Industry Benchmark · June 2026 · Smart Wearables + Smart Home
AI Customer Service Benchmark: Smart Devices
Resolution rate (not deflection), CSAT, and cost per resolution across 10 connected-device deployments — graded on the metrics that actually map to outcomes and unit economics.
By Alex Gomez, Sr. Analyst · Published June 2026 · Sources re-checked 24 August 2026
84–82% resolution is the agentic ceiling today. The best disclosed connected-device deployments resolve more than four out of five contacts end-to-end. Disclosed leaders: WHOOP 84%, OPPO 83%, Coros 82% (chat + email). Deflection-first chatbots cluster far lower, at 25–55%.
AI-resolved CSAT matches or beats humans. Category CSAT runs ~4.4–4.8 / 5. When AI resolves rather than deflects, satisfaction holds up — Coros achieves 93% positive CSAT (on par with its human agents); CLEAR runs Sierra at 4.7/5.
Unit economics swing 4–12×. An AI resolution costs ~$0.50–$2.00 versus ~$6–$13 for a human-handled contact. Coros runs its support line with 50% fewer resources at human-equivalent satisfaction, and cut time-to-resolution 10×.
Disclosure is the real bottleneck. Only a handful of brands publish support outcomes (Coros, WHOOP, ADT, OPPO, Swytch). Most large incumbents treat AI as a product feature and keep support metrics private — so several rows in the table are clearly labeled category models, not company data.
Why resolution rate beats deflection rate
Deflection rate measures avoidance; resolution rate measures outcomes. A bot that pushes a customer into a dead-end FAQ and marks it "deflected" looks identical, on a deflection dashboard, to a bot that genuinely solved the problem — which is why a benchmark built on deflection flatters every vendor and tells a buyer almost nothing.
The share of contacts where the customer's issue was actually solved, autonomously and end-to-end, without a human stepping in. This is the number to contract on.
The share of contacts that never reached a human — including customers who abandoned, rage-quit, or never got an answer. High deflection can coexist with low satisfaction.
Two cautions before reading any figure below — including the disclosed ones. First, vendors define "resolved" differently: some count "no reply within X hours" as resolved; others require explicit confirmation. Second, measure CSAT 48 hours after close, not immediately, and watch reopen rates, to catch tickets that were contained but not resolved.
10 connected-device support deployments
Ordered by resolution rate among disclosed brands, then by provenance strength. Every figure carries a provenance tag. Where a brand has not disclosed a metric, the cell shows a modeled category range (Est.) derived from benchmark bands and that brand's known support model — directional, not factual.
| # | Brand · segment | AI stack | Resolution rate | CSAT (/5 or % pos.) | Cost / resolution | Data |
|---|---|---|---|---|---|---|
| 1 | WHOOPwearable (recovery) | Intercom Fin | 84% | ~4.6–4.8 est. | ~$0.99 (per-resolution) | Disclosed |
| 2 | OPPOwearables + electronics | Sobot | 83% | ~4.5–4.8 est. | ~$1–2 est. | Disclosed |
| 3 | Coroswearable (performance GPS) | Aissist.io AgentMesh | 82% (chat + email) | 93% pos. | $0.50 | Disclosed |
| 4 | Sonossmart home (audio) | Sierra | not published | not published | outcome-based (~$1–3 est.) | Reported |
| 5 | ADTsmart home (security) | Sierra | up to ~90% automation | ~4.5–4.7 est. | outcome-based est. | Disclosed |
| 6 | Swytchconnected hardware (e-bike) | Gorgias Automate | 26–56% (automate band) | 92% pos. | $0.90 (list) | Disclosed |
| 7 | Ourawearable (smart ring) | In-app AI agent | 70–80% est. | ~4.4–4.7 est. | ~$1–2 est. | Reported |
| 8 | Ring (Amazon)smart home (security) | Kustomer AI | 40–60% est. | ~4.2–4.5 est. | ~$0.60–2 est. | Reported |
| 9 | Garminwearable (sports watch) | KB assistant + human | 45–65% est. | ~4.3–4.6 est. | ~$2–5 est. (blended) | Est. |
| 10 | Fitbit (Google)wearable | Google CCAI + human | 45–65% est. | ~4.2–4.6 est. | ~$2–5 est. (blended) | Est. |
Read the bands, not the decimals. Garmin and Fitbit have not published AI support-resolution metrics; their figures are category models. Ring's stack (Kustomer AI) is confirmed but its resolution rate is undisclosed. ADT's ~90% figure is automation rate, not verified end-to-end resolution. Treat every Est. cell as directional.
