26 Feb 2025
Raise your hand if you used CSAT to measure your team's success in customer support.
What about case closure or even case productivity?
I have a confession. I used those metrics.
Guilty as charged.
But there has to be a better way to create team-spirited metrics that are customer-focused!!!
As support leaders, we have answers at our fingertips, but the questions remain unanswered. It's high time we delve deeper, challenge norms, and harness the full power of the data. Beyond traditional metrics lie insights waiting to transform customer experiences, drive business growth, and unlock untapped potential. Let's shift our focus, challenge ourselves, and reimagine what's possible.
Let's analyze case closure in customer support and confront a pressing query: Does measuring customer case closure truly encapsulate case resolution, or are we inadvertently prioritizing metrics over genuine customer satisfaction?
As leaders in the SaaS industry, we often celebrate teams that hit quantitative targets, but are we overlooking a critical aspect of our business – the customer support teams?
Customer support is the defense in our SaaS business, ensuring customers stay satisfied, renew subscriptions, and advocate for our brand. Yet when we measure, we do not measure customer support teams as value adds but rather as cost centers because that is easy.
That is the status quo. We often measure things we should not measure, things that do not matter, but we measure because it is easy. Instead, we should measure customers' behavior after customer support interactions.
We should measure if customers become better users.
We should measure if customers can achieve more with the product.
If you can measure your customer support correctly, you can become a value center vs a cost center.
Next steps?
This is a great use case for AI. Imagine you have your scoring rubric—you might measure the quality of your case resolution, the quality of preventing the next case, the customer's effort score, or all of it.
With AI, you can measure 100% of your cases without bias and relying on the CSAT score. Now, that is a great use case for CX leaders.
Real-time sales coaching addresses the limitations of traditional, post-call feedback by providing actionable, in-the-moment guidance to sales representatives while they are actively engaged with customers. By utilizing a framework that identifies successful patterns across all calls, delivers live prompts to reps during conversations, and scales those winning tactics across the entire organization, this approach ensures that coaching is proactive rather than reactive. This shift from post-call reviews to live intervention allows teams to correct mistakes immediately, improve close rates by 15–31%, accelerate onboarding for new hires, and foster consistent performance by surfacing top-tier behaviors for every member of the revenue team.
Eylul Genc
25 Jun 2026
The Core Insight:
Your CSMs control 60% of your ARR but get fraction of the coaching investment your sales team receives. While sales teams get real-time guidance during calls, CS teams are operating blind at the exact moments when coaching matters most—resulting in preventable churn and missed expansion.
The Problem:
What Real-Time CS Coaching Changes:
The Financial Impact:
For a 200-customer base at $100K ACV:
The Timing:
CS coaching is where sales was in 2017-2018—right before it became table-stakes. Teams that prioritize it first gain a compounding growth advantage.
Bottom Line: Real-time coaching transforms reactive retention management into proactive revenue growth.
Eylul Genc
17 Jun 2026
Just like football teams review matches, study patterns, and adjust their strategy in real time, revenue teams need to analyze their sales calls to understand buyer signals, objections, and turning points. This blog explores how post-match analysis, meeting intelligence, and AI sales coaching help sales teams improve performance and turn every conversation into a smarter next move.
Nisa Meray
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