Tag: SaaS pricing

Inside My Pricing Playbook: Building Value-Based Packaging That Balances Growth and Profit

Pricing looks deceptively simple from the outside; inside, it’s anything but. Over the years, I’ve learned that every price tag is really a strategic statement about value, priorities, and the future we’re building toward.

At Fin, pricing and packaging (P&P) is more than a finishing touch. It’s a research problem, a forecasting challenge, a commercial decision, and ultimately, a strategic statement, requiring deep cross-functional work. We must balance the needs and wants of our customers, the value delivered by our product, and the broader vision we are building towards.

Our approach keeps evolving as our product and market mature. I treat it as a living system—continuously informed by research, GTM learning, and customer behavior, never "set and forget."

Here’s how I run the process in practice, especially when we launch something new that needs to be monetized, like Fin, our AI Agent. The work moves from qualitative discovery to quantitative validation to commercial modeling, with tight partnership across product, research, data science, finance, GTM, and engineering.

Step 1: Foundational research

I start by talking to buyers to understand their mental models of value. How do they define ROI? What pricing models do they expect in this category? What feels intuitive, and what feels off? This early discovery shapes two crucial choices: the pricing model and the pricing metric.

The pricing model is the overall structure; value-based, usage-based, access-based, fixed fee, and so on. With Fin, we chose a value-based model: you only pay when Fin delivers value. Our research clearly showed that buyers don’t want to pay for usage, they want to pay for results.

The pricing metric is the unit of value within that model, the unit we anchor pricing to. For Fin, the pricing metric is “outcomes.” An outcome is defined by Fin successfully handling a customer service query.

Small definitional changes can dramatically alter how customers perceive value, so I obsess over details. Buyers rarely hand us the “right” model; they reveal how they evaluate value, and I translate that into a model and metric that align with their goals and expectations.

Throughout, I loop in execs, finance, GTM, and engineering to ensure alignment before proceeding. Pricing choices cut across the business; they can’t be made in isolation.

Step 2: Willingness to pay

Once we have a model and metric, I quantify what the market will bear. This is where rigorous willingness-to-pay (WTP) research comes in, grounded in the language we validated through the qualitative work.

Here’s the kind of framing I use in surveys to keep things concrete and consistent with our model and metric:

You would only pay when Fin delivers an outcome (→ the model). An outcome is counted when the AI Agent resolves a customer query with no further help needed (→ the metric). Would you be willing to pay $X per outcome for Fin?

The foundational qual is so important as a first step. It helps us decide what we should be asking about before we start asking how much people will pay. Without the qual ground work, you risk building a very convincing answer to the wrong question.

The goal isn’t to find a perfect price. That doesn’t exist. The goal is to ground our discussions in the reality of the market.

I use methods like Gabor-Granger and Van Westendorp to understand WTP and to shape a demand curve that informs strategy, not just a single number.

This chart shows us what percentage of the market is willing to buy the product at various price points. The demand curve shows that 69% of buyers were willing to pay for the product at $0.86 per outcome, whereas only 39% were willing to pay at $1.42.

The dashed line shows the price point at which revenue for the business would be maximized (by multiplying adoption by the dollar amount).

This allows us to debate knotty questions like: What’s the right balance between growth and revenue? How sensitive is demand to price changes? At what price do we start losing the market? If we wanted to increase adoption, would lowering our prices by $X make a meaningful difference?

Those conversations help me weigh customer value and business outcomes side by side. At this stage, decisions feel more tangible, but I don’t finalize a price until I’ve modeled the operational realities.

Step 3: Modeling

By now I have a validated model, a clear metric, and a strong WTP signal. Next I translate theory into a commercially workable plan—this is where data science and finance are indispensable.

I start with a list price aligned to our strategy and commercial goals. Then I adjust for likely discounting to estimate realized price. Next, I analyze beta usage to project outcomes per customer by segment and derive average ARR. I combine usage projections with WTP to model attach rates across conservative-to-optimistic scenarios. Finally, I connect the dots in our long-range plan—logos, ARR, margins—iterating until the numbers and narrative cohere.

The modeling step is important because willingness-to-pay data is somewhat theoretical. It reflects intent, not behavior. Modeling helps us bridge that gap.

The goal of this step is to land on a price point recommendation, alongside forecasts for ARR and adoption. It allows us to understand the real business impact of the decisions we’re making.

Alongside all of this, we need to ensure any decision we make falls in line with our pricing principles and broader business objectives.

Step 4: Sign-off and execution

With the analysis complete, I consolidate everything into a clear P&P recommendation for executive approval. Once approved, the real work begins: enabling sales, communicating changes to customers, instrumenting ROI proof points, and monitoring performance so we can learn and iterate.

Do we run the full process every time?

Not always. This is the ideal process, and I apply it end-to-end for the most material decisions. In reality, time and resource constraints require judgment; rigor should mirror impact. When uncertainty crops up midstream, I run scrappier, targeted research rather than forcing a linear path.

The ongoing challenge

As Fin’s breadth has expanded, our pricing system has had to evolve, too. For a while, modular pricing worked well—each product had its own logic tied to a crisp outcome. As we add more products, more Agent capabilities, and more outcomes, the question shifts from “what is the right P&P for this one product?” to “how does everything fit together into a coherent pricing system?”

We must recognize that pricing isn’t something you set once and leave alone. As products evolve, especially in a world where AI is rapidly changing how value is created and delivered, it’s important to regularly step back and review the bigger picture, not just the component parts.

For example, outcome-based pricing has served us well, particularly when our products were tightly tied to clear, measurable outcomes. But as our products become more varied, and as we continue building toward a broader platform, it becomes less straightforward to apply a single model cleanly everywhere.

