Pricing optimization apparatus and method

Abstract

A method or apparatus for automatically determining an optimum price for an offer by a first entity to a customer entity, comprising: obtaining a demand function representative of the customer, using the demand function and other factors include an intended product margin, building a goal function representative of goals of said first entity, and automatically optimizing the goal function for the margin, therefrom to generate the offer.

1 . A method of automatically determining optimum prices of a given product, said prices comprising respective margins, for offers by a first entity to a portfolio of customer entities, comprising the steps of: 
 obtaining a set of demand functions individually parameterizable for respective customer entities of said portfolio,    using said set of demand functions and respective product margins, constructing a goal function representative of goals of said first entity, and    automatically maximizing said goal function using said set, by varying said margins under constraints calculated over said portfolio of customers, therefrom to generate individualized margins for respective customer entities, said prices thereby being optimized for said respective customer entities for said given product.    
   
   
       2 . The method of  claim 1 , wherein said demand function comprises expected behavior in respect of said offer together with expected behavior in respect of future offers, and wherein said automatically optimizing comprises taking into account said expected behaviors in respect of future offers.  
   
   
       3 . The method of  claim 1 , wherein said incorporating of said constraints comprises building an effective goal function using a lagrangian multiplier to represent said constraints.  
   
   
       4 . The method of  claim 3 , further comprising forming a set of non-linear equations from said Lagrangian multipliers and solving said non-linear equations.  
   
   
       5 . The method of  claim 4 , wherein said solving of said non-linear equations comprises Newton Raphson iteration.  
   
   
       6 . The method of  claim 1 , wherein said automatically maximizing is to generate optimized offers for said given product to corresponding customer entities of said portfolio and 
 wherein said goal function comprises a term for other income derived from products linked to a product being a subject of said offer.    
   
   
       7 . The method of  claim 1 , wherein said constructing of said goal function comprises incorporating a time horizon therein.  
   
   
       8 . The method of  claim 1 , wherein said automatically optimizing comprises using dynamic programming.  
   
   
       9 . The method of  claim 1 , wherein said obtaining a demand function comprises applying logistic regression to respective customer profiles.  
   
   
       10 . The method of  claim 1 , wherein: 
 said set of demand functions are individually parameterized for respective customer entities of said portfolio,    including in said goal function a term for multi year renewal by respective customer entities, said term comprising future margins and future demand functions, and 
 automatically maximizing said goal function using said set of demand functions, by varying said margins under constraints calculated over said portfolio of customers, therefrom to generate optimized individualized offers to corresponding customer entities of said portfolio, said optimization including said term for multi-year renewal.  
   
   
   
       11 . Apparatus for automatically determining optimum prices for offers for a given product by a first entity to a portfolio of customer entities, said prices comprising margins, the apparatus comprising: 
 a processor configured with:    a set of demand functions individually parameterized for respective customer entities,    a goal function constructor, associated with said set, for using said set and said margins, therefrom to build a goal function representative of goals of said first entity, and    a goal function maximizer, associated with said goal function constructor, for automatically maximizing said goal function for said margins using said set of demand functions, under constraints calculated over said portfolio of customers, therefrom to generate optimized individualized offers to corresponding customer entities.    
   
   
       12 . The apparatus of  claim 11 , wherein said demand function comprises expected behavior in respect of said offer together with expected behavior in respect of future offers, and wherein said optimizer is configured to take into account said, expected behaviors in respect of future offers.  
   
   
       13 . The apparatus of  claim 11 , wherein said demand function representative of said customer is a demand function generated per customer.  
   
   
       14 . Apparatus according to  claim 11 , wherein said goal function comprises a term for other income derived from products linked to a product being a subject of said offer.  
   
   
       15 . The apparatus of  claim 11 , wherein said goal function builder is further configured to incorporate a time horizon therein.  
   
   
       16 . The apparatus of  claim 11 , wherein said goal function optimizer is operable to use dynamic programming in order to carry out said automatically optimizing.  
   
   
       17 . The apparatus of  claim 11 , wherein said demand function input is operable to apply logistic regression to a customer profile, therefrom to obtain said demand function representative of a respective customer.  
   
   
       18 . The method of  claim 1 , further comprising: 
 obtaining respective probability demand functions characterized by variable cost modeling for respective customer entities of said portfolio,    using said respective demand functions and product margins, constructing a goal function representative of goals of said first entity using a Lagrangian multiplier to represent constraints, and    automatically maximizing said goal function using respective demand functions by varying said margins, therefrom to generate individualized offers to corresponding customer entities of said portfolio.    
   
   
       19 . Apparatus according to  claim 11 , wherein said goal function constructor comprises a constraint imposing unit to incorporate constraints for said customer entities of said portfolio, and said goal function constructor is further configured to construct a goal function including a term for multi year renewal by respective customer entities of said portfolio.  
   
   
       20 . The method of  claim 1 , wherein said goal function further comprises at least one of expected and variable costs characterizing said respective customer entities.