Asana unifies customer data and teams using Twilio Segment

Challenge

Asana struggled with slow, manual audience creation and siloed data spread across multiple sources. They needed a faster, unified way to activate accurate customer insights.

Solution

Twilio Segment unified Asana’s customer data across systems, empowering marketing teams to build self-serve audiences and act on real-time insights while increasing engineering efficiency and scalability.

Teamwork doesn’t “work” the same as it did ten years ago. On the surface, it’s easier to connect than ever before. But in practice, the abundance of tools, devices, and channels breeds friction. Information gets siloed, context gets lost, and what should be seamless collaboration often feels like a hard session of hot yoga.

That’s where Asana comes in. Asana allows humans and AI to collaborate effectively, helping teams achieve goals smarter and faster. As Asana grew over the past 15+ years from startup to enterprise, the company faced a challenge common to fast-scaling organizations: evolving their data infrastructure to match their growth. 

Systems and processes that work efficiently for smaller teams often require reimagining as organizations scale to thousands of employees across different functions and geographies. With multiple point-to-point integrations, data becomes fragmented across platforms, and what once required simple coordination now demands sophisticated orchestration. 

What Asana needed was a new approach—one that works not just across devices, but across functions and distances. One that connects their data as seamlessly as they connect people.

When Asana partnered with Twilio, they started a journey to rethink how teams create audiences and act on the data behind it. Along the way, they unlocked a new way of working, where marketing, customer success, and engineering are in sync.

Before Twilio, customer engagement at Asana was complex and time-consuming. Every campaign started with a familiar pain point—data wrangling.

Teams like Customer Success and Marketing Operations faced repeated delays in accessing reliable customer data. For instance, identifying high-value user cohorts for outreach required pulling data from multiple systems–including customer relationship management tools, product usage analytics, and internal spreadsheets–then manually stitching them into static lists. This process could take 9-10 hours each quarter and risked producing out-of-date segments by the time campaigns launched.

Marketing faced similar delays. Building an audience for a new campaign meant submitting a request to analytics and waiting up to two days of turnaround. These bottlenecks slowed time-to-market, limited experimentation, and made it difficult to react to changing customer behaviors in real time. 

From a technical perspective, engineering was managing numerous data pipelines across downstream systems. Without a centralized framework for defining and reusing audience segments, teams often recreated similar audiences in parallel, leading to duplication of effort and time-intensive maintenance work. 

Asana knew something had to change.

“We saw an opportunity to move away from point-to-point integrations and toward a composable architecture that could scale across teams,” said Grace Liu, Product Owner for the CDP. “It meant thinking holistically—about identity resolution, real-time activation needs, warehouse integration, and how we could reduce engineering burden while improving campaign agility.”

As the company evaluated solutions, Twilio Segment stood out. Its composable architecture gave Asana the flexibility they needed to bring together both real-time event data and warehouse insights, while still maintaining control and compliance over what data flowed where.

“We didn’t want to reinvent the wheel every time a new use case or data request came up,” said Sai Kesavamatam, Data Architect at Asana. “We were intentional about our architecture design from the start.”

So after careful planning, Asana and Twilio got to work:

• The transformation started with Segment Connections. This enabled Asana to selectively capture critical product and web events in real time. Instead of flooding the system with every possible datapoint, Asana built guidelines to decide which events warranted real-time collection and which could live with batch updates. This pragmatic balance kept costs in check while enabling use cases like onboarding triggers.

• Next, Segment Unify gave them the ability to stitch together anonymous and known user data. When a user engaged with a help article on the website and later logged into the product, those actions were connected into one unified profile. This shift gives the CS team visibility into intent signals they’d never had access to before.

• The real breakthrough came with Segment Engage’s Linked Audiences. Instead of relying solely on Reverse ETL—where data is pushed in batches from the warehouse—Asana could now query the warehouse directly in a zero-copy model. “With Linked Audiences, data sync runs and computes in real time on top of our data warehouse, and we can get a close to real time reflection of the audience population and makeup,” Liu explains.   

• To tie it all together, engineering leaned on Data Graph. This allows them to model data once and make it available for analytics, CDP activation, and downstream tools. What once required duplicate work across pipelines could now be done once, cleanly, and reused endlessly. “That's where the engineering efficiency comes through,” Kesavamatam explains. “We create one object, make it accessible for the data layer, and the same object is available for Segment to directly access.”

What benefits has Asana experienced since solving its CDP predicament? First and foremost, speed. 

Marketing and CS teams went from 2-day wait times to same-day execution. Now they can create audiences themselves, on demand, with confidence that the data is accurate. That means faster campaigns, more experiments, and the ability to respond to customers in the moment instead of after the fact. In just over a year since implementation, teams have built audiences and saved over 250 working days through the audiences built in Segment.

Accuracy and targeting effectiveness have also improved. With unified identity resolution, Asana can connect the dots between web activity, in-product behavior, and enrichment data. 

• A customer who read a help doc but failed to follow through in the product can be flagged for proactive outreach. 

• A trial user can receive onboarding messages triggered by real-time product events. 

• Even Asana’s AI Studio activation emails can be sent the moment an account is provisioned. 

Through various experiences launched, Asana saw up to a 57% increase in web leads through paid media campaigns, a 48% boost in Academy course engagement, and a 4% CS email-to-meeting conversion rate—delivering measurable results across key touchpoints.

The effect on engineering was just as powerful. Instead of maintaining three or four parallel pipelines feeding the same data into different systems, they now manage a single unified object in the warehouse. That object can be used for reporting, activation, or any future use case—without additional lift.

Downstream activation also became smoother. Linked Audiences make it simple to sync segments into paid media and email channels, giving marketers more control while ensuring that engineers don’t have to build custom integrations for every new request.

Perhaps most importantly, Asana’s teams are empowered to build what they need without bottlenecks. 

The final result: customers experience more timely, relevant engagement at every stage of their journey.

In just a short time, Asana evolved from a foundational data strategy to one that’s agile, integrated, and activation-ready. 

Today, audience creation is fast and simple. Customer profiles are holistic, stitched across web, product, and enrichment data. Campaigns can be triggered by real-time events, whether it’s trial onboarding or AI Studio activation. Engineering is no longer stuck building redundant pipelines, thanks to a single, unified data graph.

As Liu put it, “Now it’s about experimenting more and figuring out the most effective way to market to our customers—while making sure our data foundation is set up for whatever comes next, whether that’s AI-driven predictive audiences or new product experiences.”

In today’s experience-driven economy, that’s not just a nice-to-have. It’s the difference between keeping up and leading the way.