SIGNALS
What began as a question about understanding product usage became a new product-intelligence capability for Oracle Cloud Applications.
THE OPPORTUNITY
Cloud app product teams were shipping increasingly sophisticated enterprise applications without an easy way to understand what happened after they shipped. Were people actually using what had been built? Which capabilities were being adopted? Where were people struggling? And ultimately, was the product delivering the outcomes it was intended to create?
Customers faced the other side of the same problem. They were making significant investments in enterprise software without an easy way to understand whether employees were adopting it, how effectively it was being used, or where there were opportunities to get more value from that investment.
The data wasn’t entirely absent. Pieces of server-side telemetry existed in legacy systems. But it was difficult to access, hard to make sense of, and even harder to translate into any concrete action.
THE INSIGHT
Telemetry has traditionally been an afterthought in software development. Relying on individual development teams to instrument every experience rarely produces the scale, consistency, or completeness needed to understand how a product is actually being used.
Third-party analytics products could solve parts of the problem. But introducing another analytics layer wasn’t necessarily the right answer for enterprise customers, particularly those operating within highly controlled data environments.
There was a more fundamental opportunity. Instead of treating analytics as something added to an application after it was built, what if product intelligence were an inherent capability of the platform itself?
THE VISION
HUMAN UNDERSTANDING + BEHAVIORAL EVIDENCE
Behavioral evidence at scale showed us what was happening across Fusion customers. But the number itself was rarely the answer.
A drop in adoption could signal a product bug, a confusion in the experience, a training gap, or something particular to a customer’s implementation. Performance telemetry could show where an experience was slow or failing; understanding why required deeper technical or customer investigation.
Bringing the triangulated forms of evidence together helped teams move from seeing a metric to understanding what was happening, why it might be happening, and what to do next.
From Insight to Product
I set the vision and the business case, led the proof of concept, and built the multidisciplinary team to take it from an idea to a product offering. We partnered with data infrastructure to connect raw telemetry to the questions product and business leaders were actually asking, and defined a consistent measurement framework that held up across Oracle’s application portfolio.
Signals turned telemetry from something engineers collected into intelligence that Product, Design, Engineering, Customer Success, Support, and Executives could use to make decisions.
Adoption
Identify what customers were using, and where lower adoption warranted deeper investigation or proactive engagement.
Context of Use
Understand which capabilities were being used and which were not, to inform roadmap decisions on whether to sustain and invest in the feature or sunset it.
Observability
Understand the context in which applications are used, including browsers, devices, resolutions, and environments, to inform testing strategies and design decisions such as the visual scale and density of Redwood.
Performance
Give Product Engineering and Support visibility into performance problems and errors, then investigate their source and act.
A PLATFORM CAPABILITY, NOT JUST A DASHBOARD
Building product intelligence into the platform meant that the capability could scale with the applications. As new experiences were built on the platform, the underlying telemetry and measures could be available as part of the foundation.
It also created a new source of value for customers considering the new platform. They weren’t simply migrating to a new experience. They could gain greater visibility into adoption and usage as part of that investment, without having to independently assemble the same capability.
Product intelligence became part of the value proposition of the platform itself.
Making the Case to Go External
Internal adoption revealed a bigger opportunity.
Internal adoption had already demonstrated the value of Signals. But we were seeing the same need from customers. They were proactively asking for greater visibility into how their products were being used, and my team was already delivering some of those insights through bespoke customer engagements.
At the same time, customers increasingly expected usage visibility as part of their enterprise software. Because Signals was native to the platform, we could provide richer product intelligence without requiring customers to add and manage another analytics layer.
And we weren’t starting from scratch. We had already built and proven the capability internally. The investment was about adapting that foundation for customers—determining what could scale across organizations, productizing it, and making it suitable for external use.
I took that case to leadership: demonstrated customer demand, a growing competitive expectation, the differentiated value of native product intelligence, and a proven internal capability we could extend rather than rebuild. We secured the investment and mandate to turn Signals into a customer-facing product.
PROVING IT INSIDE ORACLE
Proving it inside Oracle first.
We deliberately proved the capability internally first.
Signals grew from nothing to thousands of internal users, spanning Engineering, Product, Executive Leadership, Customer Success, Support, and Sales. The platform established 40+ internal metrics, giving teams a common source of evidence about how Oracle Cloud Applications were actually being used.
That breadth mattered. Signals was no longer useful to only a research or analytics team. The same intelligence informed product investment, customer conversations, support, adoption strategy, and executive decisions.
The Impact
PRODUCT TEAMS
Gained a common evidence base for adoption, usage, and experience—and firmer ground for the decisions that follow from it.
CUSTOMERS
Gained visibility into whether the software they invested in was being adopted and used, without standing up and managing a separate analytics product.
ORACLE
Turned an underutilized stream of behavioral data into a scalable capability that informs product investment and customer conversations, while adding durable value to the next-generation application platform.
Thousands
Internal users
40+
Measures built from scratch