Abstract:
Systems and methods for method of measuring the efficiency of a plurality of marketing programs for a campaign involving a plurality of saleables and at least one distribution channel. The steps of the methods include: determining a plurality of marketing program segments, wherein each segment corresponds to a unique combination of marketing programs; uniquely correlating each combination of a saleable and distribution channel to a corresponding segment; determining the quantity of at least one measurable for each saleable; and calculating an efficiency value for at least one segment. The systems include a segmentor operatively coupled to a data storage and configured to determine a plurality of marketing program segments, wherein each segment corresponds to a unique combination of marketing programs. The systems also include a correlator operatively coupled to the data storage and configured |to | correlate each combination of a saleable and distribution channel to one marketing program segment; and an efficiency calculator configured to calculate an efficiency value for at least one segment.

Description:
CONTINUITY  
       [0001]     This application is a continuation of U.S. patent application No. 60/740,287 filed Nov. 29, 2005 which is incorporated by reference herein in its entirety. 
     
    
     FIELD OF THE INVENTION  
       [0002]     The present invention relates generally to the field of marketing, with common but by no means exclusive application to evaluating the efficiency of marketing programs. As used herein, the term “marketing program” and as will be understood should be interpreted broadly and includes the advertising or promotional medium or methods like direct mail, telemarketing, space advertising, radio and television commercials, price reduction, gifts, awards, prizes or commemoratives.  
       BACKGROUND OF THE INVENTION  
       [0003]     There is a growing need for businesses to justify their marketing expenditures. Businesses must evaluate the responses to different marketing programs, in order to determine if their marketing dollars are being spent wisely. Businesses need to know which elements of their advertising plan helped achieve their goals in the most efficient manner and which did not, in order to be able to allocate their budgets on an ongoing basis.  
         [0004]     Typically, marketing “success” has been measured in the context of the following types of analyses: 
        A. Existence: Proof of Advertising Performance, which tracks the delivery of advertising;     B. Effectiveness: Return on Objective (ROO), which examines marketplace response to advertising; and     C. Efficiency: Return on Investment (ROI) or Return on Marketing Investment (ROMI), which measures the relative efficiency of various marketing tactics (such as advertising/promotion mix, media mix, etc.).        
 
         [0008]     Advances in technology have allowed more complex data to be analyzed. Techniques such as marketing mix modeling were introduced in the 1990s and have expanded in scope to meet the increased ROI measurement demand for marketers. Marketing mix modeling is a statistical technique based primarily on pattern recognition. This analysis compares week-by-week, market-by-market patterns in advertising and marketing elements to patterns in sales. When matching patterns are located, conclusions are drawn about the positive (or negative) effect the advertising elements had on their corresponding sales. Marketing mix modeling techniques include, but are not limited to, multiple regression analysis, logistic regression, neural net analysis, and genetic algorithm analysis. A marketing mix model is a specialized version of an econometric model.  
         [0009]     The inventors have recognized a need for alternative systems and methods for evaluating the efficiency of marketing programs.  
       SUMMARY OF THE INVENTION  
       [0010]     In a first aspect, the present invention is directed towards a method of measuring the efficiency of a plurality of marketing programs for a campaign involving a plurality of saleables and at least one distribution channel. The steps of the method include: determining a plurality of marketing program segments, wherein each segment corresponds to a unique combination of marketing programs; uniquely correlating each combination of a saleable and distribution channel to a corresponding segment; determining the quantity of at least one measurable for each saleable; and calculating an efficiency value for at least one segment.  
         [0011]     In another aspect, the present invention is directed towards a system for measuring the efficiency of marketing programs. The system includes a data storage configured to store: marketing programs data comprising data corresponding to a plurality of marketing programs; saleables data correlated to a plurality of saleables, wherein at least one saleable is correlated to at least one marketing program; and measurables data correlated to the value of sales for each saleable. The system also includes a segmentor, a correlator and an efficiency calculator. The segmentor is operatively coupled to the data storage and configured to determine a plurality of marketing program segments, wherein each segment corresponds to a unique combination of marketing programs and wherein the data storage is configured to store marketing program segment data correlated to the marketing program segments. The correlator is operatively coupled to the data storage and configured to correlate each saleable to one marketing program segment. The efficiency calculator is configured to calculate an efficiency value for at least one segment. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0012]     The present invention will now be described, by way of example only, with reference to the following drawings, in which like reference numerals refer to like parts and in which:  
         [0013]      FIG. 1  is a schematic diagram of a marketing programs efficiency system made in accordance with the present invention.  
