to gather insightful user feedback for a specific feature.

With the new statistics module you can easily track the response rates and make sure that the automation you created is running smoothly.

For every feature that has Live Satisfaction enabled, you get a quick visual overview of how many users were asked, how many responded and how many dismissed the feedback widget in the past 7 days. It looks like this:

You can find the new statistics in the feedback tab for every feature that has Live Satisfaction enabled. If you have not enabled Live Satisfaction for that specific feature, you'll see instructions on how you can enable it.

Use Cases

In addition to checking the general health and response rates for your Live Satisfaction automations, you can also use the response rates to identify and quickly react to different scenarios. For example:

Use the subsegmentation feature to check response rates for different segments of your users, highlighting not only how different customers respond to the feedback prompt, but also how likely they are to provide feedback overall.

You can then dive deeper into the actual provided feedback from different user segments (e.g. power users vs. new customers) to help you make informed decisions about future product iterations.

Iterate on when to ask for feedback

Another scenario: Let’s say you are asking your users for feedback after they tried your newly released feature for the first time, but most of the feedback prompts are getting dismissed – this might indicate that users haven’t formed an opinion yet or that you are asking at the wrong time.

You can quickly go into the settings of the feature and change when you want to ask users for feedback – for example by increasing the number of required interactions, users now have to try a feature for multiple times before getting asked for feedback.

Finetune timings

Alternatively, if the statistics show a high rate of ignored feedback requests for a particular feature, it could indicate that the timing of the feedback prompt is not ideal.

Since the feedback widget is fully customizable, you can not only adjust the required amount of interactions, but also the timings of the feedback widget.

More updates soon!

We’ve just shipped an improvement that allows you to add, remove and reorder table columns across the Bucket app. We have also added more metrics to the feature view table.

See it in action

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To start customizing the table view, click on the ⚙️ icon on the right. You can now:

• Show or hide selected metrics simply by toggling the checkmark
• Re-order the table columns via drag-and-drop
• Use “Reset” to revert to the default table view

This improvement works across the whole Bucket app – everywhere where data is presented in a table format. But it really shines in the feature view table:

How can I use this?

For example, you can now add the “Tried” and “Adopted” metric to the feature table view, then sort the table by release date and get a quick overview of how your recently launched features are performing.

More Use Cases

• By selecting specific subsegments and adding the STARS metrics to the feature view table, you can visualize the STARS funnel for all your features for specific segments.
• Go into focus mode by only showing specific metrics in the feature table view, for example during different stages of a product release cycle.
• Show the feedback count and Satisfaction rate to quickly gauge what features are receiving feedback and how satisfied your users are with it.
• Add the “Live Satisfaction” and “Slack Reports” columns to see which features have them enabled.
• Save specific table layouts or make them accessible for other team members by sharing the custom URL with them.

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.

Having everything we need to make product decisions in one tool is a big time-saver for us.

Robert H., Product Owner @ Billetto

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

We are introducing Live Satisfaction – a no-code approach to collect qualitative user feedback for your features right from within Bucket. This makes Bucket your one stop shop for both quantitative product insights and qualitative user feedback on a feature level. Every feedback and satisfaction rating flows directly into the “Satisfied” metric in Bucket – which you can explore in the feature report. This will help you understand whether customers are satisfied with a feature in general.

How it works

Gathering insightful feedback is pivotal for steering your product in the right direction and to evaluate what your users think of a specific feature. And gathering the right feedback at the right time is just as important.

Watch the video below to see how you can easily start collecting feedback for your features from your users, right within Bucket:

The importance of qualitative user feedback

Feedback from your actual users is a rich source of insights that provides a deeper understanding of how your users think about your product.

While quantitative data is excellent for measuring metrics and identifying trends, qualitative data, for example the written feedback provided by your users, uncovers the “why” behind those numbers and helps to understand the motivations of your users, their frustrations, and overall sentiment – painting a complete picture of how your features are performing and perceived.

Bucket enabled us to start collecting qualitative user feedback, which plays a critical role in how we evaluate features.

Robert H., Product Owner @ Billetto

Today, most product teams either use different tools to gather qualitative user feedback, only collect company-wide NPS or don’t collect any user feedback on a feature level.

Getting started is easy

To get started, head over to the Bucket app, select the feature you would like to start collecting feedback on, go to Settings and click on “Enable Live Satisfaction”.

That’s it! You’re now collecting satisfaction - live!

In its default state, the feedback widget collects a simple customer satisfaction score and also gives the user the option to leave a comment. Please note that this widget will be hovering on the bottom right of your app.

You can now:

• Define the question you would like to ask your users.
• Define the minimum interactions before a user gets asked for feedback. This is useful if you want to collect feedback from users at different stages of the STARS funnel.
• Set the minimum and maximum time (in seconds) after an interaction for the feedback widget to be shown.

To learn more, head over to our documentation here.

Fully customizable

As product builders ourselves, we understand you want to have maximum control over the experience in your application. And to enable you to make collecting feedback in line with your design and user interaction guidelines, the behaviour, language, as well as the design of the feedback widget, are fully customizable via CSS.

