Abstract: The disclosed technology improves the process of generating recommended prices for retail products. First, the present technology makes it possible to model shopper demand when sales data includes time periods with zero unit sales without hypothesizing whether the time periods are out-of-stock events or zero sales. This can be accomplished by applying a truncated Poisson distribution and the Newton-Raphson method to the non-zero unit sales to generate a coefficient vector that maximizes the likelihood of the observations in the sales data. Second, the present technology can be used to generate recommended prices for a group of products that optimize revenue and profit while limiting the number of products that require price changes to a predefined threshold value. This can be accomplished by iteratively replacing a current best value solution with a next best value solution across a collection of product networks until an acceptable number of unchanged prices is achieved.

Abstract: A demand rule can handle simultaneous retail purchasing options available to users such as brick and mortar, online, mobile, catalog, kiosk, coupon, loyalty, volume discounts, and multiples. The method can include a demand rule for each single purchasing option, a rule for marketing influence, and a rule for partitioning user demand across purchasing options. The demand rule for each single purchasing option, rule for marketing influence, and rule for partitioning user demand across purchasing options can each be measured independently to enable computationally efficient methods for forecasting and sales optimization.

Abstract: The disclosed technology improves the process of generating recommended prices for retail products by optimizing revenue and profit while complying with a set of business rules by assigning a monetary value to each business rule. Then for each decision price that violates a business rule constraint, a penalty value is added to the monetary value. If the monetary value including the penalty is better than an original monetary value, the decision price is included in the recommended prices.

Abstract: A demand rule can handle simultaneous retail purchasing options available to users such as brick and mortar, online, mobile, catalog, kiosk, coupon, loyalty, volume discounts, and multiples. The method can include a demand rule for each single purchasing option, a rule for marketing influence, and a rule for partitioning user demand across purchasing options. The demand rule for each single purchasing option, rule for marketing influence, and rule for partitioning user demand across purchasing options can each be measured independently to enable computationally efficient methods for forecasting and sales optimization.

Abstract: The disclosed technology improves the process of generating recommended prices for retail products by optimizing revenue and profit while complying with a set of business rules by assigning a monetary value to each business rule. Then for each decision price that violates a business rule constraint, a penalty value is added to the monetary value. If the monetary value including the penalty is better than an original monetary value, the decision price is included in the recommended prices.

Abstract: The disclosed technology improves the process of generating recommended prices for retail products by optimizing revenue and profit while complying with a set of business rules by assigning a monetary value to each business rule. Then for each decision price that violates a business rule constraint, a penalty value is added to the monetary value. If the monetary value including the penalty is better than an original monetary value, the decision price is included in the recommended prices.

Abstract: Business rules can govern a single price or they can define relationships between different products involving two or more decision prices. One problem that retailers can face is that business rules are generally not codified and are rarely followed consistently, and thus it can be difficult to articulate existing business rules or generate new business rules. However, existing price information inherently contains industry knowledge and experience, even if retailers find it difficult to express that knowledge. The disclosed technology generates business rules by reverse engineering rule bounds and coefficients from existing price information. The reverse engineered rule bounds and coefficients can be used in price optimization to generate recommended prices for retail products that optimize revenue and profit while complying with a set of business rules.

Abstract: The disclosed technology improves the process of generating recommended prices for retail products by optimizing revenue and profit while complying with a set of business rules by assigning a monetary value to each business rule. Then for each decision price that violates a business rule constraint, a penalty value is added to the monetary value. If the monetary value including the penalty is better than an original monetary value, the decision price is included in the recommended prices.

Abstract: The disclosed technology improves the process of generating recommended prices for retail products by optimizing revenue and profit while complying with a set of business rules by assigning a monetary value to each business rule. Then for each decision price that violates a business rule constraint, a penalty value is added to the monetary value. If the monetary value including the penalty is better than an original monetary value, the decision price is included in the recommended prices.

Abstract: Business rules can govern a single price or they can define relationships between different products involving two or more decision prices. One problem that retailers can face is that business rules are generally not codified and are rarely followed consistently, and thus it can be difficult to articulate existing business rules or generate new business rules. However, existing price information inherently contains industry knowledge and experience, even if retailers find it difficult to express that knowledge. The disclosed technology generates business rules by reverse engineering rule bounds and coefficients from existing price information. The reverse engineered rule bounds and coefficients can be used in price optimization to generate recommended prices for retail products that optimize revenue and profit while complying with a set of business rules.