         [0014]      FIG. 2  is a schematic diagram of example marketing program records data as may be stored in the marketing program data storage entity of the system of  FIG. 1 ;  
         [0015]      FIG. 3  is a schematic diagram of example campaign data  25  as may be stored in the campaign data storage entity of the system of  FIG. 1 ,  
         [0016]      FIG. 4  is a schematic diagram of example saleables data as may be stored in the saleables data storage entity of the system of  FIG. 1 ;  
         [0017]      FIG. 5  is a schematic diagram of example location data which may be stored in the location data storage entity of the system of  FIG. 1 ;  
         [0018]      FIG. 6  is a schematic diagram of example association data as may be stored in the association data storage entity of the system of  FIG. 1 ;  
         [0019]      FIG. 7  is a schematic diagram of example measurables data typically stored in the measurables data storage entity of the system of  FIG. 1 ;  
         [0020]      FIG. 8  is a schematic diagram of example marketing program segment data records as may be determined and stored in the program segment data storage entity by the system of  FIG. 1 ;  
         [0021]      FIG. 9  is a schematic diagram of example segmented saleables data records as may be generated and stored in the segmented saleables data storage entity by the system of  FIG. 1 ;  
         [0022]      FIG. 10  is a schematic diagram of example segmented measurables data as may be generated and stored in the segmented measurables data storage entity by the system of  FIG. 1 ;  
         [0023]      FIG. 11  is a flow diagram illustrating the steps of a method of the present invention.  
         [0024]      FIG. 12  is a schematic diagram illustrating the potential plurality of relationships between saleables and marketing programs in raw data.  
         [0025]      FIG. 13  is a schematic diagram illustrating the unique relationship between each saleables and a corresponding segmented marketing program once the marketing program segments have been determined and associated with the raw data in accordance with the method of the present invention.  
         [0026]      FIG. 14A  is a schematic diagram illustrating a ROMI efficiency calculation report as may be generated by the system of  FIG. 1 .  
         [0027]      FIG. 14B  is a schematic diagram illustrating an alternate efficiency calculation report as may be generated by the system of  FIG. 1 . 
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0028]     Referring to  FIG. 1 , illustrated therein is a marketing programs efficiency system, referred to generally as  10 , made in accordance with the present invention. The system  10  comprises a processor or central processing unit (CPU)  12  such as a standard personal computer (PC) running on a WINDOWS™ operating system and having a suitably programmed efficiency engine  14 . As will of course be understood, other types of suitable hardware and operating systems may be used.  
         [0029]     An input/output device  19  (typically including an input component  19   A  such as a keyboard, and output components such as a display  19   B ) is also operatively coupled to the CPU  12 .  
         [0030]     Data storage  20  is also provided, although as will be understood, the storage  20  may be local to or remote from the CPU  12  and portions of the data stored may be stored in different physical or electronic storage locations. The data storage  20  will preferably include a marketing program data storage entity  22  storing marketing program data records  23 , a campaign data storage entity  24  storing campaign data  25 , a saleables data storage entity  26  storing saleables data  27 , a measurables data storage entity  28  storing measurables data  29 , an association data storage entity  30  storing association data  31 . If the company or other entity utilizing the system  10  has multiple stores or other centres of operation, the data storage  20  may also include a location data storage entity  34  storing location data  35 . As will be understood, much of the sales and campaign data  23 ,  25 ,  27 ,  29 ,  31 ,  35  may be previously generated or collected by marketing or other systems and communicated to or extracted by the system  10  (such communicated or extracted data referred to generally herein as raw data  13 ).  
