Month: December 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
Teach AI to Think Like an Analyst: My Amplitude-Inspired Playbook for Faster Decisions

I’ve spent years trying to bottle the judgment of a great product analyst and pour it into our AI workflows. The hardest part isn’t access to data; it’s encoding the nuance of analytical reasoning. That’s why Amplitude’s approach resonated with me—turning expert analysis into a repeatable, stepwise process AI can run with discipline and speed.

Learn how Amplitude turned its data analysis expertise into a structured, iterative process that AI can execute in moments.

In practical terms, I translate that one line into an operating model: define the decision, formalize the metrics, map the data, decompose the questions, iterate on evidence, and converge on a recommendation with clear trade-offs. This is the backbone of agentic AI for product managers—giving an LLM not just data, but a procedure that mirrors how our best analysts think.

Here’s the analyst-to-AI loop I use. First, frame the business question in decision language (what will we do differently?). Second, anchor on success metrics and guardrails, including statistical sensitivity and minimum detectable effect (MDE). Third, locate trusted sources—your unified analytics platform, experiment logs, and product instrumentation—so the AI never guesses. Fourth, generate hypotheses and segment the data (cohorts, channels, plans, geos), prioritizing signal over noise. Finally, synthesize findings into options with expected impact, risks, and next steps.

To operationalize this, I build a retrieval-first pipeline that binds Amplitude analytics to structured prompts and function calls. The AI receives exact metric definitions, event taxonomies, and governance rules, then returns a predictable schema—headlines, evidence, segments, caveats, and recommended actions. That combination of clear constraints and consistent output makes eval-driven development possible: I can test prompts and tooling against a gold set of analyses and steadily improve quality.

Consider retention analysis on a new onboarding flow. I’ll ask the system to pull activation rate, time-to-value, and day-7 retention from Amplitude, then compare cohorts by channel and plan. The AI proposes hypotheses (e.g., tooltip engagement correlates with activation), runs segmentation to validate them, and lays out product-led growth levers—like simplifying the first-run checklist or moving guidance in-app. What used to take hours of manual slicing now becomes an iterative loop that lets me spend more time on prioritization and less on tab wrangling.

Of course, speed without rigor is a trap. I guard against metric drift and hallucinations with strong definitions, lineage checks, and human-in-the-loop approvals for consequential decisions. I also log analysis steps and outcomes so we can audit reasoning, catch regressions, and keep AI grounded in our true north metrics—not just what’s easy to compute.

The big unlock isn’t a clever prompt; it’s codifying the analyst’s craft. When we treat analysis as a structured, iterative process, AI can execute it with consistency, and product teams can move faster with more confidence. If you’re building AI workflows for product insight, start by formalizing your analyst loop, connect it to your Amplitude analytics, and evaluate continuously. The result is smarter, faster decisions—and a repeatable path from raw data to action.

Inspired by this post on Amplitude – Best Practices.

December 16, 2025
Inside the Core Analytics Mindset: How I Build Products that Drive Clarity and Growth

I spend my days shaping core analytics product experiences that help teams see their business with greater clarity. When I design an analytics workflow, my goal is simple: make it effortless to ask better questions, uncover meaningful patterns, and turn insight into action. In this brief reflection, I’ll share how I approach product discovery, experimentation, and roadmapping to create analytics tools that truly move the needle.

Everything starts with outcomes. I anchor roadmaps to a clear north star and use outcomes vs output OKRs to align problem statements with measurable impact. That means instrumenting a precise event taxonomy and building guardrails for data quality so retention analysis and user activation metrics are trustworthy. When the foundation is sound, product-led growth becomes repeatable because we can connect feature usage to value creation without guesswork.

Experimentation is where conviction meets evidence. I rely on A/B testing with a disciplined view of minimum detectable effect (MDE) so we size experiments responsibly and ship with confidence. Self-serve analysis—and, when appropriate, tools like Amplitude analytics within a unified analytics platform—lets teams quickly validate hypotheses, monitor cohorts, and understand lift. The result is faster learning cycles without sacrificing statistical rigor.

On the delivery side, I practice continuous discovery and translate insights into product roadmapping and sprint planning that teams can execute. I work closely with design and engineering to reduce cognitive load in the UI, standardize tooltips and in-app guides, and ensure every chart, filter, and segment supports a clear decision. This collaboration empowers the team, shortens feedback loops, and keeps us oriented toward customer outcomes rather than feature checklists.

