Both options are costly and time-consuming, with long lead times to ROI. Optimizing pricing as a lever rarely, if ever, comes up except in the form of the cheapest of all tricks:

“Hey, we can always increase our prices.”

Most leadership teams don’t think strategically about pricing when it comes to growth, and I suspect this is because most of us are daunted by the prospect of executing these pricing initiatives.

Having been a part of the design, decision-making, and execution of some really innovative projects where pricing was the secret unlock to growth, I believe pricing is an underused lever that can lead to exponential growth when understood and executed well.

How companies traditionally think about pricing

If you ask most tech companies how they think about their pricing, chances are they are setting and forgetting an outdated structure that their founders came up with at launch.

The founders probably looked at competitive pricing pages, guessed at price points, and did not consider customer value from their products (likely because they didn’t think they knew it at the time).

Now layer on the complexity of today’s SaaS pricing structures, including:

• Subscription-based tiers

• Usage-based pricing

• Freemium/free trials

• Pay-as-you-go

• All you can eat

… and you’ve got a recipe for confusion, if not disaster.

The key to strategic pricing

Strategic pricing is about aligning the actual price to the perceived value.

This is easier said than done; deconstructing perception is a monumental challenge, so companies tend to avoid it.

But it is far from impossible, and when understood and skillfully executed, it can lead to unprecedented growth.

Case study: How we reached 80% adoption by rethinking pricing

In a past role, I was the head of product at a company focused on the SMB market. There was a desire to go upmarket to increase the TAM (Total Addressable Market) because growth was stagnant.

We debated adding several types of enterprise features, which would have taken a long time to build out. Then, we had the idea to look at a competitive product in the market that was catering to that enterprise customer.

What we realized was that it wasn’t their features that were vastly different – it was the pricing model.

Our challenge? Designing a strategy that worked for both segments of the market.

We researched the needs of each of the segments and spent our energy on a model that would cater to both audiences.

Then, with feedback from our sales team, we discovered that several large customers had used the platform and were encountering mid-market challenges, mainly around support and multi-product use.

The solution we came up with was a combination:

• À la carte options for the SMB market

• Strategically priced, all-inclusive bundles for the enterprise market

The result?

The pricing strategy turned out to be a huge success. About 20% of the customers in the smallest business segment that had a choice between à la carte and bundle options chose the bundle. It was well-priced and encouraged them to use more than one product, which led to greater stickiness.

In turn, this increased retention for that market segment, which is a pretty challenging metric to improve.

In the other, larger enterprise segments, adoption was even stronger. We had 60-80% adoption of the bundle, with the percentage of adoption increasing along with the size of the customer.

That was a far better, faster, and ultimately more valuable outcome than building a bunch of complicated enterprise features – not to mention supporting those features.

Unlocking growth through strategic pricing

In order to assess the value of a product or feature, there are several tools and approaches at your disposal.

1. Look into your own internal data

There are plenty of times when the instinct is to survey to look outside your company, even though there’s often more data than you realize right inside your company. If you have robust data tracking on what users are doing, take the time for introspection. And don’t forget your internal teams: both sales and support feedback is often a goldmine of information.

I would also lean on AI tools to help synthesize that data. Even if it isn’t 100% accurate, it will be directionally useful and can point you towards areas worth digging in further.

For example, you can cut and paste suggestions that customers put at the end of a support survey into Copilot (or your tool of choice) and ask, “summarize these thoughts into trends.” Within seconds, you’ll have four or five themes to explore.

Say one of those trends is “need better reporting.” That gives you a signal that you may need to look into what exactly is meant by “reporting.” It’s a clue that something is going on that requires further investigation and could be an opportunity because it is clearly something customers value and care about.

2. Look at the market space

The market is a good source not just for pricing, but to understand:

• What features/functionalities are gated behind paid tiers

• What features are common in a market set

• Who is leading the space, and what unique features do they offer

• What value propositions are being used on marketing websites

These are all clues to where customers are putting value and how they are buying. And, best of all, this information is essentially free!

3. Look externally to fill in the gaps

Most leaders think that this step is too time-consuming and inaccurate. But if you did the first two steps, this one can be the cherry on top. You know where to look, who to survey, which segments, and what to test further. It is the sequence of these events that leads to better outcomes.

Though it might seem like collecting data from customers and prospects on willingness to pay or product usage takes a lot of time, with the tools available today, I say it can be done quite efficiently.

Here are the steps I recommend taking:

1. Return to internal data collection first. Talk to your sales team, your customers, and other internal sources to spot opportunities for growth, or at least emerging trends

2. Identify one small test you can run. This doesn’t have to be a price test; it can be a ranking of your most popular features or asking customers what new feature would be most valuable to them. Even split testing value propositions on a marketing website (often easier to execute than in product changes) will help you learn something. Once you have some confidence in an idea or a trend, then you can design a bigger project

3. Clearly communicate the value to customers. They won’t infer it on their own, so it’s critical to work with your marketing and sales partners to be clear on the value that you want them to focus on to make pricing strategies successful

4. Have a thoughtful execution plan. Make sure it accounts for different customer segments and anticipates possible risks and risk mitigations. Experience and outside help can be very useful here, but if you are doing it on your own, start with your constraints to delivery and work out from there

Pricing is never going to be perfect or 100% predictable. You can do all the research in the world, and the market can shift (remember COVID?). But a smart pricing strategy can be the key to unlocking long-term growth, and it’s not rocket science or an accident.

It’s worth investing the time and effort to improve your odds for success with this underused, often misunderstood lever – just don’t forget the execution and risk mitigation.

ICYMI: Alo’s episode on LaunchPod

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

• Laying a solid foundation with data

• Defining a durable strategy with clear themes instead of rigid project lists

• Collaborating effectively with your go-to-market teams

Planning is more than simply listing what you’ll build. Instead, it’s creating a real, adaptable plan that can withstand the inevitable changes of the year ahead – and finding the joy in the process. Remember, this is supposed to be the fun part!

1. Build a data-based foundation for your plan

To prepare for the race ahead, you first need to understand your current and past performance. This shouldn’t be just your perception of what’s happening, but what the data says is happening. This is a great time to refresh your understanding of your customer base, their usage, and your contracts.

I like to make this a presentation that tells the story through data because, while it’s helpful information for you to have, it’s even more valuable for the teams across the organization that support you.

What to ask in these presentations:

• What’s the distribution of our customers across their size or industry?

• Is there a customer segment where we’re growing more than others?

• What features are we seeing growth or fast adoption in?

• What about the really exciting features we created this year? Are they gaining traction?

• What’s the performance of our packages across the customer base? How many customers are making the move up our tiers?

The second part of this task is to plot your usage, growth, and costs over the last 12-18 months. This will help you to forecast what you expect trends to look like over the next year if there are no substantial changes to your performance.

This:

• Establishes your current performance

• Helps you set realistic goals

• Measure the impact of your initiatives throughout the year

Tip: If you expect a particular feature to be delivered mid-year that will drive greater adoption, you can model that X% improvement into the forecast.

Even very basic assumptions of growth or retention can help you to understand what’s realistic and set goals that have a foundation in reality.

2. Define your product strategy around themes

Defining (and hopefully revisiting) your product strategy is where you’ll spend the majority of your time. If you already have a strategy that you’ve been executing against, then comparing it to your fact base and your past performance is key to measuring its effectiveness.

Either way, you’ll want to start by reviewing the larger business strategy of the company. This isn’t the same thing as your product strategy!

• Your business strategy is often reflected in how you want the market to respond to you (e.g., revenue growth, customer satisfaction, market perception, etc.)

