SCIENCE

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.

CLEARDEMAND’s Regular Pricing is an advanced third-generation solution, empowered by patented technology to help retailers tackle future challenges. Informed by market experience and leveraging Machine Learning, it analyzes vast, dynamic data sets, continuously improving its core science.

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.

What Sets Us Apart?

Competitive Pricing

CLEARDEMAND automates competitive price comparisons and alerts merchants when existing prices deviate from competitive pricing rules.

Competitive Pricing

CLEARDEMAND automates competitive price comparisons and alerts merchants when existing prices deviate from competitive pricing rules. Our unique competitive cross-elasticity model enables dynamic competitive pricing.

Merchandise Analytics

CLEARDEMAND applies an evolved optimization which constrains prices by applying retailers’ rules for competition, margin, and product-line relationships.

Merchandise Analytics

CLEARDEMAND applies an evolved optimization which constrains prices by applying retailers’ rules for competition, margin, and product-line relationships. This minimizes or eliminates pricing rules violations.

Compliant Optimization

CLEARDEMAND uses an integrated business intelligence capability from Tableau which provides visibility into sensitive items, competitive pricing and revenue opportunities.

Compliant Optimization

CLEARDEMAND uses an integrated business intelligence capability from Tableau which provides visibility into sensitive items, competitive pricing, revenue opportunities, and other business insights with interactive and drill-down tools.

Big Data Platform

CLEARDEMAND’s platform is architected from the ground up so retailers can quickly capture and analyze data from any source.

Big Data Platform

CLEARDEMAND’s platform is architected from the ground up so retailers can quickly capture and analyze data from any source – store, online, mobile, social media, loyalty programs – to weave a consistent and competitive pricing strategy.

Single Enterprise Rules Architecture

CLEARDEMAND pioneered a single Omnichannel (mobile, online, in-store) rules architecture.

Single Enterprise Rules Architecture

CLEARDEMAND pioneered a single Omnichannel (mobile, online, in-store) rules architecture. Retailers can project a unified pricing environment so shoppers perceive “one company” across digital and physical store domains.
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