Tag: product discovery

Build Powerful AI Writing Workflows with Claude Code: A No‑Code, Step‑by‑Step Playbook

My writing process used to be messy. Even in my role leading product strategy, I’d start strong and then stall because I hadn’t clarified what I truly wanted to say.

I’d begin with a brain dump—everything swirling in my head. I’d try to shape it into an outline, lose patience, and just start writing. A few paragraphs later, I’d realize I didn’t know where I was going, stop, and return to the outline. It was a tortured loop between writing and structuring.

Now I do it differently. When I get stuck, I don’t start writing. I ask Claude for help.

Claude reviews my outline and helps me fill in gaps. It often suggests things that I don’t like. This is good. It helps me figure out the core of what I want to say. Instead of writing my way to what I think, I discuss my way to what I think.

Claude isn’t just a sounding board. I also use it to help me brainstorm headlines, explore outline alternatives, critique each section as I write, conduct supporting research, act as my thesaurus and dictionary, make SEO recommendations, and so much more. As a result, I am writing way more.

I didn’t design this workflow in one sitting. I built it iteratively, the same way I build products: by asking, "How can Claude help with this?" and evolving from there.

If you haven’t been following along, I’m deep in a series about Claude Code and how it helps me work better. Here’s what we’ve covered so far: Claude Code: What It Is, How It’s Different, and Why Non-Technical People Should Use It, Stop Repeating Yourself: Give Claude Code a Memory, How to Use Claude Code Safely: A Non-Technical Guide to Managing Risk, and How to Choose Which Tasks to Automate with AI (+50 Real Examples).

This week, I’m diving into how to design personal AI workflows. I’ll use my writing workflow to illustrate each step, and I encourage you to follow along with your own process so you end with something tangible.

Claude breaks down an AI workflow article and suggests three paywall points, weighing trade-offs to guide conversion strategy. A clear, structured example of planning content and automation steps with Claude Code.

Designing AI workflows looks a lot like designing product solutions. I lean on "discovery" habits—clarifying outcomes, mapping the journey, and testing assumptions—to make the work both reliable and repeatable.

This series is inspired by my personal usage of Claude Code. I have not received any compensation from Anthropic for writing this series. And you can trust that if that ever changes, I will disclose it. This is not only required by the FTC here in the US, but I strongly believe it is the right thing to do. You can count on me to do so.

First, I map out what I do to complete the task. Once you’ve identified the AI workflow you want to create, start by mapping exactly what you do when you do it yourself. If this feels hard, do the task a few more times and jot down each step as you go.

Here’s what I do when I write a blog post: I choose a topic; I write down everything I can think of related to that topic; I structure it into an outline; I do some research to fill in gaps; I write each section; I edit each section; I think about SEO tactics; I brainstorm headlines; I decide what images to add; and I send it to my editor.

If this looks a lot like story mapping, that’s because it is. Instead of mapping what a customer has to do to get value from a solution, I’m mapping what I do to complete a task. The benefit is the same: I can see what must happen and ask, "Where can AI help?"

From here, I focus on four moves: choose one step to automate or augment with AI; decide on the right automation (or augmentation) strategy—code vs. LLMs; prototype the first workflow with detailed instructions; and test and iterate until it meets my bar for quality and speed.

My goal is to give you enough guidance that you can follow along and end with a draft of your first AI workflow. If you apply continuous discovery to your own process, you’ll not only accelerate output—you’ll improve the clarity and quality of your thinking along the way.

Inspired by this post on Product Talk.

December 10, 2025
Train Leaders First: How Product Leadership Unlocks Real Transformation and Discovery

I recently listened to Role of Leadership in Transformations – All Things Product Podcast with Teresa Torres & Petra Wille, and it crystallized a pattern I’ve seen across multiple transformations: teams often get trained in continuous discovery, but nothing changes because leadership habits stay the same. If you want to move from projects to true product thinking, “train your leaders first” isn’t a catchy mantra—it’s a prerequisite.

The episode digs into why discovery training can be stellar while adoption still stalls. I’ve witnessed this firsthand: teams return excited to interview customers and test ideas, but leaders continue to manage via features, roadmaps, and approvals. The result is predictable—discovery fades. When leaders evolve how they evaluate work, talk about outcomes, and shape rituals, discovery sticks. Without that shift, even energized, empowered product teams drift back to output.

