I look for product marketing leaders who translate market noise into clear decisions that move roadmap, revenue, and relationships. In that context, Darshil Gandhi exemplifies how competitive rigor and technical depth can sharpen product strategy and accelerate go-to-market strategy across empowered product teams.
Darshil leads competitive intelligence, partner product marketing and technical marketing at Amplitude. He is a former solutions engineering team principal.
That blend matters: a solutions engineering mindset grounds messaging in real implementation details, while competitive intelligence and partner product marketing align product positioning, points of parity, and competitive differentiation with what buyers actually evaluate. At a company centered on Amplitude analytics, that cross-functional view helps transform behavioral data into a crisp value proposition customers can feel in evaluations and expansions.
In practice, I prioritize a few patterns when partnering with leaders who span these domains: align on a single competitive narrative using driver trees that connect capabilities to outcomes; use Amplitude analytics to validate claims and win themes; co-create partner playbooks that make integrations repeatable; and ensure technical marketing closes the loop by pressure-testing demos, docs-as-code, and reference architectures with field feedback. This strengthens stakeholder management across sales, solutions engineering, and product trios, reducing ambiguity and speeding decisions.
The net effect is clarity: sharper differentiation in the field, cleaner handoffs between teams, and faster feedback cycles that de-risk launches. It’s a model I trust when stakes are high—use the truth of implementation to tell a compelling story, then let the market confirm it.
Inspired by this post on Amplitude – Perspectives.
I spend a lot of my time asking a deceptively simple question: what does excellent marketing actually look like in 2026? From the vantage point of product leadership, the answer isn’t a spreadsheet or a channel plan—it’s a feeling. Beloved tech brands earn the benefit of the doubt, create gravity around their roadmap, and make customers proud to belong. That kind of momentum is not an accident; it’s a system.
Here’s the hard truth I’ve learned building and scaling products: giving teams different goals creates dysfunction. When brand, demand gen, product marketing, and comms run on fragmented OKRs, you manufacture internal headwinds. “Marketing is one engine – not separate pieces.” One strategy, one narrative, one set of outcomes—expressed through different craft disciplines and time horizons.
That unity of purpose clarifies executive roles, too. The real difference between an SVP and a CMO is scope and narrative ownership. A great CMO architects the whole system—portfolio allocation, brand architecture, integrated go-to-market strategy, and the bar for creative taste—while refusing to get dragged into decisions they should never be making (for example, approving every headline or micromanaging channel tactics). Leaders should decide the outcomes, standards, and constraints; teams should control the craft.
On portfolio design, I run marketing like a portfolio of moonshots. You need a healthy mix: proven programs that compound, emergent bets that learn fast, and a small set of true moonshots that can change the slope of the curve. The point isn’t bravado; it’s risk-balanced exploration. If everything ships safely, you’re under-investing in differentiation. If everything is a swing for the fences, you’re not building a repeatable growth engine.
This is where taste becomes a strategic advantage. “Ubiquity is the opposite of cool.” If you want to be beloved, you cannot treat every channel, audience, and moment as equal. Early on, selective distribution, distinctive creative codes, and tight community loops create status and meaning. Later, you scale without sanding off the edges that made the product special.
Why do a few companies build a flywheel of momentum while others stall? They align story, product, and distribution. The product earns trust, the narrative creates aspiration, and the go-to-market strategy ensures the right customers experience both at the right time. Then perception cycles kick in—the Silicon Valley clock turns—and irrational optimism or skepticism can amplify signals. The antidote is compounding proof: consistent product shipping, community advocacy, and creative that makes people care.
Scaling taste across an organization is teachable. I codify brand principles, narrative guardrails, and examples of “right” versus “almost right.” I replace abstract feedback with decision rubrics—what we keep, kill, or revise and why. I run recurring creative reviews with a small cross-functional council, so judgment compounds. Taste can’t be fully automated, but it can be operationalized: shared references, a story bible, and a high bar for craft that’s explicit, not mystical.
