Tag: product management leadership

Healthcare Product Benchmarks That Matter: Actionable Metrics and Playbooks From Our Report

I rely on product benchmarks to align teams, sharpen strategy, and accelerate outcomes—especially in healthcare, where stakes are high and complexity is real. Over the years, I’ve learned that the right metrics create clarity across product, engineering, compliance, and go-to-market, enabling faster, safer decisions that translate into measurable impact.

Discover exclusive data and strategies from our Product Benchmark Report. Compare the healthcare technology industry’s performance across key product metrics.

When I evaluate a healthcare product’s health, I focus on a few essentials: activation rate and time-to-value for new users, weekly active usage and feature adoption for clinicians and admins, and cohort-based retention analysis to understand whether value compounds over time. I also look at funnel friction (onboarding drop-off, failed setup steps), support load per account, and reliability signals that influence trust—because in healthcare, trust fuels growth.

Benchmarks turn those metrics into context. They help me answer, “Are we good, or just lucky?” By comparing our numbers to industry peers, I can prioritize the few bets that matter, set outcomes vs output OKRs, and guide empowered product teams to focus on the highest-leverage improvements.

Operationally, I instrument products with a unified analytics platform and tools like Amplitude analytics and Pendo to track user activation, feature adoption, and in-product journeys. Pairing that with continuous discovery keeps insights fresh, while A/B testing and clear minimum detectable effect (MDE) thresholds ensure we ship with statistical confidence.

In practice, my playbook for healthcare product-led growth is straightforward: simplify onboarding with targeted product tours and in-app guides, tighten the first-win loop to reduce time-to-value, and eliminate blockers surfaced by behavioral analytics. Then, reinforce the loop with lifecycle messaging, role-specific education, and clear value propositions for clinicians, operations teams, and executives.

Of course, none of this works without strong governance. Data governance and regulatory compliance aren’t just guardrails; they’re growth enablers. Clear audit trails, privacy-by-design, and reliable incident management build the trust that keeps adoption high and churn low.

If you’re ready to benchmark your roadmap against the market, this report gives you the clarity to spot gaps, the language to align stakeholders, and the metrics to execute with precision. Use it to calibrate your product strategy, guide your next set of experiments, and confidently scale what works across the healthcare technology ecosystem.

Inspired by this post on Amplitude – Perspectives.

December 29, 2025
Game-Changing Product Benchmarks Every Media & Entertainment Leader Must Know

Benchmarks are my reality check. In the fast-moving media and entertainment space, I rely on concrete product metrics to align strategy, prioritize roadmaps, and drive product-led growth with confidence. When my team and I calibrate against industry benchmarks, we turn opinions into outcomes and ensure our bets are tied to measurable impact.

Discover exclusive data and strategies from our Product Benchmark Report. Compare the media and entertainment industry’s performance across key product metrics.

Here’s how I think about what matters most in this report: user activation and time-to-value to understand onboarding effectiveness, retention analysis to quantify staying power, feature adoption to validate value delivery, and engagement depth to see whether we’re building habit loops—not just generating clicks. I also look at experimentation maturity (A/B testing volume and velocity), release cadence, and how we structure outcomes vs output OKRs to keep teams accountable to real customer impact.

Benchmarks aren’t scorecards—they’re decision accelerators. I use them to run a gap analysis, set clear targets, and focus the roadmap on the few bets most likely to move our leading indicators. For example, if activation lags, we invest in clearer in-app guides, product tours, and progressive onboarding; if retention stalls, we refine the value proposition and instrument cohorts to isolate which segments respond best.

Operationally, I instrument a unified analytics platform with Amplitude analytics for cohorting and funnel analysis, and Pendo for in-app guidance and feature adoption insight. Weekly product health reviews keep the team oriented around activation, retention, and engagement. When we A/B test, we set a minimum detectable effect (MDE) up front and tie experiments to specific OKRs, so decisions aren’t swayed by noise. This discipline helps empowered product teams ship faster without sacrificing rigor.

If you’re building in media and entertainment, use these benchmarks to define what “good” looks like for your model, then localize targets to your audience and content format. Start by instrumenting the essentials, align leaders on the few metrics that matter, and iterate with high-velocity experiments. The right benchmarks will sharpen your product strategy, improve stakeholder confidence, and turn your roadmap into a reliable engine for growth.