What resonated most was how organizational dynamics kick in the moment teams start bringing real customer evidence to the table. Discovery uncovers conflicts. Sales, account management, stakeholders, and executives all feel the impact when the old “my job is to tell teams what to build” mindset collides with evidence-driven practices. Hierarchy also clashes with modern product practices—because in discovery, “all ideas come equal.” Product culture isn’t an accident; it must be intentionally created through norms, expectations, and systems that prioritize outcomes over output.

I’ve also seen the leadership skills gap up close. Many product leaders never learned continuous discovery themselves, so they aren’t equipped to coach it, critique it, or celebrate it. This is where great product management leadership shows up: the ability to assess discovery quality, reinforce outcomes vs output OKRs, and run cadences that create momentum. Leaders who invest in building these muscles—often through communities of practice and structured coaching—transform the operating environment for product trios and cross-functional teams.

The episode’s discussion of pilot teams is spot-on. Start small to surface hidden blockers—the corporate “immune system”—before going broad. Pilots expose decision bottlenecks, misaligned incentives, and policy friction that standard training never reveals. Tools like the Product Leadership Wheel help set clearer expectations for the craft of product leadership, while a coherent Product Operating Model makes the path from pilots to full transformation explicit and durable. I’m particularly excited about resources like the Discovery Habits Toolbox because they give leaders practical ways to coach continuous discovery without reverting to feature policing.

Here are the big takeaways I’m carrying forward. Skills training isn’t enough—if leaders still manage through feature requests and static roadmaps, teams will abandon discovery even if they loved the training. Leaders need training too—they must know how to evaluate discovery work, talk about outcomes, and create rituals that reinforce new habits. Discovery will surface conflicts—plan for stakeholder management, alignment with sales and account teams, and executive sponsorship. Product leadership is a craft—seniority alone doesn’t create clarity, systems, or culture. And transformations should start with leaders and pilot teams—because that’s where the real blockers live.

If you want to go deeper, listen to this episode on Spotify: https://open.spotify.com/episode/5cBTEbYX1YW3BF6icAPXzi or Apple Podcasts: https://podcasts.apple.com/kh/podcast/role-of-leadership-in-transformations/id1794203808?i=1000740342572. It’s a concise masterclass on why leadership behaviors—not just team skills—determine whether continuous discovery thrives.

For further exploration, I recommend these resources. Follow Teresa Torres: https://ProductTalk.org. Follow Petra Wille: https://Petra-Wille.com. Product Talk Academy’s Train Your Team by Teresa Torres: https://learn.producttalk.org/train-your-team. Melissa Perri’s “Train leaders first, not last.” Linkedin post: https://www.linkedin.com/posts/melissajeanperri_train-leaders-first-not-last-most-product-activity-7380927349732839424-sqBJ/. Coaching for Product Leaders/Executives by Petra Wille: https://www.petra-wille.com/coaching-packages. Product Leadership Wheel by Petra: https://www.petra-wille.com/plwheel.

To get hands-on with discovery skills, check out Story-Based Customer Interviews: https://learn.producttalk.org/course/story-based-customer-interviews. For visual management, see An idea board—do we see enough potential?: https://images.squarespace-cdn.com/…/idea_board3.png and Four Taskboards in a simple illustration: Idea Board, Product Overview Board, Product Discovery Board and Development Team Board: https://images.squarespace-cdn.com/…/boards.png. Opportunity Assessment: Do We Want to Invest in Discovering This Idea?: https://www.petra-wille.com/blog/opportunity-assessment-do-we-want-to-invest-in-discovering-this-idea?rq=taskboard.

If you’re preparing your organization to adopt a product operating model, read Is Your Organization Ready to Adopt the Product Operating Model?: https://www.producttalk.org/organizational-readiness/ and The Product Operating Model Explained: From Pilot Teams to Full Transformation: https://www.producttalk.org/the-product-operating-model/. Communities of practice can accelerate leadership growth: Community of Practice by Petra: https://www.petra-wille.com/community-of-practice. For foundational texts, see TRANSFORMED: Moving to the Product Operating Model: https://www.svpg.com/books/transformed-moving-to-the-product-operating-model/ and EMPOWERED: Ordinary People, Extraordinary Products: https://www.svpg.com/books/empowered-ordinary-people-extraordinary-products/.

I’d love to hear how you’re enabling continuous discovery in your context. What leadership behaviors have made the biggest difference? Where does your corporate immune system show up, and how are you addressing it with pilot teams, clearer expectations, and a consistent product operating model? Share your perspective—I read every comment.