Great analytics products give people confidence. By aligning on outcomes, instrumenting clean data, testing with discipline, and shipping thoughtfully, I’ve seen teams unlock deeper understanding and sustained growth. If you care about building products that illuminate the path forward, start with the questions customers need to answer—and let your analytics experience make those answers obvious.

Inspired by this post on Amplitude – Best Practices.

December 16, 2025
2026 Support Capacity Playbook: Bold AI Automation, Smarter Staffing, Zero‑Surprise SLAs

Capacity planning has always been a high-stakes exercise in customer service, and when you miss, the signal shows up fast in backlogs and SLAs. I’ve lived that pressure across multiple cycles, and 2026 will reward teams that plan differently. AI fundamentally changes capacity planning because it changes the work. It resolves the bulk of your volume, speeds up execution, and elevates the complexity and value of what humans handle. The consequence is simple: planning models must evolve. This is the final installment in my 2026 customer service planning series, and I’m focusing on the tension every leader feels right now—be ambitious about automation, but avoid the trap of understaffing if your assumptions don’t hold. My goal is to share how AI changes the logic of capacity planning, what I’ve learned implementing these practices with my team and with customers, and the common traps to avoid. Traditional planning rests on relatively stable assumptions: volume grows predictably, work types stay consistent, handle times don’t swing dramatically, and productivity improves slowly with better tools and training. In an AI-first model, none of that is guaranteed, and the fundamentals flip. The mix of work changes as AI absorbs a growing share of simpler conversations, leaving humans with deeper, more time-consuming issues that demand human-to-human connection. Demand can actually increase when you remove friction, so AI can both resolve more and attract more volume. Human time splits differently as teammates solve customer problems and also review AI behavior, give feedback, improve content, and support system-level work. Performance becomes dynamic, not fixed—automation rate isn’t a one-time number; it can rise with care and fall with neglect. If you plan for 2026 using a pre-AI model—assuming similar productivity, similar work mix, and a linear relationship between volume and headcount—you will underestimate what it now takes to run a high-performing support organization. There are many metrics you can track, but the one to put at the center is automation rate (AI Agent involvement rate × AI Agent resolution rate). This single construct tells me what share of total volume AI actually resolves, how much work remains for humans, how much additional demand humans can absorb, and how ambitious I can be with headcount. Early in the journey, I prioritize raising involvement—getting the AI involved in more conversations. Once involvement is high, I shift to resolution on the hardest remaining work, where each additional 1% of automation can represent several people’s worth of capacity. In my 2026 plans, automation rate sits alongside projected inbound volume, average “output” per person for the more complex work that remains, and occupancy—how much time is allocated to customer-facing interactions versus operational and strategic work. Together, those inputs give a realistic picture of how many people you need and where they should spend their time. First, plan boldly on automation, but match it with investment. I do not cap automation assumptions at 40–50% “because AI is new.” Many teams are already modeling 60%, 70%, even 80%+ for 2026—when they invest in AI ownership and content. The investment is non-negotiable: named ownership for AI performance (AI ops, knowledge management, conversation design), clear automation targets by work type (e.g., informational vs. personalized vs. actions vs. deep troubleshooting), realistic expectations for what’s easy to automate and what’s not, and a concrete plan to raise automation over time in monthly or quarterly steps rather than a single jump. To decide where to invest first, I dig into the data. I start with the biggest volume drivers, separate content-led issues from those dependent on data or complex procedures, assume higher resolution potential for content-led topics once the knowledge base is in shape, and set more modest initial resolution expectations for system-dependent flows. Then I stair-step improvements as the systems, data contracts, and workflows mature. In short, bold automation goals only work when paired with the team structure, content, and systems required to reach them—and the discipline to iterate. Second, expect human “output” per person to go down. That’s a mindset shift. Historically, we assumed individual productivity would stay flat or tick up as tools improved. In an AI-first model, humans handle fewer conversations but more complex, cross-functional issues—and create more value despite lower case counts. I model a lower “cases closed per person” than prior-year baselines, explicitly assume the remaining work is more complex and time-consuming, and redefine productivity to include system-level work like AI Agent improvements, content updates, and policy or workflow change management. I also report “capacity created” from automation alongside human outputs, so leadership sees the full picture. Third, rethink occupancy: more time off the queues, on higher-value work. Traditional occupancy splits time between inbox and training, meetings, and breaks. Now there’s an expanding “out-of-inbox” portfolio that directly affects AI performance and overall capacity: reviewing AI-handled conversations, improving AI Agent triaging and handovers, contributing to content and procedures, feeding insights to product and engineering, and supporting system changes that reduce future volume. I set lower inbox occupancy targets than before and make the rationale explicit. People aren’t working less—they’re working differently. In planning, I assume more time spent on improvement and system work, make it visible (for example, X% in inbox and Y% on AI and system improvement), and treat this as critical, not a “nice to have.” If you don’t proactively allocate it, it won’t happen—and your automation and performance targets will suffer. Fourth, work with the finance team early, and treat your plan as a set of assumptions. Capacity planning with AI is a set of bets across automation rate, human output, demand growth, occupancy, and where surplus capacity (if any) goes. I bring finance in early, show that the plan is dynamic and directly tied to AI performance, and label every lever as an assumption with ranges. I commit to a quarterly review cadence with finance to compare assumptions versus reality and adjust headcount, targets, and investment as needed. The risks are real: if automation grows slower than expected and you stop backfilling too early, you’ll be understaffed for months. Hiring and onboarding take time, so course-correcting late creates strain. If you do produce surplus capacity, have a clear strategy to reallocate those teammates to higher-value work—improving systems, feeding insights back to product, supporting new channels, and driving proactive CX—rather than defaulting to reductions. I also set explicit guardrails—if automation rate misses by five points for two consecutive months, we pause planned reductions and revisit hiring gates. If it over-performs, we shift people into backlog eradication, content upgrades, or proactive outreach, so we bank compounding value. To set your team up for success in 2026, anchor your plan on automation rate, be honest that humans will handle fewer but harder conversations, and protect time for system improvements. Partner early and often with finance, avoid shrinking too fast, and design a plan for surplus capacity so you’re never caught flat-footed. If AI is going to handle the majority of your customer conversations, your plan has to be designed to help it do that well and to keep your team set up for meaningful, sustainable work. A 2026 plan built on adaptable assumptions—not fixed predictions—will hold up as your work, your systems, and your customers’ expectations continue to change. If you’d like future editions like this, subscribe and stay close—I’ll keep sharing what’s working, what isn’t, and how to tune your customer support AI strategy in real time.