In a post-LLM world, the fundamentals haven’t changed—but the frontier has. Generative tools supercharge iteration and research, yet the artistry never really left. You still need a point of view, a tension worth resolving, and a value proposition that’s felt, not just stated. Can taste be encoded in software? Parts of it—pattern libraries, style constraints, data-driven feedback—absolutely. But the spark that makes work unforgettable remains human: judgment, risk tolerance, and the courage to ship something that might not fit the playbook.
That’s why telling an optimistic, yet realistic story about AI matters. Over-automation drains humanity; under-automation wastes potential. The best work pairs AI Strategy with craft leadership: LLMs for rapid exploration, humans for narrative decisions and ethical judgment. Your message should show how AI expands customer agency, not just efficiency.
The brand-versus-growth debate is a false choice. The right story accelerates pipeline, and the right demand programs reinforce the brand. Look at Apple’s discipline around product truth and design codes, or Google Chrome’s “The Web Is What You Make of It (Dear Sophie)” for proof that emotion and utility can co-exist. Notion, Pinterest, Square, HubSpot, and Harley-Davidson show how community, identity, and product-led growth interlock when the company knows exactly what it stands for.
When it comes to launches, I’ve learned that announcement videos full of humans, lack humanity. Overproduced gloss often dilutes the truth customers seek: what problem does this solve, how quickly can I feel the value, and why does it matter now? Real users, real context, and a crisp arc from problem to promise will outperform most theatrics.
Practically, I architect my week to protect taste and outcomes. Early-week for strategy, portfolio reviews, and cross-functional alignment; mid-week for deep creative and product marketing work; late-week for decision clears and postmortems. I time-box “disruptive energy”—space to chase non-obvious ideas—and I guard it like any critical meeting. Without protected cycles for exploration, the urgent will always suffocate the important.
If there’s a single takeaway: playbooks are obsolete, but the fundamentals are not. The channels change; the psychology doesn’t. Run one engine. Allocate a true portfolio. Scale taste with rigor. In the AI era, make people care. That’s how beloved tech brands are built—and how they endure.
Sometimes a corporate rename lands with such obvious inevitability—and such lateness—that it feels like a quiet confession. As a product leader, I’ve wrestled with that timing question: move early and risk confusion, or wait and risk stagnation. In this case, the industry finally received the clarity it has been circling for years.
The announcement was clear: “we’re changing the name of our company to Fin.” Crucially, the name Intercom will continue as the customer service software platform that many of the best brands rely on as their primary help desk. The team also “just launched a complete rebuild, Intercom 2,” and is doubling down investment in that product. In other words, the company brand now matches its leading customer agent platform—Fin—while Intercom remains the flagship product line.
From a product strategy and brand architecture perspective, this move aligns the corporate identity with the growth engine. I’ve seen too many winners of a prior era cling to yesterday’s positioning while markets shift under their feet. The phrase that keeps echoing in my mind—because it’s true in practice—is that “the only path to success in the future is through destroying your past.” Culture, pricing models, product lineup, investment priorities—those can evolve. But until the company name evolves, the market’s mental model often does not.
It’s telling that three years ago, when the team effectively created the service agent category, they led with Fin and kept Intercom in the background. That wasn’t indecision—it was smart category design. Humans don’t frequently remap old concepts; we add new ones. We don’t wake up reinterpreting what a chair is, but we do invest energy to understand a new kind of drone or an intelligent software agent. New categories deserve new names, or they’ll be dragged back into old expectations.
This is where product positioning meets competitive differentiation. Newcomers without legacy baggage enjoy a clean slate; they never have to convince the market they’ve changed because they never had an old position to defend. Even with provably superior technology, an incumbent can find itself explaining rather than advancing. I’ve led naming and repositioning work where the hardest task wasn’t shipping new capabilities—it was unseating the entrenched narrative in customers’ heads.
So, “baggage be gone.” Fin is clearly positioned as the future of the customer agent category and is poised to become the largest part of the business. Intercom, as a product brand, very much lives on—and with “Intercom 2” now in the world, the product roadmap and investment thesis are unambiguous. The core takeaway for product management leadership: align corporate naming with your category-creating bet, then let go. That’s how you turn momentum into market leadership.
