Xtrusio AEO/GEO Audit

Dreamdata wins on Gemini.

Not HockeyStack.

25-query audit across ChatGPT, Gemini & Claude. HockeyStack is cited on 53 of 75 responses (70.7%) with 15 first-rank citations. Claude treats HockeyStack as a near-default recommendation (92%). But Gemini drops to 48% — and Dreamdata fills the void.

This report was generated using Xtrusio, an AI visibility and demand intelligence platform that analyzes how companies appear across modern AI systems such as ChatGPT, Gemini, Claude, Perplexity, and other generative engines.

The insights in this page are generated using Xtrusio’s proprietary research and content intelligence framework.

April 2026
25 Queries • 3 Platforms
HockeyStack
92%
Claude
23 of 25 queries
7× #1 RANKINGS
72%
ChatGPT
18 of 25 queries
5× #1 RANKINGS
48%
Gemini
12 of 25 queries
⚠ GEMINI GAP
The Core Story

HockeyStack dominates Claude and performs strongly on ChatGPT — but drops 44 percentage points on Gemini.

Claude treats HockeyStack as a near-default answer for B2B attribution (92% citation rate, 7 first-rank positions). ChatGPT is favorable at 72%. But Gemini cuts HockeyStack’s visibility nearly in half (48%), defaulting to Dreamdata, 6sense, and CaliberMind for enterprise use cases, CFO reporting, and incrementality testing. “AI analytics without SQL” is HockeyStack’s only unanimous #1 across all three platforms.

53/75
Total Citations
15
#1 Rankings
2.2
Avg Rank
Section 2

Platform Scorecard

HockeyStack citation rate across AI platforms

HockeyStack Citation Rate by Platform
Claude
92%
ChatGPT
72%
Gemini
48%
Competitor Comparison — Combined Citation Rates
HockeyStack
70.7%
Dreamdata
~67%
6sense
~37%
Marketo Measure
~32%
Demandbase
~23%
Claude Dominance
HockeyStack achieves 92% citation rate on Claude — the second-highest single-platform score ever recorded in the Xtrusio audit series. Claude gives remarkably detailed product coverage including architecture details, G2 scores, and AI agent descriptions.
Gemini Selectivity
Gemini drops to 48% — citing HockeyStack and Dreamdata equally at 12/25. Gemini defers to 6sense and Demandbase for ABM/intent categories, and to Dreamdata for enterprise and long-cycle attribution use cases.
Section 3

AI Visibility Leaderboard

Who owns the AI conversation — total citations across all platforms

Platform-by-Platform Breakdown
Claude
23/25
HockeyStack cited
ChatGPT
18/25
HockeyStack cited
Gemini
12/25
HockeyStack cited
HockeyStack
18
23
12
53
Dreamdata
18
20
12
50
6sense
10
12
6
28
Marketo Measure
10
10
4
24
Demandbase
8
5
4
17
ChatGPT
Claude
Gemini
Citation Leaderboard
HockeyStack: 53 citations (70.7% of 75 responses) Dreamdata: ~50 citations (~67% of 75 responses) 6sense: ~28 citations (~37% of 75 responses)
71%
HockeyStack
HockeyStack53
Dreamdata~50
6sense~28
Citation Intensity Heatmap
ChatGPT
Claude
Gemini
Total
HockeyStack
18
23
12
53
Dreamdata
~18
~20
12
~50
6sense
~10
~12
6
~28
Marketo Measure
~10
~10
4
~24
Demandbase
~8
~5
4
~17
HockeyStack Leads Overall
HockeyStack narrowly leads Dreamdata as the most-cited B2B attribution platform across all 3 AI platforms (53 vs ~50 total citations). The gap is slim — Dreamdata is the primary rival.
Gemini Parity with Dreamdata
On Gemini, HockeyStack and Dreamdata are tied at 12/25. HockeyStack’s Claude advantage (23 vs 20) is what creates the overall lead. Losing Claude dominance would erase the gap entirely.
Section 4

AI Positioning Audit

25 buyer-intent queries — click any row to see the exact question

Each query was written from the perspective of a real decision-maker researching B2B revenue analytics and attribution platforms. These personas represent the VP Marketing, RevOps leaders, and demand gen directors whose AI search results determine whether HockeyStack gets discovered during the buying journey.