Inspired by this post on The Intercom Blog.

December 16, 2025
Year-End Reflection for Product Leaders: Values, Themes, and the 100‑Wishes Reset

I’ve been closing the year with a deliberate reflection ritual for more than a decade, and this season I found fresh energy for it after listening to an insightful conversation with Teresa Torres and Petra Wille on All Things Product. Their approaches mirror the evolution many product leaders experience: moving from rigid annual goal-setting to values-led themes, longer time horizons, and a healthier respect for spaciousness. In my own practice, that shift has created better focus, less pressure, and far more meaningful outcomes.

Prefer to listen? You can find this episode here: Spotify | Apple Podcasts. I took notes with my team in mind and translated the discussion into a simple, values-driven framework that any product organization can adopt.

Why does annual reflection matter for product people? Because our work lives at the intersection of ambiguity, trade-offs, and time. If we only measure ourselves by shipped output or quarterly OKRs, we overlook the compounding value of learning, relationships, and judgement. I treat this ritual as a strategic reset: a chance to surface patterns, adjust expectations, and recommit to outcomes over output.

My own reflection habit started scrappy—paper notebooks, messy timelines, and even artful visualizations inspired by Dear Data by Giorgia Lupi & Stefanie Posavec. Like Petra, I’ve found that tactile, analog artifacts unlock insights I miss in a spreadsheet. Over time, I’ve kept the spirit and simplified the mechanics: a “what went well” review, a short list of hard lessons, and a handful of decisions that paid off—or didn’t.

The biggest evolution for me has been moving from rigid annual goals to values and themes. I still run OKRs, but I use them to track progress, not identity. The lens of process vs. outcome goals—reinforced by ideas from Atomic Habits—helped me set fewer, better commitments. For example, instead of “launch X by Y,” I’ll emphasize the cadence of customer discovery, the health of the product trio, and the quality of decisions made along the way.