         [0031]     The data storage  20  will also preferably include a marketing program segment data storage entity  32  storing marketing program segment data  33 , a segmented saleables data storage entity  36  storing segmented saleables data records  37 , and also a segmented measurables data storage entity  38  storing segmented measurables data records  39  (collectively referred to herein as segmented data  15 ).  
         [0032]     The efficiency engine  14  may include several modules. A main executable module  40  is preferably provided for controlling the operation of the various sub-modules: a segmentor module  42 , a correlator module  44 , and an efficiency calculator module  46 . As will be discussed in greater detail below, the segmented measurables data records  39  and segmented marketing program data records  37  will typically be generated by the segmentor module  42  and the correlator module  44  using the raw data  13 .  
         [0033]     Referring now to  FIG. 2 , illustrated therein is an example of the type of marketing program records  23  data typically stored in the marketing program data storage entity  22 . While the sample data in  FIG. 2  for simplicity only illustrates three marketing program records  23 , typically the system  10  will be capable of handling complex data involving dozens or more of different marketing programs  23 . The marketing program records  23  data may be input into the marketing program data storage entity  22  by a system  10  user, and updated as new marketing programs are implemented or old marketing programs are discontinued.  
         [0034]     Each marketing program record  23  will typically include a unique marketing program identifier  60 , as well as a marketing program name  62  corresponding to the different marketing programs which may have been implemented in the various marketing campaigns. The marketing program records may also store costing data  64 . Typically the costing data  64  corresponds to the cost per saleable  27  of the marketing program  60 . In the example data, marketing programs such as a store display (with a per saleable cost  64  of $15), a loyalty card points promotion (with a per saleable cost  64  of $10) or a gift promotion (with a per saleable cost  64  of $12.50) are illustrated, but as will be understood, other types of marketing programs may be used such as media advertising, promotional events, etc.  
         [0035]     It should also be understood that the example marketing program records  23  illustrate simplified data for illustrative purposes. As will be understood, the costing data  64  may vary by campaign  25  (for example, a more expensive gift may be provided through a gift promotion  23  during some campaigns  25  than others). Accordingly, a more complicated marketing program record  23  may also include a campaign identifier.  
         [0036]     As well, depending on the number and hence complexity of the various marketing programs, it should be understood that a marketing program record  23  may represent a collection of subsidiary promotions—for example, the store display program record  23  may represent a collection of specific display programs such as shelf displays, aisle displays and store-front window displays etc. Similarly, while separate marketing program records may be created for a radio advertisement promotion and a newspaper advertisement promotion, in appropriate circumstances (such as the size of the respective promotions) it may be helpful to combine such promotions into a single marketing program record  23  for “advertisements” generally.  
         [0037]     Referring now to  FIG. 3 , illustrated therein is an example of the type of campaign data  25  typically stored in the campaign data storage entity  24 . In the example data shown in  FIG. 3 , only ten different campaign records  25  are illustrated. However, the system  10  will preferably be configured to implement and track a more expansive data set involving dozens or hundreds or more such campaigns  25 . The campaign records  25  data may be input into the campaign data storage entity  24  by a system  10  user, and updated as new campaigns are implemented.  
         [0038]     Each campaign record  25  will typically include a unique campaign identifier  70 , as well as data corresponding to the campaign name  72 , such as “Mother&#39;s Day” or “Easter”. Typically, the campaign records  25  will also include other data relating to the implementation of each campaign, such as the campaign start  74  and end  76  dates. Typically, no two campaigns will overlap.  
         [0039]     Referring now to  FIG. 4 , illustrated therein is an example of the type of saleables data  27  typically stored in the saleables data storage entity  26 . While the sample data in  FIG. 4  illustrates fourteen different saleables  27 , the system  10  will preferably be configured to handle and store a more complicated data set involving hundreds or thousands or more of saleables  27 . The saleables records  27  data may be input into the saleable storage entity  26  by a system  10  user, and updated for each new saleable. Typically the saleables data  27  includes a unique saleables identifier  80  together with data corresponding to the saleable name  82 . As will be understood, the term “saleables” as used herein is intended to refer broadly to goods or services or other intangibles that a company may sell or provide. Preferably, the saleables data  27  will include an entry for each saleable in the company&#39;s entire inventory or eligible for promotion. The saleables identifier  80  will preferably correspond to the company&#39;s saleable identifier such as a stock keeping unit number (SKU#).  
