Automatic data export for custom analysis

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.