Changelog

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! 📊

We’ve just made it easier to debug event data and to turn any event into a Bucket feature.

The Tracking view now shows event attribute values when inspecting an event. This enables you to ensure and/or debug that the correct attributes and attribute values are in fact attached to the respective event.

In the new Related features section, we’ll show any features that are associated with the respective event. If none are, you can click Track new feature to easily transform the raw event into a Bucket feature with a single click.

More updates soon!

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We’ve just shipped an update to the company view that enables you to view feature engagement and satisfaction through the lens of a single company account.

In the screenshot, we’re looking at the “Feature usage”-tab for the “Prodash” company.

For each tracked feature, we can see where the company is in the STARS funnel, if they’ve provided any qualitative feedback to the respective feature, when they used it last, and what their feature activity looks like over the past 30 days.

We’ve also added tabs to this view so that you can browse feature usage (screenshot), all feedback and satisfaction scores provided by the customer, users in the account, and all attributes for the company.

The view unlocks a bunch of possibilities, like ensuring that key accounts are aware of, and using, most key features, upsell opportunities and for checking in on new feature adoption and satisfaction by certain accounts.

More updates soon!

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It’s now possible to sort the feature views by column. This enables you to quickly get a list of features with the highest retention, the lowest satisfaction, most frequency of use or latest release date.

This can help you detect key features with high retention but low satisfaction scores more easily. To understand why the satisfaction scores are low, and why the customers might be a churn risk, dive into the respective features and check the qualitative feedback from your customers.

Or, like shown below, this can help you surface opportunities. For example, features that are successful - with high frequency and/or satisfaction - but with low adoption or awareness.

More updates next week!

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Using the STARS framework on Bucket, you can quickly establish a consistent baseline for customer engagement and satisfaction of any feature.

But how does one feature compare to other key features in your product? And how do those comparisons change over time?

Answering those critical questions has just gotten a whole lot easier as we’re introducing the Bucket Audit Matrix!

The latest Bucket update introduces an essential feature for any product leader. An intuitive visual that charts all of your features side by side including changes over time with trendlines.

The Audit Matrix

The matrix is a wonderful visualization for comparing features side by side across two dimensions. The default typically shows adoption on the x-axis and frequency of use on the y-axis.

In B2B SaaS, most customers should be using key features at least once every subscription cycle. If customers don’t, they’re likely becoming a churn risk.

Therefore, if you have a monthly subscription cycle, you want to make sure that the key feature frequency is at least monthly, and ideally weekly or bi-weekly. If the feature isn’t used often and is a key feature for customers, you want to work on moving the feature upwards.

Then once the feature is sticky, you want to move it to the right and get more customers to use it.

Lastly, once the metrics look good, you want to increase customer satisfaction to make sure customers are happily retained.

Demo

Let’s see what the Bucket Audit Matrix looks like in action!

In this example, we’re looking at our fictional Slack competitor “Slick”. We’re tracking two recent feature releases - huddles and video recordings - and comparing them to one of our key features - sending chat messages.

From the list view, we jump to the matrix comparison view. We can quickly tell that our new features (marked yellow as in evaluation period) aren’t seeing the same engagement as our key feature.

We can even see in the trendlines that the engagement has been decreasing drastically since the initial release. Users showed interest and tried the features but didn’t really find them valuable enough to keep using them.

That’s alarming! The huddles feature is intended to be a new key feature in our Slick product.

To investigate further, we swap the frequency axis with satisfaction - the qualitative layer on Bucket - to see how the features compare in this dimension. As feared, the satisfaction scores aren’t very high.

To learn why, we click the huddle feature to dive into the actual customer feedback. Here’s one piece of feedback:

‍"We'll stick with Zoom until we can use huddles for all-hands, too"

This indicates that the huddles feature is appealing but simply not feature-rich enough to be a viable alternative to something like Zoom.

So, we’ve validated the feature idea based on initial adoption (interest) and qualitative feedback, but we’ve also discovered that we need to invest in this feature to make it successful. If we do so, there’s a good chance we can move it up and to the right on the audit matrix, and grab a chunk of Zoom!

Get started today

The Audit Matrix is one of the most powerful visualization methods in a product leader’s toolbox and indispensable at any roadmap planning meeting. It works out of the box on Bucket as all features are tracked using the STARS framework and therefore comparable on the same axes.

To get started, simply start at Free trial and navigate to the Matrix view in the top right corner.

Happy roadmapping! 🏄

Our mission is to empower product teams to deliver impactful features that delight and retain customers. Today, we’ve added a major addition to our service that brings us closer to our mission:

Introducing in-app qualitative feedback!

Bucket already provides product teams with turn-key engagement metrics for the features they ship. We instantly answer common questions, like “Who adopted the feature?”, “Who’s retained?”, “Churned?”, “How do these metrics look for our enterprise customers?”.

Today, we’re adding the ability to also collect in-app customer feedback, so you can complete the feedback loop. By combining quantitative analytics with qualitative feedback you’ll be getting the full picture - in one place - so you can quickly determine customer satisfaction of any feature.

Collecting in-app feedback

Whenever you release a significant feature update, use in-app feedback to provide your customers with an easy way to let you know what they think of it.

Here’s an example of what that could look like:

Once submitted, the feedback gets shared on Slack. As you can tell, the feedback is already associated with the “Account revenue chart” feature on Bucket.

On the “Account revenue chart” feature page on Bucket, you’ll now see a Feedback tab where all the feedback about this particular feature is listed:

Having feature engagement metrics and feedback on one place is powerful for several reasons:

Firstly, it provides you and your product team with a single pane of glass for your features, which enables you to get an overview of customer engagement and satisfaction, and act on it fast.

