ASK’EM Pt 3: The feedback platform
(Post 3 of 5) How we built ASK’EM Into a scalable insight platform
A few weeks ago, I was invited to speak at the HEB PM Guild, where product teams wanted to dig into how we built ASK’EM at Indeed—and what made it different from typical feedback tools. It was a chance to reflect on the core problem ASK’EM solved, why it mattered, and why it struck a chord with product teams facing the same challenges elsewhere.
What we developed wasn’t just a way to collect feedback more consistently across Indeed. It was a system designed to tie customer sentiment to business outcomes—to help teams understand not just how users felt, but why, and what to do about it.
ASK’EM became a platform that embedded customer insight into everyday decision-making—connecting product, UX, engineering, CS, and data science around a shared definition of value.
Because ASK’EM wasn’t just a survey tool. It became our feedback infrastructure.
The problem we had to solve
Before ASK’EM, data was everywhere—and insight nowhere.
In a single year, teams across the company sent hundreds of thousands of email surveys, each with their own goals, branding, and methodologies. The intent was good. The impact? Noise.
Signal quality declined, customer fatigue increased, and the one thing we actually needed—clarity—was in short supply.
And surveys were only the tip of the iceberg.
We had product telemetry and metrics spread across hundreds of indices and dozens of dashboards, CS tickets in other systems, and customer value metrics that meant something different to every team. Everyone was measuring, but no one was aligned.
Signals that could—and should—have been complementary were competing for attention.
Meanwhile, live A/B experiments were shipping daily, but we had no shared visibility into how those changes were experienced by customers. Was satisfaction going up? Effort going down? Were we fixing problems—or just shifting them?
We didn’t lack data. We lacked a shared system to translate it into meaning, and a way to connect that meaning back to the product experience. Feedback was noisy. Experiments were isolated. And decisions were disconnected from the people we were building for.
“The PMs I work with already use survey tools and detailed VoC reports, but told me afterward that much of it turns into noise—making it hard to get actionable insight, especially about app and website experience.
What stood out most was how ASK’EM filled that gap: clear feedback plus a baseline to measure improvements—something every PM wants but rarely has.”
What we built: A system, not a tool
We didn’t need more data, or another dashboard. We needed a foundation.
ASK’EM became the insight layer our organization had been missing—a self-serve, structured system that made gathering feedback effortless for teams, while preserving the scientific rigor and brand consistency that had been slipping through the cracks.
It started as a branded, easy-to-use survey launcher that supported in-app feedback. But that was just the surface. Behind ASK’EM was a carefully constructed platform that wove durable insight generation into our operational DNA.
“Once in-app survey deployment went from weeks/months of engineering time to hours/days, it really unlocked being able to quickly adapt our surveys or create new ones to accommodate the changing needs of the organization.”
What made it work
It wasn’t just an in-app survey launcher. It was the connective tissue between teams, tools, and truths.
Self-Serve, with structure: Any team could deploy a survey in hours—but every launch adhered to shared standards for branding, logic, and measurement.
Balanced sampling, baked in: Automated guardrails ensured quality data without manual oversight. Teams couldn’t oversample or skew results, even unintentionally.
Automated index creation: Every survey was auto-tagged with unique identifiers—mapped to product area, journey step, and segment—allowing for clean, unified joins in downstream analysis.
Data pipeline integration: Responses fed directly into our warehouse, pre-structured for dashboards and modeling. No manual stitching. No delay.
Dashboard-ready by default: From launch to live view, results were routed into real-time dashboards already embedded in product planning and performance reviews.
“Before ASK’EM, teams collected their own feedback—leading to duplicated effort and inconsistent data.
ASK’EM made collecting feedback fast, structured, and immediately usable—without teams needing to fix or reinvent anything.”
ASK’EM as an insight delivery system
A system that collects feedback is useful. A system that delivers actionable insight across the org? That’s infrastructure.
Monitor. Improve. Investigate. Contextualize. Alert. Attribute. These six modules made up MIICAA—our modular insight engine.
Monitor: Track key metrics in real time and pace progress against OKRs
Improve: Surface targeted recommendations and experience drivers by segment
Investigate: Drill into anomalies and build individual experience profiles
Contextualize: Link sentiment data with behavior, journeys, and segments for deeper insight
Alert: Flag critical issues and emerging risks automatically
Attribute: Estimate causal uplift and connect feedback shifts to specific product changes
Together, they transformed ASK’EM data into real-time, decision-ready guidance for product, UX, support, and leadership teams alike.
But building MIICAA wasn’t just about modeling complexity. It was about making insight approachable, relevant, and immediately useful—no matter your role or comfort level with data.
Rather than digging through charts, teams could get clear, contextual answers that changed as the underlying data did. And to ensure the system could scale across roles and contexts, we designed a curated dashboard layer tailored to the people using it.
Each MIICAA view was organized by:
Use case (e.g., improving onboarding, diagnosing pain points)
Audience (e.g., GM-level views, designer-focused views, CS ops)
Level of complexity (quick pulse vs. deep dive)
The result was a system that felt native to your role—a place where PMs, designers, CS leads, and executives alike could each find what mattered most to them, without having to learn a new language or dig through irrelevant metrics.
We also embedded tooltips, video tutorials, and hover-based explanations to support progressive learning, so users could build confidence over time.
The goal wasn’t just to democratize access to data—it was to curate a thoughtful, navigable path through it. Together, they formed a closed-loop system: from signal → insight → action → validation.
How we built it
This wasn’t a solo build. It was a coalition effort, powered by myself, engineers, TPMs, PMs, designers, data scientists, and BI analysts who believed in the same thing: That feedback shouldn't be fragile. It should be foundational.
We built ASK’EM to scale with the organization—without sacrificing quality. Balanced sampling wasn’t a layer we added later; it was foundational. Indexing happened automatically. Every survey generated clean, structured metadata, so teams could access dashboard-ready insights.
Behind the scenes, we made deliberate trade-offs between flexibility and consistency. We architected data pipelines to adapt to different product areas, journeys, and feedback needs. No wrangling, no hand-stitching—just ready-to-use signals where and when teams needed them. It was about embedding customer understanding into the operating system of the company.
At every step, we came back to one principle:
Make user experience easy to capture, impossible to ignore, and powerful enough to shape what comes next.
What’s next?
I’ll share how ASK’EM and the insight delivery system evolved from measurement tools into planning infrastructure—driving unified OKRs from GM strategy to feature team execution.
You’ll see how teams used this system to:
Prioritize the right experience improvements
Track satisfaction pacing across journeys and segments
Align CS, Product, and UX on a shared definition of value
Then in Part 3, we’ll go deeper into forecasting, causal impact modeling, and automation—the final piece of the loop that made this system not just insightful, but intelligent.
Throughout the series, you’ll continue hear from the team who helped make it real—because infrastructure this integrated doesn’t happen alone.
‘Til next time, I’m Bianca
