The Numbers That Tell Our Story: Metrics in TPM and AI

In the wake of a challenging product launch, the importance of metrics becomes painfully clear. This reflective essay delves into how TPMs interpret metrics, the balance of leading and lagging indicators, and the art of storytelling through data—all essential for navigating AI readiness.

Finding Meaning Beyond The Metrics

As I sat on a cold conference room chair, staring at the post-mortem slides from our recent product launch, I could feel the weight of the room. The air was thick with disappointment, the kind that comes after pouring countless hours into a project that didn’t quite land as we had hoped. I glanced at the metrics we were reviewing—some glowing, some not—and realized that in the chaos, we had lost the story behind the numbers. This is a lesson I find myself revisiting often as a Technical Program Manager (TPM) in the fast-paced world of artificial intelligence.

Metrics are our guiding stars, but without understanding their nuances, we can easily find ourselves lost in the vast galaxy of data. Metrics can be broadly categorized into leading and lagging indicators, and recognizing the difference is crucial. Leading indicators are like the early signs of spring; they signal what’s to come. In our world of AI, this might be the number of users engaging with a beta feature or the speed of algorithm training. These metrics provide insight into potential future outcomes. On the other hand, lagging indicators are the results of past events—like the final sales numbers after a product launch. While they tell us what happened, they don’t help us predict what might happen next.

I remember a particular project where our leading indicators were promising. User engagement was high, and feedback was positive. However, when we launched, the lagging indicators told a different story: adoption rates were abysmally low. This disparity became a critical lesson for us. We had focused too heavily on leading indicators without understanding the context—they were shiny, but they didn’t reflect the real-world complexities of user needs and behaviors.

To navigate this, we developed a KPI tree, a visual representation that helped us break down our objectives into measurable outcomes. Each branch of the tree represented a different aspect of our project, from user acquisition to retention and satisfaction. This structure not only made it easier to track our performance but also illuminated the tradeoffs we were making. For instance, we could see that while we focused on acquiring new users, we were neglecting retention strategies. The KPI tree was our roadmap, but it required careful navigation to avoid the pitfalls of vanity metrics.

Ah, vanity metrics—a term that can bring even the most seasoned TPMs to their knees. These are the numbers that look good on paper but don’t necessarily translate to meaningful progress. Think of them as the Instagram likes of the business world. They can be incredibly tempting to showcase, but if they don’t correlate to real engagement or revenue, they can lead us astray. In the realm of AI, where the allure of data can be intoxicating, it’s vital to remain grounded. I’ve seen teams proudly display user sign-up numbers after a marketing blitz, only to realize that those users had no intention of engaging with the product.

In our post-launch analysis, we also turned to health dashboards, a powerful tool for visualizing our metrics in real time. These dashboards provided a snapshot of our project’s health, allowing us to monitor key performance indicators at a glance. However, the danger lies in the temptation to treat these dashboards as definitive answers. They are snapshots, not stories. They require interpretation, context, and a narrative to truly understand what’s happening beneath the surface.

Storytelling with data is an art form that every TPM should master. It’s about weaving a narrative that connects the dots between numbers and real-world impacts. For instance, instead of simply presenting the number of users who completed a task, I learned to frame it within the larger context of user experience. What does that number mean for our overall goals? How does it reflect on our team’s efforts and the challenges we faced? This narrative approach not only engages stakeholders but also fosters a deeper understanding of our work.

As I reflect on these experiences, I find that the true value of metrics lies not just in their ability to inform decisions but in their power to reveal tradeoffs and potential futures. In a world where AI is transforming industries, we must remain vigilant in our approach to metrics. They are not just numbers; they are reflections of our strategic choices, our team’s efforts, and the user experiences we strive to create.

In closing, I urge my fellow TPMs—especially those who are newer to this intricate dance of data—don’t be fooled by the allure of metrics alone. Embrace the stories they tell. Seek to understand the leading and lagging indicators, build your KPI trees, and craft your health dashboards with care. Most importantly, remember that every metric has a tradeoff, and every number has a narrative waiting to be told.

Transforming Data Into Inspired Innovation

As we continue to navigate the complexities of AI, let’s ensure that our data doesn’t just inform us—it inspires us to create better products and user experiences.