One exercise that changed my practice is the “100 wishes” list. It’s powerful—and surprisingly difficult. Pushing past 30 or 40 wishes forces me to name latent interests and long-range intentions I rarely say out loud. Combined with decade-level themes, the list helps me balance ambition with patience. I don’t try to do it all next year; I use it to spotlight direction, not deadlines.

I also review patterns across years: Where did over-scheduling create hidden costs? When did I protect focus time and what did that unlock? Paul Graham’s Maker’s Schedule, Manager’s Schedule remains a useful calibration tool here. And when I feel the pull toward constant throughput, I revisit Stefan Sagmeister’s The Power of Time Off (TED Talk) to remind myself why strategically creating space often yields the most valuable ideas.

Of course, not every year follows plan—and that’s normal. Reflection helps me spot unrealistic expectations early and let them go. When setbacks hit, I’ll rewatch Dealing with Setbacks and re-ground in continuous discovery. The question isn’t “Did we do everything?” but “Did we learn fast, protect customer value, and make trade-offs aligned with our values?” That’s how empowered product teams compound impact.

My sharing philosophy has become more nuanced over time. Some reflections are public to invite dialogue and accountability; others stay private so I can process honestly. I’ve found it helpful to publish what I’m saying no to, capture a theme for the year ahead, and keep the rest for myself and my team. This balance preserves motivation while still contributing to the broader product management leadership community.

If you’re designing your own ritual, consider this lightweight flow: review wins and tough calls, write your “100 wishes,” extract a few values-based themes, then translate those into process goals for Q1. Revisit monthly, not just annually. If you like structured prompts, Chris Guillebeau’s How to Conduct Your Own Annual Review from The Art of Nonconformity offers a practical template you can adapt to your context.

For deeper dives and complementary ideas, I bookmarked these as part of my year-end reset: What I’m Saying No to This Year—And Why, Ask Teresa: My Leaders Still Want Roadmaps with Timelines—What Should I Do?, Scaling Impact: A Look at the Year Ahead (2022), Let’s Connect in 2025: A Look at the Year Ahead, The Interview Coach, and Petra’s own year-ahead reflections (here and her 2026 version). I also recommend revisiting the prior conversation on leadership and change: Role of Leadership in Transformations.

I’d love to hear how you approach your end-of-year reflection. What questions bring you the most clarity? Which practices help you set an intentional, values-driven path for the next year? Share your process—I’m always looking to learn from other product creators and leaders.

Inspired by this post on Product Talk.

December 16, 2025
AI in Product Design: My Proven Playbook, Real Use Cases, and the Tools That Win Faster

In product design, AI has shifted from novelty to non-negotiable. I’ve watched teams accelerate discovery, compress prototyping cycles, and turn ambiguous ideas into validated experiences faster than ever—without sacrificing quality or customer trust.

AI in product design has quickly moved from new to necessary. Here are the AI product design tools and approaches you need to stay relevant in this decade.

From my vantage point leading product teams, “necessary” means AI is woven throughout the product lifecycle—discovery, prioritization, prototyping, validation, and iteration—not bolted on. The goal isn’t to chase hype; it’s to build durable advantage with clear AI Strategy, disciplined execution, and measurable outcomes.

First, anchor the work in strategy. Tie every AI initiative to a specific customer problem and value proposition, then express that linkage with outcomes vs output OKRs. This keeps teams focused on real impact and avoids feature-chasing. It also sharpens product positioning and clarifies where AI can deliver competitive differentiation versus simple points of parity.

Second, upgrade discovery. I rely on AI workflows to synthesize interviews, cluster themes, and surface insights at scale. A retrieval-first pipeline—grounding models in our own data—improves factuality and reduces hallucinations. Combine this with strong data governance and privacy-by-design so insights are trustworthy and compliant from day one.

Third, make quality measurable. Adopt eval-driven development: define evaluation sets and acceptance thresholds that reflect real user tasks before you ship. Pair that with A/B testing and minimum detectable effect (MDE) discipline, so you learn quickly and confidently. Add safety guardrails (red-teaming prompts, content filters, and bias checks) to manage AI risk without slowing the pace.