         [0040]     Turning to  FIG. 5 , illustrated therein is an example of the type of location data  35  which may be stored in the location data storage entity  34 . The example data in  FIG. 5  illustrates two different location data records  35 . As will be understood, the system  10  will preferably be configured to handle and store a more expansive data set involving hundreds or thousands or more of locations  35  or other distribution channels (including without limitation such as the Internet, or direct mailing). The location records  35  data may be input into the location data storage entity  34  by a system  10  user, and updated for each new location. A unique location identifier  84  will preferably be provided, together with data corresponding to the location name  86 .  
         [0041]     Referring now to  FIG. 6 , illustrated therein is an example of the type of association data  31  typically stored in the association data storage entity  30 . The association records  31  data may be input into the association storage entity  30  by a system  10  user, and updated for each new campaign. Typically the association data  31  includes a campaign identifier  100  corresponding to the campaign identifiers  70  in the campaign records  25 . The association data  31  will typically also include saleables identifiers  102  corresponding to the saleables identifiers  80  stored in the saleables data records  27 .  
         [0042]     For marketing programs involving multiple locations, the association data  31  may also include location identifier data  103  corresponding to the location identifiers  84  in the locations data records  35  together with a marketing program identifier  104  corresponding to the marketing program identifiers  60  in the marketing programs records  23 . As will be understood, if the marketing programs  23  in a campaign  25  do not apply at the distribution channel level (eg. location  35 ), then the user may elect not to include distribution channel data such as the location identifier data  103  in the association data  31 . For example, if all of the marketing programs  23  apply to all of the locations  35  in a campaign  25 , then it may not required to include a location identifier data  103  in the association data records  31 .  
         [0043]     As shown in the example data record  31 ′ in  FIG. 6  (while simultaneously cross-referencing the data in  FIGS. 2, 3  and  4 ), for the Halloween campaign (campaign identifier  70 ,  100 =“C 200509 ”), the covering stick (saleable identifier  80 ,  102 =“S 2 ”) was sold in the Toronto store (location identifier  84 ,  103 =“L 1 ”) in association with the store display and gift marketing program (marketing program identifiers  62 ,  104 =“SD” and “GF”), and also in the Ottawa store (location identifier  84 ,  103 =“L 2 ”) in association with just the gift marketing program (marketing program identifier  62 ,  104 =“GF”).  
         [0044]     As will be understood, typically, for every campaign  70 ,  100 , an association record  31  is created for each marketing program  104  (and location  103  combination, if applicable) that a saleable  102  is marketed in association with.  
         [0045]     As will be understood, marketing programs may be selectively applied to saleables  102  through different locations  103  or other saleable distribution channels. Accordingly, if a saleable  102  is marketed with two different marketing programs  104  (in only one location  103 ) during a particular campaign  100 , then two association records  31  will preferably be created.  
         [0046]     Referring now to  FIG. 7 , illustrated therein is an example of the type of measurables data  29  typically stored in the measurables data storage entity  28 . The measurables records  29  data may be input into the measurable storage entity  28  by system  10  user, and updated for each new campaign, The measurables data  29  preferably includes a campaign identifier  90  corresponding to the campaign identifiers  70  in the campaign data storage entity  24 . The measurables data  29  will typically also include a saleables identifier  92  corresponding to the saleables identifier  80  stored in the saleables data records  27 .  
         [0047]     As noted previously, for corporations or other entities distributing and tracking saleables from multiple locations, the measurables data  29  may also include location identifier data  93  corresponding to the location identifiers  84  in the locations data records  35 . The measurables data  29  will also include quantity data  94  typically referred to as “sales” correlated to the value or quantity of each saleable  92  sold or charitable funds received (eg. for a charity application) during the corresponding campaign  90 . Typically, the quantity data  94  will be represented in currency, such as dollars, but it should be understood that for some applications, the quantity data  94  may correspond to other measurements, such as number of units sold, cost, price, inventory etc.  
