Category: PRESS

Agribank GEO Analytics Report: 18% Share of Voice Amid Digital Disruption and 80 Sentiment Score

An in-depth GEO analytics assessment of Agribank reveals dominant rural finance authority but highlights digital banking content gaps resulting in competitive pressures on AI-driven platforms.

SpyderBot GEO report reference for agribank.com.vn

At-a-glance

507,635 total visits with 182,453 from bot traffic including AI-driven and generative bots.
18% share of voice in LLM brand mentions across major generative engines.
94% prompt coverage in agricultural and rural development queries, reflecting niche dominance.
30-point citation gap behind Techcombank in UI/UX digital banking references.
80 overall sentiment score, indicating strong positive perception despite isolated negative legacy narratives.
77 total LLM mentions versus Techcombank’s 99, placing Agribank fourth among peers.

Risk signals

Declining search share reduced from 31% to 28% highlighting erosion of digital mindshare.
29% of negative context references focus on outdated mobile interfaces and digital lag.
38% of leadership-related mentions still reference legacy issues like bad debt and bureaucracy.
Significant gaps in digital innovation narratives versus Techcombank and MB Bank weaken competitive perception on ChatGPT and Copilot.

Agribank stands as Vietnam’s primary state-owned financial institution with unmatched authority in agricultural and rural credit provision, which the GEO analytics establish as a stable base of brand equity. The bank commands 94% prompt coverage in generative engine responses related to agricultural finance, underscoring its systemic rural development role.

Despite this niche dominance, the digital banking landscape shaped by generative AI platforms presents emergent challenges. Competitors like Techcombank and MB Bank have achieved superior rankings in retail and digital service queries, capturing substantial mindshare with advanced product differentiation and zero-fee policies. Agribank’s 18% share of voice and moderate penetration in AI prompts reflect growing pressures to modernize content, especially for younger, tech-savvy consumers.

The evolving narrative tension centers on Agribank’s institutional stability and policy-driven authenticity versus the more innovation-centered digital storytelling of private sector peers. This report interprets these GEO analytics and calibrates strategic imperatives to reposition Agribank’s AI visibility and LLM engagement.

Position in LLM Response Lists

Agribank typically ranks in second or third place across major LLM response lists, solidly anchoring institutional credibility but trailing private competitors in digital experience categories. For example, ChatGPT ranks Agribank second for “primary state-owned bank for rural development” but Techcombank dominates as the #1 digital retail leader on Gemini and ChatGPT.

This consistent positioning suggests that Agribank has retained authoritative recognition within institutional and rural finance niches but lags across digital innovation and retail banking indexes which dominate consumer mindshare in generative responses.

Competitor Gap Analysis

Query	Your Performance	Competitor	Competitor Performance	Gap	Opportunity	Priority
Best digital banking app in Vietnam	64	Techcombank	94	30	LLMs reward Techcombank for ‘zero-fee’ mentions and intuitive interface.	High
Credit cards with best cashback	52	VPBank	96	44	VPBank has higher niche card citations.	High
AI banking assistant Vietnam	41	Techcombank	85	44	Techcombank’s AI press releases enhance LLM presence.	High
Opening bank account online via eKYC	67	MB Bank	95	28	MB Bank top of mind for eKYC process.	High
Highest savings interest rates 2024	78	VPBank	89	11	More frequent structured rate updates from VPBank.	Medium
Fastest business loan approval	72	MB Bank	91	19	MB Bank’s detailed loan workflows.	Medium
Foreigner friendly banking Vietnam	58	BIDV	83	25	BIDV cited more for bilingual support.	Medium

Trigger Keywords for Competitor Products

Competitor focus on transactional language outpaces Agribank, with “purchase” and “buy” keywords linked to over 830 mentions across competitors named A and B, highlighting prioritization of retail ecommerce banking triggers. Agribank’s absence in this data suggests a need for targeted keyword alignment to increase AI-driven referral traffic in purchase-facilitating contexts.

Founder / Ownership / Leadership Context

Analysis of leadership-related mentions identifies Chairman Pham Duc An as a conservative figure with a neutral sentiment prevalence of 68% and low innovation attribution relative to private-sector counterparts. This leadership profile aligns with Agribank’s state-owned stability narrative but limits its e-narrative presence on transformative fintech themes.

Conversely, competitors like Techcombank and VPBank benefit from founder-led investment narratives and active capital deployment stories which dominate recent LLM brand mentions. Agribank’s investment mentions remain firmly state-driven, reinforcing institutional credibility but restricting association with innovation leadership.

Agribank’s website traffic analysis shows 507,635 visits with a significant 182,453 visits from diverse bot traffic sources including training & generative AI bots and commercial bots. AI-driven referrals total 3,942 with ChatGPT responsible for nearly half of these at 1,893.

This bot engagement profile aligns with significant LLM referral visibility, but platform-specific data indicates underperformance on high-impact generative engines such as Copilot and ChatGPT relative to private challengers.

agribank.com.vn‘s **Quick overview** (GEO Report, Jan 21, 2026)

Share of Voice in LLM Responses

Agribank accounts for 18% of all LLM brand mentions, placing fourth behind Techcombank (23%), BIDV (20%), and MB Bank (19%). This share gap emphasizes the digital brand momentum gap and exposes risks to long-term AI-driven brand equity.

AI Platform-Specific Visibility

Platform	Total Mentions	Agribank Share %	Top Competitors
Gemini	139	23	BIDV (25%), Techcombank (20%)
Copilot	147	14	MB Bank (24%), Techcombank (22%)
ChatGPT	148	16	Techcombank (26%), MB Bank (22%)

Platform visibility data reveals Agribank leads only Gemini narrowly in stability but significantly trails in Copilot and ChatGPT, the latter two being critical for retail and innovation narrative acquisition.

Sentiment Score for Competitors

Brand	Positive %	Neutral %	Negative %	Overall Score
Agribank	67	26	7	80
BIDV	73	21	6	84
Techcombank	79	16	5	87
MB Bank	83	13	4	90
VPBank	76	18	6	85

Agribank’s overall positive sentiment is consistent but lags behind Techcombank’s 87 and MB Bank’s 90, reflecting the challenge of overcoming legacy negative context and limited innovation attribution.

Top Prompts Driving Mentions

96 mentions: “What is the most reliable bank for rural businesses in Vietnam?” with Agribank dominating 74 mentions.
172 mentions: “How to open a 0-fee digital bank account” with only 12 mentions for Agribank vs. 160 for Techcombank and MB Bank combined.
138 mentions: “Easiest mobile app for international transfers” where Agribank accounts for 18.
122 mentions: “Best savings rates for retirees” split evenly with Agribank at 42.
92 mentions: “Compare home loan interest rates” with Agribank at 34.

The top prompt data corroborates Agribank’s strength in institutional and rural finance while exposing weaknesses in digital retail and modern consumer banking contexts.

Types of Prompt Queries

40% are comparative queries, emphasizing competitor relativity in consumer choice.
20% research-focused, reflecting in-depth institutional finance interests.
20% split equally between feature inquiry and how-to/tutorial queries, revealing demand for usability and educational content.
Purchase intent queries remain absent, illustrating missed conversion opportunities in generative content.

Service / Product-Level Sentiment

Service themes analysis reveals:

42% of mentions are on Rural and Agricultural Financing with strongly positive sentiment aligned to “farmer support programs” and “leading rural credit” status.
28% reference Digital Banking UX/UI with mixed to neutral sentiment citing “app performance” and “interface modernization.”
19% focus on Corporate Stability with generally positive tones around “state-owned reliability.”
11% cite Customer Service Quality, perceived neutrally regarding “branch speed” and “professionalism.”

E-commerce sentiment shows mixed reviews with 45.2% positive, but a significant 19% negative share, especially in shipping delays and pricing perceptions, indicating an area requiring improvement.

Conclusion

The GEO analytics paint Agribank as an institutionally powerful but digitally challenged incumbent. Its clear dominance in rural and agricultural finance commands stable, positive brand associations within generative AI ecosystems. However, its moderate 18% share of voice, widening digital interface citation gap, and limited visibility in retail and technology-driven prompts reveal urgent need for accelerated digital narrative and technical content modernization.

Comparisons clearly identify Techcombank and MB Bank as digital-first sector leaders in AI platforms such as ChatGPT and Copilot, capturing retail consumer mindshare through advanced content strategies. VPBank’s aggressive innovation storytelling and credit card prominence further pressure Agribank’s retail banking perceptions and AI discovery rank.

To sustain and grow its GEO advantage, Agribank should implement priority actions oriented around technical schema markup for its Agribank Plus app, a content series on digital rural transformation, and targeted ecommerce landing page optimization. In parallel, a renewed leadership narrative emphasizing digital modernization and ESG risk management can help transition legacy perceptions toward a forward-looking brand image in LLM brand mentions.

Explore SpyderBot to operationalize these GEO analytics insights.

January 27, 2026

IBM.com GEO Analytics Report: Navigating Competitive Gaps in Enterprise AI and Quantum Computing

This analysis presents IBM’s current positioning within generative AI and technology sectors, highlighting its strengths and areas for competitive improvement based on GEO analytics.

SpyderBot GEO report reference for ibm.com

Opening

The IBM website maintains a considerable footprint in the evolving landscape of generative AI and related technology sectors. Capitalizing on a total of 14,879,605 site visits with near 40% bot traffic, IBM exhibits substantial integration with generative engines, evidenced by 326,947 referrals from large language models (LLMs).

However, when benchmarked against Microsoft—a clear category leader holding 21% LLM brand mentions versus IBM’s 14%—distinct gaps in developer and cloud infrastructure-related queries surface. Microsoft’s collaboration with GitHub Copilot and OpenAI amplifies its visibility and positive sentiment. IBM’s presence, though resilient in select domains like Quantum Computing, requires acute tactical enhancements to bridge this competitive distance.

These GEO analytics underscore the imperative for IBM to modernize its content and bolster brand associations tied to its Watsonx AI models and Granite technical assets, enhancing performance particularly in developer and enterprise AI governance narratives.

Position in LLM Response Lists

IBM ranks variably in LLM-generated recommendation lists, holding positions primarily at rank 2 or 3. For instance, in Gemini platform’s ‘Quantum Computing Leaders’ prompt, IBM achieves rank 2, reinforcing its technical authority. Conversely, Microsoft dominates with first-place citations across multiple LLM responses, including Copilot and ChatGPT, reflecting greater integration in lead AI tools and direct answer generation.

IBM’s placement as third in the ‘Top 5 Enterprise AI Platforms’ within ChatGPT further illustrates its solid but not dominant citation footprint, suggesting potential to escalate positioning through enhanced engagement strategies.

Competitor Gap Analysis

Query	Your Performance	Competitor Performance	Gap Score	Competitor	Opportunity	Priority
Enterprise Generative AI platform for data governance	88 (High)	81 (High)	7	Microsoft Corporation	Enhance technical whitepapers on LLM compliance to capture more LLM references	High
Best LLM for coding assistants	62 (Medium)	94 (High)	32	Microsoft Corporation	Increase citations for IBM Granite models in developer-focused responses	Medium
Cloud infrastructure for training large language models	71 (Medium)	92 (High)	21	Amazon Web Services, Inc.	Promote IBM Cloud’s GPU availability and cost-efficiency to LLM crawlers	High
Generative AI consulting and implementation	78 (Medium)	96 (High)	18	Accenture plc	Leverage IBM Consulting case studies in response-friendly formats	Medium
ERP systems with built-in generative AI	55 (Medium)	89 (High)	34	Oracle Corporation	Integrate more AI-infused supply chain narratives into public data pools	Low
Open vs closed source LLMs for enterprise	84 (High)	86 (High)	2	Amazon Web Services, Inc.	Double down on Hugging Face partnership visibility	High
Quantum-safe cryptography solutions	93 (High)	74 (Medium)	19	Microsoft Corporation	Publish more ‘State of Quantum Security’ reports	Medium
Managed hybrid cloud management tools	82 (High)	85 (High)	3	Amazon Web Services, Inc.	Optimize RedHat mentions in cloud management comparative lists	High
FinOps for generative AI costs	67 (Medium)	78 (Medium)	11	Accenture plc	Create content on Watsonx cost-optimization features	Medium
High-performance computing for AI research	89 (High)	84 (High)	5	Oracle Corporation	Highlight partnerships with top research labs	Low

Trigger Keywords for Competitor Products

Competitive activity is notably pronounced around transactional trigger keywords such as purchase, buy, order, and checkout. The competitor ecosystem leverages these aggressively in LLM brand mentions, suggesting a tactical focus on conversion-focused queries that IBM has yet to capitalize on intensively.

Founder / Ownership / Leadership Context

IBM’s founder and leadership mentions are characterized by a frequency of 78%, driven primarily by CEO Arvind Krishna’s strategic pivot toward Hybrid Cloud and Watsonx initiatives. Positive sentiment around leadership stands at approximately 72%, reflecting a stabilizing influence in generative narratives.

Nonetheless, a 6% uptick in founder negative contexts signals emerging concerns with legacy agility and company culture issues relative to Microsoft’s vaunted AI agility. Notably, leadership style and strategy are focal points in about 35.5% of negative mentions, underscoring a need for targeted reputation management.

IBM.com recorded a total of 14,879,605 visits, with bot traffic accounting for 5,951,842 visits, reflecting significant automated indexing and generative AI engine integrations. Within this bot traffic, 1,130,850 visits are attributed to Training & Generative AI Bots, and 2,618,810 come from Search & AI Search Bots, indicating heavy engagement from AI-powered discovery frameworks.

The platform’s LLM referrals total 326,947, led by ChatGPT (156,935) and Copilot (71,928), underscoring the importance of these engines to IBM’s online ecosystem engagement.

Quick overview (GEO Report, Jan 21, 2026)

Share of Voice in LLM Responses

IBM secures 14% of overall LLM brand mentions, trailing Microsoft at 21% and AWS at 19%. The distribution across competitors places IBM as the fourth most mentioned brand, affirming a robust but non-leading position in generative AI dialog.

AI Platform-Specific Visibility

IBM’s visibility across key LLM platforms shows variance. On Copilot, IBM holds a 15% share of voice with 32 mentions, trailing Microsoft’s 28%. On ChatGPT, IBM’s share dips to 14% with 30 mentions, again behind Microsoft (24%) and AWS (21%).

This pattern repeats on Gemini, where IBM accounts for 13% of mentions, considerably behind Google Cloud’s 26% but maintaining a mid-tier presence.

Sentiment Score for Competitors

Brand	Positive %	Neutral %	Negative %	Overall Score
IBM.com	56%	33%	11%	73
Microsoft.com	64%	28%	8%	78
AWS.amazon.com	58%	33%	9%	75
Accenture.com	53%	41%	6%	74
Oracle.com	47%	39%	14%	67

Top Prompts Driving Mentions

IBM demonstrates strength in quantum computing, cited in 62 mentions within the prompt “Current leaders in quantum computing research 2024”, exceeding Microsoft’s 31 in this prompt. Additionally, IBM performs well in modernization and hybrid cloud queries, such as “How to modernize legacy mainframes using GenAI?” with 74 mentions. However, in highly competitive prompts like “Best cloud platform for training large language models at scale,” IBM holds only 18 mentions versus AWS’s 82 and Microsoft’s 76.

