Overview Leaderboard Methodology
Industry Report

Mobile Analytics SDKs

SDK-based analytics services focused on in-app event tracking, crash reporting, and user engagement metrics for mobile applications.

Brands tracked: 36
Brands analyzed: 36
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 281
Total responses: 1,435
Top Brand Overall?
Firebase
96/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Firebase
89

Highest score on the LBA metric in this industry.

Authority Leader?
Firebase
100

Highest score on the Authority metric in this industry.

TOM Leader?
Firebase
100

Highest score on the TOM metric in this industry.

Firebase is the default answer in AI responses for Mobile Analytics SDKs

Firebase and Amplitude together dominate AI responses for Mobile Analytics SDKs. 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 36 Mobile Analytics SDKs 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
Firebase
firebase.google.com
 89
100
100
96
2
Amplitude
amplitude.com
 89
93
98
94
3
Mixpanel
mixpanel.com
 87
70
90
82
4
AppsFlyer
appsflyer.com
 87
31
74
59
5
Adjust
adjust.com
 88
20
72
51
6
PostHog
posthog.com
 77
32
43
48
7
Segment
segment.com
 84
11
55
39
8
Google
google.com
 89
9
30
31
9
Branch
branch.io
 77
8
22
25
10
Braze
braze.com
 87
9
14
24
11
Sentry
sentry.io
 84
8
16
24
12
Heap
heap.io
 69
7
15
21
13
Countly
countly.com
 65
6
15
20
14
CleverTap
clevertap.com
 78
8
8
19
15
Kochava
kochava.com
 71
7
11
19
16
Singular
singular.net
 79
8
8
19
17
Bugsnag
bugsnag.com
 72
7
7
18
18
FullStory
fullstory.com
 72
7
7
18
19
MoEngage
moengage.com
 73
7
7
18
20
mParticle
mparticle.com
 72
7
8
18
21
Pendo
pendo.io
 74
7
7
18
22
Flurry
flurry.com
 53
5
13
17
23
Instabug
instabug.com
 69
7
7
17
24
RudderStack
rudderstack.com
 63
6
9
17
25
Airship
airship.com
 66
7
7
16
26
AppMetrica
appmetrica.yandex.com
 61
6
6
15
27
Glassbox
glassbox.com
 59
6
6
15
28
Smartlook
smartlook.com
 63
6
6
15
29
T
Taplytics
taplytics.com
 59
6
6
15
30
UXCam
uxcam.com
 60
6
6
15
31
Embrace
embrace.io
 55
5
5
14
32
Leanplum
leanplum.com
 58
6
6
14
33
Quantum Metric
quantummetric.com
 46
5
5
12
34
Swrve
swrve.com
 41
4
4
11
35
TelemetryDeck
telemetrydeck.com
 41
4
4
11
36
Kumulos
kumulos.com
 32
3
3
9

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 281 shared Mobile Analytics SDKs 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 281 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 →