The 10 Product Strategy Mistakes I Keep Seeing (After 10+ Years in SaaS)

An enamel pin about Product Management

I’ve made every one of these mistakes. Some of them more than once. Product strategy reads well in a blog post, but in practice it’s a minefield of competing priorities, stakeholder pressure, and the constant temptation to say yes to everything.

After more than a decade leading product and growth for SaaS companies – including subscription products serving millions of users – I’ve developed a pretty reliable list of strategy mistakes that kill momentum. Not the theoretical kind you read about in business school. The real kind. The ones that cost you quarters.

Here are the 10 pitfalls I keep coming back to, the ones that have cost me the most time, energy, and momentum over the years.

What is Product Strategy, Really?

Before we get into the mistakes, let’s get aligned on what product strategy actually is – because the lack of a shared definition is often the first problem.

Product strategy is the set of choices that connect your company’s vision to the work your team does every day. It answers three questions:

  1.  Who are we building for? (target audience)
  2.  What problem are we solving for them? (value proposition)
  3. How does this create value for the business? (business model)

Marty Cagan, author of Inspired and founding partner at Silicon Valley Product Group, puts it simply: strategy is about deciding which problems are worth solving. Roman Pichler frames it as the path to your product vision – the high-level plan for achieving your goals.

The important thing is that strategy is about CHOICES. Not a roadmap. Not a feature list. Choices about what you’ll do, and more importantly, what you won’t do.

With that foundation, here are the 10 mistakes that undermine those choices.

Mistake 1: Confusing Activity with Progress

This is the one that gets almost everyone. You ship a feature. Then another. Then another. Your release notes look great. Your team feels productive.

But the metrics aren’t changing.

I’ve lived this. We shipped feature after feature and our conversion numbers stayed flat. Lots of effort, but no forward motion. The problem was that we were building things that were nice to have, not things that moved the needle.

This is what the Jobs-to-be-Done (JTBD) framework helps you avoid. When you understand the actual job your customer is hiring your product to do, it becomes much easier to evaluate whether a feature advances that job or just adds noise. Clayton Christensen’s insight was that customers don’t buy products – they hire them to make progress. If your feature doesn’t help the customer make progress on their core job, it’s activity, not progress.

How to avoid it: Before greenlighting any feature, ask “which metric does this move, and by how much?” If the team can’t answer that clearly, the feature isn’t ready to build. This is easy to say, but extremely difficult to do. Use a prioritization framework like RICE scoring (Reach, Impact, Confidence, Effort) to force the conversation beyond gut feel.

Mistake 2: Strategy by Consensus

There’s a version of inclusive leadership that sounds great in theory but kills strategy in practice. You bring everyone to the table. You gather input. You synthesize. You try to find a path that makes all stakeholders happy.

… and you end up with a strategy that offends no one and inspires no one.

Real strategy requires choices. Hard ones. The kind where someone in the room won’t like the answer. If your strategy document doesn’t explicitly state what you’re NOT doing, it’s a wish list.

This is what killed products like Google+. Google had the engineering talent, the distribution, and the resources to build a social network. But the strategy tried to be everything to everyone – a Facebook competitor, a Twitter alternative, an identity platform, a photo sharing service. No hard choices were made and the product sadly died a slow death by committee.

How to avoid it: I’ve learned (the hard way) that my job is to make everyone feel heard, synthesize the inputs, make a clear decision, and then communicate the reasoning. People can disagree with a well-reasoned decision, what they can’t work with is ambiguity. Write down your strategy in one page. If it doesn’t fit on one page, you haven’t made enough choices yet.

Mistake 3: Copying the Competition

Your competitor launches a feature. Your sales team forwards the announcement. Your CEO asks “why don’t we have this?” And suddenly your roadmap has a new top priority that wasn’t there yesterday – classic!

I’ve fallen into this trap more than I’d like to admit. You absolutely should know what your competitors are doing. The real risk is letting their decisions drive YOUR strategy.

When you copy a competitor’s feature, you’re solving for THEIR customers with THEIR context.