• Your product strategy describes what your product needs to look like to make that true

You probably have a backlog of projects that customers have requested or ones you’ve really wanted to get to. But just laying those projects out into a Gantt chart is not going to feel like a real strategy to the team.

What to do instead:

Level up those efforts as themes or initiatives, like “building endurance” or “improving finishing speed.” Allocate a percentage of investment against those themes and decide which one(s) you want to prioritize.

These projects then become examples of future investments. They also give you the freedom to learn more and evolve as the year goes on. If they’re just a list of projects, that list might (read: probably will) change within the first four months of the year, and you’ll feel like you’ve lost your strategy.

Instead, focus on themes to give your strategy staying power.

3. Create flexible roadmaps

A roadmap is one of the most visible deliverables of your annual planning. If you’re concerned that people will consider the roadmap a delivery plan, keep it framed among your themes and share it broadly, but set good expectations.

In general, people are quite understanding of the following guidelines:

• In this quarter, the roadmap is 80% accurate

• In the next quarter, 60% accurate

• Beyond 6 months, 20% accurate

Those declining percentages aren’t just execution hedging, but also an acknowledgement that any good trainer adjusts the plan based on the athlete’s progress, preventing injury and capitalizing on strengths as we learn more, new things pop up, or we want to keep working on something that is working.

4. Collaborate across the org

We can build the best product, but if we can’t get it into the hands of customers, we likely won’t see success. This is why your go-to-market strategy needs to live alongside your roadmap.

Questions to ask your team:

• Which market, or segment, do we expect to grow into? Is there a market we’re moving away from?

• What type of marketing or messaging adjustment do we need to reach this new group?

• Is it time for a price adjustment? Do we need to create more differentiation in our tiers?

• Does our support structure need to change to drive greater satisfaction?

• Would changes to our distribution strategy help open up new customers?

You might not know the answers to all of these questions, so collaboration with your GTM teams during annual planning is crucial.

5. Use AI to analyze the market and stress-test your plan

As you plot your annual plan, it’s important to have a good grasp on the competitive landscape and the market.

It can be time-consuming to stay up-to-date, and while it’s important to understand the details of how you compare to competitors, the customer’s view is what matters most. Because many customers will start their research with publicly available information, LLMs can be a great way for you to get these insights. You’ll need to validate them, but AI tools are a great starting point.

Here’s a prompt to get you started. I am usually quite specific about the output structure I want, so I can spend less time reformatting:

Act as a senior product strategist preparing a competitive analysis for annual planning. I need a concise summary of our competitor, ‘Competitor X’, focusing specifically on their product strategy for ‘our market’.

Please structure your response in the following sections:

1. Core Value Proposition: What is the main promise they make to their target customers?

2. Target Audience: Describe their ideal customer profile (e.g., SMB, Enterprise, D2C brands, etc.).

3. Key Strengths: List their top 3 most-marketed or best-known product features.

4. Potential Weaknesses: Based on public information (like G2 reviews or forum discussions), what are 2-3 common complaints or perceived gaps in their product?

5. Strategic Threat Level: On a scale of Low-Medium-High, how would you rate them as a direct threat to a company like ours, and provide a one-sentence justification.”

After you’ve prepared the elements of your plan, you need to stress-test it.

My advice?

Use a well-worded prompt that looks at your plan with a pessimistic view. This can help you identify areas you might need to address or improve. It may also highlight some blind spots you missed in your first pass.

Here’s an example of a good prompt that goes beyond “What do you think of my strategy?”

Act as my strategic advisor with the following context:

• You’re brutally honest and direct

• You’ve built multiple billion-dollar companies

• You have deep expertise in psychology, strategy, and execution

• You focus on leverage points that create maximum impact

• You think in systems and root causes, not surface-level fixes

Your mission is to:

• Identify the critical gaps

• Design specific action plans to close those gaps

• Call out my blind spots and rationalizations

• Force me to think bigger and bolder

• Hold me accountable to high standards

• Provide specific frameworks and mental models

For each response:

• Start with the hard truth I need to hear

• Follow with specific, actionable steps

• End with a direct challenge or assignment

6. Communicate your plan

With your fact base, modeling, product strategy, roadmap, and GTM considerations complete, your next step is to generate excitement across the team.

This won’t be a one-size-fits-all communication package.

This is how I often think about it:

• The elevator pitch (2 minutes): Short, clear, and repeatable

• The presentation (20 minutes): This is the version you’ll present across the organization

• The written detail (2 hours): This includes the data and assumptions behind your strategy

By grounding your training in data, aligning with your support crew, and leaving room to adapt as the process unfolds, you can turn annual planning from feeling like a chore to being a driver of your success.

Want to hear more from Ben? Check out his episode on LogRocket’s LaunchPod:

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

We’ve been around since 2007, and while we have grown and thrived, we’ve also built a graveyard of features that are difficult to find, unclear in their value proposition, and paired with an onboarding experience that almost seems to unintentionally discourage users from continuing to engage.

I used to think this was just a Sporcle problem. Turns out, it's everywhere.

The cultural problem: Smart teams, dumb assumptions

Product teams often buy into the myth that good products don't need promotion. We get so focused on building that we completely forget about the other half: getting people to discover, understand, and use what we've created.

The "build, ship, and pray" mentality is expensive. It wastes engineering cycles, demoralizes teams who watch their hard work disappear into the void, and prevents us from serving users who would genuinely love what we've built.

At Sporcle, we fall into traps that a lot of smart, well-intentioned product teams do:

• The field of dreams fallacy: We assume good products don't need promotion. If we have to promote it, maybe it wasn't good enough

• The perfectionist paralysis trap: We wait for features to be "amazing" before telling anyone. By the time they're "ready," the moment has passed

• The ROI measurement problem: We demand measurable returns before investing in promotion, but we can't get proof without trying

• The desperation delusion: We've convinced ourselves that promotion feels like desperation rather than service to users who might actually want what we've built

• The iteration timing challenge: We love shipping quickly, but this creates a go-to-market timing problem: When do you promote an evolving feature?

Our Lessons feature is a good example of this. We launched it on quiz pages and did a full newsletter promotion, then lost our engineer for two months to another project. The landing page, lessons tool, and curated topics came in spurts later.

While this gave us time to create content for homepage features, the challenge remains: how do you sustain promotion momentum when the product is constantly evolving?

Go-to-market failures at Sporcle

These aren't just theoretical problems. Here are three real examples from our own recent history that show exactly how expensive this GTM blind spot can be:

• The invisible app: We completely rebuilt our flagship mobile app with heavy, ongoing investment. Downloads were OK, but nowhere near the potential. Why? Almost zero promotion. It's not above the fold on our site, we've never sent emails about it, and there are no viral mechanics built in

  • Lesson: Great product, invisible execution = expensive mediocrity

• Sporcle Lessons and the stealth launch: After months of development on this educational quiz feature, our launch strategy was a button next to the play quiz button and a spot in the hamburger menu. When we finally added a homepage box months later, it drove real usage

  • Lesson: Promotion should be built into the product from day one

• Tournaments, the exception that proves the rule: This is closer to a success story. Full site modal, newsletter, and social push on launch led to strong initial traction. But even here, we face the ongoing challenge: how do new users discover existing features?

  • Lesson: Discovery and promotion need to be built into the launch plan

The good die young: Good features fail without promotion

Most of the features we launch have to survive this three-tiered issue:

1. Users don’t know the feature exists because we don't tell them

2. If they somehow stumble across it, the benefit isn't clear

3. If they're motivated enough to try it, the onboarding misses the mark so much that users bounce before experiencing any value

When features fail, it represents a systematic problem that compounds over time. Our work starts to feel like it has cumulative invisibility. Team morale takes a hit. "Why doesn't anyone use our stuff?" becomes the recurring question hanging over every launch.