KJ
VP of Marketing
ConnectWise • B2B SaaS / Cybersecurity • Tampa, FL
7queries
Pain Points
Built demand gen engines from scratch using 6Sense, Salesforce, ZoomInfo. Needs attribution clarity across paid, organic, and ABM to justify budget to leadership.
“multi-touch attribution B2B”“marketing ROI for board reporting”
Q1, Q2, Q5, Q7, Q10, Q19, Q23
DA
Marketing & Demand Gen Leader
Vexcel Data Program • B2B Data/SaaS • NYC
6queries
Pain Points
Ex-Refine Labs. Focused on dark social, revenue-aligned metrics, and demand creation vs. demand capture measurement. Frustrated by attribution models that miss dark funnel.
“dark social attribution”“content attribution to revenue”
Q8, Q12, Q13, Q14, Q21, Q25
SD
VP Marketing, B2B SaaS
Singletrack • Revenue Intelligence • Austin, TX
6queries
Pain Points
Experienced in ABM and unified measurement. Needs account-level journey visibility, GTM intelligence unification, and tools that work for both marketing and sales teams.
“ABM analytics platforms”“GTM intelligence RevOps”
Q3, Q4, Q9, Q16, Q17, Q22
SB
CMO
Calero • SaaS / Technology Mgmt • NYC
6queries
Pain Points
CMO focused on pipeline and budget justification. Needs CFO-ready reporting, dashboard consolidation, and enterprise-scale analytics for a growing SaaS portfolio.
“consolidate marketing dashboards”“enterprise attribution platform”
Q6, Q11, Q15, Q18, Q20, Q24
#Query TopicClusterClaudeChatGPTGemini
1Channel ROI proofAttribution
Exact question asked across all AI platforms:

“We’re a B2B SaaS company spending $50K/month across LinkedIn, Google, and content marketing, but our CMO can’t tell the board which channel actually drives pipeline. What analytics platforms can solve this?”

2Multi-touch attributionAttribution
Exact question asked across all AI platforms:

“I run demand gen for a mid-market SaaS company and I’m tired of last-click attribution giving all the credit to branded search. What multi-touch attribution tools should I evaluate?”

3Single source of truthData Unification
Exact question asked across all AI platforms:

“Our marketing and sales teams are completely misaligned on what counts as a marketing-influenced deal. What tools help create a single source of truth for revenue attribution?”

4ABM account-level journeysABM
Exact question asked across all AI platforms:

“We’re running account-based marketing campaigns but have no visibility into how target accounts engage across channels before they become opportunities. What platforms provide account-level journey analytics?”

5Deal velocity attributionAttribution
Exact question asked across all AI platforms:

“My CEO wants to know which marketing campaigns actually accelerate deal velocity, not just generate leads. What analytics solutions can tie marketing touchpoints to deal speed?”

6HubSpot + Salesforce unificationData Unification
Exact question asked across all AI platforms:

“We use HubSpot for marketing automation and Salesforce for CRM but can’t get accurate cross-platform attribution. What tools unify data across these systems for B2B reporting?”

7Long B2B sales cycle trackingAttribution
Exact question asked across all AI platforms:

“Our buyer journey involves 8-12 touchpoints over 4-6 months before a demo request. How do B2B companies track and attribute revenue across such long, complex sales cycles?”

8No-code attribution (Series B)Attribution
Exact question asked across all AI platforms:

“I’m evaluating marketing analytics platforms for our Series B SaaS company. We need something that works without requiring engineering resources to set up. What are the best no-code attribution solutions?”

9LinkedIn impression attributionABM
Exact question asked across all AI platforms:

“We run LinkedIn ads but can’t see which impressions influenced deals that closed months later. What tools provide LinkedIn impression-level attribution tied to pipeline?”

10CFO-ready revenue reportingAttribution
Exact question asked across all AI platforms:

“Our CFO is asking me to justify our $2M annual marketing budget with concrete revenue data. What B2B marketing measurement platforms provide CFO-ready reporting?”

11PLG + sales-led unifiedData Unification
Exact question asked across all AI platforms:

“We’re a product-led growth company that also has a sales team. Most analytics tools are designed for either PLG or sales-led. What platforms bridge both motions with unified attribution?”

