Rules are everywhere.  Your mother had rules. Your teachers had rules.  And retail pricing has rules.  Pricing rules are critical to your business and brand.  A retailers pricing communicates your price image and defines your retail brand to your customers every day!  That is why it is so critical to invest in a solution that will helps you manage this complex world of retail pricing and the rules that must be followed.

Retailers constrain their pricing by rules such as ending numbers, national brand to private label gap, size parity, margin, markdown budget, price image, state minimums, and competitive price relationships. These rules must be respected by price optimization to achieve compliant prices that maximize category profit and revenue. Clear Demand was the first solution to bring truly constrained optimization to pricing. We feel that it is so critical to achieving true pricing flexibility that we went out and patented the approach.

Other solutions either applied the rules after optimization or blended optimization with heuristics that approximated soft rules. In this post, we will describe how rules and optimization work together to achieve constrained optimization.

Rule enforcement can be either Hard or Soft.   Hard Rules define boundaries that are not to be broken. Remember your mom’s stern command to be home by 10pm no exceptions?  Think something like that. The boundaries may be relative to a competitor price, cost, current price, or another item’s current or recommended price. Hard Rules are prioritized to resolve conflicts between rules.

Soft Rules are weighted and monetized. The Rules Engine uses the weights to generate a penalty function that monetizes the cost of breaking the rule by various degrees of violation. It then considers the product’s elasticity and the penalty function to arrive at the recommended price. Soft rules maintain the spirit of your intent but they can easily lose their impact if they aren’t properly supported by some hard and fast guidelines.

A competent Rules Engine will resolve Hard Rules first in order of priority by identifying feasible price ranges and feasible price relationships that satisfies as many rules as possible. The feasible range is defined in relationship to cost or competitor price. The feasible relationship is defined between the prices of related items. The Rules Engine solves for price recommendations that satisfy feasible ranges and relationships in tandem. If a lower-priority rule conflicts with a higher-priority rule, the Rules Engine will identify a range or relationship that minimizes the violation of the unsatisfied lower-priority rules. After the Hard Rule feasible range and relationship is known, optimization uses elasticity and Soft Rules to find the best price that satisfy the feasible range and relationship.

At the end of the day retailers want to be able to achieve the full value of optimization while still complying with whatever business rules are defined by their corporate strategy.  The challenge is in striking the right balance between the two.  Interested in learning more?  Let’s chat!