Signal vs. Noise: Why 80% of Customer Feedback is Lying to You.
Every scaling company runs on feedback. Your inbox is flooded with customer support tickets, NPS surveys, sales objections, and feature requests. The collective voice is loud, urgent, and often contradictory. You are drowning in data, yet starving for insight.
The problem isn’t that you lack feedback. The problem is that 80% of direct customer feedback is noise; it’s a symptom, a want, or a suggestion that solves a $10 problem, not the $1,000 problem that defines your existence. Prioritizing this noise leads to “feature bloat,” wasted R&D capital, and ultimately, mission drift.
The defining skill of a strategic executive is to be a relentless filter. Personally, I use a simple framework to distinguish the few inputs that matter (Signal) from the vast majority that don’t (Noise).
The Framework: The Revenue Leverage Index (RLI)
The Revenue Leverage Index (RLI, often noted as ICE) is a scoring framework that forces me to evaluate every piece of customer feedback, from a feature request to a sales objection, based on its potential to create verifiable, measurable leverage. It is designed to kill the non-strategic, “nice-to-have” projects before they consume engineering time.
The formula conceptualizes the goal: RLI = (IMPACT X CONFIDENCE)/EFFORT
The Mandate: Only prioritize feedback that generates the highest RLI score.
Note: This framework is commonly known as RICE, an acronym for Reach, Impact, Confidence, and Effort. However, I find that the Reach component tends to carry disproportionate weight in the overall calculation. For that reason, I prefer to exclude it and instead use the RLI (or simply ICE) framework, which focuses on Impact, Confidence, and Effort for a more balanced evaluation.
The Variables for Clarity
To quantify the RLI, you must first define the variables with ruthless, objective clarity:
1. Impact: The Customer Value Test
Impact must be scored not on how loud the customer is, but on whether the feature drives one of two outcomes:
Acquisition Signal: Will this feature enable you to close a new, higher-ACV segment of customers?
Retention Signal: Will this feature directly reduce churn among your highest LTV customers?
Any request that fails to significantly impact either of these two metrics is immediately scored as Low Impact (Noise).
2. Confidence: The Behavior over Intent Rule
This variable is the counterweight to the optimistic survey. People are kind in surveys and lie about their intent. Behavior tells the truth.
Low Confidence: The request came only from Surveys, Sales Teams, or Internal Opinions. (Intent).
High Confidence: The request is validated by Usage Data, Churn Data, or Power User Interviews. (Behavior).
You must reduce the weight of all feedback that is not corroborated by a quantitative signal that proves a customer is willing to suffer or pay for the solution.
3. Effort (The Denominator): The Resource Constraint
Effort should be scored by the engineering or operational cost required to deliver the solution. By placing Effort in the denominator, the RLI framework mathematically mandates that you prioritize quick wins with disproportionate impact.
The Result: Strategic Focus
Teams that fail to apply a rigorous decision framework suffer from Product Paranoia; the fear that they are missing something crucial, leading them to chase every available suggestion. This creates feature bloat, slows time-to-market, and degrades the core product experience.
By contrast, organizations that apply the RLI (or similar prioritization frameworks like RICE, which I do heavily in roadmap planning or Kano) operate with Strategic Focus. They ruthlessly protect engineering cycles for the few features that statistically prove they will accelerate market adoption and reduce customer friction. High RLI scores are your Signal; they are the few, non-negotiable inputs that accelerate your path to sustained growth.