For leaders working through similar decisions, here’s the lesson I’m taking to my own teams: rebrands aren’t about logos, they’re about narrative clarity and execution velocity. When the corporate name and the breakout product share the same story, go-to-market motions get sharper, customer understanding improves, and AI strategy integrates more naturally into customer support workflows. Naming follows strategy—not the other way around.
Disruption is the only sustainable strategy in product. When a platform meaningfully changes how we build and operate, I pay attention—not just as a product leader, but as someone accountable for turning AI Strategy into durable competitive differentiation. That’s why the launch of the Fin API platform stands out: it’s a concrete step toward agentic AI at enterprise scale.
Today, I’m diving into what this launch includes, why it matters for product strategy, and how I’d navigate the build vs buy decision in this new landscape. My goal is to translate the announcement into actionable guidance for product teams, CX leaders, and forward-deployed engineers who are building the next generation of customer support and product-led experiences.
Fin is a customer agent platform that at present resolves over 2M customer issues a week, growing at a rapid exponential pace. It’s relied on by the best brands, large and small, in every vertical you can imagine. From Atlassian and Riot Games, to smaller hot upstarts like Mercury and Polymarket. It runs on a family of models trained by its AI group. Last week, they announced Apex, which is the world’s first specialized customer service LLM. In production tests over the last 6 months, it beat every single frontier model, including those from Anthropic and OpenAI, on resolution rate, latency, hallucination rate, and cost.
With this launch, teams can access the platform’s core capabilities and underlying models directly via API, with contracts starting at $250k per year, and usage rates that are by far the cheapest in the industry for each of the model’s subcategories. For leaders evaluating total cost of ownership, this is a meaningful data point: it shifts the economics of scaled automation from experimental to operational.
Why now? Because builders want options. I hear from teams daily that want to design their own agents, tune prompts and policies, and integrate with bespoke CRMs, data lakes, and product surfaces. The Fin announcement meets that demand with three clear build-paths, each mapping to a different operating model and maturity stage.
First, for the vast majority of companies, the Fin Agent Platform is the pragmatic starting point. Fin reports ~8k companies on it today. It addresses 99% of customer needs out of the box—without exhausting consulting engagements—while delivering top-tier resolution rates. If your priority is time-to-value, governance, and platform scalability, this route de-risks implementation and accelerates outcomes.
Second, for teams that need custom surfaces or channels, the Fin Agent API lets you present Fin in unique contexts. You get the Fin platform’s orchestration and controls, but you’re free to bypass the default messenger, email, voice, or any prebuilt channel and embed the agent natively in your product. I see this as the sweet spot for product-led growth motions where conversation design and UX writing are strategic levers.
Third, for companies building hyper-specific agents—think service plus in-product actions—the new API access to Apex and the broader collection of models is the obvious move. Unlike generalized models, these are purpose-trained for customer service scenarios and operational policies. If you have strong in-house solutions engineering, a retrieval-first pipeline, and eval-driven development in place, this path maximizes control without reinventing the model layer.
This also opens the door for vertical specialists. Fin-like businesses focused on deep domains can emerge quickly—Fin for dentists? Why not? Fin for car dealerships? Sure. I expect startups and modern CX providers (including players like Decagon and Sierra) to carve out niches where domain data, workflows, and compliance are the real moats. That’s where differentiated AI beats generic capability.
There’s a defensive reason to pay attention here. The software landscape is shifting fast: the moat is no longer feature parity—it’s the quality of your agents and the data flywheels powering them. Building software is simply less hard now, and I’ve watched engineering teams more than double measurable productivity as they adopt AI-assisted development. The implication is clear: the interface-and-features era is giving way to an agents-and-outcomes era.
Serious software companies must evolve from being a features company to an agents company—and build those agents on differentiated AI. More value will accrue at the model and orchestration layers, where safety, latency, cost, and resolution quality are won. That puts a premium on prompt engineering discipline, policy routing, continuous discovery of edge cases, and rigorous offline/online evals to keep hallucination rates low while maintaining speed.