Inspired by this post on Amplitude – Perspectives.

December 23, 2025
Master Burger Prompting: Build a High-Impact AI Resume Coach with Proven LLM Structures

I turned the playful idea of “burger prompting” into a rigorous framework for building an AI resume coach that delivers consistent, high‑quality guidance. In product management, repeatability matters: I want dependable LLM behavior, tight control of outputs, and measurable outcomes. This approach gives me exactly that—clear roles, crisp constraints, and an evaluation loop that raises the quality bar with each iteration.

Here’s the metaphor in practice. The top bun sets the role and goal; the middle layers stack context, examples, constraints, and tools; the patty is the core algorithm and output schema; and the bottom bun locks in the quality bar and follow-up behavior. When I apply this structure to an AI resume coach, I get results that feel expert, empathetic, and actionable—without rewriting the prompt every time.

Top bun: I define the system role and success criteria. I’ll say, “Act as an experienced hiring manager and resume coach for SaaS product roles” and specify the north star: improve clarity, impact, and ATS alignment without fabricating experience. I also name the audience (mid-career PMs, early-career candidates, or executives) so tone and calibration stay consistent across sessions.

First layer: I load precise context. That includes the candidate’s resume, the target job description, and any constraints (for example: keep bullets under 22 words, lead with impact, quantify outcomes). I also clarify non-goals (no inflated titles, no unverifiable claims). This is where I set the voice: confident, concise, and supportive, not generic or robotic.

Second layer: I attach the tools and references that anchor outputs. A skill taxonomy for product roles, a style guide for resume bullets, and a scoring rubric (impact, clarity, relevance, keyword coverage) help the model prioritize. To protect quality, I call out context window management rules—what to include or trim—and how to summarize long inputs without losing signal.

Third layer: I add exemplars. Few-shot examples of excellent resume bullets (“before” and “after”) teach the model what “great” looks like. I also include a counterexample or two to prevent bad habits (for instance, over-indexing on buzzwords). Exemplars act like taste buds; they steer nuance without overfitting.

Patty: I define the core algorithm and the output schema. The algorithm moves in stages: diagnose the resume against the job, identify 3–5 high-leverage improvements, rewrite bullets with quantified outcomes, and propose a summary that highlights relevant wins. I then specify the output sections: a brief diagnosis, rewritten bullets mapped to the job’s requirements, an ATS keyword coverage table, and a confidence score with rationale. A tight schema produces consistent, scannable outputs that are easy to evaluate—and easy to ship.

Bottom bun: I lock in the quality bar and the follow-up behavior. If inputs are incomplete, the coach must ask clarifying questions before rewriting. If claims lack evidence, it should suggest proof points (metrics, scope, stakeholders) rather than embellish. Finally, I require a self-check pass where the coach verifies that each bullet demonstrates impact, relevance, and clarity before presenting the final result.

Implementation blueprint: I create a reusable prompt template with clear system and user sections, then parameterize it for different roles (PM, design, data). If I have a library of style guides or skill matrices, I wire it into a retrieval layer so the model references the right material for each job. This setup makes the coach portable across tools and easy to maintain as the taxonomy evolves.

Evaluation and iteration: I practice eval-driven development. I assemble a small, representative test set of resumes and job descriptions, define acceptance criteria (readability score, keyword coverage, human rater alignment), and A/B test prompt variants. I track drift and tighten the schema whenever outputs start to meander. The goal isn’t just impressive demos—it’s reliable performance at scale.

Governance guardrails: A trustworthy resume coach respects privacy-by-design. I strip PII where possible, avoid storing raw resumes beyond what’s necessary, and document bias checks so advice doesn’t disadvantage non-traditional candidates. Clear data governance and risk management keep the product shippable and compliant as it grows.

When I apply burger prompting end to end, the AI resume coach becomes a repeatable system: fast, accurate, and measurably helpful. The structure teaches the model how to behave; the evals keep it honest; and the schema makes the result easy to review, refine, and ship. If you want dependable LLM outcomes, start with a great bun—and don’t skimp on the patty.

Inspired by this post on Pendo – Best Practices.

December 19, 2025
Inside Google’s Product Model: Hard-Won Lessons to Build Empowered, Outcome-Driven Teams

I’ve been systematically exploring how the product model shows up inside iconic companies. After studying “The Product Model at Spotify” and “The Product Model at Amazon,” I’m turning my lens to Google—specifically, how the product operating model, product culture, and product strategy manifest in practice and what we can pragmatically take back to our own organizations.

