Overview Leaderboard Methodology
Industry Report

Smartwatches

Wearable wrist devices that combine timekeeping with health tracking, notifications, and app integrations from mainstream consumer tech brands.

Brands tracked: 30
Brands analyzed: 30
Last updated: 2026-04-23
Model: OpenAI GPT-5
Prompts: 245
Total responses: 1,225
Top Brand Overall?
Apple
96/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Apple
100

Highest score on the LBA metric in this industry.

Authority Leader?
Apple
100

Highest score on the Authority metric in this industry.

TOM Leader?
Apple
87

Highest score on the TOM metric in this industry.

Apple is the default answer in AI responses for Smartwatches

When users ask ChatGPT, Claude, or Gemini about Smartwatches, Apple is the brand that surfaces first - unprompted, consistently, and usually at the top of any list the model generates. Samsung is a close second, but the gap between them is meaningful. If you're competing in this space and you're not in the top handful, you're effectively invisible to AI-driven discovery.

Brand Leaderboard All 30 Smartwatches brands ranked

Ranked by overall AI Visibility Score (smoothed geometric mean of LBA, Authority, and TOM with an LBA-based floor on Authority and TOM, see methodology). Click any brand for the full report.

# Brand LBA Authority TOM Overall
1
Apple
apple.com
 100
100
87
96
2
Samsung
samsung.com
 93
68
83
80
3
Garmin
garmin.com
 80
66
80
75
4
Google
google.com
 84
52
81
71
5
Fitbit
fitbit.com
 84
31
61
55
6
Amazfit
amazfit.com
 70
28
32
40
7
OnePlus
oneplus.com
 73
7
23
25
8
Withings
withings.com
 77
8
12
21
9
Xiaomi
xiaomi.com
 72
7
14
21
10
TAG Heuer
tagheuer.com
 78
8
8
19
11
boAt
boat-lifestyle.com
 73
7
7
18
12
COROS
coros.com
 75
8
8
18
13
Huawei
huawei.com
 69
7
9
18
14
Montblanc
montblanc.com
 73
7
7
18
15
Polar
polar.com
 76
8
8
18
16
Suunto
suunto.com
 76
8
8
18
17
Casio
casio.com
 70
7
7
17
18
Michael Kors
michaelkors.com
 71
7
7
17
19
Noise
gonoise.com
 68
7
7
17
20
OPPO
oppo.com
 69
7
7
17
21
realme
realme.com
 68
7
7
17
22
Fossil
fossil.com
 64
6
6
16
23
T
TicWatch
ticwatch.com
 64
6
6
16
24
Citizen
citizenwatch.com
 62
6
6
15
25
Fire-Boltt
fireboltt.com
 60
6
6
15
26
HONOR
honor.com
 58
6
6
14
27
Skagen
skagen.com
 56
6
6
14
28
vivo
vivo.com
 52
5
5
13
29
Haylou
haylou.com
 46
5
5
12
30
Crossbeats
crossbeats.com
 44
4
4
11

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 245 shared Smartwatches prompts. The same prompts, same model, same iterations. So differences in scores reflect actual differences in AI visibility, not differences in measurement.

Overall AI Visibility Score
Smoothed geometric mean of LBA, Authority and TOM. Authority and TOM are floored at LBA × 0.1 before the geometric mean (the same floor used in the per-metric cards above, so brand cards and the composite tell the same story). Formula: composite = ((LBA + 5)(Authority + 5)(TOM + 5))^(1/3) - 5. The floor keeps brands the model clearly recognises but doesn't yet recommend from collapsing to zero, while a single genuinely weak metric still pulls the composite down. Full methodology.
Shared industry prompts
For Authority and TOM, all brands in the industry are scored against the same 245 category prompts (e.g. "best SEO tools for agencies"). This makes brand-to-brand comparisons valid - everyone faces identical inputs. LBA prompts are per-brand because they ask brand-specific questions.
Latent Brand Association (LBA)
5 brand probes + 1 control prompt, each run 5 times in recall mode (no web search). LBA = quality × meta × stability × share × recognition × 100. Read the full LBA methodology →
LLM Authority
50 organic category prompts (discovery, comparison, problem and transactional intents), each run once in recall mode and once in retrieval mode. Score = frequency × log-decayed prominence × intent weight, then 50/50 averaged across the two modes. Read the full Authority methodology →
Top of Mind (TOM)
15 high-volume discovery prompts (sourced from Keywords Everywhere search-volume data), each run 5 times in pure recall mode (no web). Score = frequency × (0.5 + 0.5 × log-prominence), volume-weighted. Read the full TOM methodology →