You don’t know why they built it. You don’t know if it’s working. You don’t know if they’re about to kill it. You’re making a strategic bet based on a press release.

Gibson Biddle, former VP of Product at Netflix, uses what he calls the DHM Model – Delight, Hard-to-Copy, and Margin-Enhancing. The “hard-to-copy” piece is key but with AI it’s getting more difficult. If your strategy is just replicating what competitors build, you’ll always be behind AND you’ll never build anything that’s uniquely valuable to your users.

How to avoid it: Understand what problem the competitor is trying to solve, then ask whether YOUR users have that same problem. Sometimes they do, and then you should solve it in a way that fits your product, your architecture, and your users’ workflow. Sometimes they don’t, and the right answer is “we’re not building that” – Jeff Bezos has a great framework for this kind of decision.

Mistake 4: Ignoring the Metrics That Actually Matter

Vanity metrics are seductive. Page views are up! Sign-ups are growing! App downloads hit a new record!

But if your churn rate is climbing at the same time, you’ve got a leaky bucket. And no amount of top-of-funnel growth fixes a retention problem.

I’ve been in situations where the dashboards looked green but the business was struggling, and situations where the top-line numbers looked concerning but the underlying health was strong. The difference was which metrics we were watching.

This is what the North Star Metric concept helps solve. Your North Star is the single metric that best captures the core value your product delivers to customers. For Spotify, it’s time spent listening. For Airbnb, it’s nights booked. For a subscription SaaS product, it might be weekly active usage or feature adoption depth.

How to avoid it: For any subscription product, the metrics that matter are: how many people start a trial, how many convert to paid, how many cancel, and what’s the net change. Everything else is context. Build your dashboard around these numbers first, THEN add the supporting metrics that explain why they’re moving.

Mistake 5: Trying to Serve Everyone

This one is especially hard in mission-driven organizations. You WANT to help everyone. Every user segment seems important. Every use case feels valid.

But trying to serve everyone equally means serving no one well.

Your onboarding can’t be optimized for beginners AND power users simultaneously. Your pricing can’t be accessible to individuals AND competitive for enterprises without compromise.

Trying to serve everyone equally means serving no one well.

Kodak learned this the hard way. They saw digital photography coming but tried to straddle both worlds – maintaining their film business while half-heartedly investing in digital. They served neither audience well, and a company that once dominated an entire industry filed for bankruptcy in 2012.

How to avoid it: The best products I’ve used (and the best products I’ve built) made clear choices about who they were for. They explicitly prioritized one audience and designed everything around their needs first. When you do that well, other segments often benefit anyway, from a focused, coherent product rather than a compromised one. Define your primary persona. Write it on the wall. When someone asks “but what about this other segment?” you have your answer ready.

Mistake 6: Having No Strategy at All

This sounds obvious, but it’s shockingly common. My last few roles I’ve called “The Fixer” because years of the company running hard has caused them to lose their focus and they suddenly realize they don’t have a strategy. They have a roadmap. They have a backlog. They have quarterly goals. They ship things on time.

But there’s no unifying thesis about WHERE the product is going and WHY.

Roman Pichler calls this the most common product strategy mistake he encounters. Teams jump straight from vision to execution without the strategic layer that connects them. The result is a collection of features that individually make sense, but collectively don’t tell a coherent story.

How to avoid it: Your strategy should be a testable hypothesis, not a document that lives somewhere on the server. Try this format: “We believe that [target audience] struggles with [problem]. If we build [solution], we’ll see [measurable outcome] within [timeframe].” If you can’t fill in those blanks, you don’t have a strategy yet. You have a to-do list.

Mistake 7: Treating Strategy as Static

You spend weeks crafting the perfect strategy document. Leadership signs off. The team aligns. You print it out and pin it to the wall.

Six months later, the market has shifted, a competitor has launched something unexpected, and your customers are telling you something you didn’t anticipate. But the strategy is “locked.”

Eric Ries built the entire Lean Startup methodology around this problem. The Build-Measure-Learn loop isn’t just for startups – it’s for any team that operates in uncertainty, which is literally every product team. Your strategy should have built-in checkpoints where you evaluate whether your assumptions still hold.

