??Solving the Right Problem in Product Operations: The Elevator Problem Approach

In Product Ops, the pressure to solve issues quickly can often lead to jumping to conclusions. However, solving the right problem—rather than the most obvious one—is critical for ensuring that resources are used effectively and long-term solutions are achieved. The Elevator Problem, a popular MBA case, illustrates this point perfectly and provides a useful framework for approaching problem-solving in Product Ops.

?? The Elevator Problem: A Classic Lesson in Solving the Right Problem

Imagine a scenario where tenants in a building are complaining about long wait times for the elevators. Initially, the problem seems obvious: the elevators are too slow. Building management might think of expensive solutions, such as installing new elevator or upgrading the current system to speed them up.

However, after digging deeper, they realize the true problem isn’t the speed of the elevator —it’s the perception of time during the wait. The solution? They install mirrors near the elevator, so people are distracted while waiting. Complaints drop significantly without the need for costly upgrades. ??

This story teaches us an important lesson: solving the wrong problem, even efficiently, won’t address the real issue. In Product Ops, this approach can save valuable time and resources while delivering more impactful results.

??? Applying the Elevator Problem in Product Operations

Product Ops teams face similar challenges: when a problem arises, the instinct is to solve the most visible symptom. But like the Elevator Problem, it’s essential to question the problem itself before jumping to a solution.

?? 1. Efficiency vs. Effectiveness: Digging Deeper

In Product Ops, solving surface-level problems can lead to quick fixes that don’t have long-lasting effects. It’s crucial to probe deeper into the issue to ensure you're solving what really matters.

Take a recent example from my work. We were tackling the design and redesign of courses simultaneously, and the design team asked which courses should be prioritized. This question immediately led to the assumption that design capacity was lower than the number of courses waiting to enter the design pipeline. But instead of rushing into prioritizing courses, I questioned the problem itself.

Rather than immediately solving the prioritization issue, I first sought to understand whether there was truly a mismatch between design capacity and the courses needing design by a certain deadline to launch in the target FY Q. After reviewing the situation, I discovered that with proper timeline planning, we actually had sufficient design capacity to handle both new designs and redesigns. The problem wasn’t prioritization—it was about optimizing timelines to meet course launch targets.

?? 2. Resource Allocation: Solving the Right Bottleneck

In the Elevator Problem, installing mirrors was a simple, cost-effective solution that didn’t require expensive system upgrades. Similarly, in Product Ops, misdiagnosing the problem can lead to misallocating resources.

In my case, if we had rushed to prioritize courses based on the false assumption of limited design capacity, we might have delayed projects unnecessarily or overburdened certain teams. By questioning the initial problem, we avoided the need to overcomplicate the process and planned more efficiently for the entire course pipeline waiting to be in design.

?? How to Apply the Elevator Problem Thinking in Product Ops:

1. Challenge Assumptions: Don’t rush into solving the first problem you see—probe deeper to identify the real issue. ?
2. Validate with Data: Before implementing a solution, make sure to gather data that supports your approach. ??
3. Pilot and Plan: Test solutions in smaller steps to confirm they address the root cause before scaling them up. ???
4. Collaborate Across Teams: Engage with stakeholders to ensure you’re aligned on the real issue and its impact on overall goals. ??