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.
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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.
Focusing on metrics like '40 calls a day' leads to burnout. Modern sales leaders should measure team well-being and the ability to avoid overwhelm as primary KPIs. A psychologically healthy team is more profitable than a team purely focused on volume.
The most effective way for a salesperson to challenge a perceived unfair quota is not through complaints, but through data. By presenting an analysis of their own average deal size, sales cycle length, and win rates, they can build a logical case for what is achievable and force a more constructive conversation with leadership.
The common frustration of a dropped customer service call is often not an accident. Call center agents are measured on "average handle time" and are penalized if calls are too long, incentivizing them to hang up on complex calls to avoid punishment.
According to Goodhart's Law, when a measure becomes a target, it ceases to be a good measure. If you incentivize employees on AI-driven metrics like 'emails sent,' they will optimize for the number, not quality, corrupting the data and giving false signals of productivity.
Setting rigid targets incentivizes employees to present favorable numbers, even subconsciously. This "performance theater" discourages them from investigating negative results, which are often the source of valuable learning. The muscle for detective work atrophies, and real problems remain hidden beneath good-looking metrics.
Effective coaching follows a three-step process: Identify a metric-based performance gap, validate the specific rep behaviors causing it, and then co-create a coaching plan focused on improving those behaviors, not just the lagging metric.
A common OKR failure is assigning teams high-level business metrics (like ARR) which they can only contribute to, not directly influence. Success requires focusing on influenceable customer behaviors while demonstrating how they correlate to the company's broader contribution-level goals.
Alan Chang argues that incentivizing metrics can have negative second-order effects. For example, a recruiter bonused on 'hires per month' may be motivated to convince hiring managers to lower the talent bar just to hit their target, which is detrimental to the company's long-term goals.
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.