The challenge becomes less about replacing one model with another, and more about continually looking up and asking: what pricing philosophy best reflects the value we’re delivering today? And how do we deliver that philosophy in a way that still feels right for customers?

In short, there is no finish line, pricing is never “done” – and that’s exactly how it should be.

Why this work matters

Pricing and packaging is often noticeable only when it goes wrong. A confusing model, a bad metric, or a price that feels disconnected from value. And we hear about those quickly.

When pricing is done well, it becomes nearly invisible—but it still does a lot of work. It shapes how people perceive value, clarifies what they’re paying for, and makes the product easier to sell, easier to buy, and easier to scale. Most importantly, it forces us to be honest about what the product is really worth. That’s why I take it so seriously—and why I treat pricing as a product in its own right.

Inspired by this post on The Intercom Blog.

May 20, 2026
Why I Bet on First-Time Executives: Inside Figma’s Playbook for AI, IPO Readiness, and Scale

Founders should bet on first-time executives. I’ve seen it pay off repeatedly, and a recent deep dive with Praveer Melwani, CFO at Figma, reinforced exactly why. Praveer joined Figma in 2017 as the company’s first business operations and finance hire—when the team was around 30 people and not yet charging for the product—and stepped into the CFO seat in 2022, helping to lead the company’s IPO in 2025. His journey from IC to CFO isn’t just a career arc; it’s a blueprint for scaling leadership capacity in high-velocity environments.

What struck me first was the clarity of the step functions that took him from operator to “whole-company” leader. Early on, he optimized for doing the work—building driver trees, stress-testing go-to-market assumptions, and putting the basics of board management in place. As the business matured, he shifted from answering questions to defining them, owning capital allocation, and shaping the operating cadence. That evolution—from execution to orchestration—is exactly the arc I look for when I’m hiring first-time VPs.

Another takeaway: Figma started acting like a public company three years before its IPO. That wasn’t optics; it was operating discipline. Quarterly rhythms, tight controls, an audit-proof close, and forward-looking narrative management helped the company move faster, not slower. In my experience, this kind of public-company readiness clarifies trade-offs, compresses decision cycles, and strengthens cross-functional trust—especially between product, finance, and go-to-market leadership.

We also unpacked what separates world-class finance leaders from a traffic-cop CFO. The latter enforces rules and guards budgets; the former uses first principles decision making to direct resources toward asymmetric upside. World-class CFOs help the company understand risk in a post-ChatGPT world, design SaaS pricing that matches product reality, and build reliable instrumentation for outcomes—not just outputs. They’re partners in product strategy as much as stewards of the balance sheet.

On pricing, I appreciated the courage behind selling the exec team on AI consumption pricing. Consumption SaaS pricing introduces variance, but it also aligns value with usage and accelerates time-to-value—especially for AI-driven features whose unit economics evolve rapidly. It requires tight stakeholder management, robust telemetry, and a crisp value proposition, but when executed well it can unlock both growth and discipline.

One of the boldest moves: Figma intentionally cut its 90% gross margin to invest in AI. That’s a masterclass in capital allocation. The reflex to protect margins is strong, but durable advantage often comes from compounding learning loops, not short-term optics. Framed correctly, AI Strategy isn’t a cost center—it’s an option on multiple future S-curves. The key is to define decision guardrails, instrument usage, and keep a living risk register for AI risk management.

I was also intrigued by how AI is changing the CFO craft itself. Tools like Claude Code are now part of the financial leader’s toolbox—useful for scenario modeling, policy drafting, and exploring new domains without slowing down the team. Paired with strong data governance and controls, this is where FinOps meets executive leverage: faster cycles, tighter experiments, and better communication with product and engineering.

Leadership transitions can catalyze phase shifts. When a COO leaves or a company re-architects its operating model, great executives don’t just fill gaps—they redesign the system. That’s when clarity about swimlanes, escalation paths, and decision rights matters most. The lesson for founders: hire for adaptability, not just pedigree, and look for people who can turn ambiguity into momentum.

Hiring leaders in functions you don’t deeply understand is a common founder challenge. The best antidote is a first-principles test for hiring VPs: can the candidate map the business model, define success metrics, and explain trade-offs in plain language? Do they show how they’d build the team, not just run it? Can they teach you something new in 30 minutes? I use this pattern across executive hiring because it scales better than relying on domain buzzwords.

Another practice I recommend: build an internal board of peer CFOs and operators. Regular, no-agenda check-ins create a community of practice that shortens feedback loops and surfaces non-obvious risks. It’s one of the most efficient ways to de-risk capital allocation and sharpen strategic narratives ahead of real board meetings.

We talked about scope versus depth: how deeply in the details should a CFO be? My view aligns with what I heard here—be in the details often enough to validate the model and coach the team, but not so deep that you become the bottleneck. The executive job is to raise the quality of decisions at scale, not to personally make every decision.

There were personal lessons, too—from the nine-year working relationship with Dylan Field to foundational team-building insights from time at Dropbox. Strong teams are built on crisp roles, tight feedback loops, and a bias for writing things down. That muscle—organizational development through clarity—is what separates resilient companies from merely lucky ones.

If you’re a founder weighing whether to back a rising operator or recruit a “proven” exec, this story tips the scale toward the former. Bet on slope, not just intercept. Create the scaffolding—public-company behaviors early, transparent metrics, and a culture that rewards learning—and your first-time executives will scale with the business. Done right, it’s the highest-LEVERAGE people decision you can make.

May 14, 2026
Outcome-Based Pricing That Delivers: Pay $10 Only for Qualified Leads with Fin for Sales

Our outcome-based pricing model hinges on one principle: you pay when Fin delivers value.

