How to create technology that fits people, not the other way around

Main take-aways

• Ask users what they’re trying to achieve without referencing your product to uncover their true goals and needs.
• Set aside technology during discovery to focus purely on understanding user behaviors and uncovering real problems.
• Use collaborative diagrams to align stakeholders and create a shared, evolving product vision.

Who are you in a nutshell? What do you do, and why do you do it?

Hi, I'm Naomi—a Product Leader with experience at &Open, Intercom, Typeform, and Microsoft SwiftKey. I focus on building human-centered experiences, leading high-performing and collaborative product teams, and I have a particular inclination towards AI-powered products.

I work in product management to help people and solve significant problems. My emphasis is on the human side of product development, always striving to make technology adapt to people rather than the other way around.

What's your setup? What tools, frameworks, and products do you use? Where do you work, and what is your work schedule like?

I have a pretty traditional desk setup: a Mac, a monitor, a webcam—everything you'd expect. I've also invested in a great standing desk, so I try to spend half my day standing. I'm really into movement, so my yoga mat is always rolled out behind me.

As for my tools and schedule, I use various applications to scale my productivity. When you're trying to drive everyone in the company in the same direction or convey insights and concepts, one-on-one or even group meetings can become bottlenecks. To mitigate this, I send a lot of Loom videos, voice notes in Slack, document everything in Notion, and use Miro for diagrams. This combination allows me to collaborate with key people and gather input and feedback from a broader group than I could through meetings alone.

I also use pen and paper a lot to clarify my thoughts and organize my time. Each day can look quite different, but I know that if I have over four hours of Zoom meetings in a day, I won't be productive. So, I block out focus time throughout the day and reserve time in the morning to organize my thoughts before diving in. Sometimes this works; other times, it's not realistic—but that's the aim!

What’s the biggest challenge for you at the moment, and how do you plan to overcome it?

My biggest challenge right now is figuring out what my next big endeavor will be that allows me to grow and learn. I have extensive experience in B2B SaaS, AI-powered products, and scaling products with a unique selling proposition in human-centered user experiences, all while leading high-performing, collaborative product teams.

However, I'm grappling with how to apply this knowledge and experience to tackle bigger problems the world is facing today. My plan is to focus on two areas: FemTech and Climate.

In FemTech, one of the significant issues has been the lack of data. Even in clinical trials, women and people with cycles have only relatively recently been included, so much of the healthcare industry operates on outdated data. Companies like Clue and Daye are now making more data available, which is both exciting and necessary.

The next step is where I see my experience being most helpful: taking this data and creating personalized, easy-to-understand, and useful actions and insights to solve real problems for people.

In your opinion, what defines a top 1% product management professional?

A top-tier product manager knows when to delve into details and when enough is enough. There are countless frameworks, strategies, podcasts, content pieces, insights, data points, user research findings, market analyses, KPIs, and OKRs. It's easy to get overwhelmed by the sheer volume of "things you are supposed to do" and "things you are supposed to know."

The best product managers I've worked with understand the critical data points and insights. They know which processes or actions will help unblock their next immediate step, but they also recognize when they don't need to get involved at a detailed level. It's a classic prioritization problem—not just prioritizing problems to solve or your roadmap, but also prioritizing your time and energy. This means knowing what to say no to or when you have enough information to move forward.

How do you consistently identify high-impact opportunities that others might overlook?

For me, this isn't about a quick hack. I'm always searching for the sweet spot where technical solutions align with the behaviors of real people using the software, creating value for both customers and the business.

I typically start by looking at four areas holistically:

• Customer Insights: What do our customers' workflows look like? What are their biggest problems? Why are they doing what they're doing, and what are they trying to achieve?
• Technical Understanding: How does our current system work end-to-end? Where do we see the most use? Where do we see drop-offs?
• Market Insights: Where do we have gaps compared to competitors? Where are we best in class? What's trending right now?
• Business Value: What is our biggest problem to solve as a business? Is it acquisition, engagement, retention, overall growth, revenue, contribution margins, or expansion?

With a high-level view of each of these areas, significant gaps, problems, or opportunity spaces usually start to emerge. If you're lucky, you might be able to tackle more than one at once.

How do you rapidly validate ideas with minimal resources before deciding to invest more?