When I talk about the product model, I’m looking at the machinery that connects strategy to outcomes: empowered product teams, clear decision rights, tight product trios, continuous discovery, data-informed bets, and an operating cadence that enables learning at speed. My goal here is to unpack how those elements come together at Google and translate them into repeatable patterns you can adopt.

At a high level, I focus on how teams are empowered to solve problems rather than ship outputs, how outcomes vs output OKRs clarify what matters, and how experimentation (from rapid prototyping to A/B testing) de-risks decisions before they scale. I also examine how engineering and product partner to balance platform scalability with customer value, and how stakeholder management reinforces alignment without slowing teams down.

Why does this matter? Because the product model is a lever for resilience and speed. When product strategy is explicit and the operating model is built for learning, organizations multiply the impact of talented people. That’s how small, focused teams repeatedly deliver outsized results—even in complex, regulated, or high-scale environments like Google.

In the sections that follow, I’ll synthesize what I see as the core patterns behind Google’s approach and distill them into actionable guidance: how to structure product trios, how to run continuous discovery alongside delivery, how to set and calibrate OKRs for outcomes, and how to evolve your product culture so empowered product teams can do their best work. My aim is not to idolize a model, but to extract what’s portable and help you adapt it to your context.

Inspired by this post on SVPG.

December 19, 2025
How I Make Diagnostic AI Trustworthy: Confidence Levels, Citations, and Evals That Win Trust

Trust is the true currency of diagnostic analytics. If customers can’t verify why a system reached a conclusion—or how confident it is—adoption stalls. That’s why this line resonated so strongly with my own playbook: Amplitude used confidence levels, citations, and evals to build a diagnostic AI tool accurate enough to earn customer trust.

Confidence levels are my first non-negotiable. When a model flags a root cause or prescribes a next step, I want the UI to state its certainty upfront and in plain language—ideally with calibrated ranges and a brief rationale. This simple pattern sets the right expectations, reduces over-trust, and supports AI risk management by making uncertainty visible. In practice, we pair this with clear UX writing so users understand what “High,” “Medium,” or “Low” confidence really means in their workflow.

Citations are the second pillar. Every diagnostic needs a breadcrumb trail back to source data: which metrics were analyzed, what time window was used, and how the insight was derived. Linking directly to the underlying chart, query, or dashboard reinforces data governance and shortens the path from “interesting” to “actionable.” When customers can click through to verify the evidence, they gain the confidence to make decisions—fast.

Evals complete the trio. Before and after launch, I hold the team to eval-driven development: offline benchmarks, targeted scenario tests, and live performance monitoring that mirrors real customer use. We define success criteria for precision/recall, false-positive thresholds, and latency, then wire those checks into CI/CD so regressions are caught early. Continuous evals aren’t just QA; they’re the heartbeat of an AI workflow that keeps insights reliable at scale.

Operationally, these practices compound. Confidence levels help prioritize follow-up analysis, citations accelerate collaboration across product and data teams, and evals keep quality high even as models, data, and usage evolve. Together, they form a pragmatic AI strategy that aligns product discovery with measurable outcomes and safeguards customer trust where it matters most—inside daily decisions.

If you’re building a diagnostic AI tool, start with these three building blocks and resist the urge to hide uncertainty. Make it legible. Make it verifiable. And measure it continuously. That’s how we turn powerful models into trustworthy products customers depend on.

Inspired by this post on Amplitude – Perspectives.

December 18, 2025
Retail & Ecommerce Product Benchmarks That Win: Data-Backed Metrics to Outperform Competitors

Every week, retail and ecommerce leaders ask me the same thing: which product metrics truly separate the winners from the rest? As a VP of Product Management at HighLevel, Inc., I rely on benchmarks to translate strategy into measurable, repeatable outcomes—so I built a simple way to use them to guide roadmaps, experiments, and executive alignment.

Discover exclusive data and strategies from our Product Benchmark Report. Compare the ecommerce industry’s performance across key product metrics.

Benchmarks aren’t just numbers on a chart; they’re context. They help me calibrate goals, set outcomes vs output OKRs, and focus our product-led growth efforts on the handful of inputs that actually move revenue, loyalty, and lifetime value in retail and ecommerce.