As Fin takes on new roles, that principle doesn’t change, but the definition of value does.

Fin for Sales qualifies leads, engages prospects, and routes high-intent buyers to your sales team. The value it creates isn’t a resolved query, but a pipeline of qualified opportunities. So we price accordingly: $10 per qualified lead. And you, the customer, define what “qualified” means, not Fin.

This is the first outcome-based pricing model for an AI Agent for sales. Here’s why I believe it’s the right approach and how I’ve seen it change the way teams think about SaaS pricing and ROI.

Over the years, I’ve learned that the fastest way to earn trust with sales and finance leaders is to align pricing with outcomes they actually report on. The core finding from our research was unambiguous: zero buyers preferred paying for activity. They wanted to pay for results.

That insight shaped how we priced Fin for its service role, $0.99 per resolution, where a resolution means the customer’s issue is fully solved without human intervention. More recently, we evolved that model to outcomes, reflecting the broader ways Fin delivers value across complex workflows. We believe pricing should be aligned with value delivery, and the vendor should carry risk when the product doesn’t perform. In sales, the best unit of value is pipeline.

Most sales teams today are overwhelmed by leads. Early in my career, I watched reps spend hours chasing form fills that looked promising but went nowhere. That experience cemented a lesson I still use: volume is vanity; qualification is sanity.

Ensuring the right opportunities promptly reach your sales team is what makes a difference. When a prospect visits your site, engages with Fin, answers qualifying questions, and is directed to a sales rep, Fin is identifying whether the opportunity is worth your team’s time and delivering value.

Charging per conversation would penalize businesses for every curious visitor who asks a question but isn’t a buyer. And charging per token, well, that’s always been a model that protects the vendor, not the customer.

We needed a metric that captures the actual value Fin creates in a sales context: qualified leads.

The purest version of outcome-based pricing for Fin’s sales role would be a percentage of closed revenue. Fin qualifies the lead, a rep closes the deal, and we take a cut. On paper, it looks elegant; in practice, I found it breaks down for two reasons that matter to operators.

First, attribution. Between the moment Fin qualifies a lead and the moment a deal closes, dozens of things can impact the final result. The quality of human-led demos can differ, products can have outages, prospects’ budgets can get cut. Tying Fin’s price to the final outcome holds it accountable for variables entirely outside its control.

Second, measurement. To track closed revenue, we’d need deep integration into every customer’s CRM, tracking each opportunity from qualification through to close. That’s a significant implementation burden that slows time to value, which is the opposite of what we want.

So we asked: what’s the most honest proxy for the value Fin delivers, where Fin is clearly the one creating it?

A qualified lead is that proxy. It represents the moment Fin has done its job. It has engaged the prospect, gathered the relevant information, evaluated them against your criteria, and determined they’re qualified. Everything up to that point is Fin’s work. Everything after it is the rep’s. At $10 per qualified lead, the pricing reflects this boundary.

There are two key components to how this pricing model works.

First, the customer defines success. With Fin’s sales role, the customer sets their own qualification criteria based on their business context. A company with high average contract values might set a lower bar because they can’t afford to miss anyone. A company where rep time is scarce and deal sizes are smaller might set a much higher bar, filtering aggressively to only surface the most promising prospects. The criteria flex to match the business.

Second, the economics are different by design. As a Customer Agent, Fin can switch between roles like sales and service. So if you’ve deployed Fin for Sales, it can still handle support queries like prospects asking a product question. Those queries are charged at $1 per resolution, consistent with our service pricing. Disqualifications, where Fin determines a prospect doesn’t meet the criteria, are also $1. The $10 price point for qualified leads reflects the higher value of pipeline creation compared to issue resolution.

The ROI speaks for itself. Early customers are reporting significant returns using Fin for Sales. One shared a perspective that mirrors what I hear in executive QBRs:

“I would say it’s at least 10 times the value. You’re now giving the business exactly what it needs as opposed to just activity. We say this expression in sales leadership all the time – ‘I don’t pay my sales team for activity. I pay them for results.’ I want my AI engine to be the same way.”

When you compare the cost of a qualified lead from Fin against the fully loaded cost of an SDR—salary, benefits, tooling, ramp time—the economics are compelling. For many businesses, particularly those that never had SDRs in the first place, Fin for Sales isn’t just replacing headcount, but creating an entirely new capability that wasn’t economically viable before.

This pricing model came from extensive customer research—qualitative interviews and quantitative studies—exploring how buyers want to pay for AI in a sales context. We tested multiple concepts: per-conversation, per-token, per-seat, revenue share, and per-qualified-lead. The research consistently pointed to outcome-aligned pricing as the preferred model, with the qualified lead emerging as the metric that best balances value alignment, measurability, and practical implementation.

Outcome-based pricing is still rare in AI, but we think that will change. For Sales Agents, we’re the first to do it. Transparency is part of the model. If you understand why we price the way we do, you can evaluate whether it works for your business.

Inspired by this post on The Intercom Blog.

May 8, 2026
From Resolutions to Outcomes: How We Price AI Agents Fairly and Amplify Customer Value

I’ve long believed a simple truth about AI in customer support: if AI is going to earn trust, pricing has to be aligned with value. That principle has guided my product decisions and the way I hold our teams accountable for measurable outcomes, not activity.

When we shared our perspective on pricing AI Agents in 2023, we made a simple argument: if AI is going to earn trust, pricing has to be aligned with value. At the time for Fin, that value was clear. You pay when the AI resolves a customer’s problem. If it doesn’t, you don’t. That’s fair, easy to understand, and grounded in results, not activity. We were the first to introduce this pricing model because we believed that pricing and value should be inherently linked.