How to avoid it: Set quarterly strategy reviews. Not annual planning sessions where you redo everything – lightweight reviews where you ask: “What have we learned? What’s changed? Do our bets still make sense?” The best strategies are living documents, not manifestos. Jeff Bezos distinguishes between “one-way door” decisions (irreversible, deliberate slowly) and “two-way door” decisions (reversible, move fast). Most strategic choices are two-way doors. Treat them that way.

Mistake 8: Skipping Validation Before Committing

You have a great idea. The team is excited. Leadership is bought in. You go straight to building.

Three months later, you launch to silence. Customers don’t want it, don’t understand it, or already solved the problem another way.

I’ve seen this pattern destroy entire quarters. The excitement of a new idea creates momentum that skips right past the “should we build this?” question and lands on “how do we build this?”

How to avoid it: Before committing engineering resources, validate the problem AND the solution. Talk to 5-10 customers. Run a fake door test. Build a prototype and put it in front of real users. Teresa Torres’ Continuous Discovery framework calls this “opportunity solution trees” – mapping the opportunity space before jumping to solutions. The cost of 2 weeks of discovery is nothing compared to 3 months of building the wrong thing.

Mistake 9: Siloed Strategy Without Cross-Functional Input

Product writes the strategy. Engineering learns about it at spring planning. Design gets brought in when wireframes are needed. Marketing finds out at launch.

This isn’t strategy. It’s a relay race where nobody can actually see the finish line.

The best product strategies I’ve been part of were shaped by engineering constraints, design insights, and market intelligence from day one. Your engineers know what’s technically feasible and where the architecture creates opportunities. Your designers have insights about user behavior that data alone can’t capture. Your sales and support teams hear objections and pain points every day.

How to avoid it: Include engineering and design leads in strategy formation, not just execution. Share customer research broadly. Bring it up in meetings regularly. Make your strategy document accessible to everyone on the team, not locked into a leadership slide deck. When people understand the WHY behind the strategy, they make better decisions at every level.

Mistake 10: Being Unrealistic About Execution Capacity

This is the mistake that ties all the others together. You have a clear strategy. You’ve validated the direction. You’ve made all the hard choices about what to build.

Then you commit to 3x more than your team can actually deliver.

Your roadmap becomes a pressure cooker. Quality drops. Shortcuts get taken. The team burns out. And paradoxically, you end up delivering LESS than if you’d committed to fewer things done with excellence.

I’ve seen this cycle repeat across every company I’ve worked with. The ambition is always bigger than the capacity, and the gap gets filled with overtime and technical debt instead of honest prioritization.

How to avoid it: Be ruthlessly honest about how much your team can ship in a quarter. Then cut 20% from that estimate. Even writing that sounds crazy, but it must be done. Use the OKR framework (Objectives and Key Results) to limit your bets to 3-5 outcomes per quarter – not 3-5 per team, 3-5 total. Warren Buffett’s “two-list strategy” applies here: write down your top 25 priorities, circle the top 5, and treat the other 20 as your “avoid at all costs” list (avoid them entirely until the top 5 are achieved). The same logic applies to product strategy.

The Uncomfortable Truth

Product strategy is about having the discipline to say no to good ideas that don’t align with what matters most right now.

Every mistake on this list comes from the same root: the unwillingness to make a hard choice and live with the tradeoff.

Choose the right things. Decide clearly. Pick your own path. (I wrote about this focus in 5 things needed for business success.) Watch the honest metrics. Serve someone specific.

Strategy is the art of sacrifice. The sooner you get comfortable with that, the better your products will be.

Product Strategy Checklist

Before you finalize your next product strategy, run through this list:

  • Can you state your target audience in one sentence?
  • Can you articulate the core problem you’re solving for them?
  • Does your strategy explicitly state what you’re NOT doing?
  • Is every major initiative tied to a measurable outcome?
  • Have you validated your assumptions with real customers?
  • Does your team have the capacity to execute this quarter’s plan?
  • Have you set a date to review and adapt the strategy?
  • Can your entire team articulate the strategy without looking at a document?
  • Is there a clear North Star Metric everyone is aligned on?
  • Would you bet your own money on this plan working?

