Pricing Transparency Across Service Verticals: A Comparative Content Analysis
Abstract
This paper analyses pricing display practices across 21 service industry websites in four verticals: game outsourcing, design-as-a-service (DaaS), Australian design agencies, and GEO agencies. Price count varies widely (0–12 across sites), with DaaS providers displaying prices most prominently as a central element of their subscription model. Game outsourcing studios and traditional design agencies rarely list specific prices, reflecting the custom-project nature of their services. In the observational data, uncited sites average 4 price mentions while cited sites average only 1, suggesting an inverse relationship. However, this pattern is driven entirely by vertical composition: DaaS sites (which display prices) include both cited and uncited members, while game outsourcing and design agency sites (which do not display prices) are predominantly cited. No causal claim is supported. The pricing probe experiments (SIGI-2026-003) establish that pricing magnitude affects LLM sentiment under controlled conditions, but whether price display on a live website independently affects citation probability remains an open question requiring purpose-built experimentation.
Keywords
pricing transparency, service verticals, AI citation, price display, DaaS pricing, content analysis, observational study
1. Introduction
Pricing transparency is a strategic decision that varies significantly across service industries. Subscription-based models (such as design-as-a-service) tend toward transparent pricing to facilitate self-service purchasing, while project-based models (such as bespoke design or game development outsourcing) typically require custom quotation and therefore display little or no pricing information publicly.
From an AI citation perspective, pricing data represents a distinctive content signal. The presence of specific dollar amounts on a page provides named entities, statistical markers, and structured data that may influence how AI systems parse and evaluate the content. The SIGI pricing probe (SIGI-2026-003) demonstrated that pricing magnitude significantly affects LLM sentiment evaluation under controlled single-variable conditions. This paper asks a different question: in observational data, is the practice of displaying prices on a service website associated with citation outcomes?
2. Methodology
Price data was extracted from all 21 sites using automated parsing of homepage content. For each site, we recorded the number of price mentions (priceCount) and the specific values found (pricesFound). Sites were grouped by vertical and by citation status for comparison.
3. Results
3.1 Pricing Display by Vertical
| Vertical | Sites with Prices | Sites without Prices | Mean Price Count | Mean Citation Score |
|---|---|---|---|---|
| Vertical A (Game Outsourcing) | 1 | 7 | 0.5 | 4.6 |
| Vertical B (DaaS) | 4 | 1 | 3.8 | 6.0 |
| Vertical C (Design Agencies) | 0 | 4 | 0.0 | 6.0 |
| Vertical D (GEO Agencies) | 1 | 3 | 0.5 | 3.5 |
3.2 Price Count and Citation Status
| Group | Mean Price Count | Range | Median |
|---|---|---|---|
| Cited sites (n=13) | 1.0 | 0–6 | 0 |
| Uncited sites (n=8) | 4.0 | 0–12 | 3 |
3.3 The Correlation Analysis Context
The correlation analysis (SIGI-2026-037) recorded that price count has a 0.3x ratio between cited and uncited sites, classifying it as “UNCITED HIGHER.” However, this ratio reflects vertical composition rather than a pricing-citation relationship. DaaS sites naturally display more prices, and the uncited DaaS site contributes disproportionately to the uncited group’s average.
4. Discussion
The most important finding of this analysis is a null result: no clear correlation exists between pricing display and citation score within any individual vertical. The aggregate inverse correlation is a Simpson’s paradox artefact driven by vertical composition.
This finding illustrates a broader methodological point: aggregate correlations across heterogeneous verticals can produce misleading signals. Pricing display is primarily determined by business model (subscription vs. project), not by citation strategy. Any analysis that pools across verticals conflates business model differences with citation dynamics.
The controlled pricing probe (SIGI-2026-003) remains the authoritative source on how pricing magnitude affects LLM evaluation. That experiment isolated pricing as a single variable and found a clear U-curve sentiment pattern. The question of whether pricing visibility on a live website independently affects citation is fundamentally different and would require a purpose-built experiment.
5. Limitations
- Small within-vertical samples: With only 4–8 sites per vertical, within-vertical analysis has minimal statistical power.
- Confounded by vertical: Pricing display is a function of business model, not citation strategy.
- Homepage only: Price data was extracted from homepages only. Pricing pages deeper in site architecture were not included.
- Currency inconsistency: DaaS prices are in USD; Australian agency prices (when present) are in AUD. Currency differences may affect LLM processing.
6. Conclusions
Pricing display practices vary substantially across service verticals, driven by business model rather than citation strategy. Whether pricing transparency independently affects AI citation probability cannot be determined from this observational data. The aggregate inverse correlation between price count and citation score is a Simpson’s paradox artefact, not a meaningful signal.
Confidence: LOW for any pricing-citation relationship. Pricing display patterns are descriptive, not predictive.
References
- The Scientific Institute for Generative Intelligence. “The Price-Credibility U-Curve: How Service Pricing Magnitude Affects Large Language Model Sentiment Assessment.” SIGI-2026-003. generativeintelligence.institute, March 2026.
- The Scientific Institute for Generative Intelligence. “Structural Correlates of AI Citation: An Observational Analysis of 22 On-Page Metrics.” SIGI-2026-037. generativeintelligence.institute, March 2026.
- The Scientific Institute for Generative Intelligence. “A 60-Variable Comparative Dataset for Studying AI Citation Behavior Across Service Industry Websites.” SIGI-2026-036. generativeintelligence.institute, March 2026.