A more accurate measurement system can be intimidating because it reveals uncomfortable truths. It may show that seemingly successful activities, like generating high MQL volume, had a negligible impact on actual pipeline. Leaders must prepare to face this exposure to truly improve performance.

The critical flaw in most sales tech is its failure to correlate rep behavior with performance outcomes like quota attainment. The real value is unlocked not just by knowing what reps do, but by connecting those actions to who is succeeding, thus identifying true winning behaviors and separating A-players from C-players.

Most GTM systems track initial outreach and final outcomes but fail to quantify the critical journey in between. This "ginormous gray area" of engagement makes it impossible to understand which activities truly influence pipeline, leading to flawed, outcome-based decision-making instead of journey-based optimization.

Metrics like "Marketing Qualified Lead" are meaningless to the customer. Instead, define key performance indicators around the value a customer receives. A good KPI answers the question: "Have we delivered enough value to convince them to keep going to the next stage?"

When a useful metric like "average handling time" becomes a performance target, employees game the system. Reps may hang up on customers to meet quotas, destroying the metric's ability to reflect actual customer satisfaction.

Salespeople often project their own ROI calculations onto prospects. Instead, they must ask customers how they measure the effectiveness of past investments. This uncovers what truly matters to them, whether it's net profit, gross revenue, time saved, or even peace of mind.

Open and click rates are ineffective for measuring AI-driven, two-way conversations. Instead, leaders should adopt new KPIs: outcome metrics (e.g., meetings booked), conversational quality (tracking an agent's 'I don't know' rate to measure trust), and, ultimately, customer lifetime value.

Average teams measure success in functional silos (sales vs. marketing), leading to finger-pointing. Elite teams remove functions from the equation. They focus entirely on the customer's journey, identifying patterns that lead to pipeline and fixing those that don't, regardless of which department "owns" them.

When AI can directly analyze unstructured feedback and operational data to infer customer sentiment and identify drivers of dissatisfaction, the need to explicitly ask customers through surveys diminishes. The focus can shift from merely measuring metrics like NPS to directly fixing the underlying problems the AI identifies.