If you can’t check every box, your strategy still has gaps. Go back and make the hard choices.

Frequently Asked Questions

What are the most common product strategy mistakes?

The most common product strategy mistakes include confusing activity with progress (shipping features that don’t move metrics), strategy by consensus (avoiding hard choices to keep everyone happy), copying competitors instead of solving for your own users, ignoring retention metrics in favor of vanity metrics, and trying to serve every user segment equally. The root cause of most strategy failures is an unwillingness to make clear choices and accept tradeoffs.

What is the difference between product strategy and a product roadmap?

Product strategy defines WHERE you’re going and WHY. It’s about choices, tradeoffs, and the thesis behind your product direction. A product roadmap is the HOW and WHEN – the sequence of work that executes the strategy. A roadmap without a strategy is just a feature list. A strategy without a roadmap is just a vision. You need both, but strategy comes first.

How do you create an effective product strategy?

An effective product strategy begins with a clear understanding of your target audience, the problem you’re solving, and how solving it creates business value. Frameworks like Jobs-to-be-Done help identify what customers actually need. Validate your assumptions through customer discovery before committing resources. Set a North Star Metric to track progress. Review and adapt quarterly. Most importantly, be explicit about what you will NOT do – that’s ultimately where the real strategy lives.

How often should you update your product strategy?

Product strategy should be reviewed quarterly and updated when market conditions, customer needs, or business goals change significantly. It should NOT change weekly based on competitor moves or stakeholder requests. The best approach is setting lightweight quarterly checkpoints where you evaluate whether your core assumptions still hold, while keeping the overall strategic direction stable enough for the team to execute with confidence.

The Traffic You Depend On Is Being Answered Without You

I’ve been staring at a traffic chart for the last three weeks that I can’t stop thinking about.

It’s Chegg’s chart. The online education platform lost 34% of its organic visitors in a matter of months. That’s a cliff. Their keyword footprint went from 11.1 million to 3.5 million.

And the culprit wasn’t a competitor outranking them or a Google algorithm update penalizing thin content. It was Google answering the questions before anyone ever clicked.

The Machine That Eats Your Top of Funnel

Google’s AI Overviews are the AI-generated summaries that now appear at the top of search results, and they are fundamentally changing what it means to rank on Google. For years, the playbook was clear: create valuable content, optimize it for search, capture intent, convert visitors.

That model assumed one thing: that people would actually click through to your site.

AI Overviews break that assumption.

When someone searches “how to explain forgiveness to a congregation” or “best illustrations for an Easter sermon,” Google can now synthesize an answer from multiple sources and present it directly in the search results. No click required. No visit to your site. No entry into your funnel.

Tomasz Tunguz laid this out clearly in a recent analysis:

“Content dependency on organic search is no longer a sustainable acquisition model.”

That sentence should be pinned to the wall of every SaaS product leader who relies on organic traffic (understanding these shifts is a critical PM skill) to fill the top of their funnel.

Chegg Is the Preview

The pattern is showing up everywhere. Stack Overflow, the platform that essentially taught a generation of developers how to code (including me), is seeing the same erosion. Informational queries that used to drive millions of visits are now being answered inline by AI.

The New York Times is thriving. Why? How? A $100 million content licensing deal with Google. They’re feeding the AI, on their terms, for revenue.

Here’s what I think the data is telling us:

1. Q&A-style content is the most vulnerable. If your value proposition is answering questions that can be summarized in a paragraph, you’re in the blast radius.
2. Branded, premium, behind-the-paywall content is more defensible. AI Overviews can summarize a sermon topic, but they can’t replicate a full manuscript, a downloadable media pack, or an AI-powered sermon builder.
3. The winners will be the ones who stop treating Google as a given and start building direct relationships with their audience.

What This Means for SaaS Product Leaders

I run product and growth for a content platform that serves pastors. We have 245,000+ sermons and 50,000+ text illustrations, exactly the kind of content library that ranks well for long-tail informational queries.