         [0048]     The measurables data  29  will preferably also include growth margin data  96  correlated to the increase (or lack thereof) in value or quantity of saleables  92  sold during the campaign  90  relative to pre-campaign sales. As will be understood, the growth margin data  96  is intended to reflect the improvement to “sales” which may be attributed to the campaign  90 . Preferably the measurables data  29  will include marketing program investment data  98 . The marketing program investment data  98  reflects the total of the marketing program costs  64  of all the marketing programs  104  for a saleable  92  at a particular location  86 .  
         [0049]     Referring to the example measurables data record  29 ′ in  FIG. 7  (in conjunction with the data in  FIGS. 2, 4  &amp;  6 ), it can be seen that during the Halloween campaign (campaign identifier  70 ,  90 =“C 200509 ”), the covering stick (saleable identifier  80 ,  92 =“S 2 ”) was sold in the Toronto store (location identifier  93 ,  103 =“L 1 ”) in association with the store display and gift marketing program (marketing program identifiers  62 ,  104 =“SD” and “GF”), with a quantity value (representing sales)  94  of $500.33. The example marketing investment data  98  for the example record  29 ′ indicates a marketing program investment of $27.50, which was determined by adding the store display per saleable cost  64  of $15 to the gift marketing program per saleable cost  64  of $12.50 ($15+$12.50=$27.50). As will be understood the marketing program investment data  98  may either be calculated by the system  10  by adding the appropriate cost data  64  together, or may be previously compiled by marketing or other systems and communicated to or extracted by the system  10 .  
         [0050]     While the example measurables data records  29  indicate that each saleable  27 / 92  is sold through each distribution channel  35 / 93 , it should be understood that in some implementations, it may not be the case that a saleable  27  is sold through every distribution channel  35 . In such instance, the measurables data storage  28  may only include a measurables data record  29  corresponding to each combination of a saleable  27 / 92  and a distribution channel  35 / 93  in which the saleable  27  is distributed through such distribution channel  35 .  
         [0051]     Referring now to  FIG. 8 , illustrated therein is an example of the type of marketing program segment data records  33  typically stored in the program segment data storage entity  32 . Typically, the program segment data  33  includes a unique program segment identifier  110  together with Boolean data  112 A,  112 B,  112 C corresponding to each marketing program  60 .  
         [0052]     As will be understood, a marketing program segment data record  33  has been created for each possible combination of marketing programs  60 , including a “None” record  33 ′ in which the segment  110  includes none of the marketing programs  60 . As can be seen, the Boolean data  112 A,  112 B,  112 C indicates whether the corresponding marketing program  60  is present in the marketing program segment  110 .  
         [0053]     Referring now to  FIG. 9 , illustrated therein is an example of the type of segmented saleables data records  39  typically stored in the segmented saleables data storage entity  38 . Typically, the segmented saleables data  39  includes a campaign identifier  120  corresponding to campaign identifiers  70  in the campaign records  25 . The segmented saleables data  39  will typically also include saleables identifiers  122  corresponding to the saleables identifiers  80  stored in the saleables data records  27 , together with a location identifier  123  corresponding to the location identifiers  84  in the locations data records  35 . A marketing program segment identifier  124  corresponding to the marketing program segment identifiers  110  in the marketing program segment data records  33  is also provided.  
         [0054]     One segmented saleables data record  39  is preferably created for each saleable  92 /location  93  combination in the measurables data storage entity  28 . Each such saleable  122 ,  92 /location (or other distribution channel)  123 ,  93  combination is uniquely associated with a corresponding marketing program segment  124 ,  110 , correlated to all of the marketing programs  60  applied to the saleable  122  at that location  103  as will be discussed in greater detail below.  
         [0055]     Referring now to  FIG. 10 , illustrated therein is an example of the type of segmented measurables data  39  typically stored in the segmented measurables data storage entity  38 . The segmented measurables data  39  typically corresponds to the measurables data  29  and as a result may include campaign identifier  190 , saleables identifier  192 , location identifier data  193 , quantity data  194 , growth margin data  196 , and marketing investment data  198  corresponding to the similarly named data  90 ,  92 ,  93 ,  94 ,  96 ,  98  in the measurables data records  29 , respectively. In addition, the segmented measurables data  39  preferably includes a marketing program segment identifier  199  corresponding to the marketing program segment identifiers  110  in the marketing program segment data records  33 .  