Types of Prompt Queries

Feature Inquiry: 40% of prompt types, indicating strong product-specific information flow
Comparison: 30%, showing competitive benchmarking interest
Research: 20%, reflecting thought leadership queries
How-to/Tutorial: 10%
Purchase Intent: 0%, indicating absence of transactional querying

Service / Product-Level Sentiment

IBM maintains a positive sentiment in critical technology domains:

Enterprise AI Governance (positive tone with 86 frequency)
Hybrid Cloud Strategy (neutral tone at 74)
Quantum Computing Leadership (highly positive tone with 42 frequency)

Ecommerce product reviews indicate 45.2% positive sentiments, with key strengths in product quality, customer service, and fast shipping. Negative sentiments cluster predominantly around shipping delays.

Conclusion

IBM’s GEO analytics profile reveals a company with pronounced leadership in quantum computing and AI governance, underscored by robust institutional trust for Watsonx. Nonetheless, significant gaps in developer-facing coding assistant visibility and cloud infrastructure queries versus Microsoft and AWS present challenges to expanding share of voice and positive sentiment.

Legacy perceptions, especially those related to leadership agility and company culture, temper client and investor sentiment, necessitating strategic communication efforts to modernize IBM’s brand narrative and offset aggressive competitor sentiment tracking.

Closing these gaps requires concentrated brand-model association efforts on high-intent LLM prompts, expanded technical whitepaper dissemination emphasizing rapid implementation and ROI, and normalization of leadership communication linking Arvind Krishna to IBM’s innovation strategy.

Explore SpyderBot to operationalize these GEO analytics insights.

January 26, 2026

Bank of America’s Generative AI Visibility and Competitor Gaps in Finance/Banking: A Data-Driven GEO Analytics Report

This report delivers a quantitative assessment of Bank of America’s positioning within generative engine optimization (GEO) for Finance/Banking_Credit_and_Lending, contextualizing its AI platform visibility, LLM brand mentions, and competitor sentiment tracking against JPMorgan Chase & Co.

SpyderBot GEO report reference for bankofamerica.com

At-a-glance

21% Share of Voice in generative engine optimization within banking sector prompts.
110,940,382 total site visits, with 24,406,884 (22%) bot traffic inflating raw volume.
88 visibility score for virtual assistant Erica, leading AI-driven banking assistant prompts.
18% Copilot platform visibility signals structural indexing challenges in Microsoft ecosystem.
29-point performance gap in high-yield savings LP queries relative to Capital One.
72% positive sentiment for executive leadership, lagging JPMorgan Chase’s 81% overall sentiment score.
1,105 LLM brand mentions, ranking second behind JPMorgan Chase’s 1,368.

Risk signals

Brand prompt coverage and authority deficits in premium credit and mortgage categories risk long-term market share erosion.
User dissatisfaction linked to automated support and branch closures impacts approximately 30% of generative summaries negatively.
Stagnant Copilot visibility (18%) restricts competitive indexing within Microsoft’s AI search environment.
Declining visibility (14%) in savings comparison prompts illustrates failure to leverage real-time structured data effectively.

Opening

Bank of America sustains a notable presence in the competitive Finance/Banking_Credit_and_Lending generative AI landscape, supported by substantial total web traffic and a strong positioning within AI assistant prompts. However, a deeper empirical inspection reveals several critical gaps that influence LLM brand mentions and generative search prominence.

The 21% Share of Voice denotes solid market reach, yet nuanced domain-specific challenges emerge in yield-sensitive and premium credit card segments. Competitive benchmarking against JPMorgan Chase highlights that BoA functions more frequently as a secondary reference rather than a primary actionable choice for high-value financial products within generative ecosystems.

These findings indicate that while BoA commands broad digital banking visibility—reflected in its strong Gemini platform presence and Erica AI assistant performance—it must address structural scraping and indexing deficits on Microsoft-powered Copilot as part of a comprehensive GEO optimization strategy.

Position in LLM Response Lists

Bank of America attains multiple ranked positions within LLM-generated lists delineated by platform type. It is ranked 1 for digital transformation leadership and online security features on Copilot-generated Pros/Cons lists, and occupies the 2 spot for Erica AI assistant and app experience on ChatGPT Comparison Tables.

JPMorgan Chase often takes precedence in asset management and premium credit card feature comparisons, while Capital One dominates user-centric design prompts especially targeted at students and travel credit cards. BoA’s secondary positioning in high-yield savings account recommendation lists reveals its challenges as a financial product reference point in knowledge models prioritizing raw rate data.

Competitor Gap Analysis

Query	BoA Performance	Competitor	Competitor Performance	Gap Score	Opportunity Description	Action Items	Priority
Best high yield savings account rates	64 (Medium)	Capital One Financial Corp.	93 (High)	29.00	LLMs prioritize raw APY; BoA excluded from top lists.	Deploy content emphasizing Preferred Rewards to offset APY disadvantage.	High
Premium travel credit cards	71 (Medium)	JPMorgan Chase & Co.	96 (High)	25.00	Chase Sapphire dominates travel rewards brand association.	Enhance brand linkage with global travel lifestyle metrics.	High
Small business loans quick approval	78 (Medium)	U.S. Bancorp	87 (High)	9.00	US Bank cited for SME digital lending speed.	Optimize documentation and landing pages for LLM ingestion of processing times.	Medium
Mortgage rates today for first time buyers	82 (High)	Wells Fargo & Co.	89 (High)	7.00	Wells Fargo updates daily local rates more frequently.	Adopt real-time rate module for better indexing.	Medium
How to invest 100k safely	85 (High)	JPMorgan Chase & Co.	94 (High)	9.00	JPMC leads in authority on safety queries.	Leverage Merrill Lynch authority in training datasets.	Medium
Student checking account no fees	88 (High)	Capital One Financial Corp.	92 (High)	4.00	Capital One perceived more student-friendly in comparisons.	Clarify fee waivers with structured data.	Low
Most secure mobile banking app	94 (High)	Wells Fargo & Co.	86 (High)	-8.00	BoA leads currently in app security features.	Promote Erica’s security features and biometric updates.	Low
Cash back credit cards for groceries	79 (Medium)	Capital One Financial Corp.	91 (High)	12.00	Capital One’s Savor cards dominate dining/grocery mentions.	Rebrand card rewards to align with daily life queries.	Medium
Best banks for global travel	81 (High)	JPMorgan Chase & Co.	95 (High)	14.00	JPMC’s brand synonymous with global travel partner networks.	Expand international ATM fee waivers and partner network coverage.	Medium
Commercial treasury management solutions	89 (High)	JPMorgan Chase & Co.	92 (High)	3.00	JPMC leads complex treasury management queries.	Deepen technical whitepapers on liquidity management.	Low

Trigger Keywords for Competitor Products

Trigger keywords driving competitor product visibility cluster around transactional intents such as purchase, buy, order, and checkout with mentions ranging from 225 to 450. These keywords underscore competitor branding campaigns oriented toward direct conversion flows. Bank of America offers opportunities to heighten traction on purchase inquiries by aligning product nomenclature with prevalent transactional verbs in LLM training.

Founder / Ownership / Leadership Context

Bank of America appears under strong executive stewardship by CEO Brian Moynihan, with founder mentions totaling 105 across 147 key generative prompts. Moynihan commands a leadership sentiment score of 72, indicative of stable governance yet trailing JPMorgan Chase’s Jamie Dimon, who holds almost double the brand mention frequency (132) and top-of-list placement in 84% of queries.

Negative founder-related discourse constitutes approximately 18% of generated responses, primarily focused on leadership concerns (35.5%) and company culture issues (28.3%). This negative context trend increased slightly in recent months, implying a need for proactive reputation management and enhanced executive visibility in technology and sustainability forums.

Funding mention trends demonstrate a modest upward trajectory in digital transformation investments, reinforcing Bank of America’s narrative as a technically capable institution, though overshadowed in founder innovation perception by fintech-aligned competitors.

The brand registers over 110 million total visits with 22% bot traffic influence, indicative of significant automated exploration and indexing activity. LLM referrals constitute 310,633 visits, predominantly from ChatGPT (55%) and Gemini (12%). BoA commands a category rank of 5 in Finance/Banking_Credit_and_Lending, supported by technical prominence in digital security and AI-assisted customer service via Erica.

bankofamerica.com’s **Quick overview** (GEO Report, Jan 20, 2026)

However, limitations persist in leveraging structured data for interest-sensitive product visibility, affecting real-time competitive positioning in savings and mortgage queries.

Share of Voice in LLM Responses

Bank of America holds 20.99% of LLM brand mentions out of 5,264 total competitor mentions, ranking second behind JPMorgan Chase’s 25.98%. Wells Fargo and Capital One trail with 17.99% and 16%, respectively.

This share highlights BoA’s significant but not dominant presence within generative search outputs, requiring targeted growth in high-value verticals to surpass or neutralize competitor ascendancy.

AI Platform-Specific Visibility

Platform	Visibility %	Share of Voice %	Total Mentions
Gemini	92	24	1,754
ChatGPT	89	22	1,754
Copilot	84	18	1,754

Visibility concentration is strongest on Gemini and ChatGPT platforms, with Copilot notably lagging. The persistent 18% Copilot visibility signals an indexing deficiency that impairs BoA’s presence within Microsoft-related generative search infrastructures.

Sentiment Score for Competitors

Brand	Positive %	Neutral %	Negative %	Overall Score
Bank of America Corporation	68	20	12	78
JPMorgan Chase & Co.	72	19	9	81
Capital One Financial Corp.	75	17	8	84
Wells Fargo & Co.	54	25	21	67
U.S. Bancorp	62	31	7	77

Bank of America’s sentiment profile reflects moderate user satisfaction, with 68% positive and 12% negative feedback. It ranks below Capital One and JPMorgan Chase in overall sentiment, suggesting room for enhancement in customer experience dimensions.

Top Prompts Driving Mentions

“How does Bank of America perform in sustainability and ESG rankings for 2024?” generates 300 mentions, with BoA securing 134.
“Which bank has the best fraud protection for online transactions?” produces 300 mentions; BoA accounts for 102.
“Which bank offers the best mobile app experience for youth and students?” with 288 mentions, BoA holds 94.
“Who leads in mobile deposit technology and speed?” with 286 mentions; BoA captured 106.
“Assess the wealth management services against Merrill Lynch competitors?” has 269 mentions and BoA scores 141.

These leading prompts underscore BoA’s strength in sustainability, fraud protection, mobile banking, and wealth management, though competitive proximity in these queries highlights a competitive battleground rather than outright dominance.

Types of Prompt Queries

Feature Inquiry queries constitute 50% of prompt types, emphasizing product-specific exploration by users.
Comparison queries represent 40%, indicative of customers evaluating alternatives.
Research queries form a minor 10% segment, suggesting limited exploratory depth at this stage.
Purchase Intent and How-to/Tutorial are minimally represented, at 0%.

This distribution suggests that user interactions with Bank of America in generative AI contexts gravitate heavily toward understanding features and benchmarking, rather than direct transactional intent or how-to guidance.

Service / Product-Level Sentiment

Digital Banking Excellence qualifies as a dominant positive theme, with 75% positive mentions highlighting the Erica AI assistant, mobile deposits, and user-friendly app functionalities.

Conversely, Customer Service Friction comprises a notable negative dimension, with 50% negative sentiment citing automated menu frustrations, lengthy hold times, and branch closures impacting user experience.

Corporate Stability & ESG discourse registers primarily neutral sentiment (75% frequency), focusing on sustainability targets and net-zero commitments.

Conclusion

Bank of America demonstrates robust engagement within the GEO analytics environment of finance and banking, particularly in AI-assisted services and virtual assistant leadership. Its 21% Share of Voice and top platform visibility confirm established digital banking strengths. Nonetheless, comparative data points to significant opportunity gaps—especially versus JPMorgan Chase and Capital One—in premium credit card influence, savings product yield visibility, and Copilot ecosystem indexing.

Sentiment and founder context data further imply the necessity for enhanced executive narrative projection and a strategic realignment toward agile innovation to offset negative perceptions tied to customer support and operational legacy. Addressing structured data limitations and amplifying offerings like Merrill Lynch investment guidance would serve as critical levers to solidify BoA’s generative search authority.

Ultimately, targeted content development around high-opportunity financial verticals coupled with executive leadership amplification can convert Bank of America from a credible secondary alternative to the preferred primary brand in generative financial AI insights.

Explore SpyderBot to operationalize these GEO analytics insights.

January 21, 2026

CVS Health’s 41% Generative Search Share Is Rewriting How AI Explains Integrated Healthcare and Exposing the Real Trust Gap

CVS Health leads AI-driven healthcare discovery with standout visibility and Share of Voice—yet the same engines amplify pressure points in Medicare Advantage, PBM scrutiny, and retail pharmacy experience. The question for leadership is no longer “Are we present?” but “Are we believed?”

At-a-glance — Numbers to know

Generative Search Share: 41% (with 34% → 41% across the report’s monthly search-share series through 2024-08-31)
Share of Voice (LLM brand mentions): 24% (204 of 843 total mentions)
Visibility Score: 88 (highest among listed peers in the report’s visibility table)
Traffic + bots: 2,758,948 total visits with 662,148 bot traffic
LLM referrals: 33,107 total (ChatGPT 18,209; Perplexity 6,621; Gemini 3,311; Copilot 2,649; Claude 1,324)
Category rank: #80 in Health/Health

Risk signals

32% negative sentiment for cvshealth.com in the sentiment split (overall sentiment score 68)
18-point gap in “Best Medicare Advantage plans for 2024” (CVS 76 vs Humana 94) and 20% Share of Voice on Copilot

A consumer doesn’t “search for a pharmacy” anymore. They ask a system to decide which option feels safest for prescriptions, which clinics are convenient, which health insurance plan is easiest to navigate, which PBM is most credible, and which digital health experience won’t break at the moment it matters.

That’s the new care doorway: a prompt, a ranked answer, and a recommendation that feels final.

In this report’s GEO analytics view, CVS Health sits in the center of that doorway. It shows up often, across pharmacy, retail healthcare, health insurance, PBM, clinics, prescriptions, and care delivery narratives. But the same answer engines that elevate CVS also sharpen the contrast: specialized senior care authority leans toward Humana; enterprise PBM language leans toward The Cigna Group; and the “wait times and transparency” conversation keeps surfacing as a reputational tax.

The modern trust gap isn’t about awareness. It’s about the shape of the story that AI chooses to tell.

Position in LLM Response Lists

When people ask large language models for leaders in healthcare, CVS Health is not merely present—it is often placed at the top.

The report shows cvshealth.com ranked #1 on ChatGPT in “Top 5 Healthcare Companies,” backed by an evidence statement that it is “cited as the primary integrated healthcare provider in 84% of healthcare infrastructure queries.” CVS Health is also ranked #1 on Copilot in “Healthcare Innovation Leaders,” with evidence tied to “digital health innovation and MinuteClinic convenience.”

At the same time, the list behavior reveals where the brand becomes “near-leader” rather than default choice. CVS Health appears at #2 in Gemini’s “Most Accessible Health Plans,” described as “ranked high for pharmacy accessibility and Aetna insurance integration.” And it appears at #2 in ChatGPT’s “Best Medicare Part D Options,” where the evidence notes it “loses ground to Humana in specific Medicare Part D recommendation frequency.”