This varies depending on what you need to understand to validate further investment in a solution. Initially, I'll try to determine the next immediate step, the question we need to answer, the hypothesis we need to validate, or the risk we need to mitigate to know whether it's worth investing more time.

Often, this involves user research or running through scenarios and options. For example, at &Open, our goal was to move to a completely self-serve platform, which was a potentially large investment. The first step we took was to review the system to understand what volume of our total gifting could be improved by moving to a self-serve platform, and then identify the smallest piece we could tackle to validate whether our customers would use it.

This gave us two validation points: first, before building anything, by better understanding the data and our system; and second, by breaking down a potentially large project into a very small chunk that could be validated in isolation.

How do you balance quantitative data with qualitative insights in your decision-making process?

Balancing quantitative data with qualitative insights is similar to finding opportunities and what makes great product people—it's about knowing where to dig into more details. For me, it's about understanding the "why" behind a piece of data or the impact behind customer insights.

For example, at Intercom, we noticed a retention problem (quantitative data/business value). We dug into the "why" and discovered our customers weren't able to handle the volume of conversations coming into customer support reps (customer insight/qualitative data). AI chatbots and automation were becoming more popular (market insight), and our system wasn't set up to scale to handle a higher volume of conversations other than by hiring more customer support reps (technical understanding).

This led to a significant shift in our strategy towards automated support, increasing three-month retention by 19%.

How do you go beyond surface-level feedback to dig deeper into user needs?

For me, the most important thing is to essentially ignore technology during initial discovery. When I receive user feedback, it's often along the lines of "Feature X isn't working as expected," "It would be great if you built Feature Y," or "This feature should work like Z." Users provide this feedback with a full understanding of their context and exactly what they're trying to achieve.

However, I only have assumptions about how they're using the product and what they're trying to achieve. To better understand their real problems or needs, I use two approaches:

1. Observe Them Using the Product in Real-World Scenarios: At Microsoft SwiftKey, an AI-powered predictive keyboard for iOS and Android, our amazing user research team would bring people in every week. We'd watch them typing on a mobile device to see what worked and what didn't.
2. Ask Them to Describe Their Goals Without Our Product: I ask them what they're trying to do, why they're trying to do it, and how they would accomplish it if our product didn't exist. This is key to understanding real-world needs so we can build something that fits, rather than something that may or may not be suitable.

How do you present your product vision to skeptical stakeholders, and what do you do to maintain alignment over time?

Anyone who has worked with me knows I'm obsessed with diagrams. A diagram of how something should work in the future, backed up with qualitative, quantitative, and market insights that link directly to business impact, is a powerful tool. Diagrams are also easier for most people to collaborate on, whether in person on a whiteboard or using tools like Miro.

I usually collaborate with all stakeholders on what the vision should look like. Each time, the overall diagram evolves by incorporating insights and perspectives from around the business, as well as quantitative and qualitative insights, so it's informed holistically.

The combination of early involvement, collaboration, and continuous updates to the product vision means it's not just my vision being pushed onto everyone else; it's a collective vision we've all worked on and shaped together.

What non-traditional metrics do you use to measure product success, and how do these metrics influence your strategy?

I typically focus on two areas to define success metrics for products and platforms:

1. High-Value Feature Adoption: At Typeform, this was especially important because we saw a correlation with 3-, 6-, and 12-month retention among users who adopted our "higher value" features, such as integrations, logic/workflow building, and personalization features. These features are usually more complex and harder to get people to use for the first time, but once they do, they create immense value, leading to continued engagement.
2. Scalability: This metric is often harder to quantify but is crucial. Essentially, I look at whether a solution scales. For example:
   • At Typeform: Does this solution enable our customers to generate a higher volume of insights using our tool? Can they reach more people with their forms or handle a higher volume of responses?
   • At Intercom: Does this solution enable our customers to handle a higher volume of conversations without hiring more customer support reps?
   • At &Open: Does this solution allow our customers to send a higher volume of gifts in less time? Could we handle 10x more volume without needing to hire additional staff?

Measuring success against these two areas encourages teams to think big—not just solving immediate problems but also paving the way for more significant impact in the future. It also creates a built-in flywheel effect. If customers are adopting your most valuable features, and those features empower them or your business to scale rapidly without additional costs, it allows both you and your customers to grow exponentially with each solution you build.