         [0056]     Referring to the example segmented measurables data record  39 ′ in  FIG. 10  (in conjunction with the data in  FIGS. 2, 4 ,  6  &amp;  8 ), it can be seen that during the Halloween campaign (campaign identifier  70 ,  190 =“C 200509 ”), the hair gel (saleable identifier  192 ,  80 =“S 7 ”) was sold in the Toronto store (location identifier  193 ,  103 =“L 1 ”) in association with the store display and loyalty card marketing programs (marketing program segment identifier  199 ,  110 =“SD_LC”), with a quantity value (representing sales)  194  of $183.41. The example marketing program investment data  198  for the example record  29 ′ indicates a marketing program investment of $25.00, together with a growth margin  196  of $75.6.  
         [0057]     Another example segmented measurables data record  39 ″ indicates that during the Halloween campaign (campaign identifier  70 ,  190 =“C 200509 ”), the lipstick (saleable identifier  192 ,  80 =“S 10 ”) was sold in the Ottawa store (location identifier  193 ,  103 =“L 2 ”) without the use of any marketing program (marketing program segment identifier  199 ,  110 =“none”), with a quantity value (representing sales)  194  of $13.82. The example data  39 ″ also indicates that the sales of the lipstick had a growth margin of $2.1, with no marketing program investment  198  (which is consistent with the absence of marketing programs  199 ).  
         [0058]     Referring now to  FIG. 11 , this figure is a flow chart setting out the process  200  carried out by the system  10 . Initially, the system  10  receives the raw data  13  and stores it in the data storage (Block  202 ). As previously noted, the raw data  13  which comprises the majority of the sales and campaign data  23 ,  25 ,  27 ,  29 ,  31 ,  35  may be previously generated or collected by marketing or other systems and communicated to or extracted by the system  10 . However, it should be understood that the system  10  may comprise part of a larger sales system, in which case the system  10  may not require the step  202  of duplicating such data.  
         [0059]     The segmentor  42  determines the marketing program segments  110  (Block  204 ). The segmentor  42  may do this by accessing the marketing program data records  23  to determine the number, N, of marketing programs  60  utilized by the various marketing campaigns  70 . The segmentor  42  may then determine the number of different combinations of marketing programs  60 . As will be understood, the number of different possible combinations of marketing programs  60  (and hence marketing program segments  110 ) is 2 N .  
         [0060]     Thus, in the example marketing program data  23  illustrated in  FIG. 2 , three different marketing programs  60  are illustrated (“Store Display”, “Loyalty Card” and “Gift”). Accordingly, based on the example data  23  the segmentor  42  would determine there are 2 3 =8 potential combinations of marketing programs  60  (including an empty combination, “None”, with no marketing program  60 ), and may create the program segment data records  33 . As will be understood, each marketing program segment  33  may be determined by utilizing Boolean vector data representing values from 0 to 2 N −1 (in binary) and illustrating each possible combination with “1”s and “0”s. In the example provided by  FIG. 8  the Boolean vector data  113 A,  113 B,  113 C,  113 D,  113 E,  113 F,  113 G,  113 H have the values: “000”, “001”, “010”, “011”, “100”, “101”, “110”, and “111”, respectively.  
         [0061]     Each column of Boolean data  112 A,  112 B,  112 C corresponds to each marketing program  60 , and each “1” is a flag indicating that the corresponding marketing program  60  is present in the program segment  33  (and conversely, each “0” is a flag indicating that the corresponding marketing program  60  is not present in the particular program segment  33 ). With the exception of “None”, each segment identifier  110  preferably corresponds to a concatenation of marketing program identifiers  60  present in the segment  33 , but other appropriate identifiers  110  may also be used.  