Secondly, Bucket augments the customer feedback with the customer’s actual feature engagement, like frequency of use, what other features they’re using, how many users they have, and so on. The engagement data helps you understand if the feedback is coming from a new account that is trying the feature for the first time - or from a long-time enterprise customer that has been using it for a while.

Having that context enriches the feedback so you can understand where it’s coming from and prioritize accordingly.

Getting started

Feedback is now built-in to the Bucket SDK and supported by our HTTP API. To enable your customers to provide feedback, go to Bucket and track a new feature or find an existing one. You’ll need the “featureId” from the new Feedback tab. Then, create a custom, re-usable form that gathers feature satisfaction score (CSAT) and a comment, and send the data to Bucket via the SDK or API.

Here’s an example of how to use the Bucket SDK:

That’s it!

Now you can easily add in-app feedback collection to any new feature release.

By the way, if you gather feedback via email or calls, you can enter it manually on the Feedback tab.

More updates on this topic soon! ;)

Todo -> Doing -> Done. But what happens when a feature is marked as Done? It’s deployed and live with the customers. That’s undoubtedly the most crucial time in the feature cycle, and that’s exactly where existing workflows stop today.

It’s nuts!

At Bucket, we want to add a final step to the feature delivery workflow: The Evaluation step. In the Evaluation step product teams make sure that their customers actually like the feature they’ve released. We believe most features should go through this step.

We’ve come to expect almost every other step in the feature delivery workflow, like testing, deploying to production etc, to be automated. We believe that the evaluation of features should also be as automated as possible.

Last week we announced how you can plan features on Bucket. The Planned state is for when the feature is in development.

Today, we’re announcing the remaining states: Evaluating and Done. Together, they make up a complete, automated feature evaluation workflow.

Automated evaluation

When you log in today, you’ll see these columns: Planned -> Evaluating -> Done.

When a Planned feature is deployed (and marked as Done in JIRA or Linear), it automatically goes into Evaluating mode on Bucket.

Bucket reports engagement metrics on all features in Evaluating mode to Slack every Monday.

When you have enough insights to decide if the feature has reached the impact you expected from it, you mark it as Done on Bucket. If the feature needs more work, you rinse and repeat the workflow.

Release date

We’ve also shipped the ability to set a Release date for each feature. You can set it manually or let Bucket do it. Once data for a feature starts flowing in, the feature changes state automatically from Planned to Evaluating, and Bucket will automatically set the release date to the current date if a date isn’t already set. You can modify the release date later, if needed.

The release date will also appear in the Slack reports, so you can easily track how long a feature has been in evaluation.

After 2-4 weeks, you should start to see leading indicators on adoption and churn, which will indicate feature success or not. If you need to dive deeper and speak with customers, Bucket makes that very easy, too.

Happy shipping!

Adding tracking to a feature release is often an afterthought or forgotten altogether. Many engineers have received a DM from a PM around the time of feature release saying something frantic like:

‍“did you remember to add tracking? we need to track this feature!”

We believe there’s a better workflow for this, and that workflow unlocks some very useful automations.

Today, we’re introducing Planned features on Bucket!

When a feature goes on the roadmap, there’s often a story issue created for it in the issue tracking of choice. It describes the customer problem, maybe a potential solution and ideally also a basic tracking implementation specification (tracking spec). The tracking spec describes which instrumentation to add to the code, so it can be measured and evaluated post-release.

Now, you can plan features on Bucket and hand them off to engineering as a ticket that includes the exact tracking spec - right from the Bucket UI.

Here’s how it works.

First, track a new feature. In the new feature modal, you can now enter events and/or attributes that don't exist yet. In this example, we want to fire off the “Pinned card” event whenever a customer pins a card in our product UI.

Once created, you now have a feature tracking spec that you can share with your engineering team. It contains all the information needed for the engineers to know exactly what event or attribute to track before releasing this feature.

You can share the feature tracking spec as a link, copy as Markdown or create a Linear issue straight from Bucket. You can also add a note about when to fire the event.

With the feature created on Bucket, the system will listen for events with the name “PinnedCard”. When engineering is done with the feature and deploys it, Bucket will get notified as soon as a user interacts with the feature. If you want to, you can get a Slack notification when that happens.

Besides getting the feature tracking spec out of DMs and into the issue tracking system, there’s another huge benefit to creating planned features: Automated feature evaluation! 🥁  

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Automated evaluation

We want to empower product teams to make impactful features. To validate impact, you need to track customer engagement. If customers aren’t adopting the feature or are churning away fast, you need to act on it.

With Planned features, Bucket will know when a new feature is released and can therefore automatically start to report feature engagement metrics to your team. By default, Bucket will send you a feature engagement report to Slack every Monday – empowering everyone in the product team (engineers, PMs and designers) to act on bad engagement or adoption.

With the Bucket insights, the product team can quickly determine if the feature is “Done for now” or needs another iteration.

If you need to dig deeper and ask for customer feedback, Bucket makes it easy to figure out which customers that are relevant to reach out to. Here’s an example of that:

That’s our workflow: Repeatable and automated. Just how we like it!

More updates soon.

If you’re detecting high churn or low activation on a key feature, you want to reach out to the relevant accounts for feedback, and learn how you might improve the feature.

We’ve now made this workflow easier on Bucket.

When you dive into an account, you can now filter by feature usage and see a list of users that have interacted with the respective feature. The users are sorted by feature interaction, so you can quickly pick the most relevant user to ask for feedback. From there, click to send the user an email.

Here’s what it looks like:

Make sure to send the reserved "name" and "email" user attributes when tracking users to see the name and email link in the Bucket interface. For example:

bucket.user(“<userId>”, {
  name: “John Doe”,
  email: “john@doe.com”
}

To see all users of a company, remove any feature filter in the top right when looking at the company page.

More updates soon.