The cost of "Build, Ship, and Pray" is that we overinvest in trying to make a project perfect, underinvest in go-to-market, and end up feeling despair when good work goes unnoticed.

How Sporcle is fixing our GTM blind spots

There is a cost to invisibility. Not just for individual features, but to users who would genuinely benefit from what we've built, but may never find it.

Promotion is not desperation or an immeasurable cost, but a service. We're not creating demand, we're reducing friction for people who already have the need. But it’s a cultural shift, and changing culture is no small challenge.

1. Building better promotional infrastructure

Asking individual teams to reinvent promotion for every feature launch isn't scalable.

To address this, I’m conducting an audit of our promotional tools and spaces.

Moving forward, we will be creating shared promotional tools and spaces that can work for any new product:

• Homepage slots that can be dynamically allocated

• Email templates that don't require starting from scratch
  Social media assets that follow consistent patterns

• Consolidating forums so that we have more focused customer messaging channels

The goal is to eliminate barriers to promotion itself, so teams can focus on crafting the right message rather than building the delivery mechanism.

2. Measuring what matters

At Sporcle, we’re very data-driven. We track user sign-ups, premium subscribers, quiz plays, badges, etc. But our data hasn't been focused on discoverability.

To change that, I recently worked with a PM and an engineer to create a new Discoverability dashboard for various features. Instead of simply tracking usage, we're now tracking the gap between potential and actual participation across our feature set.

For example, our Tournaments feature is mostly for paid subscribers, so we can see how many users actually join compared to how many could. Nearly 20% of eligible users join, and they play about 30% more quizzes per session than average.

The opportunity isn’t fixing discoverability for current subscribers, it’s getting more users into the funnel and unlocking that value earlier.

3. Defining “good enough to promote” in a fast-shipping culture

We ship fast and improve constantly, but when do you promote something that’s always changing?

At Sporcle, my team and I are defining what “good enough to promote” means, so promotion can align with dev cycles instead of clashing with them.

And when engineering shifts projects, we can use those gaps to write content, talk to users, and refine messaging.

Final thoughts: Promotion is product craft

We’re still early in the shift, and changing culture takes time, but “good enough to promote” is a lower bar than you think. Promotion drives the feedback you need to improve, but only if you actually do it.

We’ve waited for “amazing” before promoting, which often means not promoting at all. Discoverability can’t be an afterthought; it’s core product craft. If users can’t find and use what you’ve built, it might as well not exist.

Building things is great, but too many product teams stop there. You also have to become a master at go-to-market strategies or risk getting left behind.

Because it turns out, if you build it, they won't just come.

You have to go get them.

Check out Derek’s recent episode on LaunchPod:

What does LogRocket do?

LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

Traditionally, the PRD documented the what, why, and how of a feature, while prototypes came later, trailing behind the spec. When they’re not in sync, teams are often left with outdated documentation and incomplete visual representations.

In today’s fast-paced and AI-powered environment, PRDs are becoming increasingly obsolete because:

• They go stale fast. By the time a feature is groomed and prioritized, the original context has shifted

• They require interpretation. Despite detailed language, engineers and designers often end up guessing

• They slow down iteration. PRDs are too heavy for experiments and MVPs. They’re built for certainty in an uncertain world

• PRDs don’t scale alignment. The more cross-functional your team, the more interpretation creeps in.

AI is upending this, not by making PRDs faster to write, but by making them unnecessary in many cases.

Picture this:

A product manager receives a feature request to improve the onboarding experience. Instead of writing a traditional spec, she opens Figma’s AI plugin, describes the flow in natural language, and generates a working prototype in under 15 minutes. Then, she sends it to her designer, who layers in branding and accessibility considerations. The same day, an engineer reviews the prototype, clarifies a few logic branches, and drops it into Vercel’s v0 to scaffold a functional build.

By the next morning, the team has:

• A shared understanding of the user flow

• Buy-in from design and engineering

• A click-through experience for stakeholders to review

There’s no ambiguity, no outdated spec, and no need for hours of alignment meetings. Instead, the prototype becomes the spec, living, testable, and ready to iterate.

This isn’t the future. It’s happening today.

How AI revolutionizes prototyping

An AI prototype is more than a fancy mockup. It’s a generated and testable user experience built from prompts and logic, often in minutes. It shifts alignment from documentation to demonstration.

What used to live in separate sections of a PRD is now directly embedded in the prototype:

• User stories → Simulated user journeys

• Feature descriptions → Interactive mockups with annotated flows

• Acceptance criteria → Edge-case walkthroughs baked into the experience

A new prototyping stack for PMs

Thanks to generative AI, product managers no longer need to wait for design or engineering to visualize an idea. But where to begin depends on the purpose. Drawing from practical frameworks like Colin Matthews’ guide to AI prototyping, here’s a comprehensive breakdown of tools by use case, fidelity, and team alignment:

• Chat-based assistants (for quick experiments and low-complexity use cases): Tools like ChatGPT and Claude can generate one-page functional prototypes like calculators, forms, or basic utilities. They’re perfect for early concept testing or internal demos

• Image generation (for ideation and visual direction): Midjourney, DALL·E, and Ideogram help PMs and designers quickly generate design inspiration, theme boards, or UI motifs. This is ideal for early brainstorming and aligning on visual tone

• Interactive mockups (for flow design, validation, and stakeholder feedback): Tools like Stitch, Uizard, and Figma AI convert written prompts or low-fidelity sketches into high-impact, clickable flows. This is best when you need to map a user journey and share it with stakeholders for review

• Code-generation and dev environments (for high-fidelity prototyping and MVP handoff): Use platforms like Replit v0, Lovable, GitHub Copilot, and Cursor for multi-page, logic-driven prototypes. These tools can generate functional apps in minutes and are ideal for validating engineering feasibility or showcasing product direction to technical teams

  

Whether you’re ideating, aligning stakeholders, or pressure-testing feasibility, these tools can accelerate your product development workflow and foster faster decision-making.

Reimagining the PRD

The traditional PRD isn’t dead; it has just fundamentally changed.

Here’s the lightweight PRD framework I recommend today:

1. Prompt + AI prototype: Start with a prompt and generate a clickable flow using tools like Stitch or Figma AI. Use clear language to describe the intended journey and core interaction

2. Contextual narrative: Write a concise two–three sentence summary that explains the user's goal, current pain point, and the value this feature unlocks. This keeps everyone aligned on the user need behind the flow

3. Edge case scenarios: Identify two to three likely edge cases that should be covered. Use Figma’s branching or comments to annotate these paths visually

4. Linked artifacts: Provide direct links to your prototype, relevant image boards, and any generated code snippets. Label them clearly for design, engineering, and leadership audiences

This is what I call the "AI Spec": part narrative, part prototype, but fully aligned.

How to start: Crawl, walk, run

If this sounds intimidating, don’t worry. Here’s how I recommend getting started, with practical actions for each stage:

• Crawl: Choose one low-risk initiative: a small UI enhancement, onboarding tweak, or internal tool. Use a tool like Stitch or Figma AI to generate a single screen or flow from a natural language prompt. Share it alongside your traditional spec and gauge how it helps clarify the idea

• Walk: For your next medium-complexity feature, replace your PRD’s feature description with an interactive prototype. Include annotations, basic edge case paths, and contextual narrative. Use the prototype as the discussion surface during sprint planning or design reviews

• Run: Adopt prototypes as your team’s central alignment artifact. Replace specs with working prototypes, use tools like Cursor or Replit to hand off to engineering, and share links with stakeholders for async feedback. Layer in documentation only when needed (i.e., compliance, legal, or deeper edge cases)

This progression not only builds confidence but also shifts your entire product development cadence toward speed, clarity, and collaboration.

