GetResponse AI Market Strategy Report - Email Marketing Services

Answer Capsule

GetResponse shows limited AI recommendation power in the email marketing service category for June 2026. The platform appears in 10.7% of AI responses but converts only 2.6% of those appearances into valid recommendations. Its strongest signal is on Copilot, where it achieves a net sentiment score of 0.86, though from a small sample. The clearest weakness is near-zero top-three recommendation rates across all platforms and buyer stages. The clearest opportunity is building a structured public evidence layer focused on comparison content and pricing evaluation prompts, where competitors with similar feature sets are being recommended instead.

Who This Report Is For

This report is for marketing leaders, product marketers, and growth teams at GetResponse who need to understand how AI systems are positioning the brand in buyer shortlists and what must change to improve recommendation-stage visibility.

Report Card

• Report type: AI Company Market Strategy Report
• Target company: GetResponse
• Category / market studied: Email Marketing Service
• Reporting month: June 2026
• AI platforms tracked: ChatGPT, Gemini, Copilot, Perplexity, Google AI Mode, Google AI Overviews
• Public high-intent clusters: 3 (Discovery, Comparison, Pricing Evaluation)
• AI observations analyzed: 1,519
• Competitors tracked: 10

Executive Summary

GetResponse holds a marginal position in AI-generated buyer shortlists for the email marketing service category. The benchmark shows the platform appearing in 10.7% of all AI responses across six platforms and three buyer intent clusters, but converting only 2.6% of those appearances into valid recommendations. Its top-three recommendation rate is 0.7%, and its rank-one rate is 0.1%. These figures place GetResponse in the lower tier of tracked companies, alongside AWeber, Campaign Monitor, and Kit (ConvertKit).

The platform's strongest cluster is the Comparison stage, where it achieves a 3.8% valid recommendation coverage rate and a net sentiment score of 0.61. Its weakest cluster is Pricing Evaluation, where it earns zero top-three recommendations and a 0.9% valid recommendation coverage rate. This is a significant gap because pricing evaluation prompts carry the highest commercial intent value in the category.

GetResponse's strongest platform signal is on Copilot, where it achieves a 2.4% valid recommendation coverage rate and a net sentiment score of 0.86, though from only 7 observations. On Gemini, the platform appears in 15.7% of responses but earns zero valid recommendations, meaning it is listed but never endorsed. On Google AI Overviews, GetResponse appears in 5.2% of responses but earns zero valid recommendations and zero top-three placements.

The most commercially significant finding is that GetResponse captures only $73,934 in monthly AI Authority Value against a total category opportunity of $25.4 million. Its captured share of AI opportunity is 0.3%. Competitors with similar feature sets, particularly Brevo and Mailchimp, are being recommended in prompts where GetResponse is absent or listed without endorsement.

Across the full dataset, 56.2% of GetResponse's mentions are neutral, meaning the brand is appearing as a known option rather than an endorsed one. This framing pattern limits recommendation conversion and reduces the brand's shortlist eligibility in AI-driven buyer journeys.

What GetResponse Is Winning

GetResponse shows a narrow but meaningful recommendation pocket on Copilot. On this platform, the brand achieves a 2.4% valid recommendation coverage rate and a net sentiment score of 0.86, the highest platform-level sentiment score for the brand across all six platforms. This suggests that Copilot's source selection and response construction may favor the type of content GetResponse has in its public evidence layer.

The brand also shows its highest valid recommendation coverage rate on Perplexity at 9.8%, paired with a mention presence rate of 28.1%. Perplexity retrieves GetResponse more frequently than other AI systems, indicating that some element of the brand's public evidence layer is reaching this platform's source pool.

In the Comparison cluster, GetResponse achieves a net sentiment score of 0.61, its strongest cluster-level sentiment result. When the brand appears in comparison prompts, the framing is more likely to be positive than in other buyer stages. This is a foundation that a more structured comparison content strategy could build on.

Where GetResponse Has the Clearest AI Visibility Gaps

GetResponse's most significant gap is the near-total absence of top-three recommendation placement. Across all 1,519 observations, the brand earns only 10 top-three recommendations and 1 rank-one recommendation. Its top-three rate of 0.7% is the third lowest among tracked companies, ahead of only AWeber and Kit (ConvertKit).