Inspired by this post on Product Talk.

December 9, 2025
Master the Kano Model: Prioritize Features That Delight and Drive Product-Led Growth

When I sit down with our product trios to shape the next quarter’s roadmap, I rely on The Kano Model to cut through the noise and focus on what actually moves the needle for customers and the business. It gives me a rigorous, human-centered lens for separating baseline expectations from differentiators and sustained value creation.

Learn how the Kano Model prioritizes the product features that matter by categorizing them into must-haves, satisfiers, and delighters.

Here’s how I think about each category in practice. Must-haves are the non-negotiables—if they’re missing or broken, no amount of innovation will save the experience. Satisfiers scale linearly with user happiness; do them better, and customers feel the improvement immediately. Delighters surprise users with unexpected value that elevates the product’s perceived quality and creates memorable moments that fuel advocacy.

In continuous discovery, I mix quantitative Kano surveys with qualitative interviews to validate which capabilities land in each bucket for specific segments. We ask both functional and dysfunctional questions (e.g., “How would you feel if this feature existed?” and “How would you feel if it didn’t?”) to avoid false positives and to distinguish true delighters from nice-to-haves. This approach de-risks assumptions and keeps our product discovery anchored in real customer voice.

Translating insights into action starts with outcomes vs output OKRs. Must-haves protect core outcomes like reliability, trust, and activation. Satisfiers inform product roadmapping and sprint planning by tying investment to measurable improvements such as speed, accuracy, or completion rate. Delighters earn a deliberate share of the roadmap to strengthen competitive differentiation and to refresh our value proposition before market expectations shift.

Kano also sharpens product-led growth motions. By aligning satisfiers with key activation steps and running retention analysis on cohorts exposed to delighters, we can see where excitement features become habit-forming behaviors. When a delighter consistently correlates with improved retention or expansion, it graduates into the backbone of our product positioning.

Stakeholder management gets easier with a shared framework. I present the portfolio as a balanced mix: must-haves that protect reputation, satisfiers that demonstrate continuous improvement, and delighters that signal vision. This narrative connects short-term reliability with long-term strategy and helps leaders understand why some high-effort ideas are best sequenced behind critical must-haves or high-yield satisfiers.

A quick caution: delighters decay. What delights today often becomes tomorrow’s must-have. I schedule periodic re-reads of our Kano results, especially after major releases or market shifts, to recalibrate where features sit. Combined with A/B testing and usage analytics, this habit prevents us from over-investing in fading differentiators and ensures our roadmap stays crisp and customer-centered.

If your roadmap feels crowded or your team debates priorities without resolution, bring The Kano Model to your next planning session. It adds structure to product discovery, clarifies trade-offs, and helps us deliver a roadmap that not only works—but wins.

Inspired by this post on Product School.

December 9, 2025
Product Analytics for Everyone: Master Funnels, Retention, and Conversion to Drive Growth

Product analytics isn’t a specialist’s sport—it’s a team capability. In my role leading product teams, I’ve seen designers, engineers, marketers, and customer success partners uncover insights that shape strategy, accelerate product-led growth, and improve outcomes for customers. When we demystify the basics and bring analytics into everyday decisions, we build truly empowered product teams.

Here’s the core promise of this approach: "Learn the product analytics fundamentals of funnels, retention, and conversion drivers so that anyone can confidently answer key product questions." That line has guided how I teach product managers to think—start with the essentials, tie them to real customer behaviors, and make the work repeatable across the organization.

I start with funnels because they tell a story—the journey from discovery to value. A simple example: track the path from sign-up to user activation to the first value event. This reveals where onboarding succeeds or stalls, what friction blocks adoption, and which moments are ripe for optimization. With tools like Amplitude analytics or Pendo, we can break down conversions by segment, channel, or feature usage to isolate where improvements matter most.

Next comes retention analysis, the clearest signal that we’re building something customers choose to return to. Cohort analysis shows who comes back and when; retention curves show where value compels a second, third, and tenth use. Tie retention to activation milestones and the outcomes customers achieve—not just logins—and you’ll quickly spot whether your product discovery assumptions hold up in the wild. A unified analytics platform makes these insights discoverable and repeatable across teams.

Conversion drivers round out the picture. Once the funnel is clear and retention is stable, I look for the behaviors and experiences that predict success: feature combinations, time-to-value, message timing, or supportive content. Whether in Amplitude analytics or Pendo, correlating these drivers with outcomes lets us prioritize roadmaps with confidence. Pair this with continuous discovery—qualitative interviews, in-product feedback, and rapid experiments—and you’ll move from interesting data to decisive actions.

