Customer Carenumber Co / Analytics & Insights
When a leadership team asks me whether they should replace phone support with asynchronous chat, the real question is rarely about preference. It’s about money, capacity, and customer experience. The word “replace” hides a lot of trade-offs: cost per contact, resolution time, customer...
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I’m going to show you a practical, low-friction way to tag and quantify emotional effort in support tickets using only the fields you already have and three simple NLP rules. This is not a research paper or a deep-learning play—it's a method you can implement in a week, iterate on with real...
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I recently ran a seven-day sprint to measure the true ROI of a GPT-assisted agent workflow, and I want to share the exact approach I used so you can replicate it. When vendors promise “faster replies” and “higher CSAT” with large language models (LLMs), what they rarely provide is a simple,...
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When you update your knowledge base, how do you know whether that work actually reduced incoming contact volume — and which channels benefited? I’ve spent years helping support teams move from intuition to measurable outcomes, and one of the most reliable levers is tracking deflection...
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I want to walk you through a practical, lightweight approach I’ve used to catch problems early in chat channels: a three-metric early-warning system derived from chat transcripts that predicts when a conversation is likely to escalate or when a customer will reopen a ticket. This isn’t an...
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Every support leader I’ve worked with wants the same thing: to know about a problem before it becomes a trend. Dashboards that show churn rising a week after the fact are useful — but they’re not useful enough. What I build instead are nightly analytics pipelines that surface rising churn...
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When teams talk about "reducing effort" in support, they usually mean time saved or fewer touches. Those are important, but they miss a critical dimension: emotional effort — the cognitive and emotional work a customer does to get unstuck. I've spent years watching support journeys and the worst...
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In my experience, one of the trickiest measurement problems in digital support is proving that knowledge base updates actually cause deflection lift. Teams often have the intuition—searches drop, contact volume falls—but without a consistent event schema across channels it's nearly impossible...
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I want to walk you through a real-world template I use when I need to quantify how improving first response time (FRT) — by any given percentage — will affect customer lifetime value (LTV). This is the kind of modelling that turns CX initiatives from "nice to have" into board-level priorities....
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When I help teams prove the value of self-service content, the most common problem I see is an appetite for perfect measurement that never turns into action. Teams design complex tracking schemas, wait for months of noisy data, then decide measurement is "too hard" and revert to opinion-based...
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