How would I choose among the three build-paths? If you’re early or resource-constrained, start with the Fin Agent Platform to validate outcomes and align stakeholders. If you need branded experiences and tighter product integration, use the Fin Agent API to control surfaces without owning the heavy lifting. If you have strong ML ops and a mature customer support ai strategy, go model-level with Apex and companions, layering in your own guardrails, context window management, and test harnesses. In each case, balance velocity, control, and risk—your build vs buy decision should be grounded in clear metrics and an explicit product strategy.
Where does this lead? We’ll see more companies expose specialized model families with clearer economics and stronger governance. For now, I’m excited to see what teams build with the Fin API platform—and how they turn agentic AI into measurable improvements in resolution rate, CSAT, cost-to-serve, and ultimately, customer loyalty.
I just watched one of the most significant leaps in customer service AI in years. Last week, a quiet but seismic release landed in CX: Fin introduced Apex, a vertical model purpose-built for support that raises the bar on speed, accuracy, and cost. As a product leader, this is exactly the kind of breakthrough that changes roadmaps, vendor strategies, and what customers can expect from modern service operations.
It’s a brand new model for Fin called Apex, and it’s objectively the highest performing, fastest, and cheapest model for customer service. It beats the very best models in the industry including GPT-5.4 and Opus 4.5.
In this analysis, I’ll unpack why the launch matters for the customer service agent category, what it signals for frontier labs and open‑weight ecosystems, and how leaders should rethink their AI Strategy, build vs buy decisions, and eval-driven development roadmaps.
Fin was already the highest performing and most sophisticated agent in the customer service space, consistently beating impressive competitors like Decagon and Sierra at an average win rate in the 70s. It operates at tremendous scale, now resolving almost 2M customer issues per week, a number that’s growing at an exponential clip. In its short life it’s grown to nearly $100M in recurring revenue.
As of last week, ~100% of all (English language, chat and email) customer conversations are now running on Apex. Since day 1, the Fin engine has comprised a system of models, and last year the team began replacing off‑the‑shelf models with custom ones trained on proprietary data. The core answering model had been a frontier labs offering—initially versions of GPT and more recently Sonnet 4.0. Now, that core answering model is Apex 1.0.
This model resolves customer issues at a materially higher rate than any other model available. One of their largest customers in the gaming space saw the resolution rate improve overnight from 68% to 75% (i.e. a reduction in unresolved conversations of 22%). The team notes they had never seen a jump this large from a single improvement since they started Fin.
Just as important, it’s dramatically faster, has fewer hallucinations, and is far cheaper than other available models—exactly the attributes operations leaders weigh most when deploying agents at scale. In practice, these are the levers that unlock higher CSAT, tighter SLAs, and better unit economics.
Achieving all three simultaneously is extraordinarily hard. Credit goes to foundational research from a 60‑person AI group run by Fergal Reid, and, crucially, to domain‑specific proprietary evals drawn from billions of human and agent interactions produced by the Fin resolution engine—already hand‑tuned to be the most effective in the category. That creates a flywheel: an eval‑driven development loop that trains models to keep improving at the edge of the system’s abilities. In other words, Apex 1.0 looks like the tip of the iceberg.
Zooming out, service is one of the few categories where generative AI has already delivered commercial impact at scale (alongside coding, and arguably the legal industry). With TAMs measured in the hundreds of billions, competition is intense and well capitalized. The pattern I’ve seen repeatedly is clear: winners in these spaces must become full‑stack AI companies. As features become ~free to build, durable competitive differentiation shifts under the hood—to proprietary data, post‑training, inference efficiency, and the quality of the eval loop.
Fin Apex raises the bar for finance-ready AI, highlighting a -65% cut in hallucinations and a quicker first token at 3.7s (0.6s faster), compared with Sonnet 4.6, Opus 4.5, and GPT-5.4 in side-by-side charts.