This is how we build empowered product teams: by making analytics a daily habit rather than a quarterly report. We bring insights into roadmap reviews, design critiques, and sprint planning; we celebrate learning from experiments as much as shipping features; and we hold ourselves accountable to customer outcomes, not just output. When everyone can interpret funnels, discuss retention, and isolate conversion drivers, we make smarter bets faster.

If you’re getting started, keep it simple. Define a clear activation metric, instrument the top of your funnel, and track a small number of cohorts. Share a weekly readout with highlights, surprises, and questions to investigate. Over time, stitch insights into narratives that drive product-led growth—and, most importantly, help customers achieve what they came for.

Product analytics isn’t just for analysts. It’s a shared language for product discovery, onboarding excellence, user activation, and long-term retention. When we practice it together, we build better products and stronger teams.

Inspired by this post on Amplitude – Best Practices.

December 5, 2025
From No-Code Hack to 10,000 Weekly Calls: Inside Perk’s Voice AI That Actually Works

I love real-world AI that ships, scales, and actually solves painful customer problems. This story checks every box. As a product leader who has brought agentic AI to production environments, I was captivated by how a small, focused team at Perk took a no-code voice AI prototype and turned it into a system that reliably makes 10,000+ calls per week to prevent failed hotel payments.

What happens when you combine a real customer problem, a no-code prototype, and a team willing to listen to every single call?

Steven Payne (Product Manager), Gabriel Stock (Senior Engineering Manager), and Philipe Steiff (Senior Software Engineer) from Perk share how they built a voice AI agent that calls hotels to verify virtual credit card payments, preventing travelers from arriving to find their rooms unpaid. This is a textbook example of linking operational pain to a high-leverage AI solution.

What started as a hackathon experiment in Make.com became a production system handling over 10,000 calls per week across multiple languages. Along the way, the team learned hard lessons about prompt engineering for voice (numbers, pronunciation, and a very "Karen-like" first version), how to break a single monolithic prompt into structured conversation stages, and why listening to actual calls beats any amount of theorizing.

From a product management perspective, this approach aligns perfectly with eval-driven development and continuous discovery. Structure the problem, instrument aggressively, ship safely, then listen—deeply—to real interactions. In my own teams, I’ve seen that nothing accelerates iteration on agentic AI like closing the loop between qualitative call reviews and quantitative evals.

They built a working prototype without writing a single line of backend code.

They structured the call into discrete stages (IVR, booking confirmation, payment) to improve reliability.

They created two eval systems: one for call success classification, another for conversational behavior.

They scaled from five calls a day to tens of thousands per week while maintaining quality.

This is a detailed look at building AI for real-time human interaction—where the stakes are high and the feedback is immediate.

Guests: Steven Payne, Product Manager, Perk; Gabriel Stock, Senior Engineering Manager, Perk; Philipe Steiff, Senior Software Engineer, Perk.

What stood out to me was how Perk's team identified an AI use case by connecting prior experimentation with a real operational problem. Why they chose Make.com for prototyping—and shipped to production without touching backend code—underscores how far no-code can take you when paired with crisp problem framing. The evolution from a single prompt to structured conversation stages (IVR handling, booking confirmation, payment request) is exactly how you harden agent behavior for production.

Breaking up the agent's task dramatically improved reliability. They also built two eval systems: classification for success rates and LLM-as-judge for conversational behavior. Even with automation, the team still listens to calls manually—a practice I strongly endorse for uncovering edge cases, trust issues, and UX nuances that dashboards can’t show.

The challenge of prompt engineering for voice—numbers, booking references, and text-to-speech markup—was non-trivial. Expanding to German revealed that prompts in native language improve results. And, as often happens with operations-heavy rollouts, this project uncovered other operational problems they didn't know existed—valuable signal for the roadmap.

Resources & Links: Perk. Make.com — No-code automation platform used for the prototype. Twilio — Voice/telephony provider. Eleven Labs — Text-to-speech provider (used in early experiments).