Getting started with AI-driven prototyping (+ Bolt AI prompts)

If you’ve never used AI tools to prototype before, it can feel overwhelming to begin. Don’t overthink it; you don’t need a complicated setup or weeks of research. You just need a tool that helps you get ideas out of your head and into a working prototype quickly.

For me, that tool is Bolt.

I’ve tried many AI prototyping platforms, and Bolt stands out for a few key reasons:

• It generates real, high-fidelity UI, not just rough wireframes

• It’s fast. I can go from an idea to a click-through experience in under 15 minutes

• The defaults feel good. I don’t have to write long, overly structured prompts to get a usable output

• It integrates well with tools like Cursor and Vercel, so getting from prototype to working code is frictionless

Prompts I use to prototype real product ideas with Bolt

Here are three examples I use when testing product ideas:

1. Vertical SaaS web app for garage owners
   Create a desktop web application for garage owners to manage jobs, track parts inventory, and handle customer bookings. Include: dashboard, job queue, parts inventory, customer profiles, and payments.

   

2. AI-powered marketing automation tool
   Generate a desktop web app for small marketing teams to automate email campaigns and social media posts. Core features: campaign builder with drag-and-drop UI, AI-generated email and post copy, audience segmentation, automated scheduling, performance dashboards, and AI-powered suggestions for send times and content optimization.

   

3. AI-enabled fitness form coaching app

   Design a mobile-first fitness app that records users performing exercises and provides real-time form feedback. Core features: workout library, exercise recording via camera, AI-powered form scoring, instant feedback on posture and movement, personalized tips to improve form, and tutorial content showing correct technique.

   

What does this mean for product teams?

Adopting AI-driven prototyping changes the dynamics across the product development lifecycle, not just in how products are scoped, but in how teams align, communicate, and build. It shifts product development from a documentation-heavy process to one that is faster, more visual, and iterative.

The pros of AI-driven prototyping:

• PMs lead with simulation. Not just specs. You’re facilitating clarity through demonstration

• Design becomes a shared surface. Not a gated function. AI lowers the barrier to entry

• Engineering gets involved earlier. Because the prototype shows intent and nuance

• Stakeholders finally get it. Because seeing > reading. And seeing = believing

With this approach, you’ll still need documentation, but that documentation will start to resemble annotations on a living prototype rather than pages of structured prose.

But the implications go deeper than workflows; they affect team culture and velocity. Teams that adopt AI-driven prototyping:

• Ship faster by getting alignment earlier in the process

• Reduce rework by exposing misunderstandings visually

• Encourage more experimentation because iteration is cheaper

• Improve stakeholder trust with clearer demos and fewer surprises

On the flip side, teams that resist this shift risk falling behind:

• Specs continue to go stale before they’re implemented

• Engineering loses time clarifying requirements that could’ve been demonstrated

• Design continues to operate in silos, slowing feedback loops

• Stakeholders disengage because they can’t “see” what’s coming

The faster teams move from idea to aligned prototype, the more quickly they learn, adjust, and win.

Don’t kill the PRD, reinvent it

PRDs still have a place. Regulatory teams still want traceability. Some stakeholders will still want to read. But if your process depends on documents alone, you’re building blind.

Next time you have an idea? Don’t just write it down. Prototype it.

What does LogRocket do?

LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

Some feature sets are an order of magnitude more valuable together than the sum of their parts.

When Aleks Bass joined Typeform as CPO, she set the team on an aggressive user research agenda. She was looking to fix what was seen as a lack of cohesive product strategy to reinvigorate their slowing growth. What she didn’t expect to find was the secret to unlocking growth in “feature constellations”: sets of features that, when combined, can 10x customer willingness to pay.

Feature constellations became the foundation of their fastest-growing product in company history: Typeform for Growth.

For the whole story, check out our LaunchPod episode where Aleks details the entire research and build process:

Here’s how they did it, and how you can reveal your product’s feature constellations.

1. What are feature constellations?

Feature constellations are sets of features that, when combined, solve a complete workflow and deliver far more value than any one of the features does alone.

For example, Typeform’s forms allowed people to submit video responses. But reviewing these video responses was labor-intensive and didn’t scale for customers. However, when paired with an AI analysis tool that pulled key themes from the video responses, value increased exponentially:

“Video answers had a decent willingness to pay. But when combined with AI analysis of themes and sentiment, all of a sudden, those two capabilities together had a much higher willingness to pay.”

Not only did the value for customers grow, but this feature constellation also made the company more revenue:

“We found that when you combine these two features, [customers are] willing to pay 5x, or even 10x what they would for each individual feature. Because you're streamlining the workflow, you're unlocking additional value.”

2. How to find your own feature constellations

Aleks and her team uncovered high-impact bundles through structured research:

Step 1: Pull everything onto the table

The team collected over 80 features and capabilities from a wide range of sources: customer success, sales, marketing, product managers, their various survey tools, and more.

Then they made sure that they were articulated clearly, in a way that the customer base could clearly understand.

Step 2: Ask customers to prioritize

The team focused broadly to start: anybody looking for form-like software. They wanted to know who they were, why they were using the software, what they were doing with it, what their budget was, and what their biggest pain points were with current form tools.

Then, they used a MaxDiff survey to force prioritization and tradeoffs across the ~80 features and capabilities uncovered above. Users were shown several variations of subsets of this feature set repeatedly. Each time, they were asked to determine:

• The relative value of each feature compared to others on the list

• A value score per feature

• Willingness to pay

Aleks details the full process here.

Step 3: Determine major customer groups and pick a focus

The team then used these MaxDiff findings to determine three major customer groups:

1. People who are doing lead generation-like activities (Marketers)

2. People who are doing talent acquisition-like activities (Recruiters)

3. People who are doing feedback, market research, customer experience, types of activities (Researchers)

Across those three groups, 75% of feature needs were overlapping. But 25% were different.

One complication, though, was that they found each group required a different maturity level of the overlapping features to find true value in them. The better a need was already met for the group, the more maturity and completeness they needed from a capability to derive value.

Budget sizes also affected willingness to pay. In the end, Marketers had a relatively high willingness to pay for the lowest maturity of solutions. They typically had higher budgets, and their needs were found to be the most unmet around the most important feature areas.

Step 4: Spot natural feature pairings

Finally, researchers at Typeform ran analyses on the data around willingness to pay and prioritization.

Patterns emerged where the prioritization and willingness to pay for certain features both increased when they were ranked with certain other features. This was consistent when these feature sets were shown in the same group for ranking, but priority and willingness to pay dropped when they were with other features.

This was because, upon deeper inspection, these features were found to unlock a complete workflow when combined. Analysis proved that the features, alone, had a fine willingness to pay and priority score. But, when grouped, priority and willingness to pay increased 5x to 10x.

For example, Typeform had released video answers, where respondents could submit a video answer. These video answers had a decent willingness to pay, but when combined with an AI analysis of themes and sentiment, all of a sudden, those two capabilities together had a much higher willingness to pay.