         [0062]     Once the program segments  33  have been determined, the correlator  44  will create an association between the segments  33  and the saleables (Block  206 ). Effectively, this provides an association between the marketing program segments  33  and the raw data  13  and particularly the measurables data  29 .  
         [0063]     One approach the correlator  44  may be programmed to perform this task is by utilizing the association data  31  in the association data storage entity  32  (together with the saleables data  23  and the location data  35 , if applicable) to generate and store the segmented saleables data  37  in the segmented saleables data storage entity  36 . For each saleable  27  (or saleable  27 /location  35  combination) for a campaign  100 , the correlator  44  may create a segmented saleables data record  37 . By scanning the marketing program identifiers  104  in the association data  31  for each saleable  27 ,  102  (or saleable  27 ,  102 /location  35 ,  123  combination) the correlator  44  may determine the unique marketing program segment identifier  110  which correlates to each of the marketing programs  104  applied to the saleable  27 ,  102  (or saleable  27 ,  102 /location  35 ,  123  combination) and save the marketing program segment identifier  110 ,  124  in the corresponding segmented saleables data record  37 . If no marketing program  104  exists for a saleable  27 ,  102  (or saleable  27 ,  102 /location  35 ,  123  combination), then the marketing program segment identifier  110 ,  124  is determined to be “None”, as will be understood (referred to herein as a “non-marketing program segment”).  
         [0064]     Referring briefly to the sample association data  31  in  FIG. 6 , it can be seen that the saleable  102  and location  103  combination “S 7 ” and “L 1 ” respectively has two corresponding association records  31 , each indicating a marketing program identifier  104  “SD” and “LC”, respectively. A corresponding segmented saleables record  37 ′ for the saleable  102  and location  103  combination “S 7 ” and “L 1 ” has been created in which the marketing program segment identifier  124  has been saved as “SD_LC”.  
         [0065]     The correlator  44  may be programmed to then generate and store the segmented measurables data  39  in the segmented measurables data storage entity  38 . By matching each saleable  122  and location  123  combination in the segmented saleables data records  37  to corresponding saleable  92  and location  93  combinations in the measurables data records  29 , corresponding segmented measurables data records  39  may be created and stored, each including the corresponding segment identifier  124 ,  199 .  
         [0066]     Turning briefly now to  FIGS. 12 and 13 , illustrated therein are schematic diagrams contrasting the potential one-to-many relationships between each of a plurality of saleables  27  and three unsegmented marketing programs  23  ( FIG. 12 ) which have been used in a campaign  25  as may exist in raw data  13  supplied to the system  10 , and the one-to-one relationship between each saleable  27  and the segmented marketing programs  110  ( FIG. 13 ) once the marketing program segments  110  have been determined and associated with the saleables  27  following Blocks  204  and  206 .  
         [0067]     For the sake of simplicity in the illustrations, the saleables data  27  illustrated in  FIGS. 12 and 13  represents data for a company having a single “location” (or possibly for which the measurables data  29  has not been separated by location), and hence does not include location data  35 . Alternatively, as noted, each saleable in the schematic diagrams ( FIGS. 12 &amp; 13 ) may represent a unique combination of a saleable  27  and location  35  (or other distribution channel).  
         [0068]     As can be seen in  FIG. 12 , the saleable  27  identified as “SA 1 ” has a plurality of relationships to marketing programs  23  as it is illustrated as having links to both the “SD” and “LC” marketing programs  23 , indicating that it has been marketed under both programs  23 .  FIG. 13  illustrates the same “SA 1 ” saleable  27  following the segmentation and association steps of blocks  204  and  206 , having a single relationship to a marketing program segment  110 , “SD_LC”.  
         [0069]     Referring again to  FIG. 11 , once the marketing program segments  33  have been determined and associated with the measurables data  29  as set out in Blocks  204  and  206 , the system  10  calculates the efficiency of the marketing program segments  33  (Block  208 ).  
         [0070]     To determine the efficiency of the segments  33 , the efficiency calculator module  46  may be programmed to determine the quantity of sales for each saleable  27  (or saleable  27  and location  35  (or other distribution channel) combination), which data can be retrieved from the sales data  194  of the segmented measurables records  39 .  