That belief hasn’t changed, it’s grown stronger over time. What’s changed is what Fin can do. As we expanded capabilities and pushed deeper into complex workflows, it became clear that measuring value solely by end-to-end resolutions no longer captured the full picture of impact.

Resolutions were the right place to start. Historically, we measured value based on whether Fin fully resolved a conversation on its own. These are known as resolutions and they gave support teams a clear way to measure ROI, easily comparing the cost of AI versus human support. They also aligned our incentives with our customers, as our revenue was directly tied to Fin’s performance.

That clarity worked. Today, more than 7,000 teams use Fin. Our average resolution rate across customers has increased every month and now stands at 67%, even as Fin increasingly handles more complex queries. That progress came from building an Agent that could take on harder problems and still deliver.

But as Fin got more powerful, “success” stopped being binary. I saw this first-hand in customer design sessions where policy, risk, and compliance needs rightly demanded human-in-the-loop confirmation. We weren’t failing to deliver value; we were delivering it differently.

Over the last couple of years, we invested heavily to ensure Fin could handle the most complex parts of support. As Fin’s capabilities expanded, customers began pushing what Fin can do for them by deploying Fin deeper into their workflows to handle the toughest queries.

In some cases, this required Fin to work in tandem with a human agent because that’s what customer policies and oversight needs dictated. Subscription changes, transaction disputes, billing issues, and other multi-step support scenarios can often require Fin to gather context, read and write to external systems, and execute actions before handing off to a human agent for confirmation.

Fin is still doing what it was configured for – intentionally handing off after doing more of the heavy lifting, saving valuable time for support teams and overall time to serve for their customers. But our pricing metric only recognized value when the conversation ended in a full “AI resolution” (i.e. a human was never involved).

That’s why we’re evolving Fin’s pricing metric from resolutions to outcomes. This shift reflects how customers now define value: not just in full automation, but in safe, efficient progress toward the right result across complex, multi-step, and policy-constrained workflows.

An outcome represents when Fin successfully completes the action it was configured to perform, as part of a conversation. Resolutions are still one type of outcome Fin can deliver, where it handles the issue end-to-end. Another type of outcome can be a Procedure where Fin gathers context, takes action, and hands the conversation off when that’s what customers configured it to do.

Kick off your journey with the #1 Agent—an AI partner designed to turn resolutions into real outcomes. Tap “Start a free trial” to explore faster, smarter customer service and see how Fin delivers value from day one.

Increasing end-to-end AI resolutions is still a core component of scaling Agents, but they are no longer the only measure of Fin's success and utility. Especially as Fin takes on more complex work. Moving to outcomes recognizes that solving a customer problem with full automation isn’t always appropriate. It’s about getting to the right result, safely, and efficiently.

As Fin’s capabilities expand, teams should feel empowered to use it in more nuanced, collaborative work. Outcomes support that by allowing customers to design workflows that meet compliance requirements and include a human agent when necessary. From a product management standpoint, this is how we align incentives, keep risk controls intact, and still accelerate time-to-value.

Fin is becoming even more powerful at handling complex, multi-step support queries. With outcomes, we can support that growth without constantly reinventing how value is measured. And this change gives us a strong pricing foundation that can scale as Fin continues to grow and take on more roles beyond service. This aligns with our vision of Fin becoming a “Customer Agent,” capable of handling the entire customer experience.

What this means for pricing is intentionally straightforward. An outcome will be counted when Fin successfully completes an action it was configured to perform, as part of a conversation. That keeps the model predictable for finance leaders while staying transparent for operators and product teams managing AI workflows.

The pricing model stays simple and the definition of value becomes more accurate. In other words, we’re doubling down on fairness, predictability, and competitiveness—core tenets for any consumption SaaS pricing strategy tied to real business impact.

When we first wrote about outcome-based pricing, we said that trust is the currency of AI. That’s still true. Trust is earned when customers see pricing move in lockstep with utility and risk posture, especially as gen AI and agentic AI take on higher-stakes tasks.

Pricing has to feel fair, it has to be predictable, and it has to stay competitive. Evolving from resolutions to outcomes isn’t a departure from that belief. It’s the natural maturation of how we measure value as AI moves from simple Q&A into complex procedures and human-in-the-loop collaboration.

Fin has grown more powerful because customers asked more of it. Outcomes are how we reflect that progress honestly, while staying true to the same principles that guided us from the start. This is product strategy in action: align incentives, measure what matters, and scale what works.

And as Fin continues to get stronger, we’ll keep holding ourselves to the same standard: price based on the value delivered. That’s how we build durable trust, sustainable ROI, and a better customer experience at scale.

Inspired by this post on The Intercom Blog.

March 12, 2026
A Proven Go-to-Market Playbook: Align ICPs, Positioning, Pricing, Channels, and Launch for Revenue

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.

Inspired by this post on Product School.

December 31, 2025
10 AI Business Models You Need Now: Proven Playbooks Turning Algorithms into Revenue

I’ve spent the past few product cycles re-architecting roadmaps around one simple reality: AI is no longer just a feature—it’s a business model. The companies winning market share are those that treat models, data, and workflows as monetizable assets with defensible moats, not science projects.

AI business models are rewriting value creation. Learn how smart teams turn algorithms into profit engines, reshaping entire industries.

From my seat in product leadership, I evaluate AI bets through three lenses: durable value (moat and differentiation), measurable outcomes (clear ROI), and unit economics (gross margins under real-world load). With that frame, here are ten AI business models I see performing now—and how I decide when to invest.