For years, that library has been our primary discovery engine. Pastors search for sermon ideas, find us, browse free content, start a trial, and convert to paid.

That model still works today, but we’re down around that same 34% mark and from what I can tell so is everyone, across all industries. But I’d be naive to assume it’ll work the same way in 18 months.

Here’s the uncomfortable math: if organic traffic drops by even 20-30%, and organic is your dominant acquisition channel, no amount of conversion rate optimization saves you. You can have a best-in-class trial-to-paid flow and still miss your numbers because not enough people are entering the funnel in the first place.

It’s an exposure problem. And it requires a fundamentally different response than what most product teams are used to.

The Diagnostic Before the Panic

Before you restructure your entire growth strategy, there’s a critical diagnostic step that teams often skip. You need to know whether AI Overviews are actually appearing on YOUR highest-value queries.

Here’s the move:

  • Pull your top 50 keywords from Google Search Console. Look at click-through rate trends over the last 90 days, segmented by week.
  • The signature you’re looking for: stable or rising impressions, but declining CTR. That pattern means Google is showing your content in results, but users aren’t clicking because the AI Overview already gave them what they needed.
  • If your impressions are dropping, that’s a competitor or algorithm problem. If impressions are stable but clicks are falling, that’s AI Overview cannibalization. Different diagnosis, different treatment.

Most teams I talk to are just making this distinction. They’re looking at traffic declines and assuming it’s an SEO problem when it might be a platform shift problem. The difference matters.

Three Moves to Make Now

I’m not going to pretend I have the full playbook figured out. But here’s where my thinking is landing:

1. Shift discovery investment toward owned channels.
Email nurture sequences, community platforms, pastoral networks, partnerships with organizations that already have the audience. Organic search should be one of many channels, not the only one. Every dollar of effort I’m putting into SEO-driven top-of-funnel content I’m asking if that same effort in email or community would be more durable.

2. Make your paywall content genuinely irreplaceable.
AI can summarize a sermon outline. It cannot replicate a curated media pack, a professionally produced video series, or a workflow tool that saves someone three hours a week. The content that survives AI summarization is the content that requires depth, production value, or interactivity: things a search snippet can’t deliver.

3. Explore whether the threat is also an opportunity.
The NYT licensing deal tells us something important: Google is willing to pay for premium vertical content. If you’re the dominant content platform in your niche, there may be a deal to be made.

A licensing partnership could convert a traffic threat into a revenue stream while maintaining brand visibility inside AI-generated results. Worth exploring.

The Bigger Lesson

I keep coming back to something I’ve learned over the last few years leading product: the most dangerous risks are the ones that look like stability. Traffic holding steady today doesn’t mean the foundation isn’t shifting underneath.

Chegg’s team didn’t wake up one morning to a 34% traffic drop. It happened gradually, then suddenly. The chart looks normal until it doesn’t.

The product leaders who navigate this well will be the ones who diagnosed early, diversified before they had to, and built value that can’t be summarized in a paragraph. The ones who don’t will be staring at a chart they can’t explain and wondering where all the visitors went.

I’d rather be asking the hard questions now than explaining the traffic decline later.

AI Just Walked Into Your Website Without Knocking

Last month I asked ChatGPT a question I’ve asked Google a thousand times: “What’s a good sermon illustration about forgiveness?”

It gave me a solid answer. Three illustrations, structured with context, application points, even a suggested closing line. It was genuinely useful.

And it never sent me to a single website.

That moment hit me differently than it would have two years ago. I run a platform with over 245,000 sermons and 50,000 illustrations. I didn’t just lose a click. I watched an AI system do what our product does, using content that likely came from sites like ours, and deliver it in a way that made visiting the source unnecessary.

That’s a revenue problem. (I wrote about the traffic implications of this shift recently.) (I wrote about the traffic implications of this shift recently.)

The Zero-Click Layer

Most product leaders I know are still thinking about AI as a feature to bolt onto their product: chatbots, smart search, AI-generated recommendations. And that matters. But there’s a bigger shift happening underneath that conversation.