Abstract: The disclosed technology improves the process of generating recommended prices for retail products by optimizing revenue and profit while complying with a set of business rules by assigning a monetary value to each business rule. Then for each decision price that violates a business rule constraint, a penalty value is added to the monetary value. If the monetary value including the penalty is better than an original monetary value, the decision price is included in the recommended prices.

Abstract: A demand rule can handle simultaneous retail purchasing options available to users such as brick and mortar, online, mobile, catalog, kiosk, coupon, loyalty, volume discounts, and multiples. The method can include a demand rule for each single purchasing option, a rule for marketing influence, and a rule for partitioning user demand across purchasing options. The demand rule for each single purchasing option, rule for marketing influence, and rule for partitioning user demand across purchasing options can each be measured independently to enable computationally efficient methods for forecasting and sales optimization.

Abstract: Business rules can govern a single price or they can define relationships between different products involving two or more decision prices. One problem that retailers can face is that business rules are generally not codified and are rarely followed consistently, and thus it can be difficult to articulate existing business rules or generate new business rules. However, existing price information inherently contains industry knowledge and experience, even if retailers find it difficult to express that knowledge. The disclosed technology generates business rules by reverse engineering rule bounds and coefficients from existing price information. The reverse engineered rule bounds and coefficients can be used in price optimization to generate recommended prices for retail products that optimize revenue and profit while complying with a set of business rules.

Abstract: The disclosed technology improves the process of generating recommended prices for retail products by optimizing revenue and profit while complying with a set of business rules by assigning a monetary value to each business rule. Then for each decision price that violates a business rule constraint, a penalty value is added to the monetary value. If the monetary value including the penalty is better than an original monetary value, the decision price is included in the recommended prices.

Abstract: Business rules can govern a single price or they can define relationships between different products involving two or more decision prices. One problem that retailers can face is that business rules are generally not codified and are rarely followed consistently, and thus it can be difficult to articulate existing business rules or generate new business rules. However, existing price information inherently contains industry knowledge and experience, even if retailers find it difficult to express that knowledge. The disclosed technology generates business rules by reverse engineering rule bounds and coefficients from existing price information. The reverse engineered rule bounds and coefficients can be used in price optimization to generate recommended prices for retail products that optimize revenue and profit while complying with a set of business rules.

Abstract: Business rules can govern a single price or they can define relationships between different products involving two or more decision prices. One problem that retailers can face is that business rules are generally not codified and are rarely followed consistently, and thus it can be difficult to articulate existing business rules or generate new business rules. However, existing price information inherently contains industry knowledge and experience, even if retailers find it difficult to express that knowledge. The disclosed technology generates business rules by reverse engineering rule bounds and coefficients from existing price information. The reverse engineered rule bounds and coefficients can be used in price optimization to generate recommended prices for retail products that optimize revenue and profit while complying with a set of business rules.

Abstract: Disclosed are system and methods of recommending price premium values of products based on price zones and factors. Retail chains have stores in several price zones of varying demographics and price level. The systems and methods use the pattern of pricing in the price zones to identify outliers where the price of a product in a price zone is not consistent with other products priced in that price zone. The price zone premium values used in evaluating individual-product prices in a price zone are calculated locally for each subclass of products and those values may be aggregated into price zone premium values for the entire enterprise.

Abstract: A method and apparatus for demand modeling and price calculation based on competitive pressure is provided. Data related to a plurality of products related to a product of interest is collected, along with the number of sales, sales price and one or more product attribute values for each of the products. Conversion factors are used to equivalize the plurality of products relative to the product of interest, and attribute weights characterize product relevance to the product of interest. Based on these factors, a competitor pressure model is generated for the product of interest and describes its estimated variation in demand as a function of its variation in price. The competitor pressure model may be used to evaluate the effect of proposed price changes and correct the proposed price changes by calculating an optimal price.

Abstract: Business rules can govern a single price or they can define relationships between different products involving two or more decision prices. One problem that retailers can face is that business rules are generally not codified and are rarely followed consistently, and thus it can be difficult to articulate existing business rules or generate new business rules. However, existing price information inherently contains industry knowledge and experience, even if retailers find it difficult to express that knowledge. The disclosed technology generates business rules by reverse engineering rule bounds and coefficients from existing price information. The reverse engineered rule bounds and coefficients can be used in price optimization to generate recommended prices for retail products that optimize revenue and profit while complying with a set of business rules.

Abstract: The disclosed technology improves the process of generating recommended prices for retail products by optimizing revenue and profit while complying with a set of business rules by assigning a monetary value to each business rule. Then for each decision price that violates a business rule constraint, a penalty value is added to the monetary value. If the monetary value including the penalty is better than an original monetary value, the decision price is included in the recommended prices.