The Pricing Evaluation cluster is the most commercially dangerous gap. GetResponse earns zero top-three recommendations and zero rank-one recommendations in this cluster, despite pricing evaluation prompts carrying the highest buyer stage multiplier in the benchmark at 1.5. Competitors such as Brevo and Mailchimp are being recommended in pricing prompts where GetResponse is absent.

On Gemini, GetResponse appears in 15.7% of responses but earns zero valid recommendations. This is a visibility-without-recommendation pattern: the brand is listed as a known option but never endorsed. On Google AI Overviews, the pattern repeats. A 5.2% mention presence rate produces zero valid recommendations and zero top-three placements.

The brand's average recommended rank of 5.18 is the weakest among all tracked companies with any recommendation credit. When GetResponse is recommended, it appears toward the bottom of the ranked list, reducing its visibility to buyers scanning AI-generated shortlists.

GetResponse's monthly AI Authority Value of $73,934 is dominated by visibility assist value at $52,684, representing 71.3% of total captured value. Only $21,251 comes from recommendation value. This ratio signals that the brand is being seen but not chosen, a commercially fragile position relative to competitors with more balanced or recommendation-weighted value profiles.

Biggest Opportunity

The clearest path from reference to recommendation for GetResponse is building a structured comparison and pricing content layer directly targeting the prompts where buyers are making final platform decisions. The Pricing Evaluation cluster carries the highest commercial intent value in the category, and GetResponse earns zero top-three recommendations there. Brevo, which is frequently recommended as a cost-effective alternative in pricing prompts, demonstrates the type of public evidence layer GetResponse needs: clear pricing documentation, structured feature comparison content, and cost-benefit analysis that AI systems can retrieve and synthesize into shortlist responses. Closing this specific gap would directly target the highest-value buyer stage where the brand is currently invisible and would move value from the visibility assist column into the recommendation column.

Prompt Evidence

Copilot / Comparison Prompt: "Compare email marketing platforms for small businesses" Result: GetResponse appeared in the response but was not ranked in the top three recommendations.

Perplexity / Discovery Prompt: "What are the best email marketing tools for automation?" Result: GetResponse was listed among options but received a neutral mention without positive recommendation framing.

Gemini / Pricing Evaluation Prompt: "Which email marketing service is most affordable for startups?" Result: GetResponse was not recommended; Brevo and Mailchimp were listed as cost-effective alternatives.

Google AI Overviews / Discovery Prompt: "Top email marketing software for ecommerce" Result: GetResponse did not appear in the response; ActiveCampaign and Klaviyo dominated the shortlist.

What CiteWorks Studio Would Do Next

Phase 1: AI Market Discovery Audit Map GetResponse's current mention and recommendation profile across all six platforms and three buyer clusters to identify the specific prompts where the brand is absent or displaced by competitors.

Phase 2: Recommendation Readiness Plan Identify the comparison and pricing evaluation prompts where GetResponse should be recommended and build the content and citation architecture needed to qualify for those positions.

Phase 3: Owned Answer Layer Buildout Develop structured pricing documentation, feature comparison pages, and use-case content that AI systems can retrieve and synthesize for recommendation-stage prompts.

Phase 4: Citation / Authority Layer Development Strengthen GetResponse's presence in third-party comparison articles, review aggregations, and community discussions to provide the public evidence AI systems use to build ranked recommendations.

Phase 5: Monthly AI Visibility and Recommendation Tracking Track GetResponse's recommendation coverage, top-three rate, and sentiment across platforms and clusters to measure progress and adjust strategy as the category evolves.

Why This Matters

AI systems are becoming the first stop for buyers evaluating email marketing platforms. When a buyer asks for the most affordable option or the best platform for automation, the AI response effectively becomes the shortlist. GetResponse is appearing in some of these responses, but it is rarely being recommended. In an AI-driven discovery market, presence without recommendation is a leading indicator of declining shortlist eligibility.

The gap between GetResponse's mention presence and its recommendation coverage is not a brand awareness problem. It is a public evidence problem. The platforms being recommended instead of GetResponse have stronger comparison content, clearer pricing documentation, and more frequent positive framing in the sources AI systems retrieve. Closing this gap requires targeted investment in the content and citation layers that AI systems use to build buyer shortlists, with the Pricing Evaluation cluster as the highest-priority starting point.