1) API-first Model-as-a-Service. I monetize foundation or specialized models via an API, priced by tokens, requests, or time-in-context. Success hinges on latency, accuracy, and “context window management” that balances quality with cost. This is where “consumption SaaS pricing” shines and where disciplined rate-limiting, observability, and SLAs build trust.

2) Vertical AI copilots. I package domain-specific expertise (legal, healthcare, finance, field service) into workflow-native assistants that surface next-best actions. Because these copilots live where work happens, I price on outcomes—time saved, revenue recovered, or risk reduced—aligning value with customer metrics and accelerating product adoption.

3) Agentic AI automation. When autonomous agents handle multi-step tasks across tools, I lean toward per-outcome or per-job pricing. Reliability is the moat, so I invest early in eval-driven development, robust guardrails, and human-in-the-loop QA. This model compounds fast once agents can execute end-to-end workflows with transparent audit trails.

4) Copilot add-ons inside existing SaaS. I’ve seen “AI Assist” tiers deliver immediate ARPU lift and retention gains. The playbook: start with high-frequency, high-friction jobs (drafts, summaries, enrichment), then expand to proactive suggestions. This aligns tightly with product strategy and lets me stage value without overhauling the core experience.

5) Insights-as-a-Service via data network effects. I transform exhaust data into benchmarking, predictions, and prescriptive recommendations—while honoring privacy-by-design and data governance. The more customers I onboard, the stronger the patterns, and the higher the switching costs. Pricing ties to seats plus an outcomes or value metric.

6) Retrieval-first pipeline for enterprise knowledge. I land with high-accuracy answers over customer data (search, summarize, cite), then expand into workflow automations. This “retrieval-first pipeline” reduces hallucinations, boosts trust, and creates defensibility through connectors, semantic indexing, and continuous relevance tuning—an ideal fit for LLMs for product managers prioritizing reliability.

7) Open source monetization. When I bet on openness, I monetize hosting, support, enterprise controls, and compliance features. The advantage is developer love and rapid iteration; the moat is operational excellence at scale, plus integrations customers rely on. This model converts community momentum into predictable revenue.

8) Marketplaces for prompts, skills, and agents. I create a platform for third-party extensions and charge a take rate on usage. The flywheel spins when developers see distribution, customers see breadth, and I enforce strong quality bars. The roadmap focuses on governance, discovery, and safe execution policies.

9) Solutions with forward deployed engineers. For complex rollouts, I pair product with specialized implementation to guarantee outcomes. Revenue blends software plus services, accelerating time-to-value and informing the roadmap with real-world constraints. Over time, learnings fold back into scalable, self-serve capabilities.

10) AI risk, security, and compliance tooling. As AI scales, so does the need for policy enforcement, monitoring, and auditability. I monetize via platform subscriptions that address model provenance, data leakage prevention, red teaming, and reporting. Strong “AI risk management” is now a purchasing requirement, not a nice-to-have.

How do I choose among these models? I start with the customer’s biggest workflow pain, map it to the fastest path to measurable outcomes, and align pricing with value creation. Then I build defensibility through data advantage, distribution, and governance. If a model deepens trust, improves margins, and compounds learning, it earns a place on the roadmap.

Inspired by this post on Product School.

December 24, 2025
Monetizing AI with Confidence: Proven Models, Smart Pricing, and ROI You Can Defend

I’ve learned the hard way that shipping an impressive AI demo is not the same as creating a durable revenue engine. In my role leading product strategy, I focus on one goal: connect AI capabilities to measurable customer outcomes, then price and package them so both value and margins are visible and defensible.

Monetizing AI features into profit isn’t trivial. Here are some clear strategies for capturing and pricing AI products and how to monetize with returns.

First, I clarify the business model. Add-on AI packs work when the value is concentrated in a specific workflow (for example, automated summarization or AI copilot assistance). Tiered packaging helps when AI elevates the overall experience across many features. Usage-based or consumption SaaS pricing is ideal when value scales with volume—tokens, documents processed, calls handled, or agents invoked—because it aligns price to realized outcomes.

Next, I align pricing mechanics with the customer’s value story. I anchor price against the baseline they know: hours saved, conversions gained, cases deflected, or risk reduced. Then I set floors based on unit economics—model inference, vector storage, and orchestration costs—so gross margins remain healthy as usage grows. Clear guardrails (quotas, rate limits, and context window management) prevent surprise bills and keep cost-to-serve predictable.

Packaging is where monetization becomes intuitive. I gate high-cadence, high-compute features behind premium tiers, and I expose quick wins (like smart suggestions) in core tiers to accelerate activation. For enterprise, I bundle governance, audit logs, data controls, and “privacy-by-design” features to justify step-up pricing and reduce procurement friction.

To sustain ROI, I run an eval-driven development loop. I define quality metrics (accuracy, helpfulness, latency, safety) and instrument the retrieval-first pipeline so I can isolate where value is created or lost. This lets me right-size models, tune prompts, and swap components without compromising outcomes or margins—critical for LLMs for product managers who must balance experience and cost.

Measurement is non-negotiable. I track activation, time-to-first-value, weekly engaged AI users, and feature-level retention. For revenue impact, I attribute uplift through A/B testing and minimum detectable effect thresholds, measuring conversion lift, ticket deflection, and cycle-time reductions. When customers see these numbers in their own dashboards, procurement turns into partnership.

Risk and compliance are part of the product, not an afterthought. I build in AI risk management, data governance, and red-teaming from day one. Clear data boundaries, human-in-the-loop controls, and transparent disclosures protect end users and make enterprise legal teams our allies rather than blockers.