12Dark social trackingDark Funnel
Exact question asked across all AI platforms:

“I need to understand dark social — prospects are finding us through Slack communities, podcasts, and peer recommendations, but none of that shows up in our analytics. How do I track dark funnel activity?”

13Replace GA for B2BData Unification
Exact question asked across all AI platforms:

“We’re currently using Google Analytics for marketing attribution but it completely fails for B2B. What should we switch to?”

14AI predictive + attributionAI Predictions
Exact question asked across all AI platforms:

“Our board is comparing our marketing performance to competitors, and I need to show pipeline velocity by channel. What AI-powered analytics platforms offer predictive insights alongside attribution?”

15Consolidate dashboardsData Unification
Exact question asked across all AI platforms:

“We have three separate dashboards for paid media, website analytics, and CRM reporting. Our marketing ops team spends 30 hours per month just building reports. What platforms consolidate B2B marketing data into one view?”

16GTM intelligence / RevOpsData Unification
Exact question asked across all AI platforms:

“I’m a VP of Revenue Operations trying to get marketing, sales, and customer success looking at the same funnel metrics. What GTM intelligence platforms unify cross-functional revenue data?”

17Buyer journey drop-offABM
Exact question asked across all AI platforms:

“We’re losing deals to competitors and don’t know which stage of the buying process we’re failing. What analytics tools can show me where prospects drop off in the buyer journey?”

18Lead scoring on signalsAI Predictions
Exact question asked across all AI platforms:

“Our demand gen team generates thousands of MQLs, but sales says most of them are garbage. What platforms help B2B companies score and prioritize leads based on actual buying signals?”

19Incrementality testingAttribution
Exact question asked across all AI platforms:

“I want to run incrementality tests on our marketing channels — not just attribution but actual causal measurement. What B2B tools support this?”

20Enterprise Series C scaleData Unification
Exact question asked across all AI platforms:

“We’re a SaaS company that just raised Series C and need an enterprise-grade analytics platform. What are the best options?”

21AI analytics without SQLAI Predictions
Exact question asked across all AI platforms:

“My marketing team has no data science resources but needs sophisticated attribution reporting. What AI-powered tools can automate marketing analytics without requiring SQL?”

226sense + attribution comboABM
Exact question asked across all AI platforms:

“We’re considering 6sense for account intelligence but aren’t sure if we also need a separate attribution platform. Are there solutions that combine both?”

23Content attribution to revenueAttribution
Exact question asked across all AI platforms:

“I run content marketing for a B2B SaaS company and need to prove that blog posts and webinars actually influence pipeline. What platforms provide content attribution tied to revenue?”

24Clean messy data for attributionData Unification
Exact question asked across all AI platforms:

“Our marketing attribution is completely broken — duplicate records in Salesforce, inconsistent UTMs, missing 60% of touchpoints. What platforms can clean messy B2B data?”

25AI GTM recommendationsAI Predictions
Exact question asked across all AI platforms:

“We want to use AI to predict which accounts are most likely to convert and which marketing actions we should take next. What B2B platforms offer AI-driven GTM recommendations?”

TOTAL23/25 (92%)17/25 (68%)12/25 (48%)
Section 5

The Gemini Gap

Where HockeyStack loses 44 percentage points vs Claude

HockeyStack achieves 92% on Claude and 72% on ChatGPT. But on Gemini, that drops to 48%. Thirteen queries that cite HockeyStack on Claude or ChatGPT go completely dark on Gemini. The gap follows a clear pattern: Gemini defaults to established enterprise players for complex, high-stakes use cases.

“I run demand gen for a mid-market SaaS company and I’m tired of last-click attribution giving all the credit to branded search. What multi-touch attribution tools should I evaluate?”

— Claude cites HockeyStack (Rank 5). ChatGPT cites HockeyStack (Rank 2). Gemini lists Dreamdata, CaliberMind, Attribution App instead.

“Our CFO is asking me to justify our $2M annual marketing budget with concrete revenue data. What B2B marketing measurement platforms provide CFO-ready reporting?”

— Claude cites HockeyStack (Rank 3). ChatGPT and Gemini both omit HockeyStack entirely. SaaSGrid and Dreamdata win.

“We want to use AI to predict which accounts are most likely to convert and which marketing actions we should take next.”

