E*TRADE AI Market Strategy Report - Roth IRAs

Answer Capsule

E*TRADE appears in AI responses at a modest rate but rarely earns recommendation credit in the Roth IRA category. The benchmark shows a 1.8% top-three recommendation rate and a 7.5% valid recommendation coverage across 1,384 observations, indicating a significant gap between visibility and shortlist eligibility. E*TRADE records zero rank-one recommendations on ChatGPT, Gemini, and Google AI Mode. The clearest weakness is the absence of recommendation-stage presence across all three buyer-stage clusters. The clearest opportunity lies in building the citation architecture needed to convert factual references into ranked recommendations.

Who This Report Is For

This report is for E*TRADE marketing, product, and strategy leaders responsible for AI-led discovery positioning, competitive visibility, and buyer shortlist eligibility in the Roth IRA category.

Report Card

• Report type: AI Company Market Strategy Report
• Target company: E*TRADE
• Category / market studied: Roth IRAs
• Reporting month: June 2026
• AI platforms tracked: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, Perplexity
• Public high-intent clusters: 3 (Discovery, Comparison, Pricing and Fees)
• AI observations analyzed: 1,384
• Competitors tracked: Charles Schwab, Fidelity, Vanguard, Robinhood, Betterment, Wealthfront, SoFi, M1 Finance, Merrill Edge

Executive Summary

E*TRADE appears in 17.3% of all AI responses in the Roth IRA category, placing it in the lower tier of measured brands. The benchmark reveals a more significant problem beneath that surface figure. E*TRADE earns a valid recommendation in only 7.5% of observations, and its top-three recommendation rate is just 1.8%. The company records zero rank-one recommendations on three of the six platforms tested: ChatGPT, Gemini, and Google AI Mode.

The gap between mention presence and recommendation coverage is the central finding. E*TRADE is known to AI systems but is not being selected as a top choice. Its average recommended rank of 4.09 means that even when it does earn recommendation credit, it appears in the middle of the list rather than at the top, which is a structural disadvantage at the decision moment.

E*TRADE captures an estimated $123K in monthly AI Authority Value, the second-lowest among the ten measured brands. Charles Schwab, the category leader, captures $1.86M. The comparison is direct. E*TRADE is present in AI responses but is not positioned to capture buyer attention where shortlists are formed.

The net sentiment score of 0.57 is the second-lowest in the category, driven by a high neutral mention rate and one negative mention. This is not a visibility problem in the traditional sense. It is a recommendation architecture problem. The evidence that AI systems need to rank E*TRADE as a top choice is not sufficiently present or structured in the public layer AI systems retrieve and synthesize from.

What E*TRADE Is Winning

E*TRADE has one narrow but measurable win. On Copilot, the company achieves a 3.7% rank-one rate, the highest of any platform in the E*TRADE dataset. This suggests that Copilot's retrieval and ranking logic surfaces E*TRADE as a first-choice recommendation in a small but meaningful share of prompts.

The company also shows a 4.4% rank-one rate on Google AI Overviews, indicating that some AI-generated overview content positions E*TRADE as a top option in that environment. These are isolated pockets of recommendation strength rather than consistent cross-platform performance.

On Perplexity, E*TRADE records a sentiment score of 0.88, the strongest of any platform in the dataset. Twenty-two of twenty-five Perplexity mentions are positive, suggesting that the public evidence layer Perplexity retrieves from is more recommendation-friendly to E*TRADE than the layers used by ChatGPT, Gemini, or Google AI Mode.

Where E*TRADE Has the Clearest AI Visibility Gaps

The most significant gap is the near-total absence of top-three recommendation placement. E*TRADE's 1.8% top-three rate is the second-lowest in the category, ahead of only Merrill Edge. On ChatGPT, Gemini, and Google AI Mode, E*TRADE records zero rank-one recommendations. When investors ask these platforms for the best Roth IRA provider, E*TRADE is almost never the first choice surfaced.

