"Because we didn’t have a shared common language or common queries across teams, the first 15 minutes of every data meeting was spent figuring out who had the right query."

About Branch

Branch offers solutions that help brands build and measure more seamless, personalized mobile experiences.  Branch’s mobile marketing and deep linking solutions are trusted to deliver experiences that increase ROI, decrease wasted spend, and eliminate siloed attribution. By integrating Branch technology into core marketing channels including apps, web, email, social media, search, and paid ads, leading brands are driving more and higher-value conversions than ever before.

The Branch platform has been chosen by 100,000+ mobile brands like Adobe, BuzzFeed, Yelp since 2014 . They’ve also raised more than $667M from investors such as NEA, Founders Fund, and Playground Ventures.

  • 10h
    Saved per week per Engineer/Analyst
  • 15
    Minutes saved per data meeting
  • Fabi Prabhakar
    Senior Director of Engineering
  • Ling Li
    Senior Data Scientist


With a data stream of billions of events per day, having the right solution in place to analyze customer data and share trusted insights is key to Branch’s continued success. 

Given the company’s hypergrowth, different teams ended up leveraging different SQL editors and IDEs in silos, so there wasn’t a unified, cross-department solution to easily understand and interpret data to inform business decisions. This meant that Branch’s technical teams, across engineering, data, and product, each had their own way of writing queries – leading to different insights to the same business questions.

“My team operates like a startup within Branch. Our challenge was mostly data and technology-driven” says Fabi Prabhakar, Senior Director of Engineering at Branch. “We look at data all day long to answer questions around customer engagement, revenue, conversion, etc.  We used DBeaver, which was helpful, but it was hard to share and discuss results across teams.”

As a fast, nimble team within a growing organization, Fabi was looking for a unified, collaborative SQL editor in which they could easily discover the data in their schemas, centralize and standardize their queries, and foster a more thorough understanding of business insights across the organization. Other than these challenges around silos and data hygiene, not having a unified solution also ate up precious employee time and productivity.

Ultimately, not having a standardized way to share queries caused friction between technical and business teams. Fabi explains, “because we didn’t have a shared common language or common queries across teams, the first 15 minutes of every data meeting was spent figuring out who had the right query.”   Senior Data Scientist Ling Li shares these sentiments with Fabi, citing the potential for SQL collaboration and governance as an opportunity for the company to improve data accuracy and productivity. “If everyone could easily access the same query, they’d be less likely to make mistakes as a whole. That means time savings on shipping the right insights and data products.”

Fabi and team boiled down their needs to find a data collaboration tool that could help them: 

  • Easily find standardized data that would correctly inform business decisions 
  • Improve cross-departmental collaboration, communication, and strategy
  • Accurately report findings to stakeholders 
  • Improve self-service by onboarding new team members faster with readily available queries
  • Rely less on tools that create silos to reduce collaboration overhead
  • Provide line of sight into what data was being analyzed by whom, so that cross-departmental team members could leverage each others’ queries and collaborate in real time to improve insight throughput


“A colleague of mine discovered PopSQL and told me he thought it could solve our problems,” Fabi recalls. “We wanted a formalized way to look at data, share it, and have internal stakeholders trust it.” 

Shortly after evaluating PopSQL, Fabi shares,  “I couldn't find another tool that was as pricing friendly and did what we needed the way PopSQL does. We couldn’t find a comparable solution to PopSQL with regard to the collaboration either.”

During their evaluation of PopSQL, several functions within the organization used PopSQL to learn more about their customers. This included individuals in sales, business development, product owners, and data scientists, so everyone could get on the same page.  They answered questions like: 

  • Are customers successful or not?
  • What do click-through rates (CTR) look like on each site and each link?
  • Why is the click-through rate lower or higher in this country or state?
  • Are customers integrating our solutions properly?

Each department was now able to more easily understand the underlying data and share their findings. With PopSQL, visibility into data manifested in big ways for productivity and data trust.


Fabi estimates at least a 25% savings in time and resources, due to easier navigation of data sets, more productive meetings, less time spent searching for queries, and better trust. The ability to collaborate in one unified workspace has created better cross-functional alignment with fewer hiccups.

Ling estimates with this streamlined collaboration workflow “analysts save several hours a day with PopSQL by not having to rewrite queries or waste time searching for a query in docs, email, or on Slack.  We also save time by not having to jump on unnecessary Zoom calls to screen share queries.”

The one critical benefit that PopSQL unlocked for Branch as a whole was faster data discovery by having an immediate lens into customer data, identifying skewed data, and acting on it quickly. 

“We might get a note from a customer like ‘Is this CTR off?’. Our team can now immediately dive in and look at it in PopSQL”, shares Fabi. “The most useful thing is having a strong starting point on what we think is the right way to look at the data. Sometimes seeing who wrote the query in PopSQL’s UI is a good validator. Having a source of truth to start with is so important. We now always know where to start with PopSQL.”

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