Go-to-market matters as much as the model. I use product-led growth tactics—free AI credits, transparent meters, and in-app guides—to let users feel the value before the paywall. Sales enablement centers on the value proposition: faster outcomes, higher quality, and lower total cost of ownership, not just “gen ai” for its own sake. Pricing pages should showcase tiers, usage bands, and outcomes, eliminating guesswork.

Here’s the simple playbook I follow: validate the problem with continuous discovery, instrument the workflow, pilot with generous caps, and collect willingness-to-pay signals early. Then iterate the price meter, refine units of value (documents, messages, or actions), and align SKUs to buyer personas. Over time, I introduce agentic AI capabilities as premium modules when they demonstrably reduce steps or automate entire objectives.

When AI monetization works, it feels effortless to customers because the price mirrors the outcome. When it doesn’t, it’s usually because packaging hides value, pricing ignores unit economics, or ROI isn’t visible. By grounding strategy in value metrics, consumption-aware pricing, and rigorous evaluation, I’ve found we can scale AI revenue with confidence—and keep both customers and margins happy.

Inspired by this post on Product School.

December 22, 2025
SaaS + AI Is Here: How Our Summer 2025 Release Builds an Intelligent Foundation to Win

Leading product at HighLevel, I’m watching the convergence of SaaS + AI reshape how we build, price, and scale software. The winners will combine a sharp AI Strategy with disciplined product management leadership to ship real outcomes, not just demos. That’s why my team and I have been focused on giving you pragmatic ways to move fast without breaking trust. Give your company an intelligent foundation for the SaaS + AI era with our Summer 2025 Release. When I set priorities for this release, I optimized for three things: speed with quality, responsible AI, and measurable business impact. Practically, that means enabling agentic AI and gen ai workflows where they actually create leverage, unifying analytics so teams can make decisions from a single source of truth, and hardwiring data governance and privacy-by-design into every layer. If you’re wondering how to keep up, here’s what’s working for us and our customers: tighten product roadmapping and sprint planning around clear outcomes, not outputs; align teams with simple, observable OKRs; and empower product trios to run lean product discovery loops. These practices reduce cycle time while raising confidence, especially when introducing AI into core experiences. On the go-to-market side, I’m doubling down on product-led growth—shipping value into the product with in-app guides, thoughtful product tours, and frictionless onboarding. Pair that with rigorous retention analysis and A/B testing, and you’ll see which AI-powered moments actually move activation, adoption, and expansion. Don’t overlook the fundamentals either: smart SaaS pricing (including consumption models where it fits) can unlock the economics that sustain AI investments. My goal is to give you a foundation that is both ambitious and accountable—a platform you can trust to scale responsibly while your teams iterate quickly. If you’re planning your 2H roadmap, this release is built to help you ship faster, de-risk AI, and create outsized customer value in the moments that matter most.

Inspired by this post on Pendo – Perspectives.

October 24, 2025
Master Points of Parity in SaaS: Nail Table Stakes, Earn Trust, and Unlock Differentiation

Early in any market, I obsess over one thing before splashy features or clever messaging: are we meeting the table stakes that buyers expect? Points of parity (POPs) are the baseline capabilities that put us on a buyer’s shortlist and establish the credibility to compete. Without them, even the best differentiators won’t land.

Understand how points of parity are crucial to getting your foot in the door. Explore different strategies to make POPs work for your SaaS business.

Here’s how I define POPs in practice: they’re the “no-regrets” features, assurances, and experiences that customers assume you have because your competitors already do. In SaaS, that often includes security certifications (e.g., SOC 2), SSO, predictable performance (SLAs/Uptime), clear pricing, responsive support, and integrations with the rest of the customer’s stack.

POPs differ from points of difference (PODs). PODs are what make you unique; POPs are what make you viable. I’ve seen teams try to lead with innovation before building credibility, only to stall in procurement. You earn the right to showcase differentiation after you meet parity.

For SaaS, POPs frequently map to procurement checklists. Think InfoSec reviews, role-based access controls, audit logs, encryption standards, user management, and integrations with systems like Salesforce, HubSpot, or Slack. These aren’t glamorous, but they remove friction, reduce perceived risk, and accelerate time-to-value—cornerstones of product-led growth and a healthy go-to-market motion.

To identify the right POPs, I triangulate across four inputs: customer interviews focused on buying criteria, win/loss analysis to understand disqualifiers, competitor teardowns to benchmark table stakes, and support data to spot recurring gaps eroding trust. Collectively, these inputs reveal the minimum viable promises we must keep.

Prioritization matters. I translate POPs into outcomes (not output) and align them with our roadmapping and sprint planning. For example, instead of “Ship SSO,” I set an objective like “Reduce enterprise security objections by 60%” and measure RFP pass rates, security review cycle time, and sales stage conversion. This keeps us anchored to impact, not just checkboxes.

Execution should be pragmatic. With POPs, “good enough” is often the right bar—reliable, discoverable, and well-documented. Over-engineering POPs slows you down and diverts resources from differentiation. I focus on stable defaults, clear UX patterns, great docs, and in-app guides that help users activate parity features without friction.

Measuring POP health is straightforward if you wire it into your system. I monitor activation rates for parity features (e.g., SSO enabled), support volume tied to trust blockers (security, performance, billing), and the presence of POP gaps in win/loss notes. Retention and expansion are the ultimate validators: when POPs are solid, renewal conversations shift from risk mitigation to value creation.

Consider two tangible examples. For a messaging platform, POPs may include 99.9% uptime, message deliverability guarantees, two-factor authentication, and role-based permissions. For a product analytics tool, POPs could include granular event tracking, user privacy controls, standard dashboards, and self-serve onboarding. None differentiate you alone, but missing any one of them can disqualify you.

