High Support Costs Mean You Have a Bad Product — Even If Retention Is High

There’s a dangerous illusion in many organizations: “Our customers love us. Our retention is great!” Maybe so. But if your support costs are high, that’s not loyalty. That’s friction being tolerated.

Retention Can Hide Rot

High retention doesn’t necessarily mean your product is strong. It might mean your customers are trapped - by contracts, integrations, or switching costs. It might mean they’ve built internal workarounds for your shortcomings. In some industries, it might just mean there’s no better option (yet).

If customers have to contact support often - for bugs, confusing UX, unclear workflows, or missing functionality - then every call, chat, or ticket is a symptom of a design flaw. High support costs are the smoke. The fire is inefficiency built into the product itself.

You can’t measure product quality by how many issues you resolve; you measure it by how few issues ever need resolving in the first place. Retention stats might look shiny on a quarterly report, but they're masking the real story: a product that's surviving on inertia, not excellence. Customers sticking around despite frequent support needs aren't evangelists; they're hostages to habit or hassle.

The Corporate Reflex: Optimize the Wrong Thing

When support costs rise, most organizations default to the same playbook:

• Add automation to support workflows.
• Improve response times.
• Launch new AI assistants to triage tickets faster.

All of that looks great on a dashboard. But it’s optimization at the wrong layer. It’s making the pain more tolerable instead of removing the cause of pain altogether.

That’s not transformation, that’s denial, dressed up in operational efficiency metrics. You're polishing the band-aid instead of healing the wound. Companies pour resources into scaling support teams or tech stacks, celebrating metrics like "reduced average handle time" as victories. Meanwhile, the root issues fester, driving up long-term costs and eroding true customer trust. It's a short-term fix that ignores the compounding damage of a flawed foundation.

A First Principles Lens

A First Principles mindset starts with this question: “Why does support exist?”

At its core, support exists because the product fails to fully meet the user’s needs intuitively. If you keep improving support efficiency without solving those gaps, you’re scaling the cost of imperfection.

Instead, trace each support driver back to its origin:

• Confusion? The UX is unclear. Redesign interfaces to be self-explanatory, not a puzzle that requires a manual.
• Feature request? The product scope doesn’t match real-world use. Realign development priorities based on actual user behavior, not assumptions.
• Workaround dependency? Your integrations or documentation are failing. Build seamless connections and proactive guides that anticipate needs.
• Repetitive bugs? Quality assurance is reactive, not proactive. Shift to rigorous testing and continuous monitoring to catch problems before they hit users.

Solving these upstream is where sustainable profit lives because every support ticket prevented increases margin and customer satisfaction simultaneously. This approach demands breaking down the product to its fundamentals: What problem are we truly solving? How can we eliminate barriers at the source? It's not about layering on more tools; it's about redesigning for simplicity and reliability from the ground up.

How Hyperscalers Are Addressing Support: Lessons in Root-Cause Elimination

Hyperscalers like AWS, Microsoft Azure, and Google Cloud Platform (GCP) exemplify the first principles approach by prioritizing product improvements, self-service empowerment, and proactive automation to minimize support needs altogether. Rather than just scaling support operations, they invest in ecosystems that prevent issues from arising, turning potential tickets into seamless user experiences. Here's how they're doing it:

AWS: Proactive Guidance and Architectural Best Practices

AWS focuses on reducing support tickets through tools that guide users toward optimal configurations from the start. AWS Trusted Advisor acts as an automated consultant, scanning environments for cost savings, security gaps, performance bottlenecks, and reliability issues often flagging problems before they escalate into support cases. The Well-Architected Framework provides blueprints for building resilient, efficient systems, emphasizing principles like operational excellence to avoid common pitfalls. Additionally, AWS uses AI-driven insights in Amazon Connect to transform contact centers into experience hubs that anticipate customer needs, reducing resolution times and overall ticket volume. By leveraging support data to inform product updates, AWS continually refines services like EC2 and S3 for intuitiveness, cutting down on repetitive queries about setup or troubleshooting.

Microsoft Azure: Intelligent Monitoring and Personalized Recommendations

Azure tackles support at the root with proactive tools like Azure Advisor, which delivers tailored recommendations to optimize resources, enhance security, and improve reliability directly addressing common pain points without human intervention. Azure Monitor collects telemetry to identify issues early, setting up alerts that empower users to self-resolve before contacting support. The platform's extensive documentation, Microsoft Learn modules, and community forums encourage self-service, while tiered plans (from Developer to Enterprise) ensure basic queries are handled via portals or AI chatbots. Azure also runs its own customer service on the platform, using it to refine features that reduce network latency and enhance capabilities, indirectly lowering support demands across the ecosystem.

Google Cloud: Reliability Engineering and Active Assistance

Google Cloud emphasizes prevention through Site Reliability Engineering (SRE) principles, embedded in tools like Mission Critical Services under Premium Support, which help customers align their setups with Google's production standards to avoid outages. Active Assist provides AI-powered recommendations for optimization, flagging inefficiencies in real-time to prevent escalations. Comprehensive documentation and community support form the backbone of Basic Support, enabling users to resolve issues independently. GCP also uses billing and cost management tools to give users visibility, reducing queries related to unexpected charges. By analyzing support patterns, Google iterates on product design, such as improving UI for Compute Engine, to make complex tasks intuitive and support-free.

These hyperscalers demonstrate that true support strategy isn't about faster ticket resolution; it's about engineering products so robust that support becomes obsolete for most users. They blend automation (e.g., AI insights) with root-cause fixes (e.g., best-practice frameworks) to drive down costs while boosting satisfaction.

Efficiency Isn’t the Goal. Elimination Is.

Support should be a failsafe, not a strategy. If your “Support Excellence” initiative is a major line item in the budget, that’s not a strength it’s a red flag.

True excellence comes from designing products that require less human intervention, not more. Imagine a world where users navigate effortlessly, features anticipate needs, and bugs are anomalies, not norms. That's the benchmark - not how fast you can apologize for failures.

So the next time someone celebrates your support metrics, ask this instead: “How can we make it so they never have to call us at all?”

That’s a First Principles question. And that’s where real innovation starts.

By focusing on elimination over efficiency, you build products that don't just retain customers; instead your products can delight customers without the hidden costs of constant fixes.