Chapters: 00:00 Introduction to the Team; 01:54 Understanding PERK's Mission; 02:59 Challenges in Travel Booking; 07:27 AI Solutions for Customer Care; 09:52 Prototyping with AI and Voice; 17:00 Implementing AI in Production; 25:51 Learning Through Trial and Error; 26:40 Prompting Challenges and Solutions; 27:58 Iterating on Prompts and Evaluations; 30:08 Scaling and Production Challenges; 32:43 Advanced Evaluation Techniques; 35:32 Real-World Applications and Success; 49:07 Future Directions and Expansion; 53:53 Conclusion and Team Reflections.

My product takeaways: Start with clear operational pain and measurable outcomes (e.g., payment verification). Use no-code to validate quickly, then progressively harden. Treat voice AI like any production system: break it into deterministic stages, add guardrails, and measure both outcome and behavior. Pair automated evals with hands-on reviews. And when going multilingual, write prompts in the native language—your accuracy will thank you.

If you’re exploring agentic AI for operations, this is the blueprint: tight scoping, Make.com for speed, Twilio for reliability, structured prompts for control, and an eval-driven loop to scale quality with confidence.

Inspired by this post on Product Talk.

December 4, 2025
Unlocking the 7% Retention Rule: How Early Activation Fuels Compounding, Long-Term Growth

I’ve learned to spot durable growth early. When we launch something new, I look for one deceptively simple signal that predicts whether the product will compound or stall: the percentage of users who come back one week later. It’s a small number with big implications for product-led growth and retention analysis.

Discover why 7% of users returning after one week signals long-term growth, and how early activation separates top-performing products from the rest.

Why does this matter so much? A 7% day-7 retention floor tells me we’ve earned a second interaction from a meaningful slice of our cohort, not just a curiosity click. That’s the first hint of habit formation and repeatable value—evidence that onboarding, user activation, and the core value proposition are doing their job. When the curve holds at or above this threshold, growth investments tend to work harder because cohorts keep giving back.

The lever behind that signal is early activation. I define the activation moment as the first time a new user experiences product value—sending a first campaign, integrating a CRM, or completing a workflow that solves their primary job. If we reduce time-to-activation and increase the activation rate, day-7 retention rises. This is where in-app guides, product tours, and thoughtful tooltip design shine: they remove friction without overwhelming the user.

Instrumentation is non-negotiable. I set up event tracking and cohort analysis in tools like Amplitude analytics and Pendo, define a crisp activation event, and review retention curves by first-seen cohorts. We run A/B testing with a clear minimum detectable effect (MDE), validate improvements in activation and day-7 retention, and then double down. The objective is always outcomes over output: fewer features, more value delivered.

Process matters as much as tooling. Product trios using continuous discovery keep us close to user problems, while empowered product teams move faster with context and clear outcomes vs output OKRs. When we connect these practices to a unified analytics view, it becomes obvious which changes move the 7% needle and which are noise.

In practice, I’ve seen a launch turn the corner by clarifying the “aha” moment, cutting onboarding steps nearly in half, and swapping a generic walkthrough for contextual in-app guides. Activation jumped, day-7 retention crossed the threshold, and suddenly our PLG motion became efficient—paid acquisition started compounding instead of leaking.

If you’re below 7%, start by tightening the activation definition, instrument the funnel, and remove the top three sources of friction. If you’re above 7%, stabilize it across segments, scale with targeted in-app guides, and keep iterating via A/B tests to protect that early win. Either way, the rule provides a clear, pragmatic checkpoint for product discovery and growth.

The takeaway is simple: focus the team on earning the second visit. Nail early activation, then build repeatable systems that make the 7% retention rule your new baseline for confident, long-term growth.

Inspired by this post on Amplitude – Perspectives.

December 3, 2025
Stop the Leaky Bucket: Proven Moves to Turn User Growth into Durable Retention in 2025

More signups are exhilarating—until the retention curve tells a colder truth. I’ve led launches where top-of-funnel spiked, only to watch active usage slide week over week. That’s the leaky bucket problem in action: acquisition outpaces activation, engagement, and retention, so net growth stalls.

Losing users as fast as you acquire them? Get exclusive insights from our 2025 Product Benchmark Report on how to fix the leaky bucket problem and drive lasting growth.

When I assess a product’s trajectory, I reframe the goal: our job isn’t to add users; it’s to create retained value. In product-led growth, durable growth comes from systematically increasing activation and Day 7/30 retention, not just traffic. That shift aligns teams on outcomes vs output and turns experiments into a compounding engine.

Diagnosis comes first. I run a retention analysis by cohort in Amplitude analytics (and corroborate with Pendo for in-app behavior) to pinpoint where the flow breaks: sign-up, onboarding, first value, habit formation, or paywall. Then I define a crisp activation metric—what specific action within a time window predicts long-term engagement—and measure time-to-value for each segment.