Fourth, enable empowered product teams. Product trios (PM, design, engineering) should co-create prompts, prototypes, and evaluation criteria. Give designers and PMs practical tools—LLMs for product managers, structured prompt templates, and reusable components—so AI-augmented work becomes the default, not a special project.

Where does AI shine in product design today? Concept exploration and market scans, turning fuzzy opportunity spaces into crisp problem statements. Rapid wireframes and interaction ideas, using gen ai for product prototyping to explore multiple design directions in minutes. UX writing that adapts tone and reduces friction across onboarding, tooltip design, and microcopy.

It also excels at guided experiences. I’ve seen strong lifts in user activation when we pair in-app guides and product tours with context-aware suggestions. For support and education use cases, a retrieval-grounded assistant can deflect tickets, shorten time-to-value, and reinforce the product’s value proposition at the exact moment a user needs help.

Voice is another frontier. A well-scoped voice AI agent can accelerate complex workflows (think data entry or multi-step configurations) when hands-free is faster or more intuitive. Just be intentional about when agentic AI adds net value versus when a simple UI tweak would do.

On the tooling side, my AI product toolbox is pragmatic and modular. For analytics and learning loops, Amplitude analytics and Pendo help quantify behavior changes and retention analysis. For in-product engagement and feedback routing, Intercom and HubSpot integrate cleanly with LLM-driven tagging and summarization. For ideation and automation, I use a ChatGPT connector and Claude Code for quick scripts, data wrangling, and prompt experiments. The constant: a retrieval-first pipeline that grounds models in approved knowledge and maintains context window management at scale.

Risk management is built in, not bolted on. Set clear AI risk management policies, catalog model and data dependencies, and document decisions. Align with regulatory compliance requirements early, and keep an audit trail of prompts, datasets, and eval results. That’s how you move fast without breaking trust.

If you’re getting started, begin small: pick one high-friction workflow, add a retrieval-grounded copilot, and measure the lift. Use the results to inform product roadmapping and sprint planning, then scale to adjacent use cases. With disciplined discovery, sharp evaluation, and the right tooling, AI becomes a force multiplier for product teams and a clear win for customers.

Inspired by this post on Product School.

December 15, 2025
Inside the Engine Room: How I Drive Scalable Analytics APIs, Reliability, and Performance

I build and scale analytics platforms with a product mindset, and the work starts with the "middleware and compute systems that power analytics at scale." In platforms like Amplitude analytics and other unified analytics platform architectures, that foundation is what makes everything else possible.

Day to day, I oversee the "APIs behind charts, cohorts, and metrics—driving performance, reliability, and platform scalability." When those APIs are fast and resilient, every product team—from growth to customer success—can trust the insights they use to ship, learn, and iterate.

From an engineering leadership standpoint, I partner closely with SRE to define SLOs and error budgets, wire CI/CD pipelines for safe deploys, and track DORA metrics so we improve speed without compromising quality. This combination reduces incident management toil and shortens MTTR while keeping data freshness and query latency within strict thresholds.

From a product management leadership lens, the goal is clarity: crisp APIs, predictable contracts, and transparent stakeholder management across data, engineering, and GTM teams. That alignment empowers product teams with reliable cohorts and metrics, accelerates experimentation, and de-risks roadmaps.

If you’re scaling analytics, invest first in the platform layer: middleware and compute, schema governance, caching strategies, and cost-aware compute. Do that well, and the visible experience—charts, cohorts, and metrics—feels effortless, even as you grow to serve billions of events with confidence.

Inspired by this post on Amplitude – Best Practices.

December 12, 2025
From Insights to Impact: Turning Amplitude Analytics into Product-Led Growth at Scale

I’ve seen time and again that when content is as data-driven as the product, adoption accelerates. Partnering closely with a data-driven content marketing manager and Amplitude power user, I watched how precise storytelling—grounded in Amplitude analytics—can unlock user activation and retention at scale.

Previously, she managed all customer identity content at Okta.

We started by translating product strategy into measurable moments in the customer journey: activation events, aha moments, and retention cohorts. Using Amplitude analytics, we built funnels and segmentations to isolate high-signal behaviors, ran A/B testing on messaging and in-app guides, and turned retention analysis into an editorial roadmap that spoke to specific use cases and jobs-to-be-done. This unified analytics platform approach ensured the content engine and product telemetry were speaking the same language.