The metrics I prioritize map to the customer journey: acquisition efficiency (visit-to-signup), activation and time-to-first-value, product-to-checkout conversion, order completion rate, repeat purchase and subscription retention, average order value, and LTV/CAC. I also track friction signals like cart abandonment, returns, and refund rates to surface hidden points of failure.

Here’s how I use the report in practice. First, baseline performance against peer benchmarks so we know whether we have a strategy or an execution gap. Second, segment by cohort (new vs. returning, mobile vs. desktop, subscription vs. one-time) to reveal where the experience is underperforming. Third, instrument clean funnels and events in our unified analytics platform—Amplitude analytics or Pendo—so every metric is observable and trustworthy.

From there, I translate gaps into a focused experimentation plan. We run A/B testing with proper guardrails, size tests using minimum detectable effect (MDE), and predefine success metrics to avoid p-hacking. Each experiment ties directly to an outcome metric, not an output, so we can attribute impact and iterate with confidence.

Strong execution requires strong alignment. I bring product, marketing, and CX together as a product trio to turn benchmark deltas into a crisp value proposition, targeted onboarding, and lifecycle messaging. That cross-functional focus turns insights into conversion, retention, and customer lifetime value—fast.

Data integrity underpins all of this. We establish clear event taxonomies, privacy-by-design practices, and governance to keep analytics reliable at scale. When the data is clean, decisions get faster, and experimentation becomes a compounding advantage.

If you’re ready to pressure-test your roadmap and accelerate growth, start with the benchmarks. Use them to prioritize opportunities, prove impact with disciplined experiments, and communicate strategy in language the business understands. That’s how retail and ecommerce teams move beyond vanity metrics and win their market.

Inspired by this post on Amplitude – Perspectives.

December 18, 2025
Beyond the Support Iceberg: Gradient Labs’ Multi‑Agent Breakthrough That Actually Gets Work Done

When a customer reports a stolen credit card, the frontline play seems straightforward—freeze it. But that’s just the visible tip of a much larger customer support iceberg. Underneath sits the real work: dispute filings, fraud investigations, merchant communications, proactive outreach, and follow-ups that unfold over days across multiple systems. Most AI support tools only touch the surface; they don’t coordinate or close the loop. That gap is exactly where my product instincts kick in—and why this story matters.

I recently listened to a conversation with Jack Taylor (Product Engineer) and Ibrahim Faruqi (AI Engineer) from Gradient Labs, an AI-native startup building agents that automate the full scope of customer support in fintech. Their approach resonated with the challenges I see every day in customer support automation: fragmented workflows, regulatory complexity, and the need for human-in-the-loop moments. Gradient Labs has architected a platform with three coordinating agents—"inbound, back office, and outbound"—all built on a shared foundation of "natural language procedures, modular skills, and configurable guardrails."

What impressed me most was how they "Let non-technical subject matter experts define agent behavior through natural language procedures—no coding required." That’s a powerful way to remove engineering bottlenecks, accelerate iteration, and keep the domain experts—those closest to fraud, disputes, and compliance—directly in control. In my experience, this design choice alone can compress lead times from weeks to hours and aligns perfectly with continuous discovery and eval-driven development.

At the heart of their platform is orchestration. They "Architected a state machine orchestrator that manages turns, triggers, and skill selection across long-running conversations." That "turn" architecture is built for the messy reality of async, multi-day support. They treat "Skills as modular agent capabilities—and how they're scoped deterministically per turn," ensuring the system stays predictable and auditable. They also confront a nuanced challenge most teams dodge: "Defining "done" for outbound agents when the customer isn't the one ending the conversation." That’s where deterministic criteria, timers, and clearly scoped outcomes matter as much as the model beneath.

Compliance is not an afterthought—it’s baked into the core. Gradient Labs "Built guardrails as binary classifiers with eval pipelines, tuning for high recall on critical regulatory checks." In regulated domains, optimizing for recall on high-stakes checks is the right call; you can tolerate a few extra reviews, but you can’t miss a potential fraud signal. More broadly, they frame "Guardrails as classification problems: balancing recall and precision for regulatory compliance." That mindset is exactly how I like to merge AI risk management with product velocity.

Crucially, they avoid the trap of fully autonomous optimism. "Ask a Human: a tool call that brings humans into the loop for approvals or missing APIs" gives the system a safety valve for novel or high-risk cases. I also appreciated the explicit "Ask A Human Tool" pattern, which cleanly integrates approvals, policy exceptions, or data gaps without derailing the workflow.