From there, we remove friction. Simplify onboarding, trim non-essential fields, and guide users to the “aha” with in-app guides, product tours, and contextual tooltips. Seed accounts with sample data, pre-built templates, and smart defaults so new users experience the core value in minutes, not days.

We prove impact with disciplined experimentation. A/B testing with a clearly calculated minimum detectable effect (MDE) prevents false positives, while a continuous discovery cadence with product trios keeps us close to real customer problems. Every test is tied to leading indicators—activation rate, Day 1/7/30 retention, and weekly engaged usage—not vanity metrics.

Activation does not live in product alone. Pricing and packaging, lifecycle messaging, and customer support all influence early habit formation. Align GTM and product on one retention-centric scorecard and instrument a unified analytics platform so every team sees the same truth.

Once the core journey holds water, we layer in expansion: prompts that surface adjacent value at the right moment, educated upsells tied to outcomes, and permissions or collaboration features that invite team adoption. That’s how growth becomes efficient and compounding instead of brittle and expensive.

If this resonates, you likely have more of a prioritization problem than a traffic problem. Fix activation, measure retention rigorously, and let acquisition follow. Patch the leaks, and growth stops being a hustle—and starts being a flywheel.

Inspired by this post on Amplitude – Perspectives.

December 3, 2025
See What AI Really Says About Your Brand with Amplitude AI Visibility: Score, Rank, Win

Every week, I ask a simple question with massive implications for our AI Strategy: what do large language models actually say about our brand? As a VP of Product Management at HighLevel, I’ve learned that competitive differentiation now lives as much in AI-generated responses as it does in traditional search or social. That’s why a reliable, unified analytics platform for AI visibility is quickly becoming table stakes for product management leadership.

Discover how Amplitude AI Visibility helps you track your visibility score, uncover competitor rankings, and prove business impact—all in one platform.

Here’s why that matters. A visibility score gives me a measurable baseline—our AI share of voice—so I can see whether our product-led growth and go-to-market strategy are landing in the places where buyers increasingly look for answers. Competitor rankings reveal points of parity and opportunities to differentiate, which directly inform product positioning and our value proposition. And the ability to prove business impact closes the loop between AI exposure and outcomes that executives care about.

Operationally, I would start by benchmarking our visibility score against key competitors, then segment by core use cases to identify where our story underperforms. Those insights feed product discovery, content strategy, and enablement—tightening the narrative to better align with buyer intent. I’d translate the findings into prioritized bets for the roadmap and partner closely with marketing to amplify wins and address gaps.

For teams exploring LLMs for product managers and GenAI-driven growth, this approach creates a disciplined feedback loop: measure what AI says, experiment to improve it, and verify the impact across the funnel. It’s a pragmatic way to connect messaging, discovery, and differentiation—without guessing what the models are surfacing about your brand.

I’ve followed Amplitude analytics for years, and Amplitude AI Visibility slots naturally into a modern operating model: one platform to monitor the signals that matter, align stakeholders, and make faster, evidence-based decisions. If your mandate includes scaling product-led growth and sharpening competitive differentiation, this is a timely, actionable way to see—and shape—how AI represents you.

Inspired by this post on Amplitude – Best Practices.

December 3, 2025
Sharper Signals, Stronger Collaboration: How Session Replay Accelerates Problem Solving

In fast-moving product cycles, weak signals slow teams down and let avoidable issues linger. I’ve been leaning on Session Replay to strengthen those signals and align stakeholders faster, especially when we’re balancing roadmap bets with day-to-day reliability fixes.

Discover how frustration analytics, error analytics, and shareable filters in Session Replay help you spot problems faster and collaborate more effectively.

Frustration analytics has become my shortcut to the moments that truly matter. Instead of sifting through countless replays, I start where friction peaks and focus on the sessions that best represent real user pain. In one onboarding flow, these insights pointed us to a confusing step that was suppressing user activation; a simple adjustment to the layout and copy led to higher completions and fewer support tickets.

Error analytics turns anecdotes into evidence. By pairing error trends with conversion and retention analysis in Amplitude analytics, we isolate the defects with the highest customer and revenue impact. That clarity helps my team sequence fixes in sprint planning with confidence—and it gives leadership a clean narrative for why certain issues deserve priority now.

