Customers

How Parabol used Twilio Segment to provide investors with customer insights and analysis to raise funds

Use Case

Customer 360

Customer data infrastructure

Customer insights and analytics

Region

NAMER

Industry

B2B SaaS and Technology

Products

Connections

Headquarters

Brooklyn, NY

The big win

Parabol’s founding team wanted to bake data into every facet of the business, including tracking funnel conversion and driving customer pipeline efficiencies. Regular reporting would signify success in the eyes of investors. Once they started to dig into their metrics and data collection capabilities, however, they realized the detail and clarity they needed just weren’t there.

The Parabol team turned to Twilio Segment to collect and unify data easily across touchpoints so that the team could easily share updates ongoing with investors.


Parabol is a software company that creates products to unlock a team’s full potential. Specifically, Parabol is a remote retrospective and meeting app that generates automatic insights about team performance from online collaborations. Parabol’s software allows distributed, cloud-based teams to run meetings from anywhere, across time zones, in real time or asynchronously. Then, team notes and tasks are automatically rolled into a meeting summary instantly posted to a team timeline and emailed to stakeholders. Teammates can also export their tasks to Jira, Confluence, Github or Slack.

Building in data-driven decision from the onset

When Parabol was a new company, they understood the importance of establishing a product/market fit right away. Founder Jordan Husney noted, “By our nature we’re experimentally minded and data oriented,” so it was imperative to bolster any of their creative business ideas with hard data.

The founding team values data so much that they wanted it to inform most of the way their business functions: they baked reviewing data directly into their operating cadence to have productive, informed conversations with teammates and advisors, driving company strategies and evaluating the merit of past decisions. Parabol also wanted to use data to track funnel conversion and drive customer pipeline efficiencies. Once they started to dig into their metrics and data collection capabilities, however, they realized the detail and clarity they needed just weren’t there.

What could support such a broad range of needs?

Implementing Twilio Segment for more visibility and flexibility 

The Parabol team quickly turned to Twilio Segment. Husney noted, “First we turned on Twilio Segment’s Slack integration to see an ambient flow of key events people were performing in our software.” Next, Parabol turned on Amplitude, and began using the data they were gathering to better understand their product/market fit. Then they turned on Hubspot to cater their approach to sales leads within their funnel. Husney enthusiastically stated, “We felt like champions because we only had to integrate Twilio Segment once, instead of spending our time integrating numerous tools' differing APIs.” Instantly, they had the information they needed.

Because of Twilio Segment’s ease-of-use, Parabol can easily turn on a new program, run a week-long experiment, gather data, and learn if they’re on the right track with their next iteration—devoting no additional resources to instrumenting their product to gain these important insights.

Granularity in reporting drives investment 

Parabol now has more than four years of accurate, easy-to-access data. For most of their company’s existence, they’ve published a subset of it in a weekly blog post they call the Parabol Friday Ship. This practice demonstrates a value the company holds dear because, as Husney said, “Everything is right there for investors and stakeholders to see clearly.” And the best part? “By being able to collect data easily and sharing that with investors, they start sharing our successes freely, and that brings in other investors.”

Parabol Graphs

Parabol has gone from 2k registered users to 12k (500+%), and fewer than 500 MAU to over 3,600 MAU (620%) in under 8 months with no change in its expenses, only constant iteration in response to its data.


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