Quality doesn’t happen by accident. They "Designed an auto-eval system that samples conversations for human review to catch edge cases and build labeled datasets" and built "Auto-eval pipelines that flag conversations for manual review and feed labeled datasets." That closed-loop evaluation flow is the backbone of sustainable performance in agentic AI. Combine this with targeted instrumentation—think CSAT, first contact resolution, deflection rate, time to resolution, and escalation rate—and you get a real Agent Analytics discipline, not just logs and dashboards.

The "iceberg" metaphor is more than a catchy visual. It’s a blueprint for scoping multi-agent platforms that work across the entire customer journey. With "inbound, back office, and outbound" agents coordinating on complex tasks like fraud disputes, the system can transition cleanly from intake to investigation to resolution—without dropping context or asking customers to repeat themselves. This is what genuine customer support automation looks like when it’s grounded in real operations.

Under the hood, the team leans into robust design choices that matter at scale: the "Complexities of Natural Language Input" are managed with explicit state and skill scoping, "Deterministic Skill Execution" reduces flakiness, and "Customer-Specific Guardrails" ensure compliance remains aligned to each client’s policies. Add their focus on "APIs and Customer Tools Integration" and the result is a platform that can actually take action—not just answer questions.

If you’re building in this space, here’s how I’d apply these lessons. Start by mapping the iceberg: enumerate back-office steps, approvals, and SLAs that follow the initial customer touchpoint. Capture those steps as "natural language procedures" owned by SMEs. Implement a "state machine orchestrator" to manage "turns, triggers, and skill selection" across multi-day workflows. Treat "guardrails as classification problems" and tune for high recall on high-stakes checks. Introduce "Ask a Human" early to handle missing APIs or policy exceptions. Finally, operationalize learning with "auto-eval pipelines" and tight, eval-driven development loops. That’s how multi-agent platforms deliver measurable outcomes in fintech support.

If you want to hear the full conversation, you can listen on Spotify or Apple Podcasts. You’ll also hear a nod to the "Incident.io episode – Referenced in the conversation," and a thoughtful take on the "Future of Multi-Agent Systems."

In short: this is a shift from simple Q&A bots to agents that can coordinate, comply, and complete. It’s the kind of multi-agent platform work that moves the needle for customer support in fintech—and a compelling template for any product leader scaling agentic AI and AI workflows beyond the tip of the iceberg.

Inspired by this post on Product Talk.

December 18, 2025
Product Manager Cover Letter Mastery for 2026: Proven Steps, Templates, and AI Workflows

Every week I review dozens of applications for PM roles, and in under 30 seconds I decide whether to keep reading. In 2026, the bar is higher than ever: clarity, outcomes, and customer insight beat buzzwords every time.

Learn how to write a standout product manager cover letter with steps, examples, templates, and smart AI workflows to make your application stand out.

I start with a crisp opening that communicates my value proposition in one sentence: the product problem I love solving, the customer I serve, and the measurable outcomes I drive. Then I connect my experience to the role’s core responsibilities—product discovery, product positioning, go-to-market strategy, and stakeholder management—without rehashing my resume.

A strong PM cover letter follows a simple structure: a hook with context, one paragraph proving product management leadership through outcomes vs output OKRs, a paragraph on how I partner with empowered product teams and engineering to ship, and a closing line that shows I understand the company’s roadmap and where I can help now.

To make this concrete, I include brief examples that show decisions, not duties: how I translated ambiguous customer signals into a roadmap, how I balanced platform scalability with speed, and how I measured success with activation, retention, and adoption—not vanity metrics.

Templates help me move fast, but I always tailor. I mirror the job’s language, highlight the few experiences that map 1:1, and cut everything else. I quantify impact where possible, link outcomes to business value, and keep it to 200–300 words so hiring managers can scan.

I also use smart AI workflows to accelerate the craft without sacrificing authenticity. My LLMs for product managers playbook: extract the role’s competencies, generate a draft outline, compare multiple versions with light A/B testing, and refine tone and clarity. Tools should augment judgment; the final voice is mine.

If you’re applying now, assemble your core template, slot in two role-specific examples, and close with a confident ask for next steps. With the right structure, clear outcomes, and a little AI leverage, your product manager cover letter will stand out in any stack.

