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.
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.
Platform Scorecard
HockeyStack citation rate across AI platforms
AI Visibility Leaderboard
Who owns the AI conversation — total citations across all platforms
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.
| # | Query Topic | Cluster | Claude | ChatGPT | Gemini |
|---|---|---|---|---|---|
| 1 | Channel ROI proof | Attribution | ✓ | ✓ | ✓ |
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?” | |||||
| 2 | Multi-touch attribution | Attribution | ✓ | ✓ | ✗ |
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?” | |||||
| 3 | Single source of truth | Data 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?” | |||||
| 4 | ABM account-level journeys | ABM | ✗ | ✓ | ✓ |
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?” | |||||
| 5 | Deal velocity attribution | Attribution | ✓ | ✗ | ✗ |
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?” | |||||
| 6 | HubSpot + Salesforce unification | Data 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?” | |||||
| 7 | Long B2B sales cycle tracking | Attribution | ✓ | ✓ | ✗ |
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?” | |||||
| 8 | No-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?” | |||||
| 9 | LinkedIn impression attribution | ABM | ✓ | ✗ | ✗ |
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?” | |||||
| 10 | CFO-ready revenue reporting | Attribution | ✓ | ✗ | ✗ |
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?” | |||||
| 11 | PLG + sales-led unified | Data 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?” | |||||
| 12 | Dark social tracking | Dark 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?” | |||||
| 13 | Replace GA for B2B | Data 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?” | |||||
| 14 | AI predictive + attribution | AI 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?” | |||||
| 15 | Consolidate dashboards | Data 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?” | |||||
| 16 | GTM intelligence / RevOps | Data 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?” | |||||
| 17 | Buyer journey drop-off | ABM | ✓ | ✓ | ✗ |
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?” | |||||
| 18 | Lead scoring on signals | AI 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?” | |||||
| 19 | Incrementality testing | Attribution | ✗ | ✗ | ✗ |
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?” | |||||
| 20 | Enterprise Series C scale | Data 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?” | |||||
| 21 | AI analytics without SQL | AI 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?” | |||||
| 22 | 6sense + attribution combo | ABM | ✓ | ✓ | ✗ |
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?” | |||||
| 23 | Content attribution to revenue | Attribution | ✓ | ✓ | ✓ |
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?” | |||||
| 24 | Clean messy data for attribution | Data 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?” | |||||
| 25 | AI GTM recommendations | AI 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?” | |||||
| TOTAL | 23/25 (92%) | 17/25 (68%) | 12/25 (48%) | ||
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?”
“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?”
“We want to use AI to predict which accounts are most likely to convert and which marketing actions we should take next.”
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.
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.
| Company | Score | Mentions | Citations | Buyer-Intent |
|---|---|---|---|---|
| hockeystack.com | 18/100 | 468 | 833 | 70.7% |
| dreamdata.io | 17/100 | 576 | 742 | ~67% |
| factors.ai | 18/100 | 702 | 3,700 | ~20% |
| calibermind.com | 21/100 | 33 | 57 | ~15% |


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%.



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.
AI Topic Authority Map
Which categories HockeyStack owns in AI answers
| Topic Category | AI Leader | HockeyStack Status |
|---|---|---|
| AI analytics without SQL | HockeyStack | UNANIMOUS #1 (3/3) |
| Dashboard consolidation | HockeyStack | 3/3 platforms (two #1s) |
| No-code attribution | HockeyStack | 3/3 platforms (two #1s) |
| PLG + sales-led bridging | HockeyStack | 3/3 platforms |
| Replace GA for B2B | HockeyStack | 3/3 platforms |
| HubSpot + Salesforce unification | HockeyStack | 3/3 platforms |
| Content attribution to revenue | HockeyStack | 3/3 platforms |
| Channel to pipeline attribution | HockeyStack / Dreamdata | 3/3 (all Rank 2) |
| GTM intelligence / RevOps | HockeyStack / Clari | 2/3 platforms |
| Enterprise scale (Series C) | Dreamdata / Marketo Measure | 2/3 platforms |
| Dark social / dark funnel | 6sense / Demandbase | Partial (2/3) |
| CFO-ready reporting | Dreamdata / Clari | Claude only (1/3) |
| Incrementality testing | SegmentStream / Bionic | INVISIBLE (0/3) |
Methodology
How we conducted this Xtrusio AEO/GEO Audit
This research is based on Xtrusio’s proprietary AI visibility analysis framework.
Recommendations
Prioritized actions to close the Gemini 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)
- 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
- 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
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.

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