That’s why competitors will need to release their own models. Many appear to be just starting to hire the talent to do so, which likely gives Fin at least a year of head start. For product leaders, this is a strong signal to revisit build vs buy assumptions, and to quantify when owning your post‑training pipeline and evals becomes the rational move.
Honestly, 2–3 years ago I expected AI application differentiation to live mostly in what we built around third‑party models. The AI game humbles all of us; today it’s obvious that vertical models paired with proprietary evals create compounding moats.
In a podcast interview last week, Andrej Karpathy said:
"I do think we should expect more speciation in the intelligences. The animal kingdom is extremely [diverse] in the brains that exist. And there’s lots of different niches of nature… And I think we should be able to see more speciation. And you don’t need this oracle that knows everything. You kind of speciate it. And then you put it on a specific task. And we should be seeing some of that because you should be able to have much smaller models that still have the cognitive core."
The frontier labs still have the very best models, but open‑weight models aren’t far behind—making pre‑training look increasingly like a commodity. The frontier is moving to post‑training, which is precisely what we see with Apex (and Cursor’s Composer 2), and what we should expect to dominate going forward.
Labs now face a dual reality. On one hand, horizontal general‑purpose models can over‑serve specific verticals (e.g., customer service doesn’t need an oracle that knows everything). On the other, open‑weight models are good enough that high‑quality, domain‑specific post‑training can produce superior models for special‑purpose jobs—and in the ways that matter for those jobs. In service, soft factors like judgement, pleasantness, and attentiveness matter alongside hard factors like resolution effectiveness, speed, and cost.
I’m still bullish on the labs. Many organizations remain heavy customers of Anthropic—whether as part of multi‑model systems or through deep usage of Claude Code in engineering teams (see this example of Claude Code adoption). Yet classic disruption (à la the late, great Clay Christensen) is now at their door. The way out is to disrupt themselves by building cheaper specialized models too, which likely requires acquiring the evals—or the companies with the evals—needed for each task. Expect creative data partnerships, M&A consolidation, and a wave of hyper‑specific model providers that compete head‑to‑head with the labs.
In the meantime, Fin appears to be the only vendor in its space with a custom model that’s also objectively superior to everything else out there. I’m excited to see it deployed broadly for end customers, and I’m watching closely for the next announcement that will accelerate that rollout. For product leaders, the message is clear: the age of vertical models and agentic AI is here—bring your evals, or bring your checkbook.
I’ve learned that the most effective partner product marketing is less about decks and more about decisions. When I collaborate with partner product marketing managers, we translate complex capabilities from a unified analytics platform into crisp, outcome-led narratives that customers can act on. This is where product positioning and go-to-market strategy intersect to create momentum for product-led growth.
In my experience, the strongest partner product marketing managers operate like solution orchestrators. They align value propositions across partners, clarify the problem-solution fit, and articulate competitive differentiation without drowning teams in feature lists. By anchoring messaging in clear customer pains and measurable gains, they help everyone—from solutions engineering to sales—tell the same story with confidence.
My playbook starts with outcomes. We define the “why” in terms customers care about, then quantify it with retention analysis, user activation, and time-to-value. That evidence shapes positioning, enables tighter points of parity and differentiation, and ensures our value proposition resonates in market. The result is faster alignment and fewer cycles spent debating messaging without data.
Cross-functional execution makes or breaks the strategy. I partner closely with solutions engineering to validate solution patterns, and with sales to balance sales-led motions alongside product-led growth. Strong stakeholder management keeps discovery loops tight: we capture objections early, refine narratives quickly, and reduce friction across the funnel.
On the tactics side, I rely on A/B testing to de-risk bold messaging changes and to optimize in-app guides and product tours. We set a minimum detectable effect upfront, instrument journeys with Amplitude analytics, and iterate quickly. This gives the team statistical confidence while keeping speed high—especially when refining narratives for complex partner solutions.
Ultimately, great partner product marketing illuminates the shortest path from capability to customer value. When we pair disciplined positioning with data-driven learning, we strengthen our go-to-market strategy and build durable competitive advantage. That’s how we turn strong solutions into market-leading stories that win—and keep—customers.