Inspired by this post on Product School.

December 18, 2025
Plan Your 2026 Product Conference Calendar: Top Events, Locations, and Insider Tips

I’m curating a living list of 2026 product conferences to help product managers, product leaders, and empowered product teams plan ahead with confidence. I use this calendar to align my team’s discovery work, roadmapping, and go-to-market strategy—and to prioritize conference networking and learning that moves the needle on product-led growth.

This list is not exhaustive. If there’s a product conference missing that should be here, please send it to conferences@producttalk.org. I’ll keep updating this as new events are announced so you have a reliable guide throughout the year.

I’ll be teaching a workshop and speaking at the Product at Heart conference in June in Hamburg, Germany. If you plan to attend, be sure to say hi.

Are you looking for the 2025 Product Conferences list? Find it here.

How I use this guide: I map events to our quarterly OKRs (outcomes vs output OKRs), focus on sessions that sharpen product discovery, stakeholder management, and product roadmapping and sprint planning, and bring a clear plan for takeaways I can apply the day I’m back. If you’re exploring AI Strategy and LLMs for product managers, you’ll find several strong options below.

January

Jan 28 — Product-Led Summit — Washington, DC, USA

Jan 30–31 — Prdkt+ — Cairo, Egypt

February

Feb 1–4 — WebSummit — Doha, Qatar

Feb 2–20 — DeveloperWeek Hackathon — San Jose, CA, USA & Virtual

Feb 4 — DDX Innovation & UX Conference — Tokyo, Japan

Feb 4–5 — UX360 Virtual Summit — Virtual

Feb 7–8 — DDX Innovation & UX Conference — Dubai, UAE

Feb 18–20 — DeveloperWeek — San Jose, CA, USA

Feb 18–20 — ProductWorld — San Jose, CA, USA

Feb 24 — ProductCon — London, UK

Feb 24–25 — axe-con — Virtual

Feb 24–25 — Product-Led Summit — Austin, TX, USA

March

Mar 9–10 — Gartner Product Leadership Conference — Grapevine, TX, USA

Mar 12–18 — SXSW — Austin, TX, USA

Mar 23–26 — The Annual ACM Conference on Intelligent User Interface — Paphos, Cyprus

