Avoiding the pitfalls: Lessons from Radical Product Thinking

Building great products isn’t just about scaling fast or shipping more features. It's about understanding the real problems your customers face and ensuring your solution truly delivers. Inspired by Pendo’s Radical Product Thinking: Vision Setting Course, here are some common "syndromes" that silently derail products and why staying aware of them is crucial to delivering value.

---

Hero Syndrome

Why it matters:
Chasing scale without impact can build a house of cards. It looks good externally, but it crumbles when real customer outcomes are needed.

What it looks like:
Chasing big wins, taking risky bets, and measuring success by buzz rather than meaningful results.

Example:
A data platform that scales rapidly in terms of user count, but struggles to deliver actionable insights due to poor data quality or lack of focus on actual user problems.

---

Strategic Swelling

Why it matters:
Without focus, you end up solving everyone's problems poorly — and no one remembers you.

What it looks like:
Trying to please too many users at once, leading to scattered efforts and a diluted value proposition.

Example:
A data product offering both real-time and batch processing, custom dashboards, and ML model predictions for every type of user — from marketing to engineering to product teams — ends up being bloated and inefficient for all of them, with weak features that fail to solve anyone’s specific problem well.

---

Obsessive Sales Disorder (OSD)

Why it matters:
A product built for individual deals doesn’t scale — it becomes a collection of one-offs instead of a coherent solution.

What it looks like:
Shipping custom features for big customers while losing the core product vision.

Example:
A data product that integrates custom features for large clients, like unique visualizations or personalized KPIs, while ignoring the needs of smaller customers who are left with generic, underwhelming features.

---

Hypermetricemia

Why it matters:
When you only optimize metrics, you miss transformative growth. Small improvements won’t save a stagnant product.

What it looks like:
Incremental tweaks everywhere, but no major leaps. Teams stuck optimizing instead of innovating.

Example:
A data product that’s focused on vanity metrics like daily active users (DAUs) or feature adoption rates, but ignores more important long-term metrics like user satisfaction, customer retention, and real business impact, thus failing to make meaningful improvements.

---

Locked-In Syndrome

Why it matters:
Technology is a tool, not the mission. When you marry a platform instead of your customer’s problem, you lose adaptability.

What it looks like:
Decisions driven by what's easy for the system, not what's right for the customer.

Example:
A data lake built on a specific cloud provider’s proprietary tool that locks customers into using that platform, even if a competitor offers better integration, scalability, or performance for the customer’s needs.

---

Narcissus Complex

Why it matters:
Self-centered products quickly lose touch. Without real empathy, you risk becoming irrelevant.

What it looks like:
Prioritizing internal goals and assumptions over actual customer needs.

Example:
A data analytics tool that focuses on complex internal architecture and advanced features that the development team loves, but the end users (e.g., business analysts or marketers) find the interface confusing and overly technical.

---

Pivotitis

Why it matters:
Constant changes erode trust. Customers and teams need consistency to believe in your product.

What it looks like:
Frequent, whiplash-inducing shifts in product direction, creating confusion inside and out.

Example:
A data product that frequently changes its core offering — for example, from being a dashboarding tool to focusing on data ingestion — without clear communication, leaving customers confused and hesitant to invest.

---

Tunnel Vision Syndrome

Why it matters:
Focusing only on one aspect of the product, such as technology or performance, without considering the broader context (customer needs, market trends) can lead to products that are out of touch.

What it looks like:
Narrow focus on optimizing a single component of the product (e.g., speed or data architecture) while ignoring the broader user experience or market needs.

Example:
A data product that boasts incredibly fast data processing speeds, but its interface is too complex for end users to adopt, resulting in a lack of user engagement.

Shiny Object Syndrome

Why it matters:
Chasing the latest trends or features without clear alignment to the product vision distracts from long-term value and can lead to wasteful development.

What it looks like:
Jumping to implement new technologies or features simply because they are trendy, even when they don't fit the core problem the product is solving.

Example:
A data analytics product that spends too much time integrating cutting-edge AI or blockchain features, when the core customers are still struggling with basic data cleaning or visualization needs.

Death by Consensus

Why it matters:
Seeking constant approval and agreement from all stakeholders can slow down decision-making, leading to missed opportunities and a lack of clear direction.

What it looks like:
Trying to get everyone’s input on every decision, leading to endless debates and delays in moving forward.

Example:
A data product development process where every feature, UI change, or product roadmap decision is delayed because it requires sign-off from multiple teams (engineering, sales, product, etc.), resulting in frustration and slow progress.

---

Final thought

Avoiding these syndromes isn’t about being perfect. It is about staying grounded in customer needs, consistent vision, and long-term value. A strong product vision will help you sidestep these pitfalls, align your teams, and deliver products that matter.

---

Check out this course if you would like to learn more on this topic: Radical Product Thinking: Vision Setting Course | Pendo.io