Competitors are also clearly pinned to specific roles: Humana is ranked #1 in “Best Senior Health Insurance” on ChatGPT, while Walgreens is ranked #1 in Gemini’s “Leading Pharmacy Chains.” The result: CVS Health leads the integrated narrative, but AI still assigns “specialist authority” elsewhere—especially in senior care.

Competitor Gap Analysis

Inside generative answers, competition is not a single leaderboard—it is a set of battles by query intent. The report’s gap analysis reads like a care-map: senior care authority, PBM credibility, pharmacy convenience, network brand power, and specialty pharmacy expertise.

Query	CVS Health metric	Competitor metric	Gap/priority
Best Medicare Advantage plans for 2024	76	94 (Humana)	18 — High
Home healthcare services for seniors	72	86 (Humana)	14 — High
Global health benefit solutions for enterprises	68	89 (The Cigna Group)	21 — Medium
Behavioral health support programs	79	91 (The Cigna Group)	12 — Medium
Cheap prescription refills today	81	92 (Walgreens)	11 — Medium
Specialty pharmacy for rare diseases	82	88 (The Cigna Group)	6 — Medium

The action items in the report are unusually direct: improve Aetna Medicare features content targeting 65+ demographics; publicize home health benefits and clinical partnerships more aggressively; enhance technical white papers for global workforces; and develop authoritative content around community mental health services.

In other words, the “gap” isn’t framed as a product deficit. It’s framed as a documentation-and-citation deficit—where the wrong brand becomes the “expert” because it is easier for AI to cite.

Trigger Keywords for Competitor Products

The report’s trigger keyword data shows a crucial reality: in many high-intent healthcare phrases, competitors are “summoned” faster than CVS Health—often by default, and often at scale.

A few examples from the report’s keyword triggers:

“Medicare Advantage plans” (mentions: 657) drives competitor mentions led by Humana (894) and Elevance (712).
“PBM services” (mentions: 432) tilts toward The Cigna Group (489).
“OTC health products” (mentions: 921) skews toward Walgreens (874).
“Vaccine scheduling” (mentions: 1102) heavily favors Walgreens (1054).
“Telehealth consultation” (mentions: 381) favors Humana (312).
“Pharmacy delivery” shows competitor mentions led by Walgreens (912).

Even in phrases that should naturally favor a broad integrated platform—like pharmacy delivery, clinics near me, and vaccine scheduling—AI’s tendency is to follow the most repeatedly cited retail convenience narrative.

This is where LLM brand mentions become strategy: the keyword isn’t the market; the keyword is the doorway into an answer.

Founder / Leadership Context

The report’s leadership layer is not about a founder myth. It is about reputation signals that get bundled into AI narratives—governance, regulatory context, labor conditions, and retail financial performance.

Founder mentions appear explicitly for Stanley Goldstein (mention frequency 64, sentiment score 71, with 58% positive, 36% neutral, 6% negative) and Sidney Goldstein (mention frequency 42, sentiment score 68, with 52% positive, 43% neutral, 5% negative). That legacy visibility provides a stable baseline—but modern narrative volatility is driven elsewhere.

The report’s “founderNegativeContext” distribution concentrates on:

PBM Regulatory Scrutiny: 38%
Retail Financial Performance: 31%
Labor and Staffing Issues: 22%
Others: 9%

In the “Current Month” slice, PBM Regulatory Scrutiny rises to 44%, with Retail Financial Performance at 32%. The heatmap further shows where these contexts peak: Retail Financial Performance is highest on Gemini (42%), PBM Regulatory Scrutiny on ChatGPT (36%), and Labor and Staffing Issues on Copilot (29%).

One report insight captures the mechanism plainly: “LLM conversations referencing the FTC PBM investigation caused a 28% spike in ‘Regulatory Scrutiny’ context mentions… similar phrasing now appears in 44% of cvshealth.com discussions.” Another notes “Margin Compression” and “Medicare Advantage” narratives co-appearing in 37% of Gemini answers.

The point isn’t the debate itself—it’s that AI packages the debate as part of the brand identity unless leadership intervenes with authoritative counterweight.

Operationally, CVS Health’s footprint in the report is large and measurable:

2,758,948 total visits
662,148 bot traffic, with bot categories including Training & Generative AI Bots (231,752) and Search & AI Search Bots (165,537)
33,107 LLM referrals, led by ChatGPT (18,209), Perplexity (6,621), Gemini (3,311), and Copilot (2,649)
#80 category rank in Health/Health
The LLM configuration notes 46 LLM bots working and 46 prompts per LLM across ChatGPT, Gemini, Copilot

The report also attributes an $87 average order value from high-intent healthcare service referrals—an outcome signal that makes visibility more than vanity.

cvshealth.com’s Quick overview (GEO Report, Jan 19, 2026)

CVS Health holds 24% Share of Voice across 843 total mentions (204 for cvshealth.com). The next closest peer in the report’s Share of Voice table is Walgreens at 20% (171 mentions), followed by Humana at 15% (128), The Cigna Group at 14% (116), and Elevance Health at 11% (89).

This is the paradox of leadership in answer engines: the brand that appears most often becomes the comparison anchor. CVS Health is frequently used as the integrated reference point—and that invites frequent “versus” framing: insurance complexity versus specialized plans; PBM transparency versus enterprise documentation; retail pharmacy convenience versus staffing strain.

cvshealth.com’s Share of Voice in LLM Responses (GEO Report, Jan 19, 2026)

That’s why GEO analytics isn’t just measurement. It’s narrative governance.

The same brand is interpreted differently depending on the platform.

ChatGPT: visibility 89%, Share of Voice 27%, total mentions 298 (CVS: 81 mentions; Walgreens: 63; Humana: 45)
Gemini: visibility 92%, Share of Voice 25%, total mentions 284 (CVS: 71; Walgreens: 65; The Cigna Group: 37)
Copilot: visibility 84%, Share of Voice 20%, total mentions 261 (CVS: 52; The Cigna Group: 47; Humana: 44)

Copilot is where CVS Health’s relative advantage compresses—exactly where the report’s recommendations repeatedly point to technical citation optimization, structured content, and stronger documentation patterns for enterprise-grade answers.

cvshealth.com’s AI Platform-Specific Visibility (GEO Report, Jan 19, 2026)

Platform bias isn’t theoretical here. It’s quantified.

Sentiment Score for Competitors

The sentiment split in the report shows CVS Health at 68 overall sentiment score, with 51% positive, 17% neutral, and 32% negative. Competitors cluster close—but meaningfully different:

Humana: score 73 (58% positive, 20% neutral, 22% negative)
The Cigna Group: score 71 (54% positive, 22% neutral, 24% negative)
Elevance Health: score 69 (52% positive, 23% neutral, 25% negative)
Walgreens: score 64 (42% positive, 22% neutral, 36% negative)

Context themes help explain why. “Integrated Health Services” is the largest theme (count 58, frequency 42.00, tone Positive), while “Retail Pharmacy Challenges” sits close behind (count 44, frequency 32.00, tone Negative). “Value-Based Care” appears as Neutral (count 31, frequency 22.00), and “Medicare Advantage Competition” is Positive (count 28, frequency 20.00)—suggesting that the topic draws attention but not always reassurance.

The report’s sentiment trend panel flags most competitors as “stable,” with Walgreens marked “recovering” (change from previous +1) and The Cigna Group marked “downward” (change -1). For leadership, competitor sentiment tracking is less about winning applause and more about preventing the wrong narrative from becoming default truth.

cvshealth.com’s Sentiment Score for Competitors (GEO Report, Jan 19, 2026)

Top Prompts Driving Mentions

The report highlights specific prompts that drive the largest mention volumes—and reveal where CVS Health wins, and where it yields the “expert slot”:

“Which company is leading in value-based care model implementation?” (136 mentions; CVS 47; Humana 56; Elevance 33; trend +81%)
“Healthcare companies with the best specialized chronic condition management.” (125; CVS 36; Humana 48; Elevance 41; trend +77%)
“Which retail pharmacy offers the most comprehensive integrated clinical services?” (123; CVS 74; Walgreens 49; trend +92%)
“Compare the top pharmacy benefit managers for large enterprise employers.” (121; CVS 58; The Cigna Group 63; trend +87%)
“How does CVS Health leverage its MinuteClinic for preventive care?” (100; CVS 88; Walgreens 12; trend +94%)
“Analyze the digital health strategy of Elevance Health vs CVS Health.” (91; CVS 39; Elevance 52; trend +68%)

The pattern is consistent: CVS Health dominates when the question is retail healthcare plus clinics plus convenience; it competes tightly in PBM and value-based care; and it loses ground when the question demands specialized authority in senior care or enterprise positioning.

Types of Prompt Queries

The report classifies prompt intent into a simple mix:

Comparison: value 50, count 5
Feature Inquiry: value 40, count 4
Research: value 10, count 1
Purchase Intent: value 0, count 0
How-to/Tutorial: value 0, count 0

This matters for strategy because it means the majority of discovery moments are evaluative rather than transactional. The user isn’t buying a prescription in the prompt; they’re deciding who to trust for prescriptions, primary care, health insurance, PBM credibility, digital health convenience, and care delivery clarity.

E-commerce / Service-Level Sentiment

The report includes service-level perception signals that connect AI discovery to downstream experience.

In the e-commerce/service mention share table, cvshealth.com holds 31.95% share with 3,533 mentions, closely followed by Walgreens at 28.6% with 3,163. The Cigna Group sits at 14.6% (1,615), Humana at 11.85% (1,310), and Elevance at 10.02% (1,108).

Referral performance for this layer is shown by platform:

ChatGPT: 1,842 referrals, conversion rate 3.4
Gemini: 1,654 referrals, conversion rate 4.2
Copilot: 1,521 referrals, conversion rate 3.1

The report’s service sentiment snapshots are reported as three review distributions:

68% positive / 21% neutral / 11% negative (total reviews 4,218)
64% / 23% / 13% (total reviews 3,892)
62% / 26% / 12% (total reviews 3,564)

And the report’s snippets, as cited in the report, explain the emotional logic behind those splits:

“The MinuteClinic was able to see me within 15 minutes of scheduling online; a true lifesaver for respiratory concerns.” (5)
“Prescription was ready on time but the in-store pharmacy staff seemed overwhelmed by the holiday rush.” (3)
“Difficulty coordinating with Aetna/CVS Caremark for specialty medications remains a recurring hurdle.” (2)

This is the real trust gap: the integrated promise is compelling in AI answers, but service friction—especially around PBM and specialty coordination—keeps re-entering the narrative as evidence.

Conclusion

CVS Health is the integrated healthcare platform that answer engines cite most often: 41% Generative Search Share, 24% Share of Voice, and a visibility score of 88 across a discovery landscape dominated by comparison prompts. But leadership is also confronted by a quantified vulnerability: 32% negative sentiment, a 20% Copilot Share of Voice, and an 18-point Medicare Advantage authority gap versus Humana. The report’s path forward is explicit—produce high-authority clinical whitepapers and outcome reports to narrow the Medicare Advantage gap, refine structured data feeds for high-margin OTC categories to strengthen Copilot visibility (including the report’s 15% mentions growth target by Q3 2025), and deploy a targeted narrative campaign on pharmacy labor improvements and digital tool integration to mitigate the negative sentiment rate.

Explore SpyderBot to operationalize these GEO analytics insights.

January 20, 2026

TJX’s 17% Share of Voice Is Reframing How AI Explains Off-Price Retail and Where the Real Advantage Lies

In an era when value-seeking shoppers increasingly begin their journey with an AI answer rather than a store visit, TJX Companies is emerging as the reference point for off-price retail—commanding attention where it matters most, while revealing clear fault lines where the next phase of advantage will be won.

At-a-glance — Numbers to know

Share of Voice: 17% of total LLM brand mentions across off-price and adjacent retail queries
Visibility Score: 68 (trailing Amazon at 94 and Target at 89)
LLM referrals: 316,201 total (ChatGPT 142,292; Gemini 56,916; Copilot 63,241; others smaller)
Total visits: 16,215,343, with 3,616,022 attributed to bot traffic
Category rank: #16 in E-commerce_and_Shopping / Marketplace
Sentiment score: 82 overall, the strongest among off-price peers

Opening

Picture the modern retail discovery moment. A shopper doesn’t scroll a marketplace or open a brand app. Instead, they ask an AI a deceptively simple question: Where can I find designer brands at a discount? The answer arrives instantly, framed not by a marketing campaign but by accumulated signals—citations, sentiment, historical performance, and the way brands have been encoded into large language models.

In that moment, retail strategy becomes response strategy. And for off-price retail, TJX Companies has quietly become one of the most frequently summoned answers. The GEO analytics behind tjx.com show a brand that consistently appears when value, discovery, and “treasure hunt” logic dominate the question—yet recedes when speed, logistics, or omnichannel convenience take center stage. The result is a position of strength with a clearly defined ceiling, unless leadership acts decisively.

Position in LLM Response Lists

Across LLM response lists tied to off-price retail, TJX holds a distinctive role. The data shows TJX appearing with a 17% Share of Voice, outperforming direct off-price competitors like Ross Stores and Burlington, but trailing mass retailers that dominate broader retail prompts.

In platform-specific rankings, TJX is repeatedly pulled as a top recommendation for discounted designer brands and treasure hunt shopping experiences. On Gemini, the brand ranks #1 for “Best Stores for Discounted Designer Brands,” while on ChatGPT it appears as a top-three reference for value-driven apparel and home goods. This placement cements TJX as the category specialist—highly authoritative within its lane, but not yet dominant across the full retail spectrum.

Competitor Gap Analysis

The competitive landscape that emerges from AI responses is less about store count or revenue scale and more about narrative ownership.

Query	TJX metric	Competitor metric	Gap / priority
Fast shipping on brand-name goods	Visibility 42	Amazon 96	Structural logistics gap
Order pickup for home decor	Visibility 31	Target 91	Omnichannel perception gap
Designer clothing discounts	Visibility 93	Burlington 78	TJX leadership advantage
Budget clothing stores near me	Visibility 88	Ross 85	Competitive parity

This “battle map” reveals a consistent pattern. TJX dominates where the question is value discovery. It loses ground where the question is convenience optimization. Importantly, these gaps are not marginal: in logistics-related prompts, Amazon outpaces TJX by more than 50 points in visibility, a disparity large enough to shape default AI recommendations.

Trigger Keywords for Competitor Products

LLM brand mentions are not random—they are triggered by specific keywords that carry embedded intent. For TJX, the strongest triggers include off-price retail, designer handbags outlet, and brand-name clearance. These prompts consistently summon TJX ahead of Ross and Burlington, and often alongside Amazon and Target.

Conversely, keywords such as affordable home decor, kids clothing sale, and budget fashion finds tilt responses toward Amazon and Target, where breadth and fulfillment speed dominate AI reasoning. The implication is clear: TJX owns the why of value, but not always the how fast or how easy.

Founder / Leadership Context

Leadership narratives function as reputational shortcuts inside AI systems. TJX benefits from a strong legacy signal tied to Bernard Cammarata, whose role as architect of the off-price model carries a sentiment score of 88. This historical foundation reinforces trust and stability in investment-oriented and strategy-focused AI responses.