Mar 26 — Chief Product Officer Summit — New York, NY, USA

Mar 26–27 — Product Operations Summit — New York, NY, USA

Mar 26–27 — Product-Led Summit — New York, NY, USA

April

Apr 1–2 — Product-Led Summit — Denver, CO, USA

Apr 11 — ProductCamp — Phoenix, AZ, USA

Apr 13–14 — Business of Software — Cambridge, UK

Apr 13–17 — ACM CHI — Barcelona, Spain

Apr 14 — Chief Product Officer Summit — Palo Alto, CA, USA

Apr 15–16 — UX Nordic — Aarhus, Denmark

Apr 15 — AI Product Summit — San Jose, CA, USA

Apr 20–21 — Product at Heart Leadership — Hamburg, Germany

April 22–23 — UX360 NA — Atlanta, GA, USA

May

May 7–8 — ProductWorld 2026 — Opatija, Croatia

May 9 — DDX Innovation & UX Conference — Munich, Germany

May 11–13 — UXDX — New York, NY, USA & Virtual

May 11–14 — Web Summit — Vancouver, Canada

May 12–13 — Product Operations Summit — Amsterdam, The Netherlands

May 12–15 — UXLx User Experience — Lisbon, Portugal

May 13 — Leading the Product Leaders Forum — Melbourne, Australia

May 13–15 — SaaStr Annual — San Mateo, CA, USA

May 14 — Leading the Product Conference — Melbourne, Australia

May 19 — La Product Conf — Paris, France

May 20 — Leading the Product Leaders Forum — Sydney, Australia

May 20 — ProductCon — New York, NY, USA

May 21 — Leading the Product Conference — Sydney, Australia

May 27–29 — UXDX EMEA — Berlin, Germany & Virtual

May 22 — La Product Conf — Madrid, Spain

May 27–28 — Dublin Tech Summit — Dublin, Ireland

May 28–29 — Chief Product Officer Summit — Amsterdam, The Netherlands

May 28–29 — Product-Led Summit — Amsterdam, The Netherlands

June

Jun 8–11 — Web Summit — Rio de Janeiro, Brazil

Jun 15–16 — #mtpcon: A Mind the Product conference — London, UK

Jun 16 — Growth Minded Superheroes — Frankfurt, Germany

Jun 17–18 — Product-Led Summit — Seattle, WA, USA

Jun 22–26 — UXPA International — Las Vegas, NV, USA

Jun 23–24 — UX360 EU — Berlin, Germany

Jun 24–25 — Product-Led Summit — London, UK

Jun 26 — Product at Heart Conference — Hamburg, Germany

July

Jul 2–3 — Agile on the Beach — Falmouth, UK

Jul 26–28 — Agile2026 — Washington, DC, USA

Jul 26–31 — HCI International — Montreal, Canada

August

Aug 5 — ProductCon AI: Online Edition — Virtual

September

Sep 16–17 — uxcon — Vienna, Austria

Sep 16–18 — Hatch Conference — Berlin, Germany & Virtual

Sep 17 — DDX Innovation & UX Conference — San Diego, CA, USA

Sep 17 — Chief Product Officer Summit — San Francisco, CA, USA

Sep 22–23 — Product-Led Summit — San Francisco, CA, USA

Sep 22–23 — Product Operations Summit — San Francisco, CA, USA

Sep 28–30 — B2B Summit EMEA — London, UK

Sep 30–Oct 2 — GOTO Copenhagen — Copenhagen, Denmark

October

Oct 14–15 — Product-Led Summit — Berlin, Germany

Oct 16 — Just Product 2026 — Munich, Germany

Oct 26–27 — Y Oslo — Oslo, Norway

Oct 28 — Product-Led Summit — Sydney, Australia

Oct 28–29 — Product-Led Summit — Boston, MA, USA

November

Nov 9–12 — Web Summit — Lisbon, Portugal

Nov 11–12 — Product-Led Summit — Toronto, Canada

Nov 11–12 — Leading Design — London, UK

If you’re attending any of these, let me know—conference networking is always better with a plan and a friendly face. And if you’ve got a must-attend event on your radar, send it to conferences@producttalk.org so I can keep this guide comprehensive for the community.

Inspired by this post on Product Talk.

December 17, 2025
Must‑Know Product Benchmarks for Financial Services: Actionable Insights to Accelerate Growth

I’ve learned that in financial services, intuition isn’t enough—rigorous product benchmarks are what separate signal from noise. When my team and I evaluate portfolio performance, we anchor our decisions to the metrics that correlate with customer trust, compliant growth, and durable revenue.

Discover exclusive data and strategies from our Product Benchmark Report. Compare the financial services industry’s performance across key product metrics.

Here’s how I use a benchmark report in practice: I calibrate our baseline against peers, identify the few levers that disproportionately drive outcomes, translate those findings into outcomes vs output OKRs, and align stakeholders across product, risk, operations, and go-to-market. Benchmarks turn debate into data and surface the opportunity cost of not fixing broken journeys.

The product metrics I zero in on typically include user activation rate, time-to-first-value, onboarding completion, funnel conversion (for example, from signup to funded account or application to approval), cohort-based retention analysis (D7/D30/D90), depth of feature adoption, weekly-to-monthly active ratios, support contact rate, and cost-to-serve. In financial services, these signals tell a clear story about trust, reliability, and product-market fit.

To operationalize these insights, I combine Amplitude analytics with Pendo in-app guides to instrument end-to-end journeys, segment by customer profile, and run disciplined A/B testing with clear guardrails. This lets us move from anecdotes to statistically defensible changes and iterate confidently on onboarding, product tours, and moments that drive activation and engagement.

Because the trust and regulatory bar is higher in financial services, I also watch for friction in verification flows, error states that erode confidence, and any gaps between intent and completion. When benchmarks show we’re lagging, I pair discovery with rapid experiments to improve the experience while maintaining privacy-by-design and strong governance.

Use this benchmark report to pinpoint where you outperform and where you lag, prioritize roadmap bets, and focus your product-led growth motion. When teams rally around a shared set of product benchmarks, execution speeds up, trade-offs become clearer, and the value proposition sharpens for both customers and the business.

Inspired by this post on Amplitude – Perspectives.