Common pitfalls I warn teams about: over-indexing on shiny features while losing deals on basics; inconsistent messaging that promises parity you can’t operationalize; ignoring pricing and packaging parity (buyers expect clear tiers and predictable billing); and underinvesting in enablement, leaving sales to “sell around” missing POPs.

Communicating POPs is as important as building them. I make sure parity shows up on our pricing page, security and reliability pages, and in crisp one-pagers for buying committees. In the product, I highlight parity features during onboarding with checklists and tooltips so customers experience trust quickly. For founder-led GTM, a tight narrative—“Yes, we meet the table stakes; here’s where we go beyond”—keeps discovery calls focused on outcomes.

My playbook is simple: meet parity fast, prove reliability visibly, and then pour fuel on your differentiators. When POPs are nailed, sales cycles shorten, support debt drops, and your unique value finally gets the stage time it deserves.

Inspired by this post on Amplitude – Best Practices.

October 23, 2025
Stop the Leaky Bucket: Proven Playbook to Turn User Acquisition into Lasting Growth

I've led products through dazzling acquisition spikes only to watch churn quietly erase the gains. More users don't automatically mean more long-term growth. In our world, that disconnect is the leaky bucket problem: every new signup pours water into a bucket riddled with holes across activation, engagement, monetization, and advocacy.

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When I diagnose this problem, I start by shifting the conversation from top-of-funnel volume to full-lifecycle health. I look at cohort retention curves, time-to-value, activation rates, depth and frequency of core actions, and expansion revenue. These metrics reveal whether we have true product-market fit, whether our onboarding accelerates value discovery, and where users fall out before they experience a durable “aha.”

My playbook is rigorous and repeatable. I instrument a unified analytics platform to produce clean, decision-grade metrics. I define a single, canonical activation moment that ties to value, and segment it by ideal customer profiles to avoid averages hiding the truth. I run product trios to close the gap between discovery and delivery. I set outcomes vs output OKRs so the team aligns on retention and engagement, not just shipping features. And I connect roadmap bets to measurable behaviors that lead indicators predict—never vanity metrics.

Onboarding is where I usually find the biggest, fastest wins. I trim steps, reduce cognitive load, and default users into best-practice templates so they achieve value in minutes, not weeks. I use contextual education, empty states that teach by doing, and lifecycle messaging triggered by real behavior. Then I close the loop with customer success by aligning QBRs vs OKRs so feedback from high-value accounts translates into clear product outcomes, not feature requests.

Pricing and packaging matter more than most teams realize. If SaaS pricing doesn’t map to realized value, expansion stalls and churn rises. I align paywalls to natural milestones in the journey (usage thresholds tied to success), avoid early friction on critical adoption paths, and make upgrades an obvious outcome of growing value rather than a forced gate.

Execution discipline turns strategy into lift. I run weekly growth reviews that pair qualitative discovery with quantitative signal, keep an experiment backlog prioritized by expected impact and confidence, and insist on clean experiment design (counterfactuals, guardrails, and holdouts). Typical high-leverage tests include reducing time-to-first-value, clarifying the core job-to-be-done in the first session, and collapsing setup with smart defaults and in-product guidance.

The pattern is consistent: when we measure what matters, build with empowered product teams, and commit to outcome-driven roadmaps, the bucket stops leaking. Acquisition starts compounding because each cohort retains better than the last. If your growth feels like running on a treadmill, it’s time to refocus on activation, engagement, and retention—and use benchmarks to calibrate where you are versus where durable growth lives.

Inspired by this post on Amplitude – Best Practices.

October 23, 2025
Build a High-Impact PLG Growth Org: Structure, Goal-Setting, and Pricing with Melissa Tan

I recently sat down with Melissa Tan to unpack the nuances between two PLG businesses and how growth strategy changes for a more complex product like Webflow. As I reflected on our conversation through the lens of product management leadership, I focused on what it really takes to design a high-impact growth organization, set rigorous goals, and evolve pricing and packaging without losing sight of customer value.
I spoke with Melissa Tan, GM of Self-Service and Head of Growth at Webflow and formerly Head of Growth and Monetization for Dropbox Business. Her experience scaling PLG motions across very different product surfaces offered practical signals on where to double down, what to sequence, and how to balance experimentation with long-term strategy.
Designing and structuring a growth org starts with mapping ownership to the user journey. I anchor cross-functional squads on outcomes across acquisition, activation, conversion, and expansion, with a shared platform and data foundation. Clear swimlanes, crisp interfaces with core product and marketing, and a consistent experimentation cadence keep velocity high while avoiding thrash. For PLG, I also recommend an embedded analytics function and strong partnerships with sales-assist and support to translate signals from self-serve to sales-led opportunities.
The right way to tackle goal-setting is outcomes-first. I favor outcomes vs output OKRs that ladder to a North Star and a small set of controllable, leading indicators (for example, activation rate, time-to-value, or successful workspace creation). I pair these with guardrail metrics to protect user experience and brand trust. Weekly reviews focus on decision quality and learning velocity, not just hit rates, so the team compounds insight even when experiments miss.
How Webflow’s pricing and packaging has evolved is a reminder that complex products require value-based packaging that clarifies who each plan is for and what milestones justify upgrade. When complexity rises, I encourage teams to simplify the fences, align packaging to clear value axes (usage, collaboration, security, or advanced workflows), and ensure in-product prompts communicate that value at the right moment in the journey.
How to calibrate pricing feedback comes down to triangulation. I segment qualitative input by customer size and use case, balance loud feedback with behavioral data (conversion by plan, downgrade reasons, add-on attach), and validate with structured research and live price tests. The aim is not to chase every request, but to isolate willingness-to-pay drivers, reduce friction for the majority, and preserve premium features that genuinely anchor expansion.
In this piece, I cover the essentials: designing and structuring a growth org, the right way to tackle goal-setting, how Webflow’s pricing and packaging has evolved, and how to calibrate pricing feedback. My goal is to leave you with a practical blueprint you can adapt to your PLG startup—one that aligns teams, accelerates learning, and translates product value into durable growth.

