This guide compares two dominant approaches in pricing software: traditional rule-based systems, and AI-driven pricing systems like 7Learnings. You’ll learn how these models differ in automation, adaptability, data usage, forecasting accuracy, and their ability to align with strategic business goals. Whether you’re assessing your current pricing tools or searching for an AI-pricing upgrade, this comparison will help you make a confident decision.
Comparison rule-based vs. predictive pricing
Rule-basedPricingSets prices based on a predefined set of rules. | PredictivePricingDynamically adjusts prices based on predicted outcomes. |
|
|---|---|---|
| Supports rule based pricing | ||
| Utilization of competitor prices | ||
| Learns automatically from past price changes | ||
| Algorithm considers all relevant data features (e.g. transactions, weather, seasonality) | ||
| Utilizes price elasticity | ||
| Predicts price change impact on KPIs | ||
| Optimal prices for private label products & bundles | ||
| Channel specific pricing (e.g. country) | ||
| Long-tail pricing & initial pricing | ||
| Goal driven steering across the assortment | ||
| Considers and cross-optimizes marketing decisions (e.g. coupons, promotions or performance marketing ) |
According to Gartner, the most impactful and easy to implement AI use case for retailers is pricing, with just a 1% improvement in pricing strategy leading to a 6% overall profit uplift. At a time when pricing decisions have become increasingly complex, reliance on manual rules to steer pricing strategies is becoming increasingly inefficient. These rule-based systems struggle to keep pace with changing consumer behavior, competitor actions, and market dynamics.
In-depth comparison by feature
Supports rule-based pricing
Both approaches can include rule-based pricing structures. Predictive pricing solutions like 7Learnings are flexible enough to enable the inclusion of rules where requested, such as brand guardrails or competitor price matching, while also optimizing beyond them.
Utilization of competitor prices
Rule-based tools use competitor prices for reactive decisions, such as matching or beating a rival’s offer. Predictive pricing leverages competitor data within a broader model that factors in consumer demand, product lifecycle, and price elasticity, ensuring more intelligent positioning.
Learns automatically from past price changes
Predictive pricing tools continuously learn from historical sales, promotions, and pricing decisions. Rule-based pricing remains static, requiring constant manual intervention to maintain relevancy.
Considers all relevant data
Rule-based pricing typically operates with basic inputs. Predictive pricing incorporates complex variables, for example product seasonality, transaction history, time of day, and even weather, to generate context-aware pricing recommendations.
Utilizes price elasticity
Elasticity is fundamental to profitability but often ignored in rule-based systems. Predictive pricing models calculate demand sensitivity to price changes, allowing for precision pricing that avoids underpricing or overpricing (thus also helping to optimize stock levels).
Predicts price change impact on KPIs
Only predictive pricing lets you simulate how a price adjustment will impact revenue, profit, or sales volume. Rule-based systems can’t forecast outcomes, so you operate without visibility into trade-offs. Rule-based is purely reactive, while predictive pricing is proactive.
Optimal prices for private label products & bundles
Rule-based tools struggle with products that lack direct comparables. Predictive models use internal data to optimize prices for unique SKUs like private label goods or product bundles, maximizing their performance.
Channel-specific pricing
Predictive pricing enables differentiated pricing by country, channel, or customer segment. Rule-based systems often lack this granularity, leading to missed localized or strategic pricing opportunities.
Long-tail & initial pricing
Predictive pricing can optimize new or long-tail products by extrapolating from similar items and using statistical inference. Rule-based methods are unable to optimize successfully for these products due to limited data.
Goal-driven steering
Rule-based pricing applies the same logic across the board. Predictive systems let you define your business goal, for example profit, revenue, or conversion, and optimize prices accordingly across your entire assortment.
Marketing cross-optimization
Predictive pricing can incorporate marketing levers such as discounts, coupons, and ad performance. This ensures coordinated, high-ROI decision-making. Rule-based tools operate in isolation, missing synergies between pricing and marketing.
What predictive pricing looks like in action
Imagine a retailer planning end-of-season markdowns across 5,000 SKUs. With a rule-based system, the pricing team may rely on static discount tiers (e.g., 20% after 30 days) without knowing how those changes will impact margins, sell-through, or remaining inventory.
Predictive pricing software, by contrast, simulates various pricing strategies and shows how each one will affect business outcomes like profit and turnover. The retailer can test different scenarios, such as aggressive markdowns, margin protection, or sell-out strategies, and choose the best option with confidence.
Predictive pricing also outperforms in fast-changing markets. For example, if a weather event or promotion shifts demand, the software adapts in near real time. This adaptability ensures pricing stays aligned with reality, which rule-based tools can’t keep up with. In short, predictive pricing turns pricing into a proactive growth lever.
Smart pricing with smarter tools
Rule-based pricing helped many retailers scale their initial eCommerce operations, but it’s no longer enough in today’s hyper-competitive market. Predictive pricing software delivers greater automation, adaptability, and strategic alignment.
From continuously learning and forecasting KPI impact to optimizing long-tail products and channel-specific strategies, predictive pricing empowers retailers to make smarter decisions at scale.
Want to see how predictive pricing can benefit your business?
Schedule a Demo or use our Profit Uplift Calculator