The Comparison cluster is particularly weak. In the Brokerage and Investment Platform Comparisons cluster, E*TRADE achieves a 3.7% top-three rate and a 4.0 average rank. Charles Schwab leads this cluster with a 56.5% top-three rate. E*TRADE is being displaced by competitors that have stronger evidence layers for comparison-stage queries, where buyers are actively evaluating alternatives.

The Pricing and Fees cluster shows the same pattern. E*TRADE achieves a 1.1% top-three rate and a 4.1 average rank. Charles Schwab leads at 51.2%. This cluster carries the highest commercial value at $18.1M, and E*TRADE is capturing only $50K of it. The scale of that gap makes this the most commercially significant visibility failure in the dataset.

The net sentiment score of 0.57 reflects a structural concern. E*TRADE has 102 neutral mentions out of 240 total mentions. The high neutral rate indicates AI systems are referencing E*TRADE as a factual option rather than a recommended one. The single negative mention, while low in volume, is a signal that some AI responses contain cautionary framing worth monitoring.

Biggest Opportunity

The clearest path from reference to recommendation is in the Pricing and Fees cluster. This decision-stage cluster carries the highest commercial value at $18.1M, and E*TRADE captures only $50K of it. Improving recommendation coverage in this cluster requires stronger evidence about fee structures, cost comparisons, and value propositions that AI systems can retrieve, evaluate, and rank.

E*TRADE's fee structure and pricing are documented on its own properties, but the benchmark suggests this evidence is not reaching AI systems in a form that earns recommendation credit at the decision moment. Building comparison-ready content, structured data, and citable third-party references for pricing and fee queries could convert neutral factual references into ranked recommendations in the cluster where buyer intent is highest.

Prompt Evidence

Perplexity / Discovery Prompt: "What are the best Roth IRA accounts for long-term investors?" Result: E*TRADE appeared with positive framing, consistent with Perplexity's 0.88 sentiment score for the brand.

ChatGPT / Discovery Prompt: "What are the best Roth IRA accounts for beginners?" Result: E*TRADE was mentioned as a factual reference but received no rank-one recommendation credit, consistent with zero rank-one performance on ChatGPT.

Gemini / Pricing and Fees Prompt: "Which brokerage has the lowest Roth IRA fees?" Result: E*TRADE did not appear in the top recommendations, consistent with a 1.1% top-three rate in this cluster.

Google AI Mode / Comparison Prompt: "Compare Roth IRA providers for low fees and investment options" Result: E*TRADE appeared in the response but was not ranked in a top-three position, and the single negative mention recorded in the dataset is associated with this platform.

What CiteWorks Studio Would Do Next

Phase 1: AI Market Discovery Audit Map every prompt where E*TRADE appears, where it is recommended, and where competitors are chosen instead, across all six platforms and three buyer-stage clusters.

Phase 2: Recommendation Readiness Plan Identify the specific evidence gaps in the Pricing and Fees and Comparison clusters that prevent E*TRADE from earning recommendation credit at the decision moment.

Phase 3: Owned Answer Layer Buildout Develop structured, citable content for fee comparison, account features, and beginner suitability that AI systems can retrieve and rank against competitors.

Phase 4: Citation and Authority Layer Development Strengthen the third-party citation footprint through comparison content, review sources, and trust signals that AI systems treat as authoritative when forming shortlists.

Phase 5: Monthly AI Visibility and Recommendation Tracking Monitor changes in mention presence, recommendation coverage, top-three rate, rank-one rate, and sentiment across all platforms and clusters on a recurring basis.

Why This Matters

Investors shopping for a Roth IRA provider are increasingly relying on AI systems to form their shortlists before they ever visit a brokerage website. E*TRADE is present in AI responses but is not being selected. The difference between being mentioned and being recommended is the difference between being considered and being chosen, and the benchmark shows that E*TRADE is losing that distinction across most platforms and both high-value clusters.