Inspired by this post on Amplitude – Best Practices.
Launching a major release is only half the battle; earning adoption inside the product is where the real wins happen. For our Summer Release, I made a deliberate choice to promote new capabilities where customers experience value—in the app—by leaning on Pendo’s in-app guides, product tours, and tooltip design. This product-led growth approach let us deliver timely, contextual education without disrupting a user’s flow, aligning our go-to-market strategy with how people actually work.
Increase revenue, cut costs, and reduce risk with Pendo’s Software Experience Management platform. Optimize the entire software experience to drive adoption and improve engagement.
I began by segmenting audiences around key jobs-to-be-done and lifecycle stages—onboarding users, power users, and specific roles—so every prompt supported a clear value proposition. We mapped the journey for each segment and placed concise guides at decision points where users naturally discover adjacent features. The goal was simple: accelerate user activation, reduce time-to-value, and make the Summer Release feel intuitive, not intrusive.
Execution hinged on progressive disclosure. Short, focused product tours introduced what changed and why it mattered, while tooltips offered deeper context when users hovered or asked for help. We paired this with behavioral targeting so guides appeared only after relevant triggers—usage patterns, page views, or completion of prerequisite steps—keeping the experience helpful and respectful.
We ran A/B testing on headlines, CTAs, and guide placement to refine messaging and reduce friction. Variants explored different tones (instructional vs. benefit-led), lengths (microguide vs. multistep tour), and formats (banner, modal, tooltip). The winning patterns emphasized outcome-first language, clear next steps, and optional deep dives for advanced users.
Measurement focused on adoption and engagement: guide view-to-click rates, feature usage uplift post-guide exposure, and downstream behaviors tied to retention analysis. While we avoided vanity metrics, we did look for sustained usage over time, not just one-time clicks. The early signals were encouraging—faster discovery of new capabilities, higher completion of key workflows, and more consistent engagement across targeted cohorts.
Cross-functionally, we aligned in-app messaging with our broader go-to-market strategy, ensuring consistency across help center content, enablement, and customer communications. This cohesion strengthened competitive differentiation and reinforced our product strategy: deliver value in context, then invite users to explore more when they are ready.
The biggest lesson? Thoughtful in-app guides and product tours are not about broadcasting release notes—they are about orchestrating moments of clarity that compound into adoption. By combining precise segmentation, disciplined experimentation, and clear success criteria, we turned a launch into sustained product-led growth. Next, we’re extending this playbook to onboarding and lifecycle milestones to keep momentum strong across releases.
I’ve led and learned from dozens of launches, and one truth holds: a sharp go-to-market strategy is the difference between shipping features and creating value. In this piece, I share the playbook I use with my product marketing teams to align product, sales, success, and growth around a single, measurable plan.
Step-by-step go-to-market strategy for product marketing: Define ICPs, positioning, pricing, channels, launch plan, and metrics to drive adoption and revenue.
I start by defining our ideal customer profiles (ICPs) with continuous discovery: blending qualitative interviews with quantitative signal from retention analysis and usage. We map jobs-to-be-done, pains, and buying triggers, then size segments and select the entry ICP that maximizes product-market fit odds. From there, we articulate points of parity and competitive differentiation to clarify where we must match the market and where we will win.
With ICPs locked, I craft positioning and messaging that ladder to a clear value proposition. I test headlines and narratives via A/B testing across ads, email, and in-app guides, and I tighten UX writing inside product tours to reinforce the promise. The goal: consistent, resonant language that sales can champion and self-serve users can understand in seconds.
Next, I align pricing and packaging to the value metric customers actually care about—keeping SaaS pricing simple to start, with room for advanced consumption SaaS pricing when usage scales. I pair pricing with onboarding that speeds user activation, removes friction with thoughtful tooltip design, and sets customers up for early wins.
Channel strategy is a focus decision. Depending on motion, I mix product-led growth, targeted outbound, partner co-marketing, and community. I ensure CRM integration and enablement content are ready on day one so marketing, sales, and success can execute in lockstep.
