Overview Leaderboard Methodology
Industry Report

Enterprise Customer Data Platforms

Large-scale CDPs designed for multinational corporations that unify customer data across complex, heterogeneous systems and support extensive segmentation, governance, and identity resolution.

Brands tracked: 35
Brands analyzed: 35
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 275
Total responses: 1,400
Top Brand Overall?
Segment
87/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Salesforce
97

Highest score on the LBA metric.

Authority Leader?
Segment
86

Highest score on the Authority metric.

TOM Leader?
Salesforce
100

Highest score on the TOM metric.

Segment is the default answer in AI responses for Enterprise Customer Data Platforms

Segment and Salesforce together dominate AI responses for Enterprise Customer Data Platforms. 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 35 Enterprise Customer Data Platforms brands ranked

Ranked by overall AI Visibility Score (geometric mean of LBA, Authority, and TOM). Click any brand for the full report.

# Brand LBA Authority TOM Overall
1
Segment
segment.com
 83
86
93
87
2
Salesforce
salesforce.com
 97
58
100
83
3
Adobe
adobe.com
 84
63
100
81
4
Tealium
tealium.com
 75
48
81
66
5
Treasure Data
treasuredata.com
 78
36
79
61
6
mParticle
mparticle.com
 73
37
72
58
7
BlueConic
blueconic.com
 64
5
35
22
8
ActionIQ
actioniq.com
 67
5
31
21
9
Hightouch
hightouch.com
 78
13
4
16
10
RudderStack
rudderstack.com
 68
18
2
14
11
Amperity
amperity.com
 67
2
21
13
12
Oracle
oracle.com
 79
0
39
3
13
Bloomreach
bloomreach.com
 66
0
5
2
14
SAP
sap.com
 68
0
22
2
15
Simon Data
simondata.com
 58
0
6
2
16
Snowflake
snowflake.com
 87
5
0
2
17
Databricks
databricks.com
 87
4
0
1
18
L
Lytics
lytics.com
 61
0
5
1
19
Adelphic
adelphic.com
 20
0
0
0
20
Blueshift
blueshift.com
 67
0
0
0
21
Celebrus
celebrus.com
 46
0
0
0
22
Commanders Act
commandersact.com
 43
0
0
0
23
CrossEngage
crossengage.io
 50
0
0
0
24
dotdigital
dotdigital.com
 52
0
0
0
25
FirstHive
firsthive.com
 37
0
0
0
26
Lemnisk
lemnisk.co
 52
0
0
0
27
Mapp
mapp.com
 36
0
0
0
28
Meiro
meiro.io
 33
0
0
0
29
NGDATA
ngdata.com
 49
0
0
0
30
Ometria
ometria.com
 59
0
0
0
31
Optimove
optimove.com
 64
0
0
0
32
Redpoint
redpointglobal.com
 53
0
0
0
33
Relay42
relay42.com
 33
0
0
0
34
Splio
splio.com
 32
0
0
0
35
Zeotap
zeotap.com
 63
0
0
0

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 275 shared Enterprise Customer Data Platforms 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
Geometric mean of LBA, Authority and TOM: (LBA × Authority × TOM)^(1/3). Geometric mean is used so that any single weak metric pulls the overall score down, rather than being masked by strength elsewhere.
Shared industry prompts
For Authority and TOM, all brands in the industry are scored against the same 275 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 →