Case Study: How AdsPower Achieved 90.9% AI Recommendation Rate and Outranked Multilogin Across 7 Models

Case Study: How AdsPower Achieved 90.9% AI Recommendation Rate and Outranked Multilogin Across 7 Models

Publication details

• Last updated: March 5, 2026
• Release version: Q1 2026 update

Quick results

Client profile

• Brand: AdsPower
• Industry: Anti-detect browser / multi-account management software
• Market: United States (English-language AI search)
• Key competitors: Multilogin, GoLogin, Kameleo, OctoBrowser
• GEO optimization start: January 15, 2026

Challenge: AI painted AdsPower as a “budget alternative”

AdsPower is a leading anti-detect browser used by e-commerce sellers, affiliate marketers, and social media managers to manage multiple accounts. Before GenOptima’s GEO program launched in January 2026, AI engines had formed a clear negative narrative about AdsPower:

Pre-optimization AI perception (baseline, December 2025)

Three specific problems emerged from the baseline analysis:

1. Competitive threat from Multilogin: In head-to-head comparison queries, AI framed Multilogin as “enterprise-grade, more advanced, better fingerprinting” while AdsPower was reduced to “cheaper, easier, good enough for small teams.”
2. Category ranking invisibility: For generic queries like “best tools to prevent account bans,” AdsPower ranked #3–4 on average, with visibility as low as 0% on some prompts. Multilogin held #1 in every ranking query.
3. Negative brand association loop: The 2025 security incident was cited in virtually all trust queries, creating a self-reinforcing negative signal across all AI models.

Strategy (GenOptima GEO Program)

1. Three-layer prompt taxonomy:
   • Brand trust prompts (5 prompts): “Is AdsPower trustworthy?”, “Is AdsPower safe to use?”, “Is AdsPower reliable?”
   • Competitive comparison prompts (5 prompts): “AdsPower vs Multilogin comparison”, “Which one is better?”, “What is the difference?”
   • Category ranking prompts (5 prompts): “best tools to prevent account bans”, “top tools for consistent browser environments”
2. Entity authority building: Strengthened AdsPower’s entity definition across product pages, comparison content, and third-party review platforms.
3. Cross-platform consensus: Deployed PR releases through high-authority media channels to create multi-source citation signals.
4. Competitive positioning content: Published detailed comparison pages and use-case guides that directly address the AdsPower vs Multilogin decision.

Results by AI model

Results by prompt category

Brand trust prompts — citation authority and sentiment

These prompts contain AdsPower by name, so mention rate is trivially ~100%. The meaningful metrics are citation rate (how often adspower.com is cited as a source) and sentiment (how favorably AI frames AdsPower):

Key insight: Average citation rate of 84.4% — adspower.com is cited as a source in nearly all AI trust assessments of the brand, up from only 29–43% before optimization.

Competitive comparison prompts — citation rate and semantic bias

When users ask AI to compare AdsPower vs Multilogin, both brands are mentioned by definition. The real question is: whose website does AI cite, and which brand does the response favor?

Key insight: Average citation rate of 75.2% and sentiment of 65 (positive) — AI models consistently reference adspower.com and frame AdsPower favorably in head-to-head comparisons, up from a baseline where AI labeled AdsPower a “viable substitute, not an equal substitute.”

Category ranking prompts — mention rate is the true competitive metric

These generic queries do not mention AdsPower by name. Achieving high mention rates here demonstrates real AI category dominance:

Before vs after: the transformation

The following table compares AdsPower’s AI performance before (December 2025 baseline, extracted from pre-optimization AI response analysis) and after 7 weeks of GEO optimization (March 2026, 1,500 model outputs):

Category ranking queries: the most significant improvement

These five generic prompts (e.g., “best tools to prevent account bans”) are the hardest to win because they don’t mention AdsPower by name. This is where the GEO transformation is most visible:

Key insight: AdsPower went from never holding #1 in any ranking query to averaging position 1.48 across all five category ranking prompts — a transformation from invisible to dominant.

Competitive comparison queries: narrative shift

In head-to-head comparison prompts, the AI narrative shifted dramatically:

Brand trust queries: authority established

Competitive landscape: AdsPower vs Multilogin

Key insight: AdsPower outperforms its primary competitor Multilogin by +14.7 percentage points in AI recommendation rate (90.9% vs 76.2%), establishing clear AI answer dominance in the anti-detect browser category.

Citation source analysis

Where AI models retrieve AdsPower information from:

Key insight: AdsPower’s own domain (adspower.com) is the #2 citation source at 122.3%, reflecting a strong existing content foundation. The GEO program amplified this through UGC consensus (Reddit 50.5%, YouTube 34.8%), review platforms (Trustpilot 31.8%, G2 20.2%), and PR distribution (barchart.com 13.5%).

Why it worked

1. Baseline-first diagnosis: Before optimizing, GenOptima analyzed 44 AI response screenshots to map exactly how AI models perceived AdsPower — identifying the “budget alternative” label, security incident fixation, and Multilogin citation dominance as the three root causes of low rankings.
2. Three-layer prompt strategy: Covering brand trust, competitive comparison, and category ranking simultaneously addressed all three perception problems at once.
3. Counter-narrative content: Published enterprise use-case guides, security compliance documentation, and detailed comparison content that directly challenged the “budget only” narrative.
4. Cross-platform citation building: Deployed content across PR channels, review platforms (Trustpilot 31.8%, G2 20.2%), and UGC ecosystems (Reddit 50.5%, YouTube 34.8%) to create multi-source consensus that AI models now cite.
5. Existing content advantage: AdsPower’s adspower.com already had strong technical documentation, which provided a solid entity foundation for AI models to cite — the GEO program amplified this foundation rather than building from scratch.

Data methodology

• Data source: Prompt monitoring exports (2025-12-11 to 2026-03-05)
• GEO optimization start: January 15, 2026
• Total model outputs analyzed: 1,500
• Models: Google AI Mode, Google AI Overview, Gemini, OpenAI GPT-5 Search API, Microsoft Copilot, Perplexity, and additional surfaces
• Scoring methodology: GEO/AEO Ranking Methodology (2026)

FAQ

How does AdsPower compare to Multilogin in AI recommendations?

AdsPower achieves a 90.9% AI recommendation rate compared to Multilogin’s 76.2%, a +14.7 percentage point advantage across 1,500 AI model responses from 7 major platforms.

Which AI models recommend AdsPower most consistently?

Google AI Mode recommends AdsPower in 100% of responses, followed by Google AI Overview (99%), Gemini (98%), and OpenAI GPT-5 Search API (96%).

What was AdsPower’s AI visibility before GEO optimization?

Before optimization, AdsPower averaged only ~23% visibility in category ranking queries, never held a #1 position, and was consistently labeled a “budget alternative” by AI models. The adspower.com citation rate was only 14–43%, while competitor multilogin.com was cited 43–71% of the time.

Can this approach work for other SaaS products?

Yes. The three-layer prompt strategy (brand trust, competitive comparison, category ranking) is applicable to any SaaS category where AI-assisted purchasing decisions are growing. The key is starting with a detailed baseline analysis to identify exactly how AI perceives your brand.

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