Adjacent-vertical calibration (not wearables or smart home, but useful for sanity-checking the bands): Casper 74% resolution & +20% CSAT (Sierra); WeightWatchers ~70% containment & 4.6 CSAT (Sierra); CLEAR 4.7/5 CSAT (Sierra); Wilson 77% resolution (Sierra); Edel Optics 25%→79% resolution at 92% CSAT (My AskAI). These reinforce the 75–85% / 4.5–4.8 bands even though the products are not wearables or smart home.
What is a good AI resolution rate for connected devices?
A well-implemented agentic deployment resolves 75–85% of inbound contacts end-to-end; deflection-first chatbots sit at 25–55%. The disclosed leaders — WHOOP (84%), OPPO (83%), Coros (82% chat + email) — all solve technical, multi-step issues (firmware faults, device setup, Wi-Fi troubleshooting, data-sync anomalies), not just FAQ routing.
| Tier | Resolution band |
|---|---|
| Leading agentic AI (Coros, WHOOP, OPPO) | 75–85% |
| Blended human + knowledge base (large incumbents) | 45–65% |
| Deflection-first chatbots | 25–55% |
The hardware nuance: connected devices generate technical tickets, not just order-status lookups. Resolution rate depends on whether the AI can reach real systems — order management, warranty records, device diagnostics — and act, versus only reading help-center articles. Sonos's agent, for example, runs a level-2/level-3 troubleshooting process of elimination (Wi-Fi vs. configuration vs. hardware) and opens a Salesforce case on escalation. The 75–85% ceiling belongs to deployments that take actions, not ones that answer questions.
Can AI support hold CSAT for premium hardware brands?
Yes — category CSAT for AI-handled connected-device support runs ~4.4–4.8 / 5 (≈85–93% positive), and AI that resolves frequently matches or exceeds the human baseline. Customers prefer a complete answer in seconds over a queue.
| Reference point | CSAT |
|---|---|
| AI-resolved, best-in-class (outcome-measured) | 4.7–4.9 / 5 |
| Category typical (connected-device AI support) | 4.4–4.8 / 5 |
| Coros (Aissist.io) · WeightWatchers (Sierra) · CLEAR (Sierra) | 93% / 4.6 / 4.7 |
| E-commerce median (reference) | ~82% positive |
For a brand serving elite athletes or design-led buyers, CSAT is the constraint that makes automation safe. The premium-brand playbook isn't “automate everything” — it's automate the resolvable majority at human-equivalent CSAT, then hand off the complex remainder with full context so the human starts informed.
How much does an AI resolution cost versus a human contact?
An AI resolution typically costs ~$0.50–$2.00; a human-handled contact costs ~$6–$13 (and a live phone call $10–$20). That's a 4–12× swing per resolved contact, before counting 24/7 coverage, multilingual reach, and instant first response. At Coros, AI leads engagement with 50% fewer support resources at human-equivalent satisfaction.
| Model | Cost |
|---|---|
| AI resolution (per-ticket / outcome-based) | $0.10 (per-ticket) · $0.50–$0.99 · up to ~$2–3.50 |
| Human-handled ticket (loaded) | $6–$13 |
| Human live phone call | $10–$20 |
Watch the unit: some vendors price per resolution (you pay for outcomes — Fin $0.99, Sierra outcome-based); others per conversation or seat (you pay regardless). A low per-resolution price on a low-resolution-rate tool is a false economy. Model cost-per-resolved-contact, not cost-per-interaction.