October 23, 2025
Mastering Org Design at Scale: My GM-Led Blueprint, Re-Org Steps, and Pricing Signals

Org design is one of the highest-leverage tools I have as a product leader. When structure, incentives, and decision rights align, execution compounds. When they don’t, even great people and strategy stall. In this narrative, I share how I approach company structure, drawing on hard-won lessons from complex re-orgs, “GM-led” models, and the realities of pricing, packaging, and planning at scale.
Here’s the backbone of my philosophy. First, the principles of effective org design matter more than any single chart. I relentlessly return to five anchors: #1 Align on goals; #2 Separate design considerations from human considerations; #3 Define clear reasons each team exists; #4 Design for durability; #5 Be very intentional with comms. These make tradeoffs explicit, reduce churn, and clarify ownership so we can move faster with more confidence.
On timing, there are clear signs your company needs a re-org. When multiple teams chase overlapping goals, when decision latency rises, when cross-functional friction becomes the norm, or when strategy evolves but responsibilities don’t, it’s time to revisit the design. I look for outcome drift in OKRs, blurry escalations, and too many “two owners” problems as leading indicators.
Tradeoffs are inevitable. I surface them early: speed vs. cohesion, specialization vs. customer journey continuity, centralization vs. autonomy. I make the tradeoffs explicit in a one-page brief and tie them back to company goals. This keeps us honest about why we are changing the system—and what we expect to get in return.
Square’s “GM-led” structure offers a useful reference point. The core idea: give a single accountable owner end-to-end responsibility for a business (product, P&L, and cross-functional performance), then architect adjacent teams to enable—not dilute—that accountability. In my practice, I define crisp swimlanes, escalation paths, and shared principles for collaboration at the seams so GMs move fast without fragmenting the customer experience.
Why Square centralized GTM speaks to a broader truth I’ve seen across SaaS: fragmentation in go-to-market creates inconsistent messaging, pricing confusion, and channel conflict. Centralizing GTM can raise the quality bar on positioning, funnel health, and field enablement—while GMs retain the voice of the customer and set product priorities. The key is a tight contract: who owns narrative, who owns quota, and how we arbitrate tradeoffs.
Managing pricing and packaging across a complex org is a system design problem. I establish a single authority for pricing strategy and guardrails, with clear input rights from GMs and Finance. We define canonical metrics for willingness to pay, price elasticity, bundle attach, and churn. This lets us run structured experiments while protecting long-term brand trust and ARR quality.
I put real weight on written principles. Examples of Square’s written principles remind me that great organizations reduce ambiguity by codifying how decisions get made. In my teams, we document decision rights (DACI/RACI), escalation patterns, and what “good” looks like for roadmaps, customer research, and launch criteria—so we don’t reinvent governance in every meeting.
How Square determines what each GM owns maps well to a pattern I use: define the customer journey first, then assign ownership in contiguous slices that minimize handoffs. If an interface is high-traffic or monetization-critical, it deserves a single accountable GM. Shared platforms (data, identity, payments) live in enablement groups with SLAs and portfolio-level success metrics.
Collaboration across GMs and products improves when we create durable seams. I use recurring GM councils, shared north-star metrics, and documented interface contracts. We align on joint bets for the year, budget for cross-org work, and maintain escalation rituals so debates are fast, respectful, and final.
Key lessons on planning and decision-making at scale: time-bound strategy, principle-led tradeoffs, and ruthless clarity on who decides. I anchor annual and quarterly planning in a brief that includes goals, constraints, risks, and assumptions. When we disagree, we escalate once, decide once, and document why—so we can move on without reopening settled questions.
Designing incentives across a massive org means aligning pay, promotions, and recognition to the outcomes we claim to value. I tie variable comp to a balanced scorecard: growth and retention, customer satisfaction, quality of execution, and platform health. If incentives reward only top-line ARR, we’ll get short-term wins at the expense of durability.
Two reasons GM structures go wrong: ambiguous decision rights and platform underinvestment. If GMs can’t tell what they truly own, or if shared services don’t meet their needs, the model will fail. I fix this by clarifying ownership in writing and by setting platform SLAs backed by leadership enforcement.
When it’s time to change the structure, I follow a disciplined playbook. 6 Step re-org walkthrough: Step 1: Triggering the re-org—document the triggers and the goals; Step 2: Sketching a proposed org design—present two to three viable options with tradeoffs; Step 3: Checking against key criteria—stress-test against strategy, customer journey, incentives, and interfaces; Step 4: Finalizing approach with leadership—drive alignment and decision in a single forum; Step 5: Planning comms—sequence messaging for managers, then teams, then partners; Step 6: Executing comms—deliver clear narratives, FAQs, and next steps on the same day.
Signals a re-org worked vs failed show up quickly: decision speed rises, escalations drop, and outcomes improve without heroics. If confusion persists, shadow processes form, or engagement declines, the design needs another pass. I run 30/60/90-day health checks and tune incentives or interfaces before small issues calcify.
I continue to learn from industry operators, including 5 lessons from Alyssa Henry, CEO at Square, that reinforce my own approach: design for clarity, empower accountable owners, write down how we work, invest in platforms early, and communicate like the strategy depends on it—because it does. When we treat org design as a product, we earn compounding execution and a culture built to scale.

October 23, 2025