From there, we aligned go-to-market strategy with lifecycle communication—product tours, onboarding sequences, and contextual education that made the value proposition unmistakable. Through continuous discovery and product discovery rituals with product trios, we iterated messaging to sharpen product positioning and reduce time-to-value. The result was content that didn’t just describe features—it moved outcomes.

To keep us honest, we instrumented outcomes vs output OKRs tied to activation rate, expansion intent, and long-term retention. We watched leading indicators (setup completion, power-user actions) roll up into lagging results (weekly active usage and cohort retention), and refined our bets in tight feedback loops.

If you’re building a product-led growth motion, pair your roadmap with a content leader who treats telemetry as a design material. When an Amplitude power user brings the same rigor to narrative that engineers bring to code, the compounding effect on adoption, engagement, and retention is unmistakable.

Inspired by this post on Amplitude – Perspectives.

December 11, 2025
Unlock B2B Product Excellence: Essential Benchmarks to Outperform Your Tech Peers

I rely on disciplined product benchmarks to turn strategic intent into measurable progress. In B2B technology, benchmarks give me and my team the clarity to prioritize what truly matters, align executives around shared outcomes, and course-correct before small gaps become growth-stalling problems.

Discover exclusive data and strategies from our Product Benchmark Report. Compare the B2B technology industry’s performance across key product metrics.

When I assess product health across a portfolio, I start with a core set of product benchmarks: activation rate, onboarding completion, time-to-first-value, weekly and monthly active accounts, feature adoption, cohort-based retention, expansion and contraction revenue, and support deflection. Together, these metrics show where the product creates value, where users get stuck, and which levers most efficiently drive product-led growth.

Benchmarks are only powerful if they inspire action. I instrument reliable analytics (Amplitude analytics) to capture consistent event data, pair that with in-app guides and product tours (Pendo, Intercom) to test friction hypotheses, and run A/B testing to validate changes with statistical rigor. From there, I translate insights into outcomes-based OKRs, so roadmapping and sprint planning focus on the few bets most likely to move our key product metrics.

I’ve seen this approach pay off repeatedly. When peer benchmarks revealed our user activation lagged, we simplified onboarding, clarified value propositions with sharper UX writing, and launched targeted in-app nudges. We didn’t just ship features—we improved the experience against a clear yardstick, and the subsequent lift in activation and early retention validated the strategy.

Cross-functional alignment is critical. I partner with customer success to interpret retention analysis by segment, with marketing to ensure messaging supports time-to-value, and with engineering to keep quality and reliability high. While product metrics lead, I also keep an eye on complementary signals like incident management and DORA metrics to ensure we’re not trading speed for stability.

If you’re leading a product organization, use benchmarks to calibrate ambition and create focus. Start by identifying the one or two metrics most predictive of long-term retention, set peer-informed targets, and iterate with continuous discovery. The result is a product strategy that is evidence-based, resilient to opinion cycles, and capable of compounding gains over time.

Ultimately, benchmarks aren’t about vanity; they are about velocity. With a shared view of where you stand against the B2B technology industry, you can make sharper bets, accelerate product-market fit, and turn your roadmap into a reliable engine for growth and customer value.

Inspired by this post on Amplitude – Perspectives.

December 11, 2025
From Concierge to AI Marketing Engine: Inside Mowie’s Document Hierarchy Playbook

I’m constantly asked by SMB owners: What if your small business could have a full marketing team—automated content calendars, customer segmentation, and channel-specific posts—without the headcount? That question is no longer hypothetical; it’s precisely the promise behind Mowie, and the way they got there is a masterclass in practical AI product development.

I recently listened to Chris O'Connor (CEO) and Jessica Valenzuela (Co-Founder) of Mowie, an AI marketing platform built for small and medium-sized businesses in restaurants, retail, and e-commerce. Their story starts with a concierge marketing service—doing the work by hand for overwhelmed owners—and evolves into a fully automated AI product.

They walk through their "document hierarchy" approach: how Mowie crawls the web to build a "dossier" about each business, infers customer segments and marketing pillars, and generates quarterly content calendars with channel-specific posts. As a product leader, this is the kind of retrieval-first pipeline that consistently outperforms naive prompt chaining because it builds durable context before generation.