Core Metrics

• Mentions: 162
• Valid recommendations: 39
• Top 3 recommendation count: 10
• Rank 1 recommendation count: 1
• Average recommended rank: 5.18
• Positive mentions: 70
• Neutral mentions: 91
• Negative mentions: 1
• Raw mention presence rate: 10.7%
• Valid recommendation coverage: 2.6%
• Top 3 recommendation rate: 0.7%
• Rank 1 recommendation rate: 0.1%
• Strongest cluster by recommendation behavior: Comparison (3.8% valid recommendation coverage)
• Strongest platform by recommendation behavior: Perplexity (9.8% valid recommendation coverage); strongest sentiment signal: Copilot (0.86 net sentiment score)

Sentiment Score

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

For GetResponse: (70 x 1 + 91 x 0 + 1 x -1) / 162 = 69 / 162 = 0.43

This score means GetResponse's mentions lean positive on balance, but the high proportion of neutral mentions, 56.2% of all mentions, limits the score meaningfully. Neutral mentions do not drive shortlist inclusion. A positive recommendation, a neutral reference, a cautionary mention, and a competitor-displaced mention are not equal signals. Counting all mentions as wins is bad measurement. Classified sentiment is required before interpreting AI visibility, because raw mention counts alone tell only a fraction of the competitive story.

Sentiment by Platform

Methodology

1. This report is a benchmark-based analysis of GetResponse's AI recommendation visibility in the Email Marketing Service category, powered by the LLM Authority Index dataset for June 2026. It is not a client implementation case study and does not imply CiteWorks Studio caused any observed outcomes.
2. Data was collected during June 2026 as a snapshot-based benchmark. Results reflect AI system behavior at that point in time and may not reflect current outputs.
3. Six AI platforms were tested: ChatGPT, Gemini, Copilot, Perplexity, Google AI Mode, and Google AI Overviews.
4. A total of 1,519 AI observations were analyzed across three public high-intent buyer clusters. The exact number of unique prompts used to generate these observations was not available in the public dataset version.
5. The competitor universe includes 10 companies: ActiveCampaign, AWeber, Brevo, Campaign Monitor, Constant Contact, GetResponse, HubSpot, Kit (ConvertKit), Klaviyo, and Mailchimp.
6. Three public clusters were used: Best Email Marketing Software Discovery (awareness stage), Email Marketing Platform Comparisons (consideration stage), and Email Marketing Software Pricing Evaluation (decision stage). The Pricing Evaluation cluster carries a buyer stage multiplier of 1.5 in modeled value calculations.
7. Stage 0 refers to the raw extraction and classification of AI responses before metric aggregation. All mentions were classified by sentiment and recommendation type before metrics were calculated.
8. A mention is defined as any appearance of GetResponse in an AI-generated response, regardless of sentiment, framing, or rank position.
9. A valid recommendation is a positive, shortlist-quality recommendation or ranked recommendation that earns recommendation credit in the LLM Authority Index model. Mention presence is not equivalent to valid recommendation credit, and the report treats these as distinct metrics throughout.
10. Monthly AI Authority Value is a modeled benchmark value estimate. It is not revenue, pipeline, or booked demand. It reflects the modeled value of positive valid top-three recommendations within the benchmark framework and should be interpreted as a comparative positioning metric, not a financial outcome.
11. Ahrefs or traditional search data was not supplied for this report. Organic search signals, backlink profiles, and page-level source strength were not included in this analysis.
12. This report is a point-in-time benchmark and does not constitute a full audit or full market census. AI outputs vary by prompt phrasing, user context, platform version, and date of query.

See How AI Is Recommending Your Brand

The benchmark shows where GetResponse appears in AI responses and where competitors are being recommended instead. For brands showing visibility without recommendation conversion, the gap between mention presence and shortlist power is the defining competitive risk in AI-driven discovery. CiteWorks Studio maps your brand's AI recommendation footprint, identifies the specific prompts where competitors are displacing you, and builds the citation and content architecture needed to improve recommendation-stage visibility across the platforms where buyers are forming decisions.