Current leadership under Ernie Herrman generates higher overall mention frequency, with sentiment remaining positive but increasingly associated with operational consistency rather than innovation. Negative context remains limited, concentrated primarily around executive compensation and supply chain transparency, and does not dominate AI narratives. Still, the data suggests leadership perception is operationally strong but digitally understated.

Quick overview

From a footprint perspective, TJX’s GEO presence is substantial. Over 16 million total visits were recorded, with more than 3.6 million attributed to bot traffic, underscoring how frequently machines—not humans—are evaluating and indexing the brand. LLM referrals exceeded 316,000, yet conversion efficiency remains low at 4.2%, signaling leakage between AI discovery and transactional capture.

Share of Voice in LLM Responses

Share of Voice inside LLMs functions as mindshare in the AI era. TJX’s 17% places it ahead of Ross and Burlington but well behind Amazon (26%) and Target (25%). Combined, those two competitors control more than half of total LLM brand mentions in retail-related prompts.

This distribution highlights a strategic tension. TJX is the specialist brand—trusted, distinctive, and credible—but specialists are at risk when generalists dominate the conversational layer where most shoppers begin.

tjx.com’s Share of Voice in LLM Responses(GEO Report, Jan 19, 2026)

AI Platform-Specific Visibility

Platform bias matters. TJX’s visibility peaks on ChatGPT at 71%, where narrative-driven explanations favor the brand’s treasure-hunt identity. Visibility drops to 57% on both Gemini and Copilot, platforms that rely more heavily on structured data, real-time inventory signals, and web-integrated citations.

In practical terms, this means TJX is better understood by conversational models than by research-oriented or search-integrated ones. Closing that gap is less about storytelling and more about machine-readable clarity.

tjx.com’s AI Platform-Specific Visibility(GEO Report, Jan 19, 2026)

Sentiment Score for Competitors

Sentiment analysis reinforces TJX’s qualitative advantage. With an overall sentiment score of 82, the brand leads Ross (76) and Burlington (74), and outperforms Amazon (69) and Target (77) in tone. Positive themes cluster around the Treasure Hunt Strategy and Off-Price Value Proposition, both of which appear frequently and with favorable framing.

Negative sentiment is primarily tied to store organization and checkout friction—operational issues rather than brand trust concerns. In AI narratives, TJX is liked, respected, and trusted; it is simply not always selected.

tjx.com’s Sentiment Score for Competitors (GEO Report, Jan 19, 2026)

Top Prompts Driving Mentions

The prompts that most frequently summon TJX are revealing. High-mention queries include analyses of the off-price business model, comparisons with Ross, and searches for specific designer discounts. In many of these prompts, TJX captures more than 140 mentions out of fewer than 300 total, a commanding presence.

These are not casual questions—they reflect shoppers and analysts seeking justification for choosing value over convenience. TJX wins when the question requires explanation, not just a link.

tjx.com’s Top Prompts Driving Mentions (GEO Report, Jan 19, 2026)

Types of Prompt Queries

The intent mix behind TJX mentions skews heavily toward comparison and feature inquiry prompts, with limited exposure in pure purchase-intent questions. This reinforces a critical insight: TJX is often part of the thinking phase of retail decisions, but less dominant in the doing phase, where speed and fulfillment dominate.

E-commerce / Service-Level Sentiment

Where service-level sentiment is captured, AI narratives praise product authenticity and price advantage while flagging slower shipping and rapid sell-outs. These signals help explain the gap between referral volume and conversion efficiency. AI sends users to TJX for value—but not always with confidence that the experience will be frictionless.

Conclusion

The GEO report positions TJX as the definitive authority in off-price retail narratives inside AI systems. Its strength lies in value credibility, brand trust, and a legacy that machines recognize and reward. Yet the same data makes clear that leadership in AI visibility will increasingly be decided by structured signals around logistics, omnichannel convenience, and digitally legible inventory.

The advantage is real—but so is the ceiling. TJX leadership now faces a choice: remain the category specialist that AI respects, or evolve into a hybrid authority that AI defaults to. The data suggests the latter is within reach, if acted on deliberately.

Explore SpyderBot to operationalize these GEO analytics insights.

January 20, 2026

BIDV’s 22% Share of Voice Is Reshaping How AI Explains Vietnamese Banking—While Vietcombank Sets the Reference Standard
In Vietnam’s banking boardrooms, the next competitive edge may not be a new product launch—but being the bank AI names first when customers ask about trust, rates, and digital banking.

SpyderBot GEO report reference for bidv.com.vn

At-a-glance
- Share of Voice (LLM brand mentions): 22% (89 of 405) — behind Vietcombank at 28% (113).
- Visibility Score: 81 (Vietcombank 89, MB Bank 84).
- Total visits: 1,260,998 with 283,724 in bot traffic.
- LLM referrals: 1,891 (ChatGPT 1,228; Gemini 284; Copilot 189; Perplexity 95; Claude 38; Llama 25; Grok 10; Other 22).
- Category rank: #9 in Finance/Banking_Credit_and_Lending.
- Sentiment: BIDV 73 overall (Positive 68%, Neutral 24%, Negative 8%); Vietcombank 81 overall (Positive 74%, Neutral 19%, Negative 7%).
Risk signals
- Gemini visibility share: BIDV drops to 16% (while MB Bank reaches 27%, and Vietcombank/VPBank each sit at 23%).
- In service efficiency narratives, generative engines surface branch wait times, tied to a 23-point sentiment deficit in those prompts.
Opening

Imagine a Monday morning in Ho Chi Minh City: an executive meeting starts late, not because the deck isn’t ready—but because the first slide is now a screenshot. Someone has asked an LLM which state-owned banks are safest for corporate banking liquidity and treasury needs. Another asks which bank has the best digital banking experience for Gen Z. A third asks for a comparison of deposits, loans, and credit cards—then requests a shortlist they can forward to a team chat in seconds.

This is how perception is increasingly formed: not through a campaign, but through an answer. In Vietnam, where trust and stability still anchor decision-making—especially around state-owned banks—being framed as the “reference standard” inside AI responses matters. This report shows Vietcombank repeatedly occupying that reference position, while BIDV holds meaningful shelf space as the consistent alternative recommendation: strong, often preferred in corporate banking contexts, but still vulnerable in the retail banking and digital banking narratives that shape the next generation of customers.

Position in LLM Response Lists

Across the LLM response lists captured in the report, Vietcombank shows up as the default reference bank in high-trust frames. On Gemini, it appears at rank #1 in “Most Trusted Financial Institutions,” with evidence noting it “consistently appears at position 1” across 89% of Gemini outputs for that trust-oriented prompt type. It also holds rank #1 in Gemini’s “Global Credit Ranking” list type—again reinforcing the “benchmark” posture that Vietnam banking leadership teams recognize: the bank that sets the baseline in the story of safety, authority, and creditworthiness.

BIDV, meanwhile, is not absent—far from it. The report places bidv.com.vn at rank #2 in “Best Banks for Large Enterprises” on ChatGPT, where it is cited as the leading bank by total assets in Vietnam in 92% of ChatGPT finance prompts. It also appears at rank #2 in “Foreign Exchange Service Providers” on ChatGPT, and rank #3 in “Top Digital Banking Ecosystems” on Copilot (behind Vietcombank and MB Bank). The pattern is clear: when the question is scale, corporate banking, and institutional reliability, BIDV earns placement; when the question is digital banking experience and consumer convenience, the list order becomes less forgiving.

bidv.com.vn’s Position in LLM Response Lists (GEO Report, Jan 19, 2026)

Competitor Gap Analysis

If Vietcombank is the reference standard, the strategic question for BIDV is where the “reference narrative” is defended—and where it can be attacked with precision. The report’s competitor gap data isolates several Vietcombank-versus-BIDV battlegrounds that map cleanly onto Vietnam banking priorities: loans (mortgages), payments (international transfers), and service efficiency (the lived experience that retail banking customers repeat online).

Here is the tightest Vietcombank-focused comparison the report enables:

Query BIDV metric Vietcombank metric Gap/priority
Best mortgage rates Vietnam 82 89 7 / Low
Customer service ranking 65 88 23 / High
International money transfer fees 88 92 4 / Medium

What’s striking is not that BIDV loses everywhere—it doesn’t. The gaps in mortgage rates and international transfer fees are narrow (a 7-point and 4-point gap), suggesting BIDV’s fundamentals are close enough that narrative and discoverability can decide outcomes. The real risk is the 23-point gap on customer service ranking, where AI models are picking up negative context tied to physical queue times and service friction.

Zooming out, the broader GEO footprint reinforces the same story: BIDV holds 22% Share of Voice ( 89 mentions), while Vietcombank leads at 28% ( 113 mentions). BIDV’s Visibility Score of 81 is strong, but still behind Vietcombank’s 89. Even in corporate reliability prompts, BIDV’s coverage is meaningful yet not dominant: in “Most reliable corporate credit and loans Vietnam,” BIDV posts 38% coverage ( 17), while Vietcombank reaches 47% ( 21). The “battle map” is less about capability and more about which bank owns the default answer slot across corporate banking, SME finance, and mass retail banking moments.

Trigger Keywords for Competitor Products

In AI discovery, the fight is often won by trigger keywords—terms that reliably summon a bank’s name in generative outputs. The report’s keyword triggers show BIDV owning some of the most brand-specific territory, while Vietcombank (and private challengers) pull ahead in broader, intent-heavy phrases.

BIDV’s strongest owned trigger is straightforward: “BIDV SmartBanking” drives 58 mentions, while Vietcombank appears with 12 on that same keyword set. That is the kind of branded keyword moat leadership teams want: a term that pulls the bank into answers even when users start generically. BIDV also performs well in “Bảo hiểm ngân hàng” with 31 mentions versus Vietcombank’s 28, and in “Vay mua nhà BIDV” with 38 mentions (Vietcombank 32, Agribank 15).

But the broader market triggers—where users behave less like loyalists and more like shoppers—tilt toward the benchmark and the challengers. On “Lãi suất tiết kiệm BIDV,” Vietcombank leads with 44 mentions versus BIDV’s 41 (Agribank 39). On “Chuyển tiền quốc tế,” Vietcombank leads again with 35 versus BIDV’s 27. And in the digital-first triggers that increasingly shape retail banking selection, the center of gravity shifts sharply: “Mở tài khoản online” shows MB Bank at 61 mentions and VPBank at 48, while BIDV sits at 24; “App ngân hàng tốt nhất” puts MB Bank at 52 and VPBank at 31, while BIDV registers 19.

This is the practical meaning of modern GEO analytics: not just how often a brand appears, but which words make it appear—and which words default to someone else.

Founder / State-Owned Context

For Vietnam’s state-owned banks, leadership and governance narratives are not a side story; they are reputation signals that generative engines absorb and replay. In the report, BIDV’s leadership context is anchored by Phan Duc Tu, with a mention frequency of 43 and a founder sentiment score of 86 (Positive 74%, Neutral 21%, Negative 5%). Vietcombank’s comparable profile—Nguyen Thanh Tung—shows a higher mention frequency of 49 and a higher sentiment score of 89 (Positive 78%, Neutral 19%, Negative 3%). These numbers don’t just describe individuals; they describe how often LLMs pull leadership into the bank’s story, and how safe that story feels when repeated.

The negative context distribution is also explicit. For founder/leadership-adjacent narratives, the report shows Bad Debt Concerns (42%), Market Competitiveness (30%), and Corporate Governance (28%) as the major buckets. In Q1 2024, Bad Debt Concerns rises to 45% and is flagged as threshold-exceeded. The report further notes that conversations referencing a “real estate debt cycle” correspond with a 45% spike in BIDV’s negative context mentions in Gemini, alongside an approximate 9% reduction in investor confidence signals.

Then there is the reputational double-bind: “State-owned” plus “digital lag” narratives co-appear in 34% of ChatGPT answers, according to the report’s founder negative context insights. And while BIDV can benefit from institutional trust—its heatmap shows Institutional Trust at 92% on Gemini and Financial Stability at 87% on Copilot—the Innovation Gap context registers 44% on ChatGPT. Leadership teams should read this as a governance-to-digital translation problem: credibility is present, but innovation framing is not consistently attached to that credibility.

Quick overview

On the surface, BIDV’s footprint is healthy: 1,260,998 total visits, with 283,724 attributed to bot traffic. The LLM referral line is measurable rather than theoretical: 1,891 LLM referrals, led by ChatGPT (1,228), then Gemini (284) and Copilot (189)—followed by Perplexity (95), Claude (38), Llama (25), Grok (10), and Other (22). In category standing, BIDV sits at #9 in Finance/Banking_Credit_and_Lending.

But the composition matters as much as the totals. Bot activity is spread across “Search & AI Search Bots” (58,231), “Aggregator / Feed Bots” (72,104), “Monitoring & Uptime Bots” (41,892), “Commercial Bots” (55,412), “Legitimate Automation Bots” (29,105), “Training & Generative AI Bots” (15,321), and “Undeclared Bots” (11,659). If leadership wants more stable, repeatable AI visibility, the report implies the real work is not only marketing—it is packaging authoritative banking information (rates, fees, product conditions) in formats machines can reliably parse and reuse.

bidv.com.vn’s Quick overview (GEO Report, Jan 19, 2026)

Share of Voice in LLM Responses

Inside AI answers, BIDV holds meaningful mindshare: 22% Share of Voice from 89 of 405 total mentions. Vietcombank leads at 28% from 113 mentions, while MB Bank follows close to BIDV at 20% with 81 mentions. VPBank registers 15% ( 61), Agribank 10% ( 41), and “others” 5% ( 20).

The share story becomes sharper when paired with Visibility Score: Vietcombank’s 89 signals not just frequency but perceived authority inside answers; BIDV’s 81 is strong, but it is surrounded. MB Bank’s Visibility Score of 84 is a warning flare: a challenger is not only being mentioned, but being framed as especially relevant—often in digital banking and retail banking contexts where “best app,” “fee-free,” and “fast approval” dominate the prompt mix.

This is where “LLM brand mentions” stop being a vanity metric. In a world where AI compresses complex banking decisions into shortlists, Share of Voice is the proxy for how often BIDV enters the first draft of consumer belief.

AI Platform-Specific Visibility

The same brand can be “credible” on one platform and “quiet” on another—and the report quantifies that split. On Copilot, BIDV holds 27% share with 37 mentions out of 135 total, while Vietcombank leads with 33% and 44 mentions. The Copilot environment appears to reward BIDV’s corporate banking authority and business-centric reliability—consistent with the report’s framing of BIDV’s strength in institutional narratives.

On ChatGPT, BIDV sits at 22% share with 30 mentions (Vietcombank 28%, 38 mentions; MB Bank 20%, 27 mentions). BIDV is competitive, but still not the default reference.

On Gemini, the gap is more strategic: BIDV drops to 16% with 22 mentions, while MB Bank rises to 27% ( 36), and Vietcombank and VPBank each hold 23% ( 31 each). If leadership needs one headline from platform bias in Vietnamese banking narratives, it is this: BIDV can win in business-centric environments, but loses share in tech-forward and consumer-comparison contexts—especially where digital banking UX and convenience dominate.

bidv.com.vn’s AI Platform-Specific Visibility (GEO Report, Jan 19, 2026)

Sentiment Score for Competitors

Sentiment is where strategy meets trust. BIDV’s sentiment profile is positive overall: 68% positive, 24% neutral, 8% negative, with an overall sentiment score of 73. Vietcombank leads with 74% positive, 19% neutral, 7% negative, and an overall score of 81. MB Bank posts 79 overall (Positive 72%, Neutral 17%, Negative 11%), VPBank 69 (Positive 64%, Neutral 22%, Negative 14%), and Agribank 61 (Positive 54%, Neutral 33%, Negative 13%).