I translate the strategy into a concrete launch plan tied to product roadmapping and sprint planning: milestones, assets, demos, and a clear owner for every dependency. We rehearse the narrative, pressure-test objections, and equip field teams with competitive battlecards and objection handling.
From the outset, we define success metrics that ladder to revenue: awareness, activation, conversion, expansion, and retention. Leading indicators beat lagging ones, so I instrument a unified analytics platform to monitor activation rate, time-to-value, and feature adoption in near real time, then feed insights back into the roadmap.
After launch, we run tight feedback loops—win/loss analysis, in-product surveys, and cohort-based retention analysis—to refine messaging, re-bundle packaging, or adjust channels. The team owns outcomes, not output: we iterate until we see durable signals of product-market fit and efficient growth.
If you need a simple way to operationalize this, print the one-liner above, share it with your cross-functional partners, and commit to weekly reviews. When everyone can state the ICP, the promise, the price, the channel plan, and the metrics, execution accelerates and the market responds.
In my role leading product management at HighLevel, I’ve learned that enterprise go-to-market lives or dies by the strength of the partnership between product and product marketing. When we operate as one team, we turn complex capabilities into clear outcomes that resonate with buyers and drive adoption at scale.
I’m especially energized by the archetype of a product marketing manager at a leading analytics platform—someone “focusing on go-to-market solutions for enterprise customers.” That mandate requires rigor across product positioning, value proposition design, competitive differentiation, and sales enablement, all while aligning deeply with engineering and customer success. In practice, it means translating signal from a unified analytics platform into narratives and plays that close deals and expand accounts.
Day-to-day, I partner with product marketing to validate messaging through continuous discovery and data. We use Amplitude analytics to instrument activation, engagement, and retention analysis—then feed those insights into product-led growth motions like in-app guides and product tours. A/B testing grounded in a clear minimum detectable effect (MDE) helps us separate noise from impact, while points of parity and true differentiation shape the story sellers can confidently carry into enterprise conversations.
This is also where outcomes vs output OKRs keep us honest. Rather than celebrating launches, we anchor on measurable behavior change: faster time-to-value, higher user activation, deeper feature adoption, and multi-threaded stakeholder engagement. Product trios provide the operating rhythm, and stakeholder management ensures sales, marketing, and success move in lockstep with the roadmap and GTM calendar.
If you’re building an enterprise GTM motion, start by tightening your value proposition to the top three pains your best-fit accounts actually feel, validate with real usage data, and then enable your field teams with crisp, data-backed talk tracks. With the right PM–PMM alignment and analytics foundation, your go-to-market strategy becomes a compounding advantage—not just a launch plan.
Inspired by this post on Amplitude – Perspectives.
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.
I build enterprise growth motions by grounding strategy in data and execution in crisp storytelling. When I partner with teams using Amplitude, I focus on architecting "go-to-market solutions for enterprise customers." That simple phrase clarifies the mandate: align product, marketing, and sales around measurable value, reduce buyer risk, and prove outcomes early and often.
My go-to-market strategy begins with rigorous segmentation and an ideal customer profile, then translates into a living narrative: the value proposition, points of parity, and competitive differentiation that underpin product positioning. I pressure-test that narrative with real customer language, executive business cases, and use-case–level messaging so every stakeholder—from procurement to security to the economic buyer—hears their priorities reflected back with credibility.
Execution is analytics-led. With Amplitude analytics as a unified analytics platform, I instrument the entire journey—from first touch to paid expansion—to expose activation, aha moments, and friction. I use A/B testing to validate in-app guides, product tours, and onboarding, and I track user activation and retention analysis to ensure product-led growth efforts compound over time. These signals inform sales enablement, content roadmaps, and launch plans so each asset moves a specific metric, not just a milestone.
Operating cadence matters as much as the plan. I rely on empowered product teams and product trios to translate strategy into product roadmapping and sprint planning, ensuring every slice of the roadmap ties directly to market impact. Clear OKRs and QBRs keep the feedback loop tight, while field insights from enterprise pilots shape rapid iteration without losing strategic intent.
