Figure: 10-year Trend in Customer Support Spend, as % of Revenue,
Sources: Bain, APQC, Quantanite Analysis

Over the last decade, spending on customer support has quietly declined. Across eight major sectors that make up the bulk of contact center activity, support costs as a percentage of revenue are down about 25%. Some of that makes sense—better technology and smarter operations have made support more efficient. Despite the high churn that’s always been part of this industry, management teams have become adept at running large, complex operations.

Yet there’s another side to this story. Procurement behavior hasn’t evolved as fast as technology or customer expectations. Many companies still treat customer support as a cost center—something to be optimized down rather than invested in. It’s an odd contradiction: customer experience is sacred, yet the function that protects it is squeezed year after year.

Part of the problem is that the sector still measures the wrong things. Average Handle Time (AHT), for instance, tells you how long something took—but not whether it was done well. Chasing shorter handle times or other post-interaction productivity metrics can mean rushing through conversations that might reveal why customers are frustrated—insights that could help fix the root cause.

Companies love tracking NPS or CSAT but rarely go deep enough to understand why customers are unhappy. Often, the real problem lies elsewhere—a wrong shipment, a renewal pushed through despite low satisfaction, or a confusing returns policy. These moments are feedback goldmines, but most organizations aren’t structured to learn from them. Few work closely enough with partners to uncover the underlying causes of dissatisfaction.

To be fair, change isn’t simple. (At Quantanite, we’re going through our own IT and systems transformation and know how hard this is.) Many enterprises are weighed down by legacy platforms never designed for modern customer journeys. One of our clients implemented a new CRM and saw efficiency drop by over 75% in the first month—not because the technology was poor, but because it wasn’t built for the job. Add fragmented data and weak vendor integration, and even the best support teams struggle to perform consistently.

The current approach isn’t sustainable—but it’s also a clear opportunity. First, recognize that customers don’t always want to talk to someone; sometimes they just want a fast, accurate answer. The right model blends technology and human expertise—sometimes AI-only, sometimes human-only, and often both.

Second, rethink what efficiency means. Ask how your partners can prevent contacts by anticipating customer needs instead of just handling them faster. And third, reclassify customer support from a cost center to a strategic investment—one that protects revenue, builds loyalty, and drives competitiveness.That’s the mindset shift we’re championing at @Quantanite.

We’re architecting customer and data support operations that learn, predict, and adapt—where AI does the heavy lifting, our people bring judgment and empathy, and customers experience the best of both.