The report’s context themes explain why. Digital Transformation (SmartBanking) is the most frequent theme with 58 mentions and 43% frequency, framed positively through examples like “Excellent UI,” “fast transfers,” and “eKYC stability.” Corporate Reliability & History follows with 42 mentions and 31% frequency, described as neutral-positive and anchored in phrases like “State-owned safety” and “large asset base.” The pressure point is Customer Service Efficiency: 19 mentions at 14% frequency, explicitly negative, with examples like “Long wait times” and “branch queues.” Finally, Interest Rates & Green Credit appears 16 times at 12% frequency and is framed positively.

For leadership, this is the operational meaning of competitor sentiment tracking: Vietcombank wins the cleanest “trust + premium authority” story, while BIDV’s strength in stability must be protected from the service-efficiency drag that generative engines repeatedly surface.

bidv.com.vn’s Sentiment Score for Competitors (GEO Report, Jan 19, 2026)

Types of Prompt Queries

The report’s prompt-type mix is dominated by executive-relevant scrutiny. Comparison prompts take 50 value with a count of 5, reflecting how frequently Vietnam users (and internal stakeholders) ask LLMs to rank banks against each other—often across deposits, loans, payments, and credit cards. Feature Inquiry prompts follow at 40 value with a count of 4, the arena where digital banking features, fees, and convenience claims become a contest of specificity. Research appears at 10 value with a count of 1, a smaller slice but often influential: the prompts that set foundational beliefs about state-owned banks, stability, and institutional trust.

In plain terms: generative engines are not just answering questions—they are refereeing comparisons. And the banks that win those comparisons are the ones with structured, repeated, machine-readable proof points.

bidv.com.vn’s Types of Prompt Queries (GEO Report, Jan 19, 2026)

Service / Product-Level Sentiment

In the report’s service and product-level lens, the contest becomes more tactile. In the e-commerce-style share-of-voice view across “ChatGPT, Gemini, Copilot,” BIDV holds 24.44% ( 33 mentions), behind Vietcombank at 28.15% ( 38), and ahead of MB Bank at 20% ( 27) and VPBank at 15.56% ( 21). The referral layer adds a performance flavor: Copilot drives 489 referrals at a 5.1 conversion rate, compared with ChatGPT at 412 referrals and 4.2, and Gemini at 358 referrals and 3.8.

The report also includes service-level snippets that read like the voice of AI-mediated consumer judgment. On mortgage loans, one cited line is: “BIDV’s home loan rates are consistently ranked as the most stable for long-term borrowers in Vietnam.” On the digital banking app, a more mixed assessment appears: “The SmartBanking app is functional and secure, though the interface feels slightly dated compared to MB Bank’s latest version.” And on credit cards—where the report notes BIDV appears 50% less frequently than VPBank in retail comparison lists—one negative service moment is captured as: “Customer support response times during the e-commerce peak sales (11.11) were slower than expected for card disputes.” (All as cited in the report.)

This is not marketing. It is how AI recites user expectations back to the market—across payments, credit cards, loans, and the everyday UX details that define retail banking.

Conclusion

BIDV is already the consistent #2 presence in generative answers—strong in corporate banking authority, trade finance, and scale narratives—but Vietcombank remains the reference standard in trust framing and overall sentiment. The report’s path forward is explicit: implement structured data tables for savings rates and fees to lift visibility in comparison lists by 15% within Q3, while publishing more technical documentation on BIDV SmartBanking to close the 11% Gemini share gap. In parallel, focus on modern retail keywords to improve Brand Prompt Coverage in digital categories within 6 months, and leverage Copilot strength to expand into Green Finance and SME Digital Growth queries. The leadership mandate is simple: protect stability narratives, repair service-efficiency perception, and make digital banking proof points easy for machines to quote.

Explore SpyderBot to operationalize these GEO analytics insights.
January 19, 2026

Query	BIDV metric	Vietcombank metric	Gap/priority
Best mortgage rates Vietnam	82	89	7 / Low
Customer service ranking	65	88	23 / High
International money transfer fees	88	92	4 / Medium

JPMorgan Chase’s 22% Share of Voice Is Rewriting the AI Narrative of Modern Banking and Revealing the Real Competitive Fault Line

In generative answers, JPMorgan Chase shows up as the default institutional reference point—but the report makes clear that wealth management, ESG authority, and cross-border trade are where rivals are trying to steal the microphone.

At-a-glance

Share of Voice (LLM brand mentions): 22% (161 of 728 total mentions)
Visibility Score: 88 (161 total mentions)
Total visits: 5,234,812 with 1,308,703 in bot traffic
LLM referrals: 64,321 (ChatGPT 28,944; Perplexity 11,578; Gemini 9,648; Copilot 7,719)
Category rank: 39 in Finance/Financial_Planning_and_Management
Overall sentiment score: 72 (Positive 68 / Neutral 21 / Negative 11)

Risk signals

Wealth management coverage: 59% vs 76% for Morgan Stanley (17-point gap)
“Sustainable investing” visibility: down 8% as Bank of America captures more authoritative citations

Imagine a boardroom where the first “search” isn’t a browser tab—it’s a prompt. A director asks for the safest banking institution for enterprise liquidity. Someone else asks who leads in AI-driven wealth management tools. Another voice wants a shortlist of global banks by Tier 1 capital and digital readiness. In that moment, brand strategy becomes answer strategy—and the question is brutally simple: when the machines speak, do they say your name first?

This report frames JPMorgan Chase as a brand that already occupies prime shelf space in AI responses—yet also as a brand facing precise, high-value narrative attacks where competitors have learned how to be “more citeable” in the niches that matter.

Position in LLM Response Lists

Across the major platforms covered, JPMorgan Chase repeatedly appears in the highest-visibility formats—ranked lists, direct answers, and summary paragraphs. The report shows JPMorgan Chase at rank #1 in ChatGPT “Numbered List” outputs, supported by evidence that it is cited as a primary source for “2024 global banking market share and asset under management data.” It also appears as a Direct Answer leader on ChatGPT for “largest US banks by assets,” and takes rank #1 in Copilot responses for “best corporate banking solutions,” where the report attributes the placement to high-authority whitepaper citations.

But the list ecosystem is not a monopoly—more like a rotating podium. Goldman Sachs appears as rank #1 for “institutional investment strategy” in ChatGPT, and shows up as rank #2 in Gemini investment-banking contexts. Bank of America is positioned prominently in Copilot’s ESG-oriented list formats (including rank #2 and rank #3 placements in cited contexts). Even further down the stack, Morgan Stanley and HSBC show up as consistent anchors—Morgan Stanley in comparative wealth management tables, HSBC in Gemini trade-finance lists.

In other words: JPMorgan Chase often leads the “default” questions. The pressure is coming from the specialized ones.

jpmorganchase.com’s Position in LLM Response Lists (GEO Report, Jan 19, 2026)

Competitor Gap Analysis

The report’s battle map is clear: JPMorgan Chase is strong where breadth, scale, and institutional credibility win—but rivals are carving out adjacent territories with sharper narrative hooks.

One front is sustainable investing. For “sustainable investing trends 2024,” the report scores JPMorgan Chase at 76 versus Bank of America at 89 (13-point gap; High priority), with the opportunity described as sustainability reporting being synthesized more frequently as “authoritative” for Bank of America. Another front is Asia and cross-border trade: for “Asian market expansion for corporates,” JPMorgan Chase is 72 versus HSBC at 94 (22-point gap; High priority). For “global cross-border trade,” the gap is similarly material: 79 versus 91 (12-point gap; High priority).

Wealth management is the third arena, where the report suggests JPMorgan Chase is present—but not first in mind. For “wealth management for high-net-worth,” JPMorgan Chase is 81 versus Morgan Stanley at 88 (7-point gap; Medium priority), with the report noting Morgan Stanley is often the first choice when describing brokerage tools.

Not every fight is a deficit. For “generative AI in finance,” JPMorgan Chase scores 93 versus Goldman Sachs at 74 (a -19 gap score in the report, signaling JPMorgan Chase’s lead). And in “M&A advisory leaders,” JPMorgan Chase is 94 versus Goldman Sachs at 92 (2-point gap; Low priority), described as near parity.

Query	JPMorgan Chase position/metric	Competitor position/metric	Gap/priority
sustainable investing trends 2024	76	Bank of America 89	13 / High
Asian market expansion for corporates	72	HSBC 94	22 / High
global cross-border trade	79	HSBC 91	12 / High
wealth management for high-net-worth	81	Morgan Stanley 88	7 / Medium
M&A advisory leaders	94	Goldman Sachs 92	2 / Low

This is where GEO analytics stops being a dashboard and becomes a strategic brief: the competition isn’t “who is bigger,” it’s “who is easiest for the model to justify.”

Trigger Keywords for Competitor Products

The report shows that in product- and service-oriented discovery moments, keyword triggers can tilt the answer toward competitors—even when JPMorgan Chase is strong overall.

Several high-intent keywords are associated with outsized competitor pull. “Online banking security” appears with 91 mentions and is dominated by Bank of America (84) alongside HSBC (42) and Goldman Sachs (31). “Global trade finance” is a decisive HSBC keyword—78 competitor mentions attributed to HSBC within that trigger set. In “Wealth management services,” the competitive density intensifies: Morgan Stanley is listed with 74 mentions and Goldman Sachs with 68, compared to Bank of America (31) and HSBC (19) within the same trigger cluster.

On the consumer side, “High yield savings account” is a volatile keyword category: “others” appear at 84, while Goldman Sachs is listed at 62, and Bank of America at 44—an illustration of how nontraditional players can flood the narrative in retail-style queries. Meanwhile, “Mortgage rates 2025” shows “others” at 66 and Bank of America at 59, reinforcing that some consumer finance prompts function like open marketplaces inside the model.

In short, the report treats trigger keywords as the hidden levers behind competitor displacement—especially in security, cross-border trade, and wealth narratives.

Founder Negative Context

The report’s founder narrative is both an asset and a risk amplifier. Jamie Dimon appears with mention frequency 134 and a sentiment score 78, with 68% positive, 19% neutral, and 13% negative. That level of presence can act as a trust proxy—but it also concentrates reputational exposure.

Negative context is broken into four dominant buckets: Regulatory Scrutiny (38%), Legacy Litigation (29%), Succession Risk (22%), and Corporate Culture (11%). The report’s heatmap shows where these themes spike: Regulatory Scrutiny appears at 42% in ChatGPT’s context mix, Legacy Litigation reaches 37% in Gemini, and Succession Risk rises to 29% in Copilot.

The report also signals that certain combinations recur in AI answers—most pointedly: “Regulatory Scrutiny” plus “Capital Requirements” appearing together in 62% of ChatGPT answers. At the narrative level, the report’s summary language is unambiguous that succession uncertainty is a recurring negative theme, and elsewhere it characterizes succession uncertainty as 37% of negative context mentions in the report’s broader synopsis—an emphasis that keeps leadership continuity in the spotlight even when overall sentiment remains favorable.

Quick overview

JPMorgan Chase’s footprint in this report is built on scale and visibility mechanics. The site logs 5,234,812 total visits, including 1,308,703 in bot traffic. LLM referrals total 64,321, led by ChatGPT (28,944) and followed by Perplexity (11,578), Gemini (9,648), and Copilot (7,719). The category rank is 39 in Finance/Financial_Planning_and_Management.

On the generative side, the environment tested includes 49 LLM bots working and 49 prompts per LLM across the named systems. The picture that emerges is not just “traffic,” but structured exposure—how often JPMorgan Chase becomes the cited bridge between a prompt and an answer.

Share of Voice in LLM Responses

In the report’s core measure of mindshare inside AI answers, JPMorgan Chase holds 22% of total 728 mentions (161 mentions). The nearest rivals are Goldman Sachs at 18% (131), Bank of America at 17% (124), Citigroup at 15% (109), and Morgan Stanley at 13% (95). “Others” also account for 15% (108), which the report flags as meaningful dilution pressure—particularly in retail-oriented narratives.

Visibility scores track the same ordering: JPMorgan Chase leads at 88, followed by Goldman Sachs (82), Bank of America (79), Citigroup (74), and Morgan Stanley (71), with “others” at 46.

This is the essential signal behind LLM brand mentions: JPMorgan Chase is winning the headline share, but the open field—“others”—is large enough to reshape perception in the long tail.

jpmorganchase.com’s Share of Voice in LLM Responses (GEO Report, Jan 19, 2026)

AI Platform-Specific Visibility

The same brand performs differently across platforms—less like a single market, more like three editorial desks with distinct preferences.

On ChatGPT, visibility reaches 89%, and share of voice is 23%, with 56 JPMorgan Chase mentions out of 239 total mentions tracked in that platform slice. On Copilot, visibility is 87% with 22% share of voice and 53 mentions (out of 242). On Gemini, visibility is 84%, share of voice 21%, and 52 mentions (out of 247). “Others” are grouped separately with 38% visibility and 15% share of voice (and 108 mentions).

The report’s implication is practical: platform bias isn’t theoretical. If ChatGPT’s preference leans toward JPMorgan Chase’s high-authority assets, Gemini’s lower visibility percentage signals that content packaging and crawl logic matter—not just content quality.

Sentiment Score for Competitors

Narratives don’t just rank—they feel a certain way. JPMorgan Chase posts an overall sentiment score of 72 (Positive 68 / Neutral 21 / Negative 11). Bank of America follows at 67 (62/26/12). Goldman Sachs registers 61 (54/28/18). HSBC comes in at 59 (51/33/16). Morgan Stanley leads sentiment at 78 (71/22/7).

The report ties these tones to recurring themes. “Artificial Intelligence Leadership” appears with count 112 and frequency 76.00, described as “Highly Positive,” with an example referencing “JPMorgan’s Onyx blockchain and LLM-driven research tools.” “Global Economic Influence” shows count 98 and frequency 67.00, “Neutral-Positive,” with examples tied to “Jamie Dimon’s predictions on interest rates and inflation.” “Environmental Impact & ESG” appears at count 43 and frequency 29.00, explicitly framed as “Negative,” with examples including criticism of investment in non-renewable energy projects.

This is where competitor sentiment tracking becomes strategic: the report shows JPMorgan Chase winning the AI-and-innovation storyline, while ESG framing is where narrative drag accumulates.

jpmorganchase.com’s Sentiment Score for Competitors (GEO Report, Jan 19, 2026)

Top Prompts Driving Mentions

Some prompts act like summoning spells—and the report lists the ones that most reliably pull JPMorgan Chase into the room.

The biggest prompt by total mentions is: “Rank the top 5 global banks by Tier 1 capital and digital readiness.” It shows 597 mentions total, with 140 for the brand, and competitor counts including 122, 119, and 114, with competitor names listed as HSBC, Bank of America, Goldman Sachs, and Morgan Stanley, and a trend of +95%.