To check out what and how to customize your feedback widget, go check out our documentation here or head over to GitHub for the full developer documentation.

Make feedback collection a part of your workflow with Bucket

Live Satisfaction is a major addition to Bucket's repeatable feature evaluation workflow:

Launching and evaluating features

Bucket is built upon the STARS framework, a funnel that, at its core, lets you understand the engagement and satisfaction of users for all your features. It measures feature satisfaction and enables you to evaluate feature performance consistently and for every feature you launch.

Compare and audit feature performance

All your features that are launched through Bucket are evaluated on a consistent framework, making it possible to analyse and compare feature performance. Use the Audit Matrix and Subsegmentation to compare feature adoption, retention, and satisfaction across different customer segments.

Measure customer satisfaction per feature

After you launch a feature or identify one that needs further investigation, it is easy to start collecting feature-specific user feedback with Live Satisfaction. All the collected feedback is seamlessly integrated into the Feature Report.

Integrate enriched Bucket data to make informed product decisions

Bucket is designed from the ground up to give Product teams fast & actionable insights. With our recently launched Data Export you can go one step further and easily integrate Bucket-enriched data into your own workflows, data warehouses or use it to strengthen cross-team collaboration by making it accessible to other teams, for example Customer Success.

Let’s recap

• You can now use Bucket to collect qualitative feedback from your users for specific features.
• To get started, head over to the settings for the feature you’d like to start collecting feedback for, enable Live Satisfaction and set a question.
• Both the design and behaviour of the feedback widget are fully customizable, making sure you are aligned with your design and UI Guidelines and allowing you to implement feedback collection seamlessly into your product.

To learn more about enabling, customizing and deploying your first feedback widget, head over to our documentation here or check out the full GitHub documentation here.

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.

Having everything we need to make product decisions in one tool is a big time-saver for us.

Robert H., Product Owner @ Billetto

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

We've rolled out Subsegmentation to enable comparisons of feature adoption, retention, and satisfaction across different customer segments  – allowing you to get a deeper understanding about what features specific customer cohorts use and how satisfied they are with them. Understanding which features matter to your customers in different segments is business critical – be it customers on different pricing plans or in different parts of the world.

Subsegmentation shines in the Audit Matrix

Subsegmentation works across the Bucket app, but is especially useful in the Audit Matrix. On the feature table and board view, you’ll find the new option to select and apply one of your company segments. In the Audit Matrix you can select multiple company segments which lets you easily compare how specific features perform with different company segments.

How do I use it?

Pick your features, as well as one or multiple segments you wish to compare. Your initial view will give you quick insights into how different customer segments have received your features.

This allows you to compare how a particular feature has been adopted in Japan versus in the US, or highlight feature satisfaction for customers on a Business pricing plan versus customers on the Enterprise pricing plan.

We’ve also added the ability to dig deeper into the data for a particular segment by clicking the dot on the matrix. This takes you to the feature report filtered for that particular segment:

The data in the segmented feature report is calculated in the same way as the regular feature report, but it only includes the companies in the segment you have selected.

These additions together let you compare and contrast feature success across different customer segments as well as dig deep into the data for a particular segment of your customers on a particular feature.

Let’s recap

• A feature’s target segment specifies which customers have access to your feature. The feature report uses this segment by default. 
• Think of Subsegmentation as a “magnifying glass” that allows you to zoom in and understand feature engagement for specific company segments.
• By comparing multiple subsegments, you can spot trends in feature engagements, for example how adoption varies between customers on different pricing plans or in different regions.

Get started with Bucket in minutes

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

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

We’ve added the option to export your Bucket data as a CSV file – so you can work with your data in your own data warehouse, your CRM or good old-fashioned Excel. 

The data export feature enables you to download the complete feature usage of every company for every feature you’re tracking. To get started, log into Bucket, go to App Settings and click on the "Data export" tab. Here's a snapshot of what you can expect from your data export:

How can I use this in my company? 

Bucket is designed from the ground up to give you fast and actionable insights into your product. By downloading Bucket’s enriched feature data, you can manipulate it as needed, import it into other systems or create custom reports to answer questions the Bucket UI does not yet answer. For example:

Create 360-degree customer profiles

Integrate key feature adoption metrics into your CRM for a comprehensive view of each customer’s interaction with your product.

Engage specific segments

Find customers with high satisfaction but low feature usage frequency, and re-engage them with targeted messaging.

Discover your power users

Just like in the screenshot above, use the Bucket data to determine who your power users are by filtering for those who are ‘retained’ for any combination of key features.

Create custom visualizations

Use the available historical data aggregations to create custom visualizations. For example, you can map out adoption rates over time or create cohort-style visualizations for feature adoption.

Time your feature launches

Plan feature releases aligned with high-usage periods based on the available historical data aggregations of the Bucket data export.

To learn more about the technical details and content of the data export, check out the documentation here. We'd also love to hear how you're planning to use the enriched data; feel free to reach out to us.

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That’s it for now​​—happy data crunching! 📊