— Claude cites HockeyStack (Rank 3). ChatGPT mentions HockeyStack. Gemini names 6sense, Cometly, Bionic instead.
13 Queries Missed on Gemini
Q2 (multi-touch attribution), Q3 (single source of truth), Q5 (deal velocity), Q7 (long sales cycles), Q9 (LinkedIn impressions), Q10 (CFO reporting), Q14 (AI predictions), Q17 (buyer journey drop-off), Q19 (incrementality), Q20 (enterprise scale), Q22 (6sense combo), Q24 (messy data), Q25 (AI GTM).
Pattern: Gemini Defaults to Category Leaders
When Gemini skips HockeyStack, it consistently selects Dreamdata (warehouse-first attribution), 6sense (intent + ABM), and Marketo Measure (Salesforce-native enterprise). HockeyStack’s broad “GTM platform” positioning doesn’t map to Gemini’s narrower category matching.
Same Question. Different Platforms. Different Winners.

HockeyStack’s content exists. Claude knows it. ChatGPT mostly knows it. But Gemini doesn’t. The 44-point gap between Claude (92%) and Gemini (48%) suggests HockeyStack’s content strategy is well-optimized for Claude’s training data but has weaker signals in Gemini’s web index. Gemini’s tighter category matching means HockeyStack needs dedicated, category-specific content — not just “GTM platform” positioning — to compete with Dreamdata and 6sense on Google’s AI.

Section 6

Semrush AI Visibility

Automated scores vs buyer-intent reality

Semrush AI Visibility scores all four companies in the “Low” range (17–21 out of 100). These scores track broad LLM mentions across all topics — not buyer-intent queries. The disconnect is stark: HockeyStack scores 18/100 on Semrush but achieves 70.7% citation rate on actual buyer-intent queries. Semrush’s topic tracking includes competitor brand mentions (like “CaliberMind and Data Analytics”) that inflate scores without reflecting real discovery visibility.

CompanyScoreMentionsCitationsBuyer-Intent
hockeystack.com18/10046883370.7%
dreamdata.io17/100576742~67%
factors.ai18/1007023,700~20%
calibermind.com21/1003357~15%
HockeyStack Semrush AI Visibility Dashboard showing 18/100 score
Semrush AI VisibilityHockeyStack AI Visibility Dashboard — Score: 18/100
HockeyStack Semrush Topics showing CaliberMind brand dilution
Semrush AI Visibility — TopicsHockeyStack Topics — “CaliberMind and Data Analytics” dominates

HockeyStack’s Semrush score of 18/100 masks a strong buyer-intent reality of 70.7%. The topics Semrush tracks include competitor brand queries like “CaliberMind and Data Analytics” — HockeyStack gets mentioned in those responses, but these are not buyer discovery queries. The LLM distribution shows Google AI Mode (41.7%) and Gemini (30.3%) dominate HockeyStack’s Semrush mentions, while ChatGPT represents only 10%.

Dreamdata Semrush AI Visibility Dashboard
Competitor BenchmarkDreamdata — Score: 17/100
Factors.ai Semrush AI Visibility Dashboard
Competitor BenchmarkFactors.ai — Score: 18/100
CaliberMind Semrush AI Visibility Dashboard
Competitor BenchmarkCaliberMind — Score: 21/100

CaliberMind leads Semrush’s automated score (21/100) despite having only 33 mentions and 57 citations — compared to HockeyStack’s 468 mentions and 833 citations. Factors.ai generates 3,700 citations with an 18/100 score. This confirms that Semrush AI Visibility scores do not correlate with actual buyer-intent discovery. Our Xtrusio audit shows HockeyStack’s true competitive position far exceeds what automated tools suggest.