Coros — 82% resolved (chat + email), 10× faster, 50% lighter
Coros builds performance GPS watches for professional athletes — a customer base with elite expectations and genuinely technical support: firmware faults, device setup, data-sync anomalies. In early 2025 it deployed Aissist.io's AgentMesh as the front line of customer engagement.
| Metric | Result |
|---|---|
| Issues resolved autonomously | 82% (chat + email) |
| Time to resolution | 10× faster |
| Support resources at equal CSAT | −50% |
| CSAT | 93% positive |
For the ~18% of complex queries that escalate, the system doesn't just hand off — it summarizes context, extracts the core issue, and suggests next steps, so the human agent starts informed. That's the pattern premium hardware brands need: high autonomous resolution on the resolvable majority, clean human handoff on the rest, CSAT held throughout.
“The AI agents handle complex customer inquiries with human-like understanding, dramatically improving our support quality and extending our reach to new channels.”
— Mike Box, AI Lead, Coros
Questions buyers ask about AI support for connected devices
Six direct answers on resolution rates, CSAT, cost benchmarks, and data provenance for the smart wearable and smart home sector.
What is a good AI resolution rate for wearable and smart home support?
Well-implemented agentic AI resolves roughly 75–85% of inbound contacts end-to-end. Disclosed examples: WHOOP 84% (Intercom Fin), OPPO 83% (Sobot), Coros 82% chat + email (Aissist.io). Sonos runs on Sierra but publishes no resolution figure. Deflection-first chatbots sit at 25–55%.
What is the difference between resolution rate and deflection rate?
Deflection rate counts any contact that didn't reach a human — including customers who abandoned. Resolution rate counts only contacts where the issue was actually solved end-to-end. Deflection can look high while real problems go unsolved; resolution rate is the outcome-true metric.
What CSAT can AI customer service achieve for connected devices?
Roughly 4.4–4.8 / 5 (≈85–93% positive). When AI resolves rather than deflects, measured satisfaction frequently matches or exceeds human agents — Coros achieves 93% positive CSAT with Aissist.io; CLEAR runs Sierra at 4.7/5.
How much does an AI resolution cost compared to a human?
About $0.50–$2.00 per AI resolution versus $6–$13 per human-handled ticket (and $10–$20 for a live call) — a 4–12× difference per resolved contact.
Which wearable and smart home brands publish AI support results?
Few do. Those with publicly reported figures include WHOOP (84%, Intercom Fin), OPPO (83%, Sobot), Coros (82% chat + email, 93% CSAT, via Aissist.io), ADT (up to ~90% automation, 2M inquiries/month, Sierra), and Swytch (92% CSAT, Gorgias).
Why are some numbers in the table marked as estimates?
Because company-disclosed support metrics are rare in this sector. Rather than print invented precision, undisclosed cells show a modeled category range (est.) derived from benchmark bands and the brand's known support model. They're directional, not factual — and labeled as such.
How this was compiled
- Compiled by
- Alex Gomez, Sr. Analyst. Figures read from their sources on 24 August 2026; source pages change, and several publish no date of their own.
- What “resolution rate” means here
- Full end-to-end AI resolution — the customer's issue finished without a human agent completing it. Deflection and containment are counted separately and are not folded into this figure.
- Measured vs. modelled
- Every brand figure is tagged by provenance: Disclosed (published by the brand or its AI vendor), Reported (deployment publicly confirmed but the metric not quantified), and Est. (a modelled category range). No point-figure is attached to a brand that has published nothing. Category bands are our synthesis of cross-vendor 2026 material, not a measurement: human tickets $6–$13 (live calls $10–$20), AI resolutions $0.10–$2.00, deflection-first automation ~26–56%, agentic resolution ~64–94% with most deployments 70–85%. A premium watch brand's technical ticket mix is harder than a subscription-billing mix — read the bands, not the decimals.
- Sourcing status
- 4 of 10 sources below are linked and dated. The rest are carried forward from earlier revisions without a recorded URL — they are marked as such rather than presented as verified, and linking them is outstanding work.
- Interest to declare
- Aissist.io publishes this and sells a product in the category it measures, so our own deployments appear among the figures and are labelled as ours. That is a reason to follow the links rather than take the summary on trust. See the cross-industry benchmark for how vendor-published claims compare with one another.
The agentic AI operational layer behind results like Coros — built to resolve, not deflect.
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