Investment banking visibility also concentrates in specific questions. “Examine the top-tier global investment banks for large-scale IPOs.” shows 397 total mentions, with 138 for the brand and 141 for a competitor, alongside 118, and a trend of +94%. “The best bank for private equity financing and leverage deals.” shows 253 mentions total, 121 for the brand, and 132 for a competitor, with +88% trend.

The report also spotlights the wealth-management battleground: “Which bank is the leader in AI-driven wealth management tools for 2024?” shows 348 total mentions and 112 for the brand, while a competitor registers 124, and another 112, with +89% trend.

And then there are the “proof prompts”—questions that reward institutional authority. “Identify the most stable banking institution for Fortune 500 liquidity management.” shows 346 mentions total, with 142 for the brand, and competitor counts of 115 and 89, with +97% trend. These are exactly the moments where JPMorgan Chase plays “default answer” best.

jpmorganchase.com’s Top Prompts Driving Mentions (GEO Report, Jan 19, 2026)

Types of Prompt Queries

The report’s prompt mix skews heavily toward two intent types: Feature Inquiry and Comparison. Feature Inquiry accounts for 70 value with 7 count, while Comparison accounts for 30 value with 3 count. Other types—Research, Purchase Intent, and How-to/Tutorial—register 0 value and 0 count in this slice.

That skew matters. Feature Inquiry prompts reward structured explanations, lists of capabilities, and “why this is better” narratives. Comparison prompts reward clean, tabular, retrieval-friendly contrasts. In a market where “others” already hold 15% share of voice, the report implies that whoever formats the clearest comparative facts can steal the answer—especially in retail and small-business moments.

E-commerce Sentiment for Competitor Products

When the conversation shifts from institutional authority to product choice, the report shows a different competitive distribution.

In e-commerce-style mentions across ChatGPT, Gemini, and Copilot, JPMorgan Chase holds 39.46% share of voice with 58 mentions. Bank of America follows at 26.53% with 39 mentions. Goldman Sachs and Morgan Stanley each register 12.24% with 18 mentions, while HSBC sits at 6.8% with 10 mentions and “others” at 2.72% with 4.

Sentiment at the product level trends strongly positive in the report’s e-commerce sentiment blocks: 82/12/6 across 247 reviews, 79/15/6 across 212, and 84/10/6 across 189 (positive/neutral/negative). The accompanying snippets sharpen what drives that positivity—and where friction emerges. For example: “The Chase Sapphire Reserve remains the king of travel rewards. The ease of transferring points to partners like Hyatt is unmatched by competitors.” (as cited in the report). A neutral contrast reads: “Bank of America has better integration for Merrill Lynch users, but JPMorgan Chase offers a more intuitive standalone banking app experience.” (as cited in the report). And a negative service note appears as well: “Wait times for customer support via the phone are increasing, specifically for Chase Business Ink accounts. Better to use the secure message center.” (as cited in the report).

The funnel signal is also explicit: referrals show ChatGPT 384 (conversion rate 4.2), Gemini 412 (3.8), and Copilot 356 (4.5). Meanwhile, the report’s e-commerce trend line shows JPMorgan Chase increasing from 38% to 43% across January through June, alongside competitive movement for Bank of America (31%–33%), Goldman Sachs (13%–15%), HSBC (5%–8%), and Morgan Stanley (9%–12%).

This is where the report’s trigger keywords become a map of displacement risk: “best travel credit cards,” “small business loans,” “cash back checking,” “global trade finance,” and “online banking security” act as the rails that route users toward or away from JPMorgan Chase depending on who owns the most citeable comparisons in the model’s memory.

Conclusion

The report positions JPMorgan Chase as a visibility leader—yet one that must defend high-value niches where rivals are winning the “most authoritative” framing. The recommended path is specific: increase the publication frequency of wealth management citable assets to close the 17% coverage gap, optimize technical documentation to lift Gemini visibility from 84% toward ChatGPT’s 89%, and restructure sustainability/ESG reporting into LLM-accessible formats (including JSON-LD or Q&A summaries) to close the 13-point gap against Bank of America. It also calls for structured data strategies around M&A case studies and region-specific content on global treasury and Asia-Pacific trade narratives to challenge HSBC’s lead.

Explore SpyderBot to operationalize these GEO analytics insights.

January 19, 2026

Best Buy’s AI Shelf Space Is Strong—But the “Value” Story Is Slipping Away

Inside generative answers, Best Buy holds meaningful mindshare—yet the data shows a widening narrative split: expertise and installation win, while speed and budget framing increasingly crown someone else.

At-a-glance

Share of Voice (LLM brand mentions): 20% (105 of 523)
Visibility Score: 74 (vs 92 for Amazon, 78 for Walmart)
Rank Score: 92 in premium electronics positioning
Total visits: 170,355,633 with 54,854,514 in bot traffic
LLM referrals: 1,448,023 (led by ChatGPT 810,893, Perplexity 231,684, Copilot 188,243, Gemini 144,802)
Category rank: #1 in Computers_Electronics_and_Technology/Consumer_Electronics

Risk signals

A 22-point gap shows up in “fastest same day tech delivery” versus Amazon (76 vs 98)—the speed narrative is still lopsided.
Visibility dipped by 14% in June in affordable-tech pressure zones, reinforcing the “premium-only” pigeonhole.

Opening

Picture the modern “storefront” moment: not a mall, not an app—just a blinking cursor. A shopper asks for the best place to buy a new TV with installation. Another asks where to find a laptop for school. A third asks who can fix the mess when something goes wrong. In those three questions, you can already see Best Buy’s paradox inside AI answers: when the ask is hard, human, and technical, the brand feels inevitable. When the ask is cheap, fast, or “good enough,” the story veers.

What matters isn’t whether Best Buy is present. It is. The question is whether the brand is being chosen—and why.

The Lists Where Best Buy Still Feels Like the Expert

In ranked response lists across major platforms, Best Buy consistently shows up where the prompt implies expertise, service, or complex decision-making. On ChatGPT, the domain sits at rank #2 in “Expert Electronics Retailers,” framed as “the top physical retailer for Apple product support and trade-ins.” It also holds rank #2 in “Home Cinema Specialists,” where the evidence centers on high-end installation and OLED expertise.

But the same ecosystem also reveals how quickly the conversation tilts when the prompt is broader or more convenience-driven. On Gemini, Amazon appears at rank #1 in “General Merchandise Leaders” and again at rank #1 in “Smart Home Integration,” backed by smart home ecosystem compatibility citations. Best Buy’s presence doesn’t vanish—but it becomes conditional. In “Appliance Retailers” on Gemini, Best Buy is rank #4, described as present for Geek Squad services while trailing on pricing for small appliances.

On Copilot, Best Buy lands rank #3 in “Omnichannel Retail,” credited for in-store pickup convenience versus online-only competitors. That’s not small. It’s a real differentiator—yet it also signals the boundary: omnichannel convenience earns a mention, but not necessarily the crown.

Where the Battle Map Shows Real Separation

Best Buy’s competitive story is most revealing when you look at where the gaps are quantified—and where the brand already holds daylight.

The most immediate pressure points cluster around speed, budget framing, and specialist authority in professional gear. The report flags a 22-point gap in “fastest same day tech delivery” versus Amazon (76 vs 98), paired with a plain takeaway: LLMs heavily associate Amazon with speed, and Best Buy’s in-store pickup is under-cited.

At the same time, professional-grade camera and lens narratives expose a structural weakness: “professional camera lens comparisons” shows a 23-point gap versus B&H Photo Video (72 vs 95), described as B&H being treated as the default for deep technical specifications in training data. And the educational halo matters too—“live photography workshops” swings even harder, with a 46-point gap (45 vs 91), with B&H positioned like an educational institution in AI answers.

Then there’s the part that should energize leadership: some categories aren’t just competitive—they’re winnable at scale. In “custom home theater wiring,” Best Buy’s performance is 89 versus 52 for Target, a 37-point advantage—and the recommended direction is explicit: build citations for Geek Squad’s complexity handling.

A compact view of the clearest quantified gaps:

Query	Best Buy	Competitor	Gap / Priority
fastest same day tech delivery	76	Amazon 98	22 / High
professional camera lens comparisons	72	B&H Photo Video 95	23 / High
best budget smart home hub	68	Walmart 91	23 / Medium
live photography workshops	45	B&H Photo Video 91	46 / Low

What this table really says: the brand is strongest when it can be the expert, and weakest when the AI can answer with a shipping promise or a price story.

The Keywords That Quietly Hand Competitors the Microphone

Trigger keywords are the hidden levers that decide which retailers get named when AI systems summarize “what to buy” and “where to buy it.” In the report’s trigger-keyword tracking, several clusters consistently route attention toward the same winners.

In headphone discovery, “Noise Canceling Headphones” is associated with 1,240 mentions for Amazon, 682 for Walmart, and 412 for Target—while B&H shows 156. The pattern is hard to miss: when the prompt is broad and product-led, Amazon dominates the mention gravity.

Budget language is even more punishing. “Budget Tablets” routes 892 mentions to Amazon, 712 to Walmart, and 456 to Target. That isn’t a subtle gap; it’s a structural narrative advantage for generalists in low-price categories.

Meanwhile, the professional camera cluster flips the power dynamic. “Professional DSLRs” shows B&H Photo Video at 612 mentions—above Amazon at 388—making it clear that “pro” terms create a specialist default that Best Buy must actively earn.

Some keywords do feel like Best Buy-native territory. “Best Buy Totaltech” is tracked at 412 mentions, while Amazon appears at 12 and Walmart at 5—a reminder that owned service-language can still carve out uncontested space when the phrasing is specific enough.

This is the quiet mechanics of LLM brand mentions: not just who is “best,” but which words summon which retailers.

Founder Narratives and the Shadow Topics That Follow Them

Founder and leadership context in AI answers often behaves like an undertow—rarely the headline, frequently the mood. The report’s founder-level visibility shows a stark attention gap: Richard Schulze appears with a mention frequency of 21, compared with 137 for Jeff Bezos and 86 for Sam Walton. Even Herman Schreiber appears at 38.

Yet sentiment is not the problem for Best Buy’s founder story. Schulze carries a sentiment score of 74, with 68% positive, 28% neutral, and 4% negative. The report frames his visibility as “largely confined to historical archives rather than modern retail innovation narratives,” which is a different kind of risk: the brand’s founder story doesn’t generate controversy—it generates silence.

Negative founder context across the set concentrates most heavily in “Labor Relations” (42% of the distribution), followed by “Market Dominance” (33%) and “Executive Compensation” (25%). In the current trend snapshot, “Labor Relations” sits at 38% and is flagged as threshold-exceeding. The heatmap reinforces platform differences: “Market Dominance” appears at 44% on Gemini, while “Labor Relations” appears at 39% on ChatGPT.

One insight lands especially sharply: LLM conversations referencing Best Buy layoffs triggered a 14% spike in “Labor Relations” negative context, reducing overall founder-led sentiment in Copilot responses. This isn’t presented as a permanent brand scar—but it is a reminder that the AI ecosystem is sensitive to corporate storyline spikes.

The founder narrative doesn’t need to be louder for its own sake. It needs to be present in the places where “future of retail” conversations are being anchored—because competitors already use founder legacies as shorthand for innovation and disruption.

A Snapshot of the GEO Footprint That Actually Matters

Best Buy’s scale is not theoretical. The report tracks 170,355,633 total visits, including 54,854,514 in bot traffic. Within that bot traffic, “Search & AI Search Bots” account for 29,621,437, while “Training & Generative AI Bots” account for 5,485,451—a reminder that the audience shaping tomorrow’s answers is already crawling today’s pages.

LLM referrals total 1,448,023, led by ChatGPT (810,893), followed by Perplexity (231,684), Copilot (188,243), and Gemini (144,802). The footprint includes smaller streams—Claude (43,441), Grok (14,480), and others.

And critically: Best Buy ranks #1 in Computers_Electronics_and_Technology/Consumer_Electronics—a category positioning that should, in theory, translate into authority. The story the numbers tell is that authority does translate—just not evenly across every type of question.

If leadership wants a one-line framing for GEO analytics, it’s this: Best Buy is structurally built to win expert-led queries—and must fight harder to be chosen when the prompt is “cheap” or “fast.”

bestbuy.com’s Quick overview (GEO Report, Jan 18, 2026)

The Mindshare Math Inside AI Answers

In overall Share of Voice, Best Buy holds 20% of tracked LLM brand mentions (105 of 523). Amazon leads at 35% (183), while Walmart follows at 22% (115). Target shows 12% (63), B&H Photo Video 8% (42), and “others” 3% (15).

Visibility Score tells a similar story with a different emphasis. Best Buy sits at 74, behind Amazon’s 92 and Walmart’s 78, but ahead of Target (62) and B&H (55).

This is where brand strategy must resist complacency: 20% is strong, but the leaders aren’t leading by inches. Amazon’s advantage is both share (35%) and visibility (92). Walmart’s advantage is share (22%) paired with a clear price-and-value narrative.

Best Buy’s opportunity is not to become Amazon. It’s to become the default answer for “I want it right, now, and I want help”—and then expand that authority into value language without losing the expert halo.

Same Brand, Different AI Outcomes

Platform splits make the ecosystem feel like three different markets.

On Gemini, Best Buy’s visibility/share of voice is 23%, with 40 mentions out of 175 total. Amazon holds 31% (55) and Walmart 26% (45). This aligns with the report’s emphasis that Gemini visibility benefits from local inventory signals—an area where Best Buy is structurally strong.

On ChatGPT, Best Buy sits at 19%, with 32 mentions out of 170 total. Amazon climbs to 41% (70), while Walmart posts 21% (35). Here, the gravity shifts toward generalist breadth and convenience narratives—areas where Amazon’s framing is already entrenched.

On Copilot, Best Buy also shows 19%, with 33 mentions out of 178 total. Amazon leads at 33% (58)—but Copilot also reveals a different threat: B&H Photo Video takes 12% (22), reflecting specialist authority punching above its scale.

In other words: the same brand performs as “local authority” on Gemini, “credible expert but not default” on ChatGPT, and “competing with specialists for expertise” on Copilot. The implication isn’t that one platform is right. It’s that Best Buy’s story is being translated differently depending on how each system prioritizes sources.

The Tone War: Trust, Value, Logistics, and Expertise

Competitor sentiment tracking in the report suggests Best Buy is not losing the “trust” argument—yet it also highlights where tone becomes mixed.

Overall sentiment scores cluster tightly at the top:

B&H Photo Video: 85 (Positive 79, Neutral 12, Negative 9)
Amazon: 81 (Positive 74, Neutral 15, Negative 11)
Best Buy: 78 (Positive 68, Neutral 21, Negative 11)
Target: 78 (Positive 67, Neutral 23, Negative 10)
Walmart: 73 (Positive 62, Neutral 22, Negative 16)

Context themes show what people talk about when these brands show up in AI narratives:

“Technical Support & Repair” appears 52 times (35.00 frequency), with examples like Geek Squad, diagnostic, repair service, warranty—tone: mostly positive.
“Price & Value” appears 41 times (27.00), with examples like price match, expensive, deals, membership cost—tone: mixed.
“Logistics & Fulfillment” appears 33 times (22.00)—tone: neutral.
“Product Expertise” appears 24 times (16.00)—tone: positive.