“Because if I'm getting 50 to 100 videos without any ability to really extract context [or] analyze them at scale, I'm left watching 50 to 100 videos, which makes me feel like that's going to be extra work. It's still valuable, it's just not quite as valuable as layering additional capabilities that then help me extract that true value from that video question capability in the form. There were a few of these interesting feature constellations that helped us from a prioritization standpoint.”

3. How to nail what you deliver

To build the right things, you need to:

• Know who you’re building for

• Know what you’re building

• Verify you’re building the right thing in the right way

The first two are accomplished from the MaxDiff research done prior, as well as honing this understanding with user interviews to more deeply understand.

But, the last one? This requires continuous usability testing throughout the development lifecycle.

And don’t just track clicks, talk to users:

“The click-through data is only going to give us small percentages of information. The context we get from having real conversations with humans improves our abilities to solve their problems tenfold.”

Run continuous user and usability tests and ask:

• Does this meet your needs?

• What do you expect this button to do?

• How do you imagine this experience unfolding?

• Does this meet minimum requirements of usability?

• Where are the traps?

• Where can we improve?

Takeaways

Isolated features check boxes. Feature constellations unlock workflows and serious business value.

They helped Typeform shift from fragmented priorities to launching one of their most successful products.

Next time you build a roadmap, don’t just ship a feature. Ship a constellation.

Inspired by LaunchPod appearances:

From working a double shift in a pizza shop to picking and fulfilling orders in an auto parts store. It’s hard to understand your users if you haven’t done the job.

Customer research is vital, but often not enough. If you want to build real empathy as a product leader (and build products that really work), sometimes you need to step behind the counter and just “make the pizza” yourself.

Product managers talk a big game about empathy. But most have never lived their users' workflows.

Here’s how these leaders turn empathy into action:

• Deepti Mendiratta, VP of Product at HungerRush

• Susan Stavitzski, Principal Product Manager at CarMax

• Julie Acosta, Dir. of eCom & Marketing Analytics at NOBULL (formerly Sr. Digital Analytics Manager at Autozone)

Live the workflow. Literally.

Support tickets and sales feedback aren’t the only way, or even the best way, to understand product pain points and opportunities for improvement.

At HungerRush, Deepti and her team actually stepped in to “make the pizza” with their users. Banging their elbows while running the drive-thru window, working double shifts, and dealing with last-minute order changes on the fly, they experienced the chaos of a quick-service restaurant firsthand.

But this allowed them to understand why certain processes had to be one-click, where user friction really was, and overall, how to improve their POS and ordering management technology in ways that drove benefit for their users.

“We’ve ingrained it in every single product person,” Deepti says. “Go live the life of your customer.”

Similarly, Julie Acosta and her team at AutoZone were required to work in physical AutoZone stores once a month, pulling parts and working at the counter, to directly experience the limitations of their digital catalog and in-store lookup system.

Action: Embed teams in frontline environments regularly, even for just a few hours. There is no better way to understand and empathize with how users interact with your product.

Translate emotion into design

You can count clicks. You have to feel frustration.

When Deepti was running the drive-thru, she tried to edit a customer’s order mid-dinner rush while three screaming kids waited in the back seat. "I panicked," she said. The system wasn’t built for quick edits, and the experience was overwhelming​.

Feeling this firsthand helped Deepti deeply empathize with the users of their product, often teenagers working stressful, busy rushes:

“I didn’t just have the number of clicks as a metric. I also had the emotion that came with it.”

Julie’s AutoZone team saw the same thing. After working store shifts, they noticed language gaps in their catalog search that didn’t just annoy, they derailed entire transactions:

“When I went and worked in stores, I noticed that the way we, as product and tech people, had written catalog search didn’t line up with how a regular customer described their problem. Sometimes it would be enough friction that we’d lose the sale.”

Takeway: Numbers shouldn’t be the only thing that convinces you to prioritize a fix; lead with emotion and data, not just a ticket count.

Build for people who tap the keys

Your buyer isn’t always your user. Build for the people who tap the keys, not just the people who cut the checks.

“The actual user of the software could be anywhere from a 20-year-old kid to somebody who started the business on pen and paper,” Deepti says.

At HungerRush, talking to frontline workers helped her product team translate user pain points into what were essentially consulting-style advice for franchise owners and corporate customers. This strengthened the relationship across stakeholder levels.

Susan Stavitzski's team at CarMax took a similar approach. They hired former store employees (“Principal Field Leads”) into product teams to ensure deep frontline empathy and immediate reality checks:

“In order for us to truly know the field, there's nothing better than having someone who has worked in the field. So there is a principal field lead on the majority of product teams. We’re quicker when it comes to discovery and understanding how we can move forward as a team because we have a person with a wealth of knowledge to help us get connected.”

Action: Map every buyer and every user. Build direct feedback loops for frontline operators, not just executives.

Walk the walk: Bake hands-on empathy into your team’s culture

Empathy can’t be a one-time thing. It needs to be baked into your culture.

HungerRush doesn’t just make the pizza once; they’ve embedded it into their team culture through structured programs and expectations. Even new hires are expected to work in-store environments.

At AutoZone, HQ employees follow store dress codes, work holiday shifts in distribution centers, and regularly shadow stores. As one team member put it: “When you put yourself in the shoes of the store AutoZoner, you start thinking about things differently.”

Action: Make hands-on customer experience a standard part of OKRs, onboarding checklists, and performance reviews for PMs and designers. Walk the walk every month or quarter, and with every hire.

Takeaways

Support tickets don't paint the full picture. Empathy lives behind the counter, at the drive-thru window, in the back kitchen.

If you want to build better products, don’t just study your users – be the users. Make the pizza.

Over the years, CallRail has undergone massive growth. Part of the secret, though, has been a dogged focus on what really matters: simplicity, speed, and customer satisfaction. Here’s Ryan’s antidote to avoid “shiny object syndrome” and help product teams scale without losing their edge.👇

Check out the full LaunchPod episode with Ryan here:

🚨 Don’t chase tools, chase value

“There’s always a new analytics or AI tool, but switching costs are real. You don’t need the shiniest tool, you need the right one.”

Instead of chasing trends, Ryan helps teams ask: Does this tool or idea make our core experience better?

At CallRail, that mindset helped the team resist distractions and stay focused on what customers actually need, not what’s trendy.

🧠 Innovate from insight, not instinct

Use customer behavior and support data to prioritize features that matter.

When CallRail noticed performance issues with a core page, they realized users needed faster access to high-level insights. So, they built a new dashboard from scratch in record time:

“We noticed really quickly, our customers [...] needed more phone numbers [...] and now we put it in front of eyes in the right place.”

The result? Better performance, happier users, and a measurable spike in customers taking action on key alerts.

⏱ Set deadlines for testing new ideas

Ryan’s rule: Time-box new ideas. If they’re not delivering value by that deadline, kill them.

“We had this North Star of a magical dashboard… but now we had this forcing function: what can we do in 45 days?”

📊Avoid data chaos: Prioritize features that add value without bloating UX

CallRail serves 200K+ users, from solo plumbers to large marketing agencies. As bigger customers came on board, they started asking for more advanced reporting and customization.

But more features aren’t always better.

“Our biggest customers were asking for more customization — but we had to balance that with keeping things intuitive. More isn’t always better.”

Instead of bloating the UI, the team built their new dashboard to surface the most relevant insights that could be used across the board.

🛠️ Align on tools or risk drowning in noise

Serving so many users and teams, data chaos was a real risk at CallRail.

“We’ve all been there where you ask the same question from three teams and they give you three answers.”

Ryan’s fix: Get cross-functional agreement on the tools and frameworks everyone uses and how data should be interpreted.

The fastest way to lose your edge? Saying yes to everything.

