Research
Personalized Pricing: How AI Increases Marginality While Maintaining Loyalty
In the context of global digitalization, businesses face a paradox: while customer data is becoming more abundant, the effectiveness of standard marketing tools is declining. Linear discounts and seasonal sales are gradually giving way to dynamic and personalized pricing. Why Mass Discounts No Longer Work The traditional approach to discounts has two critical flaws: The Mathematics of a Personalized Offer The R42 system, developed by our team, solves this problem
Research Materials
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Technical Documentation & Resources
Below are the reports, datasets, and publications produced during this research:
| Resource Type | Document Title | Format |
| Full Report | White Paper: RnD-42 Predictive Model Architecture | [PDF, 2.4 MB] |
| Dataset (Demo) | Anonymized Traffic Flow Sample (Fragment) | [CSV / JSON] |
| Presentation | Visualization of Model Training Phases and Accuracy Assessment | [PPTX / PDF] |
| Publication | Conference Paper: Intertraffic Amsterdam Presentation | [Link] |
Personalized Pricing: How AI Increases Marginality While Maintaining Loyalty
In the context of global digitalization, businesses face a paradox: while customer data is becoming more abundant, the effectiveness of standard marketing tools is declining. Linear discounts and seasonal sales are gradually giving way to dynamic and personalized pricing. Why Mass Discounts No Longer Work The traditional approach to discounts has two critical flaws: The Mathematics of a Personalized Offer The R42 system, developed by our team, solves this problem