When potential customers ask ChatGPT about CRM systems, BI tools or software providers, the answer often arrives before they visit a single website. That answer does not come from the system’s own experience. It is assembled from a mix of sources, and that mix can differ by question type and by industry.
Peak Ace’s LLM Industry Benchmark examined which sources sit behind these answers, using a fixed set of 1,000 commercially relevant prompts for Germany in August 2026. This article focuses on the SaaS & Technology source mix and what it may mean for GEO and Digital PR.
Note: This article is an excerpt from the LLM Industry Benchmark. The complete analyses, including results for all ten industries, are available in the benchmark itself.
In short
- In our benchmark, comparison formats lead three of the four search intents, both for SaaS & Technology and across all ten industries.
- For criteria and advice questions, how-to guides take the lead.
- The second-ranked format differs by intent, from listicles for recommendations to product pages for pricing.
- All findings refer to this prompt set, this time period and this setup.
Methodology and data corpus
The benchmark is based on a clearly defined data corpus. The following sections describe its structure and the key figures derived from it.
Prompt set and structure
The prompt set comprises 1,000 standardised bottom-of-funnel prompts, with 100 commercially relevant prompts per industry. These are distributed across ten topic clusters and reflect questions people ask shortly before a purchase or booking.
Each industry covers four search intents, with 25 prompts per intent:
- Best/Top/Test winner: recommendation questions, such as “Which tool is best for…?”
- Comparisons: contrasts, such as “Which is better, A or B?”
- Prices/Where to buy: questions about costs and sources, such as “Where can I book and test…?”
- Criteria/Advice: questions about selection criteria, such as “What should I pay attention to when…?”
Market, systems and repetitions
The prompts were run in German for the German market, with answers observed between 1 and 31 August 2026. Gemini, ChatGPT and Google AI Overviews were observed. Each prompt was executed 15 times, five times per system. Individual responses were treated as samples, which allows fluctuations to be classified more reliably.
Data source and key figures
The data comes from Peec AI exports. Fifty brands per industry were monitored, and reporting focuses on the top 20 brands by visibility.
- Visibility: how frequently an entity appears in responses to the prompt set.
- Share of Voice (SOV): an entity’s share of visibility within the top 20.
- Sentiment: the tone of responses mentioning an entity, rated on a scale of 0 to 100.
These are indicators within the benchmark, not market share or absolute quality ratings.
Search intent and source patterns
Search intent appears to influence the type of source that shapes a response. The SaaS & Technology figures follow the cross-industry pattern for all four intents.
SaaS & Technology in context
The industry sits close to the cross-industry averages:
| Metric | SaaS & Technology | All 10 industries |
|---|---|---|
| Visibility | 7.70% | 6.94% |
| Share of Voice | 3.60% | 3.72% |
| Sentiment | 55.4 | 54.58 |
Sentiment describes the tone of responses in the dataset. It is not an assessment of software quality, vendor reputation or real customer satisfaction.
Leading source types by intent
The SaaS & Technology scorecard publishes the order of leading source types, not their individual retrieval shares. The cross-industry shares are shown for reference.
| Intent | Leading source type in SaaS & Technology | Leading source types across all 10 industries (retrieval share) |
|---|---|---|
| Best/Top/Test winner | Comparison | Comparison 28.31%; Listicle 14.99%; Product page 12.75%; Item 9.47% |
| Comparisons | Comparison | Comparison 32.65%; Item 16.06%; Product page 10.46%; Other 9.45% |
| Criteria/Advice | How-to guide | How-to guide 22.33%; Articles 19.82%; Other 12.20%; Comparison 12.03% |
| Prices/Where to buy | Comparison | Comparison 22.64%; Product page 14.57%; Category page 12.35%; How-to guide 10.96% |
“Other” is a residual category and not a specific format.
Four patterns stand out in the data:
- Comparisons lead three out of four intents. They appear not only in explicit “A versus B” questions but also in recommendation and pricing questions.
- Advice changes the source mix. How-to guides and articles become more prominent for criteria questions. How-to guides account for 22.33 per cent of retrieval in this intent, compared with 5.81 per cent for recommendations.
- Product information appears alongside purchase questions. Product pages rank second for pricing, and category pages follow in third place.
- The pattern is not universal. The mix of sources varies by industry, so the leading format in one sector may not lead in another.
Retrieval means that a source was accessed. It does not automatically mean that the source was cited or that the brand appears in the final answer. Source types describe formats, not ownership or authority.