Good product leaders know:

• Volume doesn’t equal value

• Listening is better than guessing

• Ease of use beats feature creep every time

Alo, now SVP of Product at U.S. Bank, has found that her teams can greatly focus the scope of user discovery sessions by first talking to just 5 users.

Listen to the full episode here:

Here’s how the “Rule of Five” helps teams move faster and de-risk their biggest product decisions. 👇

1. Start small to spot the signal

Start small but strategic: talk to five users in each audience segment to uncover broad themes before investing in deeper research.

Alo says, “Five is like a good manageable number… you get 80 to 90 percent of what you need just talking to those five people.”

2. Look for patterns before overcommitting

At Wave, Alo’s team used the Rule of Five to rethink navigation and pricing:

• First, exploratory interviews revealed how users naturally described product features

• Then, they validated concepts with additional research

• Finally, they shipped and measured results in the real world

“You're doing broader research to understand some things. Then, when you’ve got some ideas, you do more validation. And then ultimately, when you launch, you’ve got the real data to compare and contrast.”

3. Pay attention when the story repeats itself

In a past pricing project, Alo’s team initially talked only to current users. But something felt off, so they interviewed five prospective users, too.

“Even within five, we figured out: something’s going on here. This pricing model doesn’t make sense to them.”

If five people flag the same issue? That’s your cue.

4. Design follow-up research with more clarity and purpose

“Go broad first… then figure out where to dive deeper and design the right study.”

Alo emphasizes asking around the question, not just “What do you want?” but “Why do you need that?” and “What’s happening around that task?”

In Alo’s previous role at Toast, field research during COVID-19 revealed that unreliable Wi-Fi, not just POS software, was a major blocker for customers trying to access Toast services.

These deeper insights wouldn’t emerge without intentional, open-ended initial research.

Good research takes time, but the Rule of Five isn’t about perfection. It’s about velocity with intention:

• Start with a few users

• Spot the patterns

• Decide where to double down

What systems have you developed for user research?👇

Recognize that most problems are already solved elsewhere

You don’t need a blank slate to start. Apply creative thinking to adapt existing solutions to your current challenge.

Mikal’s team at Nordstrom borrowed from in-store merchandising strategies to improve outfit recommendations online. Just like a salesperson helps you put a look together, they launched a digital product that recommended complete outfits to users, boosting both confidence AND conversion.

“If you have the willingness to apply some abductive thinking, you can cheat off of the past, off of related industries, to solve the problems that you face today.”

Save your team’s energy for what matters

Focus innovation on the things that matter most: strategic advantages, user delight, and differentiating your product’s feel.

“How do you increase the quality and the pace of something? Well, you deliver more value faster. And a simple way to do that is through making sure you're spending your decision friction and your decision challenges in areas where it really has impact."

Steal smart: Look for high-leverage wins

At Nordstrom, Mikal noticed that the ecommerce site forced customers to reselect their shoe size on every new product page. In-store, no good salesperson would ask you for your size twice.

His team “cheated,” using the same playbook: tracking size selections and pre-selecting them across pages.

“We did see a conversion lift [...] but ultimately, it just made sense. It took out the work from the customers and it was the right thing to do.”

How can you find these? Look for table-stakes features: search, sizing, signups. Make them so easy that they just disappear.

Build a portfolio of reusable insights

Mikal actively collects models and strategies from across industries and keeps them in mind for future reuse.

💬“I want to learn from the best […] so I can cheat off of them when I face everyday challenges that they've already solved.”

What’s a solved problem you’ve borrowed from another industry or a past strategy?👇

Dane Molter, VP of Product and Strategy at Navan, has spent years building teams that operate courageously to get outsized returns.

Here’s how to build a product team that moves fast and takes smart risks.

Check out the full episode with Dane here:

Run small, cheap, fast tests

Small tests can drive big wins. Dane urges teams to run quick, inexpensive tests rather than spend months building complex features before validating them.

In a former role at Expedia, Dane’s team ran small tests like tweaking banners, copy, and push notifications. Minor changes boosted engagement and retention without heavy development.

“You may not understand how a single percentage point change on your conversion funnel will shift revenue at first, but go run the test. It’s cheap. It’s fast. Learn from it.”

Challenge assumptions & “best practices”

Dane emphasizes that pushing against conventional wisdom leads to real breakthroughs.

“You’ll always need SMEs, but they’ll tell you, 'You can’t do it like that.' Your job as a product manager is to challenge, push, and deliver something fundamentally different.”

At LogRocket, an idea for our blog site that was initially dismissed as “dumb” went on to nearly quintuple conversion rates.

Small risks can unlock huge opportunities.

Identify leading vs. lagging indicators

Don’t just chase big outcomes. Identify small, measurable leading indicators that drive long-term success.

At Expedia, Dane’s team didn’t just track bookings. They analyzed early signals like app downloads, session length, and return visits to predict larger business outcomes.

“The way you can really contribute to the long-term impact of the business is to understand the little metrics that matter.”

Tie every experiment to a business outcome

Speed matters, but testing without purpose is just noise.

At Navan, the challenge was modernizing corporate travel booking, a traditionally human-driven process. But simply “adding AI” wasn’t enough; it needed to drive measurable value.

Instead of blindly integrating AI, the team set clear objectives: reduce time spent managing bookings and lower costs for companies. By linking experiments to these goals, they made sure their innovations delivered real impact.

Unlock courage: Failure is an option, stagnation isn’t

At Grubhub, leadership challenged him to launch 50 new markets in a year without expanding the ops team. People hesitated. What if a key piece failed?

The response? "If you don’t have the courage to fail, you’re never going to build anything."

That mindset shift drove the team to take smart risks, push past their comfort zone, and rapidly scale Grubhub’s delivery network.

Nan Yu, Head of Product at Linear, prioritizes one large, dynamic team above all else to focus on the highest-impact work.

Kevin Wang, CPO at Braze, believes focused, durable teams build long-term expertise that drives innovation and competitive advantage.

Here’s what I learned from both about building successful product teams. 👇

The dynamic approach

Nan Yu believes in Conway’s Law: the product you build will reflect your org chart.

"If you have two teams that are equal, you’re making a very strong assumption that they are equally important, and that's almost always not the case."

Dynamic prioritization means focusing on what matters most. Nan champions a constantly evolving product team structure.

It’s ideal for startups:

• Great for ever-changing environments

• Quick iterations & fast wins

• High visibility on progress

Dynamic teams rapidly shift priorities based on insights and market needs. As Nan put it, "Shift people to where you need them. It’s how you build what’s most important right now."

But dynamic comes with trade-offs:

• Risk of shallow expertise on long-term bets

• Fragmented ownership

Spreading resources too thin sacrifices impact. Dynamic works best when teams move fluidly across projects.

The focused, durable approach

At the other end of the spectrum, Kevin Wang values stability, deep expertise, and long-term ownership.

"You want experts, but you don’t just want experts in this particular problem that I decided to solve this Tuesday. You want experts in an entire slice of the market."

For example, building an AI-focused team in 2016 gave Braze a strategic advantage in today’s market. He believes focused, durable teams drive long-term innovation:

• Solves root-cause issues

• Builds deep product & market expertise

• Incentivizes long-term bets

"If a team is running on a suboptimal database, maybe they actually just need to spend six months and fix it. And if they know that they'll be on that team 24 months from now, they’re a lot more incentivized to actually fix that root cause problem and create a step-function improvement."

The risks of this model?

