Case Study  ·  PubMatic  ·  2023–2026
Measurement

How I changed how my PMs define success

Role Lead Designer & Program Manager
Category Measurement Strategy
Year 2023 – present
Duration 3 years & ongoing
Context

PubMatic is a B2B ad tech company with four product teams and ~30 PMs. As Lead Designer, I owned the analytics function and led the migration from Pendo to Amplitude from 2022 through the January 2026 cutover.

Problem

Pendo measured clicks. Leadership was asking whether revenue happened. Years of training hadn't closed the gap, because the tool let PMs ship tracking without ever defining what they were measuring.

What I did

I owned the program end-to-end: tool evaluation across six products, KPI planning with the four product teams, event taxonomy design with engineering, and a reframe of how the org defines success — from clicks to revenue outcomes. The work was less about analytics and more about establishing measurement discipline across the organization.

Impact
Jan
2026
Pendo sunset. Amplitude live.
After 3 years of migration
0
Shared KPIs across four product teams in flight for end of Q2
First time teams negotiated metrics together
0
Product areas aligned on shared measurement infrastructure
Discovery

The tool wasn't broken. The operating model around it was.

PM Pendo workflow

Three structural problems had compounded over years:

Behavior was decoupled from outcomes.
Pendo could show usage but couldn't tie usage to revenue.
No shared definition of success.
Each of the four product teams tracked its own metrics in its own way. PMs optimized within their lane.
Measurement sat in the wrong place organizationally.
The head of product had asked design to drive measurement adoption, but design had no authority and PMs had no incentive. The accountability gap was structural, not a skill gap.

What was needed wasn't a better dashboard. It was a forcing function for measurement discipline.

Program · Tool Evaluation

Why I proposed migrating tools

For four years, I tried to fix the problem inside the existing structure: quarterly Pendo workshops, written manuals, one-on-ones with PMs. The needle didn't move.

By 2024, I was clear on the diagnosis. This wasn't a training problem. Pendo's strength was that anyone could set up tracking in a few clicks, and that was also why it failed. PMs could ship measurement without ever answering the harder question: what are we trying to learn? Adding more training on top of a tool that didn't require thought wasn't going to change behavior. I'd proven that to myself the hard way.

The shift came when I realized I was treating Pendo as fixed and trying to work around it. The tool wasn't the constraint I had to live inside. It was the lever I'd been ignoring.

Tools evaluated

Amplitude
FullStory
Datadog
Mixpanel
Heap
Pendo

So I started the conversation about replacing it. Amplitude requires custom events, which require engineering time, which require PM justification. The friction the tool introduced is what would create the conversation Pendo had skipped. The tool change was the mechanism. The actual target was how the org makes decisions.

Current state · How the org was structured

Set by Leadership
Big Hairy Audacious Goals (BHAGs)
Set by Product Managers
Fuzzy product KPIs
Fuzzy product KPIs
Fuzzy product KPIs
Fuzzy product KPIs
What gets celebrated by the company
Roadmap item
Roadmap item
Roadmap item
Roadmap item
Roadmap item
Roadmap item
Roadmap item
Roadmap item

That's why this project sits at the org and infrastructure layer, not the screen layer. The deliverable wasn't an interface. It was a shared operating definition of success across four product teams.

Five decisions shaped how we approached measurement

None of these were screen-design decisions. All were decisions about how the org would operate after the migration. I'll walk through them in the order they happened, because the sequence matters.

Decision 1: Selected Amplitude for the problem we were about to have, not the one we currently had.

Amplitude and Mixpanel were nearly tied on conventional criteria. The tipping factor was AI agent measurement, which our product was about to need at scale. I selected for that (I decided the tool, my boss paid for the tool), not for today's analytics parity.

Decision 2: Sunset Pendo cleanly. Only migrate data upon request.

A counterpart wanted to ingest a year of Pendo data into Amplitude. I pushed back. Importing all the old data was a large Engineering effort with little upside. The clean break forced every new report to engage with the new operating model.

Decision 3: Made tracking a PM accountability, not a design service.

Pendo had failed because PMs treated measurement as something design produced for them. Amplitude's custom event requirement moved the work to where the decisions get made. Each PM now justifies what they're measuring, why, and how it ties to their KPIs — at the planning stage rather than after launch.

Decision 4: Pushed for 5 shared KPIs across the organization, instead of each team defining their own.

The easy path was letting each team define its own KPIs. I argued for five shared across all four teams instead. The negotiation to land those five was the actual point: it forced the first cross-team conversation about what "good" means.

Decision 5: Reframed what gets measured, from clicks to end states.

This is the work that turned the project from an analytics migration into a product strategy project.

Framework shift: from clicks to outcomes

The shift was moving PMs from measuring intent ("they clicked the button") to outcomes ("the campaign went live, revenue posted"). Each KPI became a chain across source systems, not a single event. Once PMs could see the full chain from click to dollar, prioritization conversations changed.

Results

Amplitude isn't fully rolled out yet. PM training lands this quarter, so adoption numbers aren't the right measure of impact at this stage.

Confirmed

Pendo sunset and Amplitude live, January 2026.
Five shared KPIs across the four product teams, in flight to land by end of Q2 2026.
The first time those teams have negotiated metrics together.
Head of product fully bought in.
The company's AI agent strategy made behavior-level tracking non-negotiable, and the measurement infrastructure was already in place when it was needed.

Early signals

An unexpected adoption pattern. PMs are using Amplitude in ways I hadn't projected: to identify high-usage user segments and reach out for targeted interviews. The behavior has replaced the old workflow of asking Customer Service which clients liked which features. A more sophisticated use of the data than I'd designed for.

A representative case. A designer on the team shipped a pacing visualization tool and initially read it as a failure based on raw user count: about ten users. On closer analysis, only fifty users had access, making the actual rate 20%. Still low, but a different problem. A subsequent client conversation surfaced the real issue: the feature wasn't discoverable. The tool was redesigned. Three to four months of engineering investment that would otherwise have been written off were translated into a next iteration. That kind of quantitative-and-qualitative loop wasn't happening before.

Team measurement literacy. I run a biweekly "Insight of the Week" session where designers bring a notable data point from Amplitude or Pendo. The intent is making engagement with data a default behavior, not a special event.

The framework I'm teaching across the organization

Product Manager
UX Designer
Foundational

Believes it matters. Is actually measuring.

  • Has a metric for every shipped feature
  • Can name the event tied to the feature
  • Conflates clicks with adoption
  • Asks "are we tracking this?" before shipping
  • Treats instrumentation as eng's job
  • Designs flows without measurable exits
Intermediate

Asking the right question. Denominator thinking.

  • Defines adoption against eligible users, not total
  • Segments before drawing conclusions
  • Separates discovery failures from value failures
  • Designs with measurable steps, not just happy paths
  • Contributes to event taxonomy decisions
  • Uses funnel drop-off to prioritize iterations
Advanced

Measures, shares, acts — even when the action is "don't act."

  • Shares data laterally, not just upward
  • Can make a "hold" decision and defend it with data
  • Closes the loop: ships, measures, documents
  • Brings a data point to crits and retros
  • Uses behavioral data to challenge qual research
  • Can articulate UX impact of a metric shift to stakeholders