December 16, 2025
Stop Tuning Prompts: How Context Engineering 10x’d Accuracy and Adoption in Our AI Platform

"The best AI products improve more through context engineering than prompt tinkering." I’ve seen this play out repeatedly in high-stakes, enterprise use cases: substantive gains come from how we curate, structure, and deliver context to models—not from wordsmithing. When we started treating context as a product surface, performance climbed, hallucinations dropped, and teams shipped with more confidence.

Here are four key decisions we made to improve our AI context.

First, we moved to a retrieval-first pipeline. We unified trusted sources—CRM records, support knowledge bases, product telemetry, and governance-approved docs—behind hybrid retrieval (semantic + keyword) with strong metadata ranking. This let us constrain generations to verifiable facts, apply privacy-by-design rules at the edge, and practice disciplined context window management so every token carried its weight. Freshness policies, source-level confidence scores, and lightweight schemas kept the system precise and auditable.

Second, we made eval-driven development non-negotiable. Every change to context assembly goes through offline evals and online A/B testing with clear acceptance thresholds (e.g., task success, groundedness, time-to-first-answer, and deflection rate). We sized tests with minimum detectable effect (MDE) and tied them to outcomes vs output OKRs so we weren’t just shipping more prompts—we were shipping measurable improvements that mattered to customers.

Third, we personalized context based on intent and role. We built AI workflows that detect user intent, segment by persona, and dynamically assemble context: recent account activity for customer success, policy-safe excerpts for finance, and fine-grained reasoning chains for product teams. For conversational and voice AI agent experiences, we combined short-term conversation memory with scoped, long-term account memory to preserve relevance without bloating the prompt. This agentic AI pattern ensured faster, safer, and more helpful responses.

Fourth, we operationalized context as a first-class platform capability. We invested in data governance (ownership, lineage, and redaction), instrumentation (Amplitude analytics for usage, retrieval hit rates, and failure modes), and CI/CD guardrails for context updates. Product trios partnered with SRE to monitor drift, while side-by-side comparisons and human-in-the-loop reviews turned frontline feedback into structured improvements. The result: a durable system that improves continuously instead of relying on one-off prompt tweaks.

Context engineering isn’t glamorous, but it compounds. By prioritizing retrieval-first design, rigorous evaluation, intent-aware assembly, and operational excellence, we transformed our AI features into dependable, enterprise-ready capabilities. If you’re serious about LLMs for product managers and sustainable AI Strategy, shift your energy from clever prompts to robust context—and watch adoption and trust follow.

Inspired by this post on Amplitude – Perspectives.

December 16, 2025
Inside a Staff AI Engineer’s Impact: How Cross-Functional AI Initiatives Drive Product Wins

When I think about the roles that truly move the needle on AI Strategy and product outcomes, the Staff AI Engineer stands out. This is the person who can translate research into repeatable AI workflows, partner with product to solve real user problems, and operationalize models in a way that scales. It’s where innovation meets accountability—and where product management leadership meets hands-on engineering craft.

Ram Soma is a Staff AI Engineer at Amplitude, leading various AI initiatives across the company. He has a background in data science and machine learning engineering.

What does that look like in practice from my seat? It starts with precise problem framing and measurable success criteria. I align with a Staff AI Engineer on eval-driven development and instrumentation so we can track impact from prototype to production. With Amplitude analytics operating as a unified analytics platform, we can quantify user activation, retention analysis, and feature adoption, then iterate through continuous discovery with tight feedback loops.

Execution quality hinges on robust experimentation. Together, we design A/B testing plans with minimum detectable effect (MDE) targets, isolate confounding variables, and build evaluation harnesses that reflect real-world UX constraints. We also agree on rollout strategies—staged deployments, guardrails, and observability—so we can learn safely while preserving customer trust and performance SLAs.

On the technical approach, I look for pragmatic architectures that balance speed and reliability: a retrieval-first pipeline for grounding, judicious use of LLMs for product managers to instrument prompts and policies, and agentic AI patterns only when task decomposition truly reduces complexity. Just as important are privacy-by-design and data governance practices from day one, because responsible innovation beats retrofitting controls after the fact.

Finally, the magic happens in empowered product teams and product trios. When product, design, and Staff AI Engineering operate with shared context and clear constraints, we compress decision cycles and ship value faster. That’s how AI initiatives evolve from demos to durable capabilities—and how we enable product-led growth with measurable results that customers feel, not just features they see.

Inspired by this post on Amplitude – Perspectives.

December 16, 2025