Success story
Dynamic Pricing
for E-commerce and Retail
How Rulex built an AI-powered dynamic pricing solution that protects margins and prevents product cannibalization.
profit margin
improvement
accuracy in
predictions
real-time
recommendations
transparency
behind every corrective
action suggested
A leading furniture manufacturer was managing prices across a complex e-commerce catalog. Prices needed to respond to demand, seasonality, competitor activity, and the relationships between similar products. Every pricing decision mattered, as changing one product could shift demand, influence customer behaviour, or cannibalize sales elsewhere in the catalog.
Manual pricing could not keep pace with a catalog in which every product influenced every other.
Building an accurate dynamic pricing framework
Working with Rulex, the company shifted from a manual pricing process to a dynamic pricing solution that could analyze these market signals, identifying profitable and risky price combinations and flagging corrective actions before any price reached production.
Data integration
Using product IDs, Rulex collected and integrated large volumes of data to analyze correlations between purchase history, historical price fluctuations, and competitor pricing across multiple time frames – daily and weekly.
Purchase history, competitor pricing, and catalog data collected and unified across custom timeframes
Category-level margin modeling
To avoid cannibalization, optimal margins were calculated at the category level, ensuring price shifts across similar items remained consistent and mutually reinforcing.
Optimal margins calculated per category to keep price shifts across similar items consistent and cannibalization-free.
Extraction of transparent rules
The model used feature vectors expressing price differences relative to a reference competitor – such as the market average or a low-cost leader – and extracted rules based on specific profit margin thresholds to identify optimal and suboptimal combinations for each time period.
Rulex's XAI algorithm extracts if-then rules from profit margin thresholds, identifying optimal and suboptimal price combinations.
Expert review before price update
Rulex’s Rule-Based Control tool evaluated live price vectors as they were sent to production, automatically proposing clear, precise corrective actions grounded in the extracted rules and controllable variables. Depending on the level of confidence and risk associated with each recommendation, changes could be applied automatically or routed to pricing experts for review and validation prior to implementation.
Rulex’s Rule-Based Control tool recommends corrective actions; pricing teams review and approve before any price goes live.
Explainable, automated, and ready to be updated
Rulex’s solution changed how the pricing team operated, delivering:
- 5%+ profit margin improvement, confirmed through dynamic A/B testing.
- Improved control over product cannibalization.
- Automated identification of suboptimal price combinations.
- Corrective actions suggested before prices reached production.
- More consistent pricing across product categories.
- Zero blind price changes – every corrective action reviewable before going live.
Tech point
Take a closer look at the capabilities behind Rulex Platform as an AI dynamic pricing software.
01. E-commerce data integration
Rulex Platform ingests catalog data, purchase history, and competitor pricing from multiple sources and time frames, unifying them into a structured data orchestration pipeline for dynamic pricing analytics and optimization.
02. Explainable AI for pricing decisions
At the core of Rulex’s approach is a clear-box proprietary algorithm that generates if-then rules directly as part of the training process. This distinguishes it from post-hoc explainability techniques, which infer plausible explanations for opaque predictions without exposing the actual decision logic. In Rulex’s case, every pricing rule is readable, and can be explored by the pricing team – inspecting coverage, error rate, and other validation metrics – before it affects a live catalog.
03. Rulex’s Rule-Based Control
Live price vectors are evaluated against the extracted ruleset in real time, flagging any combination that falls outside the recommended range. Because Rulex Platform distinguishes between controllable and non-controllable variables – separating levers the pricing team can act on from external signals such as competitor pricing – corrective actions are proposed only on the variables that can actually be adjusted. Each recommendation carries an associated confidence score, which can determine whether it is applied automatically or routed to expert review before production release.
Why Rulex as a dynamic pricing software
Rulex combines data integration and orchestration, explainable AI, and rule-based decision control into a governed framework for e-commerce and retail dynamic pricing. For teams, this means faster decisions, fewer manual interventions, and more transparent control over the pricing logic that affects margin performance.
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Frequently asked questions
What is dynamic pricing?
Dynamic pricing automatically adjusts prices based on market signals such as demand, competitor activity, and seasonality. Rulex adds explainability to this process, generating clear if-then rules that pricing teams can inspect and validate before any change goes live.
How does AI dynamic pricing differ from traditional rule-based pricing?
Traditional rule-based pricing relies on manually defined conditions. AI dynamic pricing uses machine learning to identify patterns across large datasets – such as competitor positioning and demand trends – and identifies the combinations most likely to protect or improve margins, with results validated by human experts.
How does Rulex prevent product cannibalization?
Rulex calculates optimal margins at the category level, ensuring price changes across similar products remain consistent and mutually reinforcing. If a proposed price shift creates a cannibalization risk – by making one product significantly more attractive than another at the expense of overall category margin – the system flags it before production.
What makes Rulex different from other dynamic pricing tools?
Most dynamic pricing tools optimize for a single metric and return a recommended price without explaining why. Rulex generates explainable IF-THEN rules traceable to the specific market conditions and margin parameters behind each recommendation, giving pricing teams full visibility and control over what gets applied.
What are real-time dynamic pricing analytics?
Real-time dynamic pricing analytics means continuously monitoring pricing signals – competitor activity, demand fluctuations, margin performance – and generating updated recommendations as conditions change. Rulex supports this through live price vector evaluation and automated corrective action suggestions across daily and weekly time frames.