The benchmark shows that AI recommendation power in the Roth IRA category is concentrating around providers with the strongest evidence layers. E*TRADE has brand awareness and a measurable mention footprint, but it lacks the citation architecture that AI systems rely on when ranking providers at the decision moment. The next move is not to increase raw visibility. It is to convert existing visibility into recommendation credit by building the structured, citable evidence that AI systems need to surface E*TRADE as a top choice.

Core Metrics

• Mentions: 240
• Valid recommendations: 104
• Top 3 recommendation count: 25
• Rank 1 recommendation count: 11
• Average recommended rank: 4.09
• Positive mentions: 137
• Neutral mentions: 102
• Negative mentions: 1
• Raw mention presence rate: 17.3%
• Valid recommendation coverage: 7.5%
• Top 3 recommendation rate: 1.8%
• Rank 1 recommendation rate: 0.8%
• Strongest cluster by recommendation behavior: Discovery (C01)
• Strongest platform by recommendation behavior: Copilot

Sentiment Score

Sentiment Score = (137 positive x 1) + (102 neutral x 0) + (1 negative x -1) / 240 total mentions = 0.57

This score means E*TRADE's framing in AI responses is more positive than negative overall, but the high neutral count of 102 mentions indicates that most references are factual rather than recommendation-driven. Unclassified mention counts are misleading because they treat a neutral reference and a positive recommendation as equal signals. Share of voice is a diagnostic metric, not a business KPI. A positive recommendation, a neutral reference, a cautionary mention, and a competitor-displaced mention carry meaningfully different commercial weight and should never be counted together as wins. Classified sentiment is required before any AI visibility figure can be interpreted accurately.

Sentiment by Platform

Methodology

1. This report is an AI Company Market Strategy Report based on the LLM Authority Index benchmark for the Roth IRA category. It is not a client engagement result or a full audit.
2. The reporting window is June 2026. All data reflects a point-in-time snapshot and should not be treated as a continuous or longitudinal measure.
3. AI platforms tested: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, and Perplexity.
4. Total observations analyzed: 1,384 across three public high-intent clusters. The exact number of unique prompts used to generate these observations was not available in the public dataset version used for this report.
5. Competitor universe: Charles Schwab, Fidelity, Vanguard, Robinhood, Betterment, Wealthfront, SoFi, M1 Finance, and Merrill Edge. This set reflects the brands included in the benchmark and is not a complete census of all Roth IRA providers.
6. Prompt clusters: Discovery (awareness-stage queries), Comparison (consideration-stage queries), and Pricing and Fees (decision-stage queries).
7. A mention is defined as any appearance of a brand in an AI-generated response, regardless of sentiment, rank, or recommendation quality.
8. A valid recommendation is defined as a positive, shortlist-quality recommendation or ranked recommendation that earns recommendation credit in the AI response. Visibility and valid recommendation are not the same signal and are not interchangeable in this report.
9. Ranking and scoring metrics used include valid recommendation coverage, top-three rate, rank-one rate, average rank, net sentiment score, and modeled monthly AI Authority Value. Modeled AI Authority Value is a benchmark estimate based on commercial intent proxies and is not revenue, pipeline, or booked demand.
10. AI outputs are probabilistic and change over time. This report reflects the AI recommendation environment as observed during the reporting window. Future outputs on the same prompts may differ.
11. The citation and source layer described in this report refers to the public evidence that AI systems may retrieve or synthesize from when forming responses. Ahrefs or search-based signals, where referenced, are used only as supporting evidence for the traditional organic search and source footprint and do not independently prove AI recommendation influence.
12. Taxonomy, cluster labels, and company names are normalized to the benchmark standard. Any discrepancies between the public report text and structured dataset values are resolved in favor of the structured dataset.

See How AI Is Recommending Your Brand

The Roth IRA category is experiencing shortlist compression, and the gap between visibility and recommendation power is widening across all major AI platforms. CiteWorks Studio can show where your brand appears, where competitors are recommended instead, which prompts carry the most commercial risk, which sources are shaping AI answers, and what needs to change to improve recommendation-stage visibility in the clusters where buyer decisions are forming.