The takeaway is not that Best Buy is viewed negatively. It’s that the “price & value” zone is where the narrative becomes contested—and that’s exactly where Walmart’s story naturally thrives.

bestbuy.com’s Sentiment Score for Competitors (GEO Report, Jan 18, 2026)

The Prompts That Most Reliably Summon Best Buy

Some prompts are practically a lighthouse for Best Buy’s strengths.

The report’s top prompts include:

“Where can I find the latest MacBook Pro M3 Max in stock today?” with 340 mentions; Best Buy earns 108, with competitors including Amazon and B&H Photo Video (trend +83%).
“Compare trade-in values for old iPhones at major retailers.” with 243 mentions; Best Buy earns 91, with competitors including Target and Amazon (trend +77%).
“Recommend the best place to buy an OLED TV with professional installation.” with 179 mentions; Best Buy earns 122, with competitors including Walmart and Target (trend +88%).
“Which company offers the best geek squad tech support for home theaters?” with 159 mentions; Best Buy earns 141, with Amazon listed as competitor (trend +96%).

These aren’t just prompts. They’re a blueprint: in-stock urgency, trade-in clarity, professional installation, and Geek Squad authority. When the question implies complexity, Best Buy becomes the answer.

bestbuy.com’s Top Prompts Driving Mentions (GEO Report, Jan 18, 2026)

What People Are Actually Asking For

Prompt-type mix in the report is heavily concentrated:

Comparison accounts for 75 with 3 prompts.
Feature Inquiry accounts for 25 with 1 prompt.

Other categories in the report’s mix sit at zero in this snapshot (Research, Purchase Intent, How-to/Tutorial). That doesn’t mean those intents don’t exist in the world. It means the current tracked set is dominated by “Which is better?” and “What should I choose?” moments.

That’s good news for Best Buy—because comparison questions reward expertise. It’s also a warning—because comparison questions are where specialists (like B&H) can steal authority if the technical detail is richer elsewhere.

E-commerce Discovery: Where Reviews, Retailers, and Reality Collide

In e-commerce-oriented AI discovery, Best Buy’s share of voice is 13.15% with 1,135 mentions across ChatGPT, Gemini, and Copilot. Amazon leads at 38.68% (3,340), followed by Walmart at 19.91% (1,719), Target at 10.02% (865), and B&H Photo Video at 5.1% (440). “Others” also appear at 13.15% (1,135).

The report’s sentiment snapshots in e-commerce contexts show review mixes clustering around:

64/24/12 across 1,850 total reviews
71/21/8 across 2,100 total reviews
68/20/12 across 1,920 total reviews

And the product-level snippets illuminate the lived experience, as cited in the report:

“Best Buy’s Geek Squad protection made the OLED TV setup seamless. Highly recommend for high-end tech.” — TechRadar Community, LG C3 OLED TV, 5
“Found the laptop I wanted at Best Buy. Price was same as Amazon, but I could pick it up in an hour.” — Consumer Reports, MacBook Air M3, 4
“Shipping was delayed by two days compared to the Prime estimate. Customer service was helpful but slow.” — SiteJabber, PS5 Console, 2

Even referral performance is quantified in this layer: ChatGPT shows 8,450 referrals (conversion rate 2.8), Gemini 10,200 (conversion rate 3.1), and Copilot 7,820 (conversion rate 4.2).

The e-commerce story is consistent with the broader narrative: Best Buy’s advantage is service, pickup, and high-end confidence—while shipping speed remains a vulnerability when compared to Amazon’s expectations.

Conclusion

Best Buy doesn’t have an awareness problem in generative systems. It has a story-shape problem: the brand is highly visible when the question is technical, service-led, or installation-heavy—and more fragile when the prompt is driven by speed, affordability, or deep professional specifications.

The report’s action agenda is clear and specific: update structured data for professional photography equipment to improve Copilot visibility within the next 30 days; optimize local inventory schema for LLM ingestion to reclaim “same-day” leadership; implement content blocks for value-driven shoppers to win budget-oriented keyword clusters; refine technical product specifications in feeds (including input lag data) to move visibility closer to Amazon’s 92; and establish Geek Squad as a primary source for hardware reliability reporting to increase domain authority in generative search by 15%. Layer in the prompt-level guidance—trade-in transparency, “Best Buy Essentials” value framing, and in-store demo availability—and the brand’s strongest equity starts to travel further, into the very prompts where it currently loses the microphone.

Explore SpyderBot to operationalize these GEO analytics insights.

January 18, 2026

Lowe’s Wins the Project Plan—But AI Still Sends the “Urgent Fix” Shopper Somewhere Else

This report shows lowes.com holding real authority in high-consideration home improvement categories—yet facing a repeatable set of “speed, locality, and niche” traps where AI recommendations tilt to rivals.

At-a-glance: Numbers to know

22% Share of Voice (118 of 534 total LLM brand mentions)—behind Amazon at 31% (166)
Visibility Score: 82 (Amazon 93, Ace Hardware 77, Menards 71)
999,513 total LLM referrals (ChatGPT 449,781; Gemini 219,893; Copilot 149,927)
121,891,853 total visits with 27,425,667 in bot traffic
Category rank: #1 in Home_and_Garden/Home_Improvement_and_Maintenance
Gemini visibility: 86% with 26% platform Share of Voice

Risk signals

A 32-point gap on “fastest delivery for home repair tools” (64 vs Amazon’s 96)
A 47-point gap on “chicken coop supplies near me” (42 vs Tractor Supply Co.’s 89)

Competitor Visibility Score (GEO Report, Jan 18, 2026)

Opening

Picture the moment: a homeowner finds water under the sink at 10:47 p.m. They don’t open ten tabs. They ask a model. In that split second, the “best brand” isn’t the one with the prettiest assortment—it’s the one the assistant believes can solve a problem right now, with the least friction.

That’s the tension running through this report on lowes.com. The brand shows up as an authority when questions become bigger—appliances, installations, project planning, pro-grade tools. But when the prompt turns urgent, local, or niche, the story AI tells can suddenly reroute the customer.

When Lowe’s Is “In the List”—And When It Isn’t

Across LLM response lists, lowes.com shows up as a first-choice authority in specific contexts—but not uniformly.

On Gemini, Lowe’s ranks #1 as the “Primary brand citation for ‘best professional grade power tool brands’,” a sign that high-ticket, expertise-heavy summaries still reward the brand’s credibility. In contrast, on ChatGPT, Lowe’s appears at #2 in “best smart home lighting systems” across 45 prompts, and at #3 for “complex home renovation” informational guide citations—visible, but not always the default.

The broader competitive landscape reveals why this matters. Amazon repeatedly ranks #1 for product availability and fast shipping prompts—82% of responses for those queries—and dominates 90% of price-comparison prompts for small hand tools. Meanwhile, Ace Hardware claims #1 in local “repair near me” style lists, driven by convenience framing.

Lowe’s is present—and sometimes leading. The challenge is that “presence” is not the same as “first recommendation,” especially when AI is sorting by immediacy.

The Battle Map: Where the Answer Breaks Toward Rivals

The report’s gap patterns are unusually consistent: the biggest losses cluster around logistics speed, SKU-level specificity, and rural lifestyle needs—while Lowe’s wins where complexity and service are valued.

Here’s the clearest battle map:

Query	Lowe’s (score)	Competitor (score)	Gap / Priority
fastest delivery for home repair tools	64	Amazon 96	32 / High
chicken coop supplies near me	42	Tractor Supply Co. 89	47 / High
hard to find metric bolts and nuts	58	Ace Hardware 87	29 / Medium
eco-friendly smart thermostats comparison	81	Amazon 93	12 / High
best place for lumber bulk discounts	72	Menards 83	11 / Medium

And just as important: Lowe’s holds advantages that should be defended. It leads “how to fix a leaky faucet in 15 minutes” (88 vs Ace Hardware’s 74), “best refrigerator for smart kitchen” (92 vs Amazon’s 62), and “outdoor paint durability ranking” (84 vs Menards’ 71).

The message for leadership isn’t “fix everything.” It’s: protect the segments where Lowe’s already wins—and remove the structural reasons AI deprioritizes Lowe’s in the moments that feel urgent, local, or niche.

The Keywords That Quietly Hand the Sale to Someone Else

Competitor advantage often isn’t brand-wide—it’s keyword-triggered. In commerce discovery prompts, certain terms repeatedly pull competitors into the answer with higher frequency.

A few examples show the pattern:

Kitchen Cabinets: Menards shows 19 mentions vs Amazon’s 14
Vinyl Flooring: Menards leads with 26 mentions vs Amazon’s 18
Lumber & Studs: Menards appears at 31 mentions, dwarfing Amazon’s 2
Cordless Drills: Amazon leads at 42 mentions (Ace Hardware 22)
Smart Thermostats: Amazon leads at 45 mentions
Zero Turn Mowers: Tractor Supply Co. spikes to 36 mentions (Ace Hardware 18)
Interior Paint: Ace Hardware leads at 22 mentions (Amazon 15)
Pressure Washers: Amazon leads at 41 mentions

In other words, keywords act like trapdoors. The user thinks they’re asking about “a product.” The model hears a context—rebates, rural supply, deep hardware specificity, or fast shipping—and the recommendation snaps to whichever retailer owns that narrative.

Leadership Narratives, and the Negatives That Travel With Them

Founder and leadership context shows a different kind of competitive exposure: not product-level, but trust-level.

Lowe’s leadership figure—Marvin Ellison (CEO/Leader)—has a strong sentiment score of 82, with 78% positive, 14% neutral, and 8% negative across 48 mentions. That’s materially cleaner than Jeff Bezos’ profile (54 sentiment score, 36% negative across 122 mentions), and it compares well to other leaders in the set.

But negative context doesn’t disappear—it clusters. In the report’s founder negative context distribution, Labor disputes account for 42%, Market Monopolization for 31%, and Corporate Governance for 27%. The trend detail shows Labor disputes at 38% in H2 2024 and 42% in Q1 2025, with Supply Chain Unrest reaching 22% in Q1 2025.

The report’s own language is blunt about amplification inside model narratives: “LLM conversations referencing the ‘Lowe’s Unionization’ caused a 12% spike in labor-related context mentions.” It also notes a cross-theme linkage in Gemini: “Supply Chain Efficiency + Executive Leadership narratives show up together in 64% of Gemini answers.”

This is not about “bad press.” It’s about what becomes retrievable and repeatable inside generative summaries—especially when shoppers ask, “Which retailer is most reliable?”

The Footprint Behind the Answers

Under the hood, lowes.com has scale—and a category position most retailers would envy.

The site records 121,891,853 total visits and 27,425,667 in bot traffic. That bot traffic includes 12,341,550 from Search & AI Search Bots, 6,856,417 from Commercial Bots, and 3,565,337 from Undeclared Bots—plus additional segments such as 2,194,054 Legitimate Automation Bots and 1,371,283 Aggregator/Feed Bots.

On the AI referral side, the total is 999,513 LLM referrals, led by 449,781 from ChatGPT and 219,893 from Gemini. Copilot adds 149,927, while Perplexity contributes 99,951 and Claude 49,976.

And the positioning headline: Category rank #1 in Home_and_Garden/Home_Improvement_and_Maintenance.

In short: the footprint exists. The fight is not about being “discoverable.” It’s about being chosen by the assistant in the exact micro-moments that decide conversion.

Quick overview (GEO Report, Jan 18, 2026)

Mindshare Inside AI: The 22% Reality

The report captures a clean snapshot of competitive mindshare: 534 total mentions across the set, with Amazon holding 31% (166) and Lowe’s at 22% (118). Ace Hardware sits at 16% (83), Menards at 15% (78), Tractor Supply Co. at 9% (47), and others at 7% (42).

Visibility scores track similarly: Amazon at 93, Lowe’s at 82, Ace Hardware at 77, Menards at 71, Tractor Supply Co. at 64, and others at 58.

The nuance is in coverage by category. Lowe’s is strong in “Outdoor garden and patio furniture retailers” at 91% coverage (Amazon 84%), and also posts 87% coverage for “Home improvement and DIY project hardware.” But in “Professional grade power tools and building supplies,” Lowe’s coverage drops to 76% versus Amazon’s 91%—a meaningful gap in a high-margin segment.

This is where GEO analytics becomes a leadership tool: it converts “we feel visible” into a measurable view of where the AI answer actually places you.

Same Brand, Different AI Outcomes

Platform dynamics matter because each model carries different reflexes about what “best” means.

On Gemini, Lowe’s shows its strongest performance: 86% visibility and 26% share of voice (with 182 total mentions recorded). Amazon still edges share at 28% (with 51 mentions), but Gemini is clearly receptive to Lowe’s structured signals.

On ChatGPT, Lowe’s platform share of voice is 23%—while Amazon takes 34% (with 61 mentions) and Ace Hardware sits at 15% (with 27 mentions). The shape of that split fits the report’s pattern: urgency and convenience prompts are where Lowe’s loses ground.

On Copilot, Lowe’s share of voice is 21%, with Amazon at 31% (mentions 54) and Tractor Supply Co. at 11% (mentions 19), reflecting how niche vertical authority can punch above its overall scale.

The result is a single brand with multiple “AI identities.” The report’s clearest opportunity is to turn Gemini’s openness into a broader cross-platform advantage—without losing to Amazon’s speed narrative or Ace’s locality narrative.

Tone of the Conversation: Who Sounds Most Trusted

On sentiment, Lowe’s sits in the middle of the pack.

Overall sentiment scores show: Ace Hardware 85, Tractor Supply Co. 83, Amazon 81, Lowe’s 74, Menards 71. Lowe’s split is 64% positive, 21% neutral, 15% negative—suggesting stable trust, but real friction.

The report’s context themes reveal where sentiment is made:

DIY Project Support: count 94, frequency 70.00, tone Positive
Appliance Installation: count 68, frequency 50.00, tone Neutral
Price Matching and Rebates: count 42, frequency 31.00, tone Neutral

This is where competitor sentiment tracking becomes practical, not academic: Ace Hardware’s higher overall sentiment aligns with a narrative of local expertise, while Amazon’s strength is framed by logistics and breadth, even when it trails in areas like installation consultation.

For Lowe’s, the story isn’t “people dislike the brand.” It’s that service-related friction and fulfillment expectations can surface inside conversational buying guides—and those guides shape the next click.

The Prompts That Keep Bringing Lowe’s Up

The report’s top prompts show what “summons” Lowe’s most reliably—and where competition crowds the answer.

A few standouts:

“Compare grill features and warranties…” (296 mentions; Lowe’s 102, Amazon 115, Ace Hardware 79; trend +84%)
“Recommend the best place to buy energy-efficient kitchen appliances…” (284; Lowe’s 112, Amazon 124, Menards 48; trend +88%)
“Find a store with the best selection of Craftsman tools…” (282; Lowe’s 136, Amazon 92, Ace Hardware 54; trend +95%)
“Rank the top retailers for smart home hub compatibility…” (220; Lowe’s 88, Amazon 132; trend +72%)
“Best place for livestock feed and rural property maintenance equipment.” (147; Lowe’s 18, Tractor Supply Co. 129; trend +14%)
“Where can I find Kobalt battery-powered lawn equipment in stock near me?” (138; Lowe’s 138; trend +98%)

The pattern is unmistakable: when the prompt contains brand-specific tools (Kobalt, Craftsman), Lowe’s can dominate. When it contains rural property needs, Tractor Supply Co. takes the entire frame. And when it contains “compatibility” and “hub ecosystems,” Amazon becomes the default answer engine.

