How SurveySensum Boosted Their AI Search Visibility by 150% in Just 6 Months

Client Profile

SurveySensum is a Net Promoter Score (NPS) software company that has built a reputation as a trusted solution for thousands of businesses worldwide. They've developed robust analytics tools and real-time feedback capabilities that integrate smoothly across various platforms. Their software helps organizations gather actionable insights to improve customer experience and build loyalty. They've earned recognition in industry forums and reports for their innovative approach to customer satisfaction measurement.

Industry: SaaS (Net Promoter Score and Customer Experience Management)

Team Size: Approximately 200 employees

Locations: Headquartered in the Greater New York Area with offices throughout the United States, India, and remote teams globally

The Challenge

While SurveySensum had established solid visibility in traditional search engines, they were missing out on a growing source of potential leads: AI-powered search platforms like ChatGPT and Perplexity. With only about 50 sporadic visits per month coming from these channels, they recognized an untapped opportunity to reach decision-makers who were increasingly turning to AI assistants for software recommendations.

They needed a strategy that would ensure their solution appeared prominently when potential customers asked AI platforms about NPS measurement, customer feedback systems, or experience management tools.

The Solution

A strategic 6-month engagement focused on optimizing the company's digital presence specifically for large language model (LLM) search behaviors. This approach wasn't about replacing traditional SEO but adding a complementary layer that addressed the unique way AI platforms discover, process, and recommend content.

Phase 1: Content Structure Enhancement

The first step involved analyzing which content types were already performing well in traditional search and had the highest potential for LLM visibility. After identifying these high-value pages, several structural improvements were implemented:

• Content Chunking: Long-form articles were reorganized into distinct, digestible sections that AI systems could efficiently process and reference
• Question-Based Headings: Traditional headings were converted into natural questions that matched how users typically phrase queries in AI interfaces
• Direct Answer Formatting: Content was restructured to provide immediate, authoritative answers following each question
• Data Optimization: Statistical information was reformatted into tables and structured formats that made it easier for LLMs to retrieve and cite

Phase 2: Authority Building for AI Citation

To increase the likelihood of the content being cited by LLMs, the strategy included:

• Strategic Community Participation: Identifying and engaging in niche forums about customer feedback methodologies where the company's research could be naturally referenced
• Enhanced Data Visualization: Creating compelling visual representations of NPS benchmarks with appropriate alt-text descriptions that LLMs could process
• Building a Citation Network: Developing relationships with industry analysts and research publications to increase mentions of the company's proprietary data

Phase 3: Technical Implementation

The technical foundation needed specific optimizations to maximize LLM crawlability:

• Schema Implementation: Deploying comprehensive JSON-LD structured data markup focused on dataset and research content types
• Clear Source Attribution: Establishing unmistakable canonical references for all proprietary data points
• Alternative Format Creation: Developing companion formats like markdown repositories and data tables for key resources

The Results

Within six months, this methodical approach transformed the company's presence in AI search results, increasing monthly sessions from LLM platforms from approximately 50 to nearly 600 – a 150% growth rate.

The most significant gains came from specific content categories:

• Industry Benchmark Pages: SurveySensum’s comprehensive NPS score comparison page jumped from minimal visibility to generating 234 monthly visits through AI platforms
• Methodology Content: Their guide on Likert scale implementation went from occasional citations to consistently delivering 126 monthly visits
• Brand Case Studies: Analyses of well-known companies like Nike, Tesla, and Disney together brought in over 200 monthly LLM-referred sessions

• Business Impact Resources: Their page connecting NPS to revenue outcomes attracted 95 monthly visits from AI search users looking for ROI evidence

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Key Insights

The project revealed clear patterns about which content types earn the most AI platform citations:

1. Data-rich resources containing original statistics and comparative benchmarks received 3.5x more citations than theoretical content
2. Direct answer formats that immediately addressed specific questions saw 2.8x higher citation rates
3. Brand-associated analyses examining well-known companies consistently attracted higher AI traffic
4. Practical methodology guides maintained steady citation rates month over month

This case demonstrates how intentionally structured, data-focused content with clear question-answer formats can significantly increase visibility in AI search platforms, creating a valuable new traffic channel while complementing traditional SEO strategies.