Shareable filters have been a quiet superpower for cross-functional collaboration. I create saved views for specific cohorts—first-time users, power users, or high‑value accounts—so engineering, design, and support can reproduce exactly what I’m seeing in Session Replay. No more screen recordings in Slack or back-and-forth on “what filters did you use?” Everyone starts from the same context and moves to decisions faster.

This workflow fits naturally into how our product trios practice continuous discovery. We pick one question each week, open a shared filter, and review a handful of targeted sessions together. Within the same unified analytics platform, we connect what we observe to metrics that matter, then translate insights directly into product roadmapping and sprint planning without losing momentum.

If your goal is sharper detection of issues and stronger collaboration across stakeholders, these capabilities deserve a place in your toolkit. They compress time-to-insight, improve stakeholder management, and fuel product-led growth by focusing attention where it delivers the most customer value.

Inspired by this post on Amplitude – Best Practices.

December 3, 2025
Stop Waiting—Run A/B Tests 3X Faster with Powerful Self‑Service Experimentation

I’ve spent enough cycles in product and growth to know the biggest drag on experimentation velocity isn’t creativity—it’s waiting. Waiting for engineering to wire events, for analysts to pull cohorts, for approvals to trickle in. When marketers can move autonomously with the right guardrails, learning accelerates and impact compounds.

“Amplitude’s new web experiment capabilities enable teams to scale experimentation 3X faster without waiting for help.” That promise hits directly at the bottlenecks I see most often across product and marketing organizations.

My takeaway: the real unlock isn’t only speed; it’s confidence. Faster learning loops power continuous discovery and product-led growth, but only if teams trust the data, align on success metrics, and can iterate without creating downstream tech debt. Self-service done right transforms scattered tests into a durable growth engine.

From a VP of Product lens (and what we practice at HighLevel), self-service experimentation means more than a new UI. I look for governance-by-design, role-based permissions, clear metric definitions, pre-built test templates, and operational best practices like minimum detectable effect (MDE) sizing and traffic allocation standards. That mix keeps A/B testing fast, statistically sound, and repeatable—without piling work onto engineering.

Here’s the playbook I recommend to teams leaning into this shift: instrument a unified analytics platform and lock a shared taxonomy; define canonical success metrics and guardrails; require lightweight pre-registration for hypotheses and MDE; stand up weekly experiment reviews; and close the loop by sharing learnings in-product and across go-to-market. When marketers, PMs, and designers operate as an empowered product trio, the flywheel spins.

To maximize value from any web experimentation stack—Amplitude analytics included—connect the dots from insight to activation. Tie experiments to CRM integration for downstream campaigns, ensure user activation metrics are first-class citizens, and keep your experimentation backlog aligned to outcomes, not outputs. The goal is fewer opinions and more evidence, shipped continuously.

Self-service also requires culture. Set expectations around statistical rigor, data governance, and post-test decisions, then celebrate the teams that sunset ideas just as quickly as they scale winners. That’s how you reduce waste, build confidence, and keep momentum high without creating hidden operational costs.

If your marketers are still waiting in ticket queues, it’s time to raise the bar. With the right foundations and process, you can go from idea to live test in hours, not weeks—learning more, shipping smarter, and unlocking 3X faster cycles where it matters most: customer value.

Inspired by this post on Amplitude – Best Practices.

December 3, 2025
Build Smarter MVPs with AI: Test Faster, Fail Cheaper, and Accelerate Product-Market Fit

I build MVPs to learn, not to launch—and AI lets me compress those learning loops from weeks into days. When the stakes are high and the clock is ticking, I default to simple architectures, ruthless scoping, and instrumentation from the very first commit. What follows is the practical playbook I use to reduce uncertainty quickly, keep risk contained, and ship value with intent.

This is a practical guide for product people who move with purpose. Build smarter, test faster, fail cheaper. This is how AI reshapes the MVP game.

I start by framing the problem in business terms and picking a single success metric tied to the customer’s core job-to-be-done. I document the riskiest assumptions, define guardrails (quality, safety, latency, cost), and choose a minimum detectable effect (MDE) so my A/B testing has statistical teeth. This forces clarity: What has to be true for this AI MVP to matter?

Then I scope the thinnest, testable slice of the experience—one clear user, one context, one outcome. I write the happy path first, instrument the key events, and resist the urge to boil the ocean. If it can’t be demoed in five minutes and measured in five days, it’s not an MVP.