Enterprise nuance is the difference-maker: longer cycles, multi-threaded buying committees, and higher switching costs demand precision. I design proofs of value that quantify outcomes early, align pricing and packaging with willingness to pay, and use customer evidence to de-risk decisions. The result is a scalable, repeatable system where positioning is consistent, the funnel is measurable, and revenue teams can predictably win with complex accounts.
Ultimately, the work is about trust. When strategy, analytics, and storytelling lock together, customers see themselves in the product—and teams see themselves in the win. That is the heart of enterprise go-to-market done right.
Inspired by this post on Amplitude – Perspectives.
AI search is reshaping how customers discover emerging products, and I’ve seen firsthand how this shift rewards startups that speak clearly to both humans and machines. Learn how LLMs like ChatGPT and Perplexity decide which startups to recommend and what signals help a brand get discovered in AI search.
In practice, AI search behaves less like a list of blue links and more like a synthesis engine. These models look for credible, consensus-backed, well-structured sources they can cite with confidence. That means your brand’s discoverability hinges on technical clarity (schema, structure, speed), topical authority (depth, citations, expert bylines), and evidence of real-world adoption (reviews, case studies, third-party validation).
I start by mapping buyer intent across the entire journey—category exploration, problem framing, solution fit, integration needs, ROI, and competitive comparisons. Then I design a page system that answers each intent with precision: clear “About” and “Use Cases” pages, integration-specific pages, objective "X vs Y" comparisons, transparent pricing, and a living FAQ that mirrors the exact questions users ask in conversational queries.
Structure matters. I add JSON-LD schema for Organization, Product, FAQPage, HowTo, and Article where appropriate; keep canonical URLs consistent; and ensure titles, meta descriptions, and Open Graph data reinforce the same story. Clean sitemaps, a sensible robots.txt, and fast, mobile-first performance reduce friction for crawlers and increase the odds that LLMs extract accurate snippets.
Authority is earned off-site as much as on-site. I prioritize third-party signals—G2/Capterra reviews, analyst mentions, reputable press, open-source repos with README clarity, academic or industry citations, and credible partner integrations. LLMs heavily weight these external proofs when recommending solutions, especially for B2B and regulated categories.
On your site, demonstrate expertise. I include expert bylines with real credentials, cite primary sources, showcase customer outcomes with verifiable metrics, and make methodologies transparent. Shallow, keyword-stuffed posts don’t help; comprehensive, up-to-date explainers with references do.
Make your content retrieval-friendly. LLMs favor text they can segment, anchor, and quote. I structure pages with descriptive headings, short paragraphs, and linkable anchors; offer HTML-first documentation (not just PDFs); and provide copyable code or configuration steps when relevant. This also sets you up for a retrieval-first pipeline in your own product experiences.
From a product and platform angle, I expose trustworthy documentation and a clear trust center—security, compliance, data governance, and privacy-by-design content. When a user asks an LLM whether they can safely deploy your solution, these pages often get pulled into the answer.
Evaluation closes the loop. I run an eval-driven development process for content: a stable prompt set that mirrors real queries, regular tests in both Perplexity and ChatGPT, and analytics to track referrals from AI-driven sources. I iterate headlines, schema, and on-page structure, then tie changes back to engagement and pipeline using A/B testing where it’s appropriate.
Don’t neglect comparison and alternatives pages. Fair, well-cited pages that address trade-offs and points of parity build trust—and they give LLMs succinct, quotable language for recommendation contexts. Clarity beats hype every time.
Finally, keep your corpus fresh. I schedule quarterly content reviews, retire outdated claims, and highlight release notes and integration updates. Freshness signals help models favor your content when they resolve time-sensitive queries.
If you treat AI search as a product surface—one that rewards precision, provenance, and performance—you’ll dramatically increase your odds of being recommended where it matters. That’s how I operationalize AI discovery for startups: intent mapping, structured content, external authority, a retrieval-friendly corpus, and a rigorous eval loop.
Inspired by this post on Amplitude – Perspectives.
