The Right Approach
To Retail
Price optimization has evolved significantly from its early days of rigid models and inflexible rules. While the core principles of price elasticities and item interactions still hold true, the implementation and best practices have greatly advanced.
Why Choose CLEARDEMAND’S Machine Learning
We do Retail Science the Right Way
Every price optimization decision boils down to a single question: "What is the effective price?" In classical economics, it is widely recognized that higher prices lead to lower sales volume, which is measured through the concept of elasticity. However, the relationship between price and demand is rarely linear. Some items, like flat screen TVs, are highly elastic, while others, like shoe polish, are minimally affected.
Demand models maximize margin on low-elasticity items and maintain competitive prices on highly-sensitive items to shape the retailer's image and foster shopper loyalty. Implementing this concept is complex, as changes in demand for one item can affect the demand for another item, either positively (affinity or halo effect) or negatively (cannibalization).
To effectively use the regular pricing model, consider the relative prices of items within each category, including national brands, budget store brands, organic and premium private labels, and different pack sizes. Develop rule sets for consistent application of price gaps that align with shopper expectations.
The Evolution of Pricing Science
First-generation pricing science was groundbreaking, but it generated a large volume of recommendations that demanded extensive manual intervention. As a result, the effectiveness of these solutions were limited by retailers' capacity to implement numerous price recommendations.
Subsequent solutions aimed for user-friendliness, relying on heuristics like competitive price matching and margin formulas to simplify and expedite the pricing process. However, this approach compromised mathematical rigor and reliability due to the reliance on "soft" rules. While the results appeared more accessible, they lacked consistency and dependability.
Machine Learning Provides Trusted Prices
Applying advanced machine learning to price optimization revolutionizes the process by utilizing powerful computing capabilities to continuously refine the model with new data. This eliminates pricing "hygiene" issues, where the line structure falls out of alignment.
Price professionals gain confidence in the vast majority of everyday price recommendations, allowing them to concentrate on reviewing only a few flagged exceptions identified by the system.
Understanding Pricing Rules
In today's retail landscape, enforcing pricing rules is crucial for a retailer's pricing strategy, competitive positioning, and delivering a consistent shopping experience. The handling of rules within optimization solutions plays a vital role.
CLEARDEMAND ensures that rules are not an afterthought, but an integral part of the optimization process. This prevents rule violations, pricing inconsistencies, and declining same-store sales.
With CLEARDEMAND, each pricing rule specifies the price upper/lower bounds, associated costs, applied strength, and confidence in elasticity. For assortments of 40,000 or more items, machine learning calculates and adjusts the relative priority of rules, unlike other solutions that rely on fixed settings determined by pricing analysts.