Brand mentions and concentration
In the SaaS & Technology prompt set, Salesforce (14 per cent visibility), HubSpot (13 per cent) and Power BI (9 per cent) carry the highest visibility. This reflects the setup of the prompt set: CRM and BI are different buying decisions pooled into one industry category. These figures describe this snapshot and are not a statement about overall market leadership.
Concentration tells a different story. Within the top 20, the HHI is 548 and the top three brands hold 24 per cent of normalised SOV. In other words, about three-quarters of the share of voice is spread across the remaining entities.
| Industry (selected) | HHI | Top 3 share |
|---|---|---|
| Telecommunications | 1,462 | 63% |
| Marketing & Advertising | 1,200 | 51% |
| Health & Wellness | 1,132 | 50% |
| SaaS & Technology | 548 | 24% |
These values suggest different distributions of mentions, not different market opportunities. HHI and top 3 share use normalised SOV shares within the top 20 and are not statements about market shares.
What this means for SEO teams and marketers
The data reveals a pattern: depending on intent and industry, responses draw on different types of sources. This may indicate where SaaS brands can position themselves strategically. The goal is not to be visible everywhere, but to understand which questions your product answers well and where visibility matters most.
- Handle answer types separately. A comparison should explain alternatives, along with their advantages and disadvantages. A guide should clarify requirements or support a task. A general feature article rarely does both equally well. Practical implication: plan comparison pages and guides as distinct formats with distinct jobs.
- Keep product details consistent. Plans, user limits, integrations, capabilities and billing conditions should match across product pages, comparisons and third-party sources. Outdated or conflicting information increases the risk of inaccurate statements in generated answers.
- Explain the criteria and link them to offers. How-to guides and articles lead for consultation questions. Explain how to test a workflow or assess a reporting requirement, then link to suitable products so that guidance and offers do not drift apart.
- Prioritise your Digital PR sources. Identify the most important third-party sources per intent and check what they say about your brand. Not every publication carries equal weight. Articles that clearly highlight your product or services are most relevant, but content higher up the sales funnel matters too.
- Expert perspective: Gordon Herenz, Team Lead Content Strategy & Digital PR at Peak Ace, recommends building comparison and list formats where they make sense, seeking placement in relevant rankings and listicles, and strengthening citable authority on independent sources through consistent Digital PR.
- Treat monitoring as an ongoing task. Track brand mentions and retrieved sources with a consistent prompt set. Split the results by intent, system and sub-category, such as CRM or BI. A broad SaaS figure can hide the questions that matter most for your product.
Limitations of the findings
The results are valid only for this specific prompt set, time period and setup. They do not represent a complete measurement of an industry’s or brand’s visibility in AI systems. In particular:
- The prompt set shapes the outcome. Different questions or wording can lead to different answers.
- Individual responses are samples. Small differences should not be overinterpreted.
- Three systems are not one system. Gemini, ChatGPT and Google AI Overviews cannot simply be equated, so patterns may differ by engine.
- Retrieval is not citation. Accessed sources show where information came from during retrieval, not how a final answer was formed.
- Sentiment is not a quality assessment. It describes the tone of responses in the dataset.
Read the full benchmark
This article provides an excerpt. The complete LLM Industry Benchmark includes:
- The source mix for each of the ten industries and all four intent types
- Visibility, SOV and sentiment within the benchmark
- HHI and top 3 share within the top 20 of each industry
- The leading entities observed per industry
- The industry scorecards and the complete methodology
Looking to strengthen your brand’s presence in AI answers? Get in touch with our LLMO and GEO team.
FAQ: The SaaS and technology industry in LLMs
How visible are SaaS and technology brands in AI systems?
No single cross-system figure can be stated reliably, because there is no unified measurement methodology across models, markets, prompts and points in time. The benchmark offers a standardised view of one fixed commercial prompt set and one measurement period.
Which SaaS brands are most visible in AI search?
In this benchmark, we can only state which brands were mentioned most often in this dataset during the observation period. Mentions shift with the model, timing, phrasing and context, so this does not indicate who is generally most visible.
Which content types do LLMs use for SaaS questions?
In our benchmark runs, comparison formats play a larger role for recommendation, comparison and pricing questions, while how-to guides lead for criteria and advice questions. This applies only to the examined prompt set and time period.
Is this benchmark relevant for SaaS brands?
It adds a perspective alongside organic traffic, demos, trials and conversions. It is not proof of success, but a way to observe touchpoints, topic coverage and consistency in AI answers under constant conditions.
How can SaaS brands improve their presence in AI answers?
Start with one software category and review key pages, such as product, comparison, FAQ and guide pages, against typical buyer questions: capabilities, integrations, plans and pricing, and differences between alternatives. Fix outdated information, then re-test with the same questions and setup so results remain comparable.