• Slower to pivot

• Requires strong product vision

Durable teams thrive when there is a clear, compelling vision. Without it, they risk focusing on the wrong problem. When paired with strong conviction, this approach can provide a competitive edge.

Dynamic and durable aren’t oppositional. Both have their place:

• In fast-changing environments, being dynamic helps you iterate quickly and adapt.

• If you know your direction, durable teams provide depth and stability for long-term innovation.

Check out both full conversations from their appearances on LaunchPod:

Check out the full episode with Kristin here:

Not every PM should be a visionary

There are different kinds of PMs:

• 0 to 1 PMs (Builders): The ones who thrive in ambiguity and love starting from scratch

• 1 to 100 PMs (Scalers): Take a scrappy but promising solution and turn it into something scalable and maintainable

• 100 to 101 (Optimizers): Detail-oriented, fine-tuning to eke out the last bit of performance

Trying to fit everyone into the same mold only leads to misalignment.

Here’s how to balance a team of builders, scalers, and optimizers.

Match the right person to the problem

Trying to fit every PM into the same mold only leads to misalignment.

“Where I’ve seen things not go so well is if you put a zero-to-one on something like risk or compliance... they’re going to struggle. And it’s not that either is better — they’re just better at different types of problems.”

As a CPO, Kristin sees her job as a strategic matchmaker:

“I love solving problems… but I can’t do it for all of them anymore. What I have to do is stay at 10,000 feet and match the most important problems with the right people to solve them.”

A good product leader spots the biggest opportunities and aligns them with people on the team who have the right mindset and skills to execute them.

Projects evolve, and so should your team structure

Don’t let your PMs get complacent — or bored. Recognize when a PM needs a new challenge to stay engaged.

Kristin shared a past experience where she felt underutilized when a scale-focused project turned into optimization work over time:

“If I’m not building something, I get really bored. I’ve had great optimizers on my team who love the 1–3% wins. But that’s not what energizes me — and I need different kinds of people for different stages.”

The high performers who thrived in the early stages might need a different kind of project 2 years in.

Create structure but leave room for flexibility

Viator uses a dual-track method that balances company-wide strategy and team-level autonomy.

Each year, leadership defines a set of top-level OKRs and picks 10–12 “big bets,” high-impact initiatives that consume roughly 50% of the product team’s capacity. The remaining 50% is driven by individual product teams.

“The goal would be: these are the big bets… and then [teams] fill in the rest with the team-driven work that helps drive their charter forward.”

This model gives clear direction without crushing autonomy.

The future of product leadership isn’t about doing it all, it’s about connecting the right people to the right problems at the right time.

How do you manage your builders, scalers, and optimizers? Which are you? I’d love to know.

Product expert Oji Udezue, formerly CPO at Typeform & Calendly, says when launching a disruptive product, you need to deliver FAR more benefit than existing options. This is the “Zone of Benefit.”

This framework helps founders, product managers, and investors decide whether their product is positioned for success–or if it’ll ultimately fail.

Here’s Oji’s framework to create products that get you into the Zone of Benefit:

1. Identify switching costs–what’s holding users back?

Most users won’t leave an existing solution just because yours is better. They stay because switching is hard:

• Financial costs

• Time investment

• Fear of making a wrong decision

“The thing you’re battling is switching costs – the hard costs and the psychological: change aversion.”

To overcome this, you need to calculate 2 things:

• The “do nothing” alternative: Inertia is often your biggest competitor

• The actual costs: Financial, competitive, emotional barriers

2. Make switching a no-brainer

Being slightly better isn’t enough — you need to be dramatically better.

“People don’t like change. To lure them, you have to deliver something, like, 3x plus benefit.”

Oji points to Calendly, which didn’t just improve scheduling; it replaced inefficiency with a near-instant solution, turning a multi-step back-and-forth into a single-click workflow.

Your product should deliver an upgrade that’s instantly obvious. If customers have to think about whether it's worth switching? They probably won’t.

3. Remove the risk–Reduce the cost of loss

Customers fear making a bad decision—wasting time, money, or credibility–more than no decision.

Remove that fear through things like:

• Low-friction onboarding, no long-term contracts, pay-as-you-go pricing, etc.

• Freemium or trials

• Social proof; build credibility through testimonials and case studies

“If I give you a product that’s 3x better but it has a free plan, or it’s freemium, it has a trial, you can check it out with no cost. Now your boss isn’t mad at you because you made a huge commitment.”

Takeaways

If you’re launching something new, ask yourself:

• What will be a user’s hesitations to switch?

• Is your product delivering 3x+ value?

• Are you making it easy and risk-free to try?

• Is adoption built into the experience?

If not, you’re asking customers to change but not giving them a real reason to.

What makes a product feel like a “no-brainer” in a crowded market?

Check out the full conversation with Oji here:

In a recent episode of LaunchPod, my long-time friend Rich shared some powerful insights about his journey as an entrepreneur and product leader, from founder to leader, to founder again, and now product leader at EF Education First.

His insight: the best teams are built on curiosity, passion, and (probably most importantly) the willingness to be a little weird.

Back in college, Rich ran a music venue with a group of other creative misfits. Collaborating with a group that was hyper-curious and obsessed with music, art, and making things happen shaped the way Rich approached his career and how he leads today.

Here’s what I learned from Rich about adopting the “weird little club” framework for building teams. 👇

1️⃣ Hire for curiosity, not just credentials

The best teammates are those who dive deep, ask questions, and never stop learning. Credentials are nice, but passion and curiosity are what drive real impact.

Rich calls himself “addicted to curiosity and allergic to apathy.” He believes that the best teams are filled with people who are always learning, exploring, and pushing boundaries.

🤩 How to apply this: When hiring, ask candidates what they’re obsessing over right now. Look for a spark, not just qualifications.

2️⃣ Empower people to care

When people genuinely care about the impact they’re making, collaboration feels natural — even when the work gets tough.

⚔️ How to apply this: Build teams of people who care about the mission. When everyone is aligned, collaboration feels natural—even when it’s hard.

3️⃣ Celebrate the crazy ideas

You have to be a little crazy to go from good to great. Obsession drives people to build amazing things.

🏋️‍♂️ How to apply this: Encourage passion and creativity, even if it’s unconventional. The best ideas come from people who push boundaries.

4️⃣ Find where you fit — or create it

Earlier in his career, Rich worked as a consultant at a Big 4 consulting firm, where he felt his curiosity and energy didn’t match the culture, so he moved on. When he found his people in the Boston startup ecosystem, work became fun–and success came naturally.

🚧 How to apply this: If you feel stuck or stagnant, it might be a sign you’re in the wrong room. Surround yourself with people who challenge and inspire you.

🔑 Takeaways

Your “weird little club” isn’t just a career hack—it’s the foundation of great teams. Passion, curiosity, and collaboration are what make work fun and turn good ideas into great outcomes.

What’s one way you’ve found (or built) your own “weird little club”?

They were focused on what drove business outcomes.

Here’s what Andy learned from building one of IBM’s first growth teams and why the best growth strategies are rarely “new.”

To see this whole conversation, check out Andy’s episode on LaunchPod:

PLG: A fresh label for proven GTM fundamentals

Long before “PLG” became a trend, Andy’s team at IBM was doing the work to grow the sign-ups and paid users. No buzzwords, just outcomes.

💬 “In retrospect, we were the growth team. But the work that we were doing was really PLG before the term PLG was coined.”

They focused on small, iterative improvements that compound over time:

• Activation

• Retention

• Conversion

• Referral

That sounds like PLG, no?

