Overview Leaderboard Methodology
Industry Report

Mobile Game Stores

App marketplaces and storefronts for smartphones and tablets that distribute mobile games, in-app purchases, and developer tools for monetization.

Brands tracked: 34
Brands analyzed: 34
Last updated: 2026-04-23
Model: OpenAI GPT-5
Prompts: 269
Total responses: 1,365
Top Brand Overall?
Apple
99/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Apple
100

Highest score on the LBA metric in this industry.

Authority Leader?
Apple
97

Highest score on the Authority metric in this industry.

TOM Leader?
Apple
100

Highest score on the TOM metric in this industry.

Apple is the default answer in AI responses for Mobile Game Stores

Apple and Google together dominate AI responses for Mobile Game Stores. Both brands consistently surface unprompted, with the model treating them as the default answers for most category queries. Brands outside the top two face a structural disadvantage: users are usually given these two before the model even considers alternatives.

Brand Leaderboard All 34 Mobile Game Stores 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
97
100
99
2
Google
google.com
 99
92
100
97
3
Samsung
samsung.com
 80
39
80
63
4
Amazon
amazon.com
 81
29
77
57
5
Epic Games Store
epicgames.com
 49
19
36
33
6
TapTap
taptap.io
 58
10
21
24
7
itch.io
itch.io
 79
8
13
22
8
Huawei
huawei.com
 69
7
14
21
9
Netflix
netflix.com
 77
8
12
21
10
F-Droid
f-droid.org
 73
7
7
18
11
Redmi
mi.com
 73
7
7
18
12
QooApp
qoo-app.com
 60
6
6
15
13
vivo
vivo.com
 62
6
6
15
14
APKPure
apkpure.com
 57
6
6
14
15
Aptoide
aptoide.com
 57
6
6
14
16
OPPO
oppo.com
 57
6
6
14
17
Uptodown
uptodown.com
 55
6
6
14
18
Cafe Bazaar
cafebazaar.ir
 52
5
5
13
19
QQ Music
qq.com
 53
5
5
13
20
9
9Apps
9apps.com
 46
5
5
12
21
GetJar
getjar.com
 45
5
5
12
22
RuStore
rustore.ru
 46
5
5
12
23
M
Mobogenie
mobogenie.com
 41
4
4
11
24
ONE store
onestore.co.kr
 45
4
4
11
25
W
Wandoujia
wandoujia.com
 44
4
4
11
26
Baidu Mobile Assistant
baidu.com
 37
4
4
10
27
Myket
myket.ir
 36
4
4
10
28
A
APKMonk
apkmonk.com
 34
3
3
9
29
Indus Appstore
indusappstore.com
 34
3
3
9
30
SlideME
slideme.org
 36
4
4
9
31
M
MoboMarket
mobomarket.net
 31
3
3
8
32
AppChina
appchina.com
 24
2
2
7
33
APKCombo
apkcombo.com
 22
2
2
6
34
Aurora Store
auroraoss.com
 0
0
0
0

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 269 shared Mobile Game Stores 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 269 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 →