Data comes next. I adopt privacy-by-design, set up basic data governance, and map inputs and outputs to avoid silent failures. I define an AI risk management checklist (prompt injection, PII leakage, hallucinations) and set budget limits to keep inference costs predictable. Responsible scaffolding early saves me from operational drag later.

On the model strategy, I prefer the simplest option that can win the experiment. I often start with an off‑the‑shelf LLM and a retrieval-first pipeline (RAG) for grounding, plus light context window management to keep prompts lean. If the workflow demands autonomous steps or tool use, I add agentic AI behaviors incrementally; fine‑tuning only comes after I’ve validated repeatable value.

For prototyping speed, I lean on my AI product toolbox: CustomGPT workflows for rapid flows, a ChatGPT connector for quick integrations, and Claude Code for code scaffolding and refactors. I stitch the MVP into the existing stack with pragmatic CRM integration, then layer in in-app guides and product tours so users immediately understand what to try and why it matters.

Measurement is non‑negotiable. I set up Amplitude analytics to track activation and retention, add Pendo for in‑product guidance and usage heatmaps, and wire Intercom for qualitative feedback inside the flow. With A/B testing in place and an agreed MDE, I can make crisp calls on whether the AI feature clears the bar or needs another iteration.

Shipping must stay frictionless. I keep a simple CI/CD pipeline, monitor deployment frequency, and prepare basic incident management with SRE hygiene appropriate to an MVP. Small, reversible releases let me learn safely while protecting user trust.

The learning loop is continuous discovery, not a one‑off demo. I run quick research sprints with product trios, capture edge cases, and turn user feedback into structured prompts, examples, and evaluation sets. As signal strengthens, I harden guardrails, improve retrieval quality, and elevate the value proposition in messaging.

When the metrics move and the experience feels reliable, I scale deliberately: tighten privacy-by-design controls, document outcomes vs output OKRs, and explore product-led growth motions. Only then do I consider pricing experiments, broader go-to-market strategy, and heavier investments like fine‑tuning or bespoke infrastructure.

If you want a simple way to start: day one, define the problem and metric; day two, wire a thin RAG prototype with guardrails; day three, put it in front of real users with analytics and a clear activation path. The goal isn’t perfection—it’s validated learning you can scale with confidence.

Inspired by this post on Product School.

December 3, 2025
How Amplitude AI Feedback Turns Noise into Product Signal You Can Ship With Confidence

I’ve spent enough time in the trenches of product management to know the hardest part isn’t collecting feedback—it’s separating signal from noise. When every channel is buzzing, the real question becomes: what should we build next, and why? That’s where Amplitude AI Feedback has changed how I work. It gives me a disciplined, data-informed way to turn messy qualitative input into clear, defensible roadmap decisions.

Learn how Amplitude AI Feedback leverages AI to transform massive volumes of customer feedback into actionable product insights.

In practice, this means I can synthesize input from support tickets, NPS responses, user interviews, sales notes, and reviews—then connect those insights to product behavior data from Amplitude analytics. The result isn’t just a list of requests; it’s a ranked problem set grounded in evidence, which makes product discovery and continuous discovery faster, clearer, and less biased.

A recent example: we were hearing recurring complaints about onboarding friction, but it wasn’t obvious which steps truly mattered. By pairing feedback themes with activation and retention signals, I could zero in on the first-session setup tasks that correlated with drop-off. That clarity guided product roadmapping and sprint planning decisions we could stand behind, and it accelerated user activation without bloating the backlog.

My workflow is straightforward: aggregate feedback, cluster themes, validate with behavioral metrics, and translate insight into outcomes. I look for patterns tied to user activation, retention analysis, and moments that drive product-led growth. When the evidence shows a request is both frequent and high-impact, it earns a place on the roadmap; when it’s loud but low-impact, it becomes a targeted experiment rather than a default commitment.

What I appreciate most is the confidence this brings to stakeholder conversations. Instead of debating opinions, we review the evidence: quantified themes, clear user stories, and measurable KPIs. That turns “Finally, Signal That Tells You What to Build” from a slogan into an operating principle, and it helps empowered product teams move faster with fewer reversals.

If you’re building your AI Strategy or exploring LLMs for product managers, this is one of the highest-leverage moves you can make: use a unified analytics platform to connect qualitative feedback with quantitative behavior. It sharpens prioritization, improves time-to-learning, and keeps the team focused on outcomes—not outputs.

Inspired by this post on Amplitude – Best Practices.

December 2, 2025