AI answer engines (ChatGPT, Google AI Overviews, Perplexity) are becoming the front door to the internet. They don’t just search. They visit your site, interpret your content, synthesize it, and serve it directly to the user. The user gets the answer. You get nothing.

Google’s featured snippets started this zero-click trend years ago. BUT what’s different now is the depth. A featured snippet pulls a paragraph. An AI answer engine can synthesize an entire page, or multiple pages, into a comprehensive response that genuinely satisfies the user’s intent.

If your business depends on organic traffic as a top-of-funnel engine, this should keep you up at night.

Your Content Library Is Both Your Greatest Asset and Your Biggest Vulnerability

Here’s the paradox I’ve been sitting with.

We spent years building one of the largest structured content libraries in our space. That library is what drives our organic traffic. It’s what Google indexes. It’s what pastors find when they search “sermon on grace” at 11pm on a Saturday night.

That same library is now what AI systems are ingesting to train their models and generate their answers. The very content that built our moat is being used to fill in the moat.

And here’s what makes it worse. The emerging AI-native competitors in our space don’t even need to win Google rankings. They ARE the AI tool. They’re built to live inside AI workflows, not compete for traditional search clicks.

I think this pattern applies to any SaaS company sitting on a large content asset. If you’ve built your growth engine on content that AI can summarize, you’re exposed.

AEO: A Genuinely Different Discipline

There’s a term gaining traction: AEO, or AI Engine Optimization. And I’ll be honest, my first reaction was skepticism. We don’t need another three-letter acronym.

But the more I’ve dug into it, the more I realize it represents a genuinely different discipline.

SEO optimizes for ranking. AEO optimizes for citation. The goal is to be the source that AI systems reference AND link back to. That requires a fundamentally different content strategy.

Here’s what that looks like in practice:

  1. Structured data becomes non-negotiable. Schema markup, clear metadata, explicit problem-solution framing in your content. AI systems parse structure, not vibes. (Schema.org is the starting point.)
  2. Content architecture matters more than keyword density. How your content is organized (headers, relationships between pages, internal linking) determines how AI systems understand your authority on a topic.
  3. Gated content is a double-edged sword. If your best content is behind a login wall, AI crawlers can’t index it. You’re invisible to the answer engine. But if everything is open, you get summarized without a click. The play is in the middle: structured preview content that AI can cite, with depth that requires the visit.
  4. Domain-specific language is your moat. Generic content gets synthesized away. Content that uses the precise language of your audience (the way a pastor describes their Saturday night prep struggle, the specific vocabulary of sermon structure) is harder for AI to replace and more likely to be cited with attribution.

What I’m Doing About It

I’m not going to pretend I have this figured out. But here’s where my head is:

Audit how AI sees us. Before optimizing anything, we need to understand how our top pages render to AI crawlers. What structured data exists? What’s behind login walls that blocks indexing?

Treat AI referral as a distinct channel. We track direct traffic, organic search, paid. AI referral needs its own lane in our analytics. We can’t optimize what we can’t measure.

Build content AI can’t summarize away. The full sermon text? AI can handle that. But a pastor’s framework for adapting a sermon to their specific congregation? A diagnostic tool for matching an illustration to a particular emotional moment in a service? That’s interactive, personalized, and requires being on the platform.

Move faster than the AI-native competitors. They have the structural advantage of being built for AI workflows. We have the structural advantage of 20+ years of trusted content and relationships. The question is whether we can adapt our distribution before they build our depth.

The Strategies That Got You Here Won’t Sustain You

I keep coming back to this. The strategies that built organic growth over the last decade won’t sustain it over the next five years.

That’s a reason to move, not a reason to panic.

The companies that treat AI answer engines as a new channel will capture disproportionate share of the next era of discovery. The ones that keep optimizing for Google page one while AI summarizes their content into zero-click answers will watch their traffic erode and wonder what happened.

I’d rather be early and wrong about the tactics than late and right about the trend.

The AI just walked into your website. The question is whether it’s going to send people your way, or make visiting you unnecessary.