They also unpack the technical challenges of structuring unstructured data and the evolution from rigid schemas to loosely structured markdown. In my experience with LLMs for product managers, markdown becomes a flexible intermediate representation that’s easy to diff, trace, and feed back into models without brittle parsing.

Equally important, they use customer feedback—from calendar approvals to regeneration requests—as their primary evaluation signal. That’s eval-driven development in practice: close the loop with lightweight evals that reflect genuine user intent, not proxy metrics.

The planning model is elegant: the three mini-calendars—public events, business-specific events, and recommended campaigns—roll up into a coherent plan that eliminates the blank-page problem and enables steady, predictable execution.

Crucially, they’re building traceability so customers can see which context documents influenced their content. This kind of transparency increases trust, accelerates edits, and supports governance in regulated categories where auditability matters.

Onboarding and data collection stay pragmatic: let the system crawl first, ask humans only for deltas, and progressively profile over time. It’s a pattern I advocate in continuous discovery and AI workflows—keep humans in the loop without overwhelming them, and make the right action the easy action.

Early on, they used Simon Sinek's Golden Circle framework to validate demand and sharpen messaging. Framing the "why" before the "what" helps teams maintain a crisp value proposition and tighten their go-to-market strategy.

Performance measurement goes beyond vanity metrics by connecting marketing performance back to point-of-sale data for attribution. The ability to tie campaigns to revenue events is the bridge from clever content to accountable outcomes.

What’s next is equally compelling: deeper attribution, omnichannel expansion, and digital out-of-home displays. For SMBs, that points to a unified analytics platform spanning email, social, and in-store touchpoints—exactly where modern marketing is headed.

My takeaways for builders: invest in a retrieval-first pipeline with a resilient document hierarchy; prefer loosely structured markdown over rigid JSON when dealing with messy inputs; design human-in-the-loop controls that double as evals; and always connect activity to business outcomes. That’s how you turn an idea into a repeatable system that scales.

If you want to explore further, start here: Mowie AI — AI marketing platform for SMBs. For early validation and storytelling, revisit Simon Sinek's Golden Circle.

Inspired by this post on Product Talk.

December 11, 2025
Automated Insights for Product Teams: Uncover Causal ‘Aha’ Moments in Minutes, Not Weeks

I’ve spent countless cycles guiding teams through the maze of dashboards, SQL pulls, and ad‑hoc analyses—only to watch truly meaningful patterns emerge far too late. Automated insights are the next frontier in product analytics: a shift from manual exploration to AI that proactively surfaces what matters most. When we let the system do the heavy lifting, we accelerate discovery, reduce bias, and give product trios the clarity to act.

Finding causal connections in product data involves exhaustive searches and tests. We trained our AI to find “aha” moments in minutes instead of weeks.

Here’s what that means in practice for product management: the platform continuously scans events, cohorts, and segments; prioritizes signals linked to activation, conversion, and retention; and highlights likely causes behind meaningful movements in your core KPIs. Instead of sifting through endless funnels and cohorts, I get ranked hypotheses I can validate with targeted A/B testing and minimum detectable effect (MDE) guardrails.

This approach turns analytics into action. Automated insights reduce time-to-learning, tighten our discovery loops, and make continuous discovery tangible—especially when we’re aligning roadmaps, designing experiments, and refining onboarding. Whether you’re using tools like Amplitude analytics or instrumenting a unified analytics platform, the value is the same: faster, clearer paths to customer impact.

I’ve seen teams unlock retention analysis breakthroughs by spotting counterintuitive patterns—like a specific feature combination or an overlooked step in onboarding—well before they would have surfaced through manual analysis. With AI workflows scanning the noise and elevating the signal, we can focus on decisions: ship or iterate, scale or sunset, double down or pivot. That’s empowered product teams in action.

If you’re building for product-led growth, this is the leverage you’ve been waiting for. Automated insights transform how we prioritize, test, and communicate strategy—bringing us from gut feel and lagging indicators to explainable, causal narratives we can stand behind. The outcome is simple: more confident bets, less waste, and a faster path to durable product-market fit.

Inspired by this post on Amplitude – Best Practices.

December 10, 2025