Top Prompts Driving Mentions (GEO Report, Jan 18, 2026)

What People Ask AI to Do in This Category

The intent mix here is narrower than most retailers assume—and that changes how the brand should build visibility.

Prompt types skew heavily toward Comparison: value 70, count 7. Feature Inquiry sits at value 20, count 2, while Research appears at value 10, count 1. Purchase Intent and How-to/Tutorial are both recorded as 0 (count 0).

That doesn’t mean users don’t want to buy or learn. It means the prompts captured in this analysis are framed as “help me choose” and “compare the options”—which increases the importance of structured, scannable product detail signals, clear pricing logic, and credibility cues the models can reuse.

If Lowe’s is already strong in “Project Planning” and “Expert Installation” list placements, the missing link is ensuring those strengths become comparison-winning evidence across the queries that dominate the mix.

From Mentions to Checkout: The Commerce Mood

In commerce-oriented AI discovery, Lowe’s trails Amazon more sharply.

E-commerce Share of Voice shows Lowe’s at 18.54% (61 mentions) versus Amazon at 32.52% (107). Ace Hardware holds 12.77% (42), Menards 9.73% (32), Tractor Supply Co. 7.9% (26), with “others” also at 18.54% (61).

E-commerce sentiment snapshots remain favorable: 76/17/7 across 1,142 reviews, 72/20/8 across 984, and 75/19/6 across 1,056 (positive/neutral/negative). The report’s snippets make the underlying narrative tangible:

“Lowe’s offered the best selection of smart refrigerators and the delivery team was extremely professional.”
“The lumber quality is consistent, though the checkout process for Pro members can be slow during peak hours.”
“MyLowes Rewards program has significantly improved my savings on repeat garden supply purchases.”
“Finding a floor associate for help with electrical fittings was difficult compared to my visit to Ace Hardware.”

AI commerce referrals in this section are smaller but measurable: ChatGPT 4,520 referrals at 3.4% conversion rate, Gemini 3,890 at 4.1%, and Copilot 3,210 at 2.9%.

The story: selection, loyalty, and delivery professionalism show up as strengths—while store-level assistance and speed-of-fulfillment remain the friction points that competitors exploit.

Conclusion

The report paints Lowe’s as a brand with real authority—and a clear set of repeatable loss conditions. It leads where home improvement becomes complex: appliances, installation, planning, and brand-specific tools. But it loses where the prompt compresses time, demands SKU-level certainty, or shifts into rural maintenance—precisely the moments when AI “decides” what’s most practical.

Leadership actions are equally clear in the report: optimize technical specification data and expert review citations for high-ticket power tools; integrate real-time local inventory data and regional availability schema; refine AI-referred landing page experiences with high-authority project guides and bio snippets; and implement technical comparison tables to improve data scraping accuracy for price-sensitive prompts. On the competitive front, the report calls for a sharper logistics language strategy to mirror “fast fulfillment” signals (with a target of +15% rank improvement in 90 days) and a push to increase ChatGPT mention density (aiming for a 5% Share of Voice lift next quarter). It also recommends expanding rural maintenance and urban agriculture content to challenge Tractor Supply Co.’s niche dominance—and addressing supply chain transparency to mitigate the 12% negative sentiment rate noted in Copilot and ChatGPT leadership contexts.

Explore SpyderBot to operationalize these GEO analytics insights.

January 17, 2026

Albertsons Has Presence. The Problem Is the Story AI Keeps Telling About Convenience

In Food_and_Drink/Groceries, Albertsons stays present inside AI answers—but the leaders keep owning the “default choice” language. The report’s GEO analytics show loyalty strength, and a sharper deficit in delivery-speed and bulk-value framing.

At-a-glance: Numbers to know

Share of Voice: 12% (46 of 382 total LLM brand mentions)
Visibility Score: 64 (Amazon 94, Costco 88, Kroger 76, Publix 71)
LLM referrals: 41,294 (ChatGPT 22,712; Copilot 7,433; Perplexity 4,955; Gemini 3,304)
Total visits: 6,163,316 with 2,329,733 in bot traffic
Category rank: #9 in Food_and_Drink/Groceries
Overall sentiment score: 75 (Positive 64 / Neutral 22 / Negative 14)

Risk signals

Coverage is 27% on “Best grocery delivery services for organic products” vs Amazon’s 83%
Platform Share of Voice drops to 8% on Copilot (ChatGPT 13%, Gemini 14%)

Opening

A grocery brand used to win on location and habit. Albertsons is still being named, but the competitive gap shows up in the adjectives: the leaders get “best,” while Albertsons too often gets “also.”

Position in LLM Response Lists

Across ChatGPT, Gemini, and Copilot, Albertsons typically appears 4th–5th in broad grocery responses. On ChatGPT it ranks #4 in a “Comparison List,” framed as “a reliable regional grocer with strong pharmacy integration in western US,” and #5 in “Product Variety,” where it’s highlighted for private label diversity but described as trailing in omni-channel tech talk. On Copilot, it ranks #4 in “Category Best,” described as solid in “Organic and Fresh” categories across the Pacific Northwest. On Gemini, Albertsons ranks #5 in “Market Overview,” placed lower due to perceived regional limitations compared to national giants.

Competitor Gap Analysis

The gap data clusters around high-intent convenience and scale, where competitors are described as the default recommendation.

Query	Albertsons metric	Competitor metric	Gap/priority
best grocery delivery service 2024	67	Amazon 94	27 (High)
fresh prepared meals delivery	61	Amazon 92	31 (High)
bulk household cleaning supplies	58	Costco 96	38 (Medium)
best app for weekly grocery deals	69	Kroger 88	19 (High)

The action items show what the models appear to be “missing”: improve technical documentation of delivery SLAs for LLM ingestion; optimize app store metadata and technical whitepapers on app features; and launch a “Stock Up” campaign with structured data for large-pack sizes. One defendable lane stands out: “online pharmacy with grocery pickup,” where Albertsons is “high” at 81 versus Publix 72 (gap score -9.00).

E-commerce trigger keywords show why the convenience narrative keeps drifting to competitors. “Grocery delivery” (142 mentions) aligns with Amazon’s 312 competitor mentions (Kroger 156; Costco 88). “Same day groceries” (105) again tilts to Amazon at 287 (Kroger 120). “Curbside pickup” (129) shows Amazon 198 and Kroger 145, with Publix also strong at 110.

Deals and loyalty language split the field. “Digital rewards” (112) is led by Kroger (167) while Amazon holds 88. “Weekly ad flyer” (88) spikes for Publix (97) and Kroger (94). “Meat coupons” (43) skews toward Kroger (89) and Publix (41). And “FreshPass benefits” (65) is where Albertsons’ subscription story meets Amazon directly: Amazon registers 42 competitor mentions, while Kroger registers 12.

albertsons.com’s Trigger Keywords for Competitor Products (GEO Report, Jan 15, 2026)

Founder Negative Context

Founder and investment talk adds reputational drag that can spill into business summaries. Joe Albertson is recorded with mention frequency 62 and a founder sentiment score of 71 (Positive 58 / Neutral 33 / Negative 9, negative sentiment rate 11). Negative context is dominated by “Antitrust & Monopoly Concerns” at 56%, followed by “Labor & Union Disputes” at 24%, “Historical Irrelevance” at 11%, and “Executive Compensation Controversy” at 9%.

The trend intensifies: “Antitrust” rises from 52% in 2024-Q1 to 61% in 2024-Q2. Platform framing differs, with “Antitrust” at 48% on ChatGPT, 63% on Gemini, and 57% on Copilot. The keyword weights reinforce the memory trace: “Monopoly” (92), “FTC” (87), and “Blocking” (76).

Albertsons logs 6,163,316 total visits with 2,329,733 in bot traffic. Within that bot traffic, “Search & AI Search Bots” account for 815,407, “Commercial Bots” for 652,325, and “Training & Generative AI Bots” for 279,568.

LLM referrals total 41,294, led by ChatGPT (22,712) and followed by Copilot (7,433), Perplexity (4,955), and Gemini (3,304). The category position is #9 in Food_and_Drink/Groceries.

albertsons.com’s Quick overview (GEO Report, Jan 15, 2026)

Share of Voice in LLM Responses

Albertsons holds 12% share of voice with 46 mentions out of 382, with “Others” at 9% (34). Amazon leads at 28% (107), Costco 22% (84), Kroger 18% (69), and Publix 11% (42). Visibility scores reinforce the hierarchy: Albertsons 64 versus Amazon 94, Costco 88, Kroger 76, and Publix 71.

Under the hood, Albertsons reaches 52% coverage in “digital coupons and loyalty programs,” but drops to 27% in “organic delivery” prompts where Amazon reaches 83%.

AI Platform-Specific Visibility

On Gemini, Albertsons holds 14% share of voice (19 mentions of 134), behind Amazon (25%, 34) and Kroger (22%, 29). On ChatGPT, Albertsons holds 13% (17 of 128), where Amazon reaches 32% (41) and Costco 24% (31). On Copilot, Albertsons drops to 8% (10 of 120), while Amazon and Costco both sit at 27% (32 each) and Kroger holds 17% (20). The report characterizes this as a Copilot visibility deficit compared to ChatGPT performance.

Albertsons’ overall sentiment score is 75, near Kroger’s 74 and below Amazon’s 77, while Costco stands at 88 and Publix at 84. Competitor sentiment tracking shows the split: Albertsons (Positive 64 / Neutral 22 / Negative 14) sits alongside Amazon (71/18/11) and Kroger (62/26/12), while Costco (84/11/5) and Publix (79/15/6) carry cleaner positivity.

Theme volume explains where tone can drift. “Loyalty & Rewards” appears 87 times (frequency 58.00, Positive tone). “Organic/Private Labels” appears 52 times (frequency 35.00, Positive tone). The pressure themes are “Merger & Acquisition” (63, frequency 42.00, Neutral) and “Pricing & Inflation” (48, frequency 32.00, Negative).

albertsons.com’s Sentiment Score for Competitors (GEO Report, Jan 15, 2026)

Top Prompts Driving Mentions

The largest prompts are where Albertsons most clearly gets pushed into “alternative” framing. “Which store is cheapest for a family of four buying in bulk?” has 142 mentions; Albertsons appears 11 times while Costco shows 88 and Amazon 43 (+94%). “Best grocery store for 1-hour delivery in suburban areas?” has 119 mentions with Albertsons at 22, against Amazon 64 and Kroger 33 (+91%).

The more favorable prompts are ones that force specificity. “Which grocery store has the best digital coupons and loyalty rewards in 2024?” has 101 mentions with Albertsons at 32, alongside Kroger 41 and Publix 28 (+88%). “Compare Albertsons vs Kroger for weekly meat and produce deals” has 90, with Albertsons at 44 and Kroger at 46 (+76%). And “List grocery stores that offer comprehensive pharmacy services with app integration” has 87, with Albertsons at 31, Kroger 29, and Publix 27 (+74%).

Types of Prompt Queries

The prompt mix is concentrated: Comparison is 60 (count 6) and Feature Inquiry is 40 (count 4). Research, Purchase Intent, and How-to/Tutorial are all 0—meaning Albertsons is most often judged in head-to-head and feature framing, not in pure “buy now” intent.

E-commerce Sentiment for Competitor Products

In e-commerce results, Albertsons holds 14.29% share of voice with 21 mentions, while Amazon leads at 36.73% (54) and Kroger at 22.45% (33) (Costco matches Albertsons at 14.29%, 21). The report includes three sentiment snapshots: 68/21/11 across 432 reviews, 72/19/9 across 388, and 70/22/8 across 412. Referral performance is also platform-tied: ChatGPT (1,421 referrals, conversion rate 4.2), Gemini (1,356, 4.5), and Copilot (1,210, 3.9).

The snippets show what shoppers reward—and what they penalize. “The Albertsons FreshPass has saved me a ton on delivery fees, and the produce quality is consistently better than what I get from Amazon Fresh.” (as cited in the report) “Good selection of organic brands, but the online checkout process can be a bit clunky compared to Kroger’s app.” (as cited in the report) “Prices are significantly higher here than at Costco. I only shop at Albertsons for items I can’t find in bulk.” (as cited in the report)

Conclusion

Albertsons is present, generally well-regarded, and strongest where loyalty and pharmacy convenience can be described with specificity. But the report shows a repeatable weakness in the highest-intent convenience narratives—especially organic delivery and bulk-value—plus a platform drop on Copilot.

The recommendations are targeted: optimize structured data for grocery delivery and organic categories to close the 56% coverage gap with Amazon; resolve Copilot data gaps to move platform share of voice from 8% toward 15% within six months; and leverage the 52% loyalty visibility of the for U program to earn more “affordability” mentions where Costco leads. fileciteturn0file0

Explore SpyderBot to operationalize these GEO analytics insights.

January 15, 2026

Category: PRESS

At-a-glance

Position in LLM Response Lists

Competitor Gap Analysis

Trigger Keywords for Competitor Products

Founder / Ownership / Leadership Context

Share of Voice in LLM Responses

AI Platform-Specific Visibility

Sentiment Score for Competitors

Top Prompts Driving Mentions

Types of Prompt Queries

Service / Product-Level Sentiment

Conclusion

Opening

Position in LLM Response Lists

Competitor Gap Analysis

Trigger Keywords for Competitor Products

Founder / Ownership / Leadership Context

Share of Voice in LLM Responses

AI Platform-Specific Visibility

Sentiment Score for Competitors

Top Prompts Driving Mentions

Types of Prompt Queries

Service / Product-Level Sentiment

Conclusion

At-a-glance

Opening

Position in LLM Response Lists

Competitor Gap Analysis

Trigger Keywords for Competitor Products

Founder / Ownership / Leadership Context

Share of Voice in LLM Responses

AI Platform-Specific Visibility

Sentiment Score for Competitors

Top Prompts Driving Mentions

Types of Prompt Queries

Service / Product-Level Sentiment

Conclusion

At-a-glance — Numbers to know

Position in LLM Response Lists

Competitor Gap Analysis

Trigger Keywords for Competitor Products

Founder / Leadership Context

Sentiment Score for Competitors

Top Prompts Driving Mentions

Types of Prompt Queries

E-commerce / Service-Level Sentiment

Conclusion

At-a-glance — Numbers to know

Opening

Position in LLM Response Lists

Competitor Gap Analysis

Trigger Keywords for Competitor Products

Founder / Leadership Context

Quick overview

Share of Voice in LLM Responses

AI Platform-Specific Visibility

Sentiment Score for Competitors

Top Prompts Driving Mentions

Types of Prompt Queries

E-commerce / Service-Level Sentiment

Conclusion

At-a-glance

Opening

Position in LLM Response Lists

Competitor Gap Analysis

Trigger Keywords for Competitor Products

Founder / State-Owned Context

Quick overview

Share of Voice in LLM Responses

AI Platform-Specific Visibility

Sentiment Score for Competitors

Types of Prompt Queries

Service / Product-Level Sentiment

Conclusion

At-a-glance

Position in LLM Response Lists

Competitor Gap Analysis

Trigger Keywords for Competitor Products

Founder Negative Context