Stop dealing with piles of tickets and cases – start utilizing the experience as a data model!
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Why Customer Service Isn’t Solved (Yet): The Hidden Gaps No One Talks About
For years, businesses have been sold the promise that new technologies—AI, automation, and machine learning—would revolutionize customer service. And yet, despite these investments, the challenges persist. Contact rates haven’t decreased. Customer satisfaction scores are stagnating.
Support teams are still overwhelmed.
So why hasn’t all this innovation solved the customer service problem? The truth is, we’ve been looking
in the wrong places.
The Illusion of Progress
On the surface, it might seem like customer service has made leaps and bounds. Companies have deployed chatbots, predictive analytics, and omnichannel systems to respond to customer needs faster. But speed alone doesn’t solve the underlying problem: why are customers reaching out in the
first place?
Take a closer look, and you’ll notice that many of these innovations are simply automating inefficiencies rather than eliminating them. For example, AI chatbots may resolve routine questions quickly, but they’re powerless when it comes to addressing complex or systemic issues. Instead of focusing on removing the root causes of contact demand, we’ve become better at reacting to them and customers are paying the price in frustration.
Dark Data: The Hidden Opportunity in Service
Here’s the untold story: companies are sitting on mountains of customer service data but failing to extract meaningful insights from it. Call logs, chat transcripts, email threads—this “dark data” often ends up siloed across systems, inaccessible and underutilized.
The real opportunity lies in connecting these disparate pieces of data to uncover patterns and trends that are otherwise invisible. For instance, why do customers keep calling about the same billing issue? Is it a problem with the billing system, or is the communication around billing simply unclear? Without a unified view of the customer journey, organizations are left guessing and customers bear the brunt of the disconnect.
The Metrics That Matter (And the Ones That Don’t)
Another common pitfall in modern service is an over-reliance on outdated metrics. Take First Contact Resolution (FCR), for example. On paper, it seems like a great way to measure efficiency: How many tickets were resolved in a single interaction? But in practice, it’s often meaningless. FCR doesn’t account for whether the customer’s issue was fully resolved or if they had to switch channels multiple times before even reaching a support agent.
Instead, forward-thinking organizations are starting to measure customer effort. Metrics like Average Minutes Per Resolution (AMPR) and Contacts Per Resolution (CPR) provide a clearer picture of how much time and effort customers are spending to get their issues solved. These insights are invaluable for identifying pain points and addressing them at the source, but they remain tragically underutilized in most organizations.
The Root Cause: Service Is Still Reactive
At its core, the customer service crisis is a symptom of a broader organizational problem: companies are still operating reactively. Customer service teams are often left cleaning up the mess created by other parts of the business, from faulty product designs to confusing marketing communications.
What if we flipped the script? Instead of treating support as a cost center, what if we used it as a diagnostic tool for the entire organization? By analyzing the reasons behind every customer interaction, companies can identify patterns that point to systemic issues. Did a recent product update cause confusion? Are unclear shipping policies driving repeat calls? These are opportunities to fix problems upstream and reduce contact demand before it even reaches the support team.
The Path Forward: Transforming How We Think About Service
The future of customer service isn’t about faster responses or more advanced AI—it’s about eliminating the need for those responses altogether. To get there, we need to shift our mindset from managing interactions to solving experiences.
This means:
• Embracing experiential data that quantifies the customer journey holistically, not just transactional outcomes like ticket closures.
• Breaking down data silos and integrating insights across systems to create a unified view of
the customer.
• Measuring success through the lens of customer effort, not internal efficiency.
An Invitation to Rethink Service – Lets face it we still have thousands of contacts daily!
At the Customer Connect Expo, we’ll be diving deeper into these issues—unpacking the hidden inefficiencies, missed opportunities, and transformative trends shaping the future of experiential service analytics. If you’ve ever wondered why contact demand isn’t going down or why AI hasn’t delivered on its promises, join us. Let’s talk about what’s next in customer service not just what’s possible, but what’s necessary to truly solve the problem!