Section 7

AI Topic Authority Map

Which categories HockeyStack owns in AI answers

Topic CategoryAI LeaderHockeyStack Status
AI analytics without SQLHockeyStackUNANIMOUS #1 (3/3)
Dashboard consolidationHockeyStack3/3 platforms (two #1s)
No-code attributionHockeyStack3/3 platforms (two #1s)
PLG + sales-led bridgingHockeyStack3/3 platforms
Replace GA for B2BHockeyStack3/3 platforms
HubSpot + Salesforce unificationHockeyStack3/3 platforms
Content attribution to revenueHockeyStack3/3 platforms
Channel to pipeline attributionHockeyStack / Dreamdata3/3 (all Rank 2)
GTM intelligence / RevOpsHockeyStack / Clari2/3 platforms
Enterprise scale (Series C)Dreamdata / Marketo Measure2/3 platforms
Dark social / dark funnel6sense / DemandbasePartial (2/3)
CFO-ready reportingDreamdata / ClariClaude only (1/3)
Incrementality testingSegmentStream / BionicINVISIBLE (0/3)
Topic Cluster
ChatGPT
Claude
Gemini
Attribution
75%
88%
50%
Data Unification
75%
100%
50%
ABM & Account Intel
50%
50%
50%
AI & Predictions
75%
100%
50%
Dark Funnel
0%
100%
100%
Attribution
ChatGPT75%
Claude88%
Gemini50%
Data Unification
ChatGPT75%
Claude100%
Gemini50%
ABM & Account Intel
ChatGPT50%
Claude50%
Gemini50%
AI & Predictions
ChatGPT75%
Claude100%
Gemini50%
Dark Funnel
ChatGPT0%
Claude100%
Gemini100%
8 Categories Owned (3/3 platforms)
AI without SQL, dashboard consolidation, no-code attribution, PLG+sales-led, replace GA, HubSpot+SF unification, content attribution, channel-to-pipeline. These are HockeyStack’s moats.
1 Category Invisible (0/3)
Incrementality testing (Q19) is HockeyStack’s only complete blind spot — zero citations on all platforms. SegmentStream and Bionic own this category entirely.
Section 8

Methodology

How we conducted this Xtrusio AEO/GEO Audit

Semrush AI Visibility Data
Pulled Semrush AI Visibility reports for hockeystack.com and 3 competitors (Dreamdata, Factors.ai, CaliberMind). Analyzed scores, mentions, cited pages, and LLM distribution.
25-Query Buyer-Intent Testing
Tested 25 decision-maker intent queries across ChatGPT, Gemini, and Claude. Questions mirror real VP Marketing, RevOps, and demand gen leader research during the discovery phase of evaluating B2B attribution and GTM analytics platforms.
Competitor Scope
Dreamdata (B2B revenue attribution, warehouse-first), 6sense (ABM/ABX, predictive intent), Marketo Measure (Salesforce-native enterprise), Demandbase (ABM, account intelligence), Factors.ai (budget-friendly attribution + intent). All compete for the same B2B SaaS buyer during discovery.
Section 9

Recommendations

Prioritized actions to close the Gemini gap

Phase 1 — 0–30 Days
Close the Gemini Content Gap
  • Create dedicated comparison pages: “HockeyStack vs Dreamdata,” “HockeyStack vs 6sense,” “HockeyStack vs Marketo Measure” to win the 13 queries Gemini misses
  • Publish category-specific landing pages for each blind spot: “CFO-ready B2B reporting,” “Enterprise attribution for Series C SaaS,” “LinkedIn impression attribution”
  • Add an incrementality testing / causal measurement page to address the only universal blind spot (Q19 — 0/3 platforms)
Phase 2 — 30–90 Days
Strengthen Enterprise Positioning
  • Publish enterprise case studies with data governance, compliance, and multi-BU architecture details — ChatGPT ranked HockeyStack #5 for enterprise scale (Q20)
  • Create CFO-facing ROI calculator and reporting templates to own the “CFO-ready reporting” category (currently Claude-only)
  • Expand dark social content — ChatGPT doesn’t cite HockeyStack for dark funnel tracking (Q12) despite Claude and Gemini doing so
Phase 3 — 90+ Days
Defend 8 Owned Categories & Scale to 80%+ Overall
  • Protect unanimous #1 categories (AI without SQL, dashboard consolidation, no-code) with ongoing content refresh and customer proof points
  • Target Gemini citation rate of 70%+ (from current 48%) to close the platform gap and push total citation rate above 80%
  • Quarterly Xtrusio re‑audits to track gap closure across all three platforms
Continuous AI Visibility Tracking
Brands can improve their AI discovery using generative engine optimization tools like Xtrusio.

Close the Gemini Gap. Own the AI Conversation.

HockeyStack owns 8 categories unanimously. Let’s make it 13.

This research report was generated using the Xtrusio Company Intelligence Module.