A great product with bad GTM still dies

For IBM Watson, Andy’s team was responsible for managing signups for Watson’s API platform. They doubled signups by moving the demo front and center on the page, improving official docs (devs were using it before signing up), and reducing signup friction.

It was still the same product.

Their developer customer base just didn’t care about marketing; they wanted to use the product.

“Developers don’t want to be sold to — they want to use and buy. They were reading the docs, looking at inputs and outputs, before even signing up. The docs were the pitch.”

There are no silver bullets

When other IBM teams asked what their “secret to success” was, they expected some silver bullet. But it was just compounding wins over time.

💬 “If I showed you everything we did, you’d be uninspired. It was just good practices done consistently.”

This approach became the blueprint for Andy’s trainings across IBM, which helped launch 20 new growth teams. The framework was always the same:

1. Pick a key metric

2. Map the user journey

3. Run experiments

4. Learn, iterate, repeat

Whether you call it PLG, growth hacking, or something else, what matters is obsessing over users, solving their problems, and shipping fast.

Don’t get distracted by shiny labels. The best growth leaders know:

• Fundamentals never go out of style

• Execution beats buzzwords

• And customer-first thinking always wins

Every growth channel starts as a gold mine. Then, inevitably, it fades into a ghost town as every thought leader on LinkedIn espouses its magic.

Aakash explained why in a recent conversation we had on LaunchPod, pointing to Andrew Chen’s "Law of Shitty Click-Throughs." More importantly, though, he talked about how to stay ahead.

So, how do you win in a game where every tactic has a shelf life? Let’s break it down:

1. Ride the Wave Early 🏄

The biggest wins come when you're ahead of the curve. Aakash points to the golden age of referral programs — think Dropbox, Uber, and Airbnb — where users were so excited that they asked for links to share with friends.

As former Director of Growth at ThredUP, Aakash saw this firsthand:

“We had people writing in to say, ‘Do you have a referral program? I want to tell my friends, but I also want credit for it.’ Now if you have a referral program, you're in the umpteenth wave.”

🔭 Lesson: Be an early adopter. The best time to capitalize on a new channel is before the market floods with competition.

2. Recognize When the Wave Is Crashing 🌊

Nothing lasts forever. Referral programs, once a marketing darling, became the digital version of banner ads — easy to ignore and full of system gamers.

Aakash compares this to display advertising: once everywhere, now practically invisible as people tune them out.

⌛ Lesson: Don’t cling to old tactics. Watch ROI closely and pivot as soon as effectiveness drops.

3. Forget the Playbook — Let User Behavior Guide Your Strategy 📓

Winning in growth isn’t about recycling old tactics. You can’t just say, “It worked before, so it’ll work again.” You need to understand why it worked, what problem it solved, and whether those conditions still exist.

User behavior, expectations, and emerging habits shape what sticks.

📊 Lesson: Don’t just repeat what worked before. Dig into the why and make sure it aligns with current user behavior before betting big.

4. Experiment Your Way Out 🧪

Before a tactic fizzles, be ready. Aakash shared how ThredUP experimented with 99-cent sales tied to referral bonuses. Some worked. Others didn’t.

Ride the wave while it’s hot, but always be looking for what’s next.

🧪 Lesson: Experimentation isn’t optional — it’s survival. Test often, fail fast, and always be searching for your next growth lever.

🔑 Takeaway

The “Law of Shitty Click-Throughs” is a reminder: no tactic lasts forever. But the leaders who stay ahead are the ones who move fast, adapt, and always keep an eye on what’s next.

Check out my full conversation with Aakash here: https://tinyurl.com/3xxpy4ss

So, do growth channels have shorter lifespans than they used to? What’s the best growth channel you’ve seen go from goldmine to ghost town?

Visibility is Everything

Sales forecasts drive business decisions—budgets, headcount, and investments. Why don’t product teams have the same rigor? A roadmap isn’t enough.

Justin emphasizes that missed product deadlines create ripple effects like:

• Delayed deals

• Missed revenue

• Operational chaos

“If you don’t have the visibility, it’s just a guessing game.”

Like in sales, product teams need transparent forecasting and data-driven accountability.

Leverage Data to Predict Success (or Failure)

Sales teams track win rates—why can’t product teams track delivery rates?

Justin recommends using historical data to forecast outcomes more accurately and build trust within the organization.

“It’s about having the data to make a better decision,” he says. “If a team historically delivers late, build that into the forecast instead of guessing.”

Don’t rely on hope. Build intentional timelines based on your team’s actual performance data.

Set Clear Priorities and Stick to Them

Justin believes in ruthless prioritization to avoid spreading teams too thin. Focused teams deliver meaningful outcomes, not just checkboxes.

“Deciding what NOT to build is 10 times more important than deciding what to build.”

The Sales-Product Connection

Sales can’t sell without trust. Product teams are no different.

Justin’s sales-first mindset shapes his product leadership philosophy: deliver what you promise, when you promise it. This alignment between product and go-to-market teams ensures stronger collaboration and better customer outcomes.

Why It Matters

Product teams often lack the operational rigor of sales teams—but they shouldn’t. With clear priorities, data-backed forecasting, and strong communication, you can build a product team that operates as predictably as a sales org.

What do you think about Justin’s idea? Is it time to manage product teams like sales teams?

I recently spoke with Oji Udezue (author of “Building Rocketships” and former CPO at Typeform & Calendly) and Eric Metelka (Head of Product at Eppo) about how to run the most effective A/B testing program possible.

Here’s how you can run the best A/B tests possible to drive your business forward. 👇

1. Have a good hypothesis

Testing isn’t “does a red button work better than a green button?” That’s just throwing spaghetti against the wall.

“Having a good hypothesis is always the start,” says Eric. “Have a belief based on quantitative or qualitative data that says, ‘if I make this change, these users are going to do this thing.’”

And understand how this change ties to your North Star goal.

2. Only test high-uncertainty areas

Not everything needs an A/B test. Eric recommends, “A/B test what has high uncertainty. If it's not high uncertainty, just build it.”

Oji adds, “A/B tests take time. I’ve done tests that take a month to reach statistical significance. A month before I can make a decision? Are you kidding me?”

Consumer tech companies like Facebook can test at scale and hit significance in days. But for B2B SaaS, long test cycles are impractical.

Consider alternatives like user research, observation, and iterative feedback.

From Oji: “Talk to customers, get into their workflows, observe them, and bring them along the journey.”

3. Balance speed and confidence

Oji flagged another common mistake: Holding tests to an unnecessarily high confidence threshold.

Eric reinforced this, pointing out that different statistical models impact test speed:

“Most companies use sequential testing, which lets you check results in real time, but that slows down test speed. Bayesian methods can actually help you run tests faster with smaller samples.”

Instead of one large, time-consuming test, teams can optimize for speed by running multiple smaller tests with slightly lower confidence levels to iterate quickly.

⚡️If you do decide to run an A/B test, here are some more best practices:

• QA your tools: Run A/A tests to ensure your testing tools work properly. You’d be surprised how often a supposed “variant” wins with >95% confidence, despite being identical to the control.

• Look beyond the numbers: Metrics don’t tell the full story. Are you trading 10 high-value leads for 15 low-quality ones? Optimize for results, not just mid-funnel gains.

• Ensure equal traffic (or understand ratios): Don’t just look at raw conversions. Unbalanced traffic may mean more conversions simply because that variant had more people to convert.

Check out both full conversations from their appearances on LaunchPod:
📺 Oji Udezue: https://tinyurl.com/hzy35t54
📺 Eric Metelka: https://tinyurl.com/mtnsz4hr

What’s your team’s go-to strategy for validating ideas quickly?👇