         [0071]      FIG. 14A  illustrates an example efficiency report  300  for a campaign  25  containing line entries  300 A based on ROMI as may be generated by the efficiency engine  14 . Such a report  300  will preferably include a campaign identifier  301  which may include a campaign code  301 A and/or a campaign name  301 B corresponding to a campaign identifier  70  and campaign name  72 , respectively, in the campaign data records  25 .  
         [0072]     The report  300  will also preferably be provided with a segment identifier  302 . Typically, the segment identifiers  302  will correspond to the segment identifiers  110 , in the marketing program data records  33 . The report  300  may also include an entry  300 A′ having a “Total” segment identifier  302  corresponding to efficiency calculations for all saleables  27  sold in association with at least one marketing program  23  (ie. for all saleables  27 ,  192  (or saleable  27 ,  102 /location  35 ,  123  combination) other than those indicating a “None” segment identifier  199  in the segmented measurables data records  39 ). As will be understood, typically the report  300  will include one entry  300 A corresponding to each marketing program segment  33 ,  199  listed on the segmented measurables data storage entity  32  (other than the “None” segment  33 ′).  
         [0073]     Each entry  300 A will also preferably include a growth margin value  304  typically determined by summing the growth margin data  196  from each segmented measurables data record  39  for the campaign  190 ,  401 A in which the segment identifier  199  corresponds to the segment identifier  302  for the entry  300 A. Similarly, a marketing investment value  306  will preferably be provided and typically determine by summing the marketing investment data  198  from each segmented measurables data record  39  for the campaign  190 ,  401 A in which the segment identifier  199  corresponds to the segment identifier  302  for the entry  300 A.  
         [0074]     A ROMI efficiency value  308  for each entry  300 A is also preferably determined and provided. To calculate the ROMI values  308  for the example ROMI efficiency report  300  entries  300 A, the efficiency engine  14  may utilize the following equation (EQ. 1) in which GM represents growth margin  304  and MPI represents marketing investment  306 :  
             ROMI   =         GM   -   MPI     MPI     .             EQ   ⁢           ⁢   1             
 
         [0075]      FIG. 14B  illustrates an alternate style of efficiency report  400  for a campaign  25  containing line entries  400 A as may be generated by the efficiency engine  14 . Such a report  400  will preferably include a campaign identifier  401  which may include a campaign code  401 A and/or a campaign name  301 B corresponding to a campaign identifier  70  and campaign name  72 , respectively, in the campaign data records  25 .  
         [0076]     If applicable, the report entries  400 A may include a location identifier  402  corresponding to a location identifier  84  in the location data records  35 .  
         [0077]     The report entries  400 A will also preferably be provided with a segment identifier  404 . Typically, the segment identifiers  404  will correspond to the segment identifiers  110 , in the marketing program data records  33 .  
         [0078]     For each location  402 , the report  400  may also include an entry  400 A′ having a “All Products in Promotion” segment identifier  404  corresponding to efficiency calculations for all saleables  27  sold in association with at least one marketing program  23  (ie. for all saleables  27 ,  192  (or saleable  27 ,  102 /location  35 ,  123  (or other distribution channel) combination) other than those indicating a “None” segment identifier  199  in the segmented measurables data records  39 ).  
         [0079]     For each location  402 , the report  400  may also include an entry  400 A″ having an “All Products Not in Promotion” segment identifier  404  corresponding to efficiency calculations for all saleables  27  for which no marketing program  23  was used (ie. for all saleables  27 ,  192  (or saleable  27 ,  102 /location  35 ,  123  combination) indicating a “None” segment identifier  199  in the segmented measurables data records  39 ).  
         [0080]     As will be understood, for each different location  402 , typically the report  400  will include one entry  400 A corresponding to each marketing program segment  33 ,  199  listed on the segmented measurables data storage entity  32  (including the “None” segment  33 ′ and an “All Products in Promotion” segment).  
         [0081]     Each entry  400 A will preferably include sales data  406  corresponding to the segment  404  for the particular location  402 . The sales data  406  is calculated by summing the sales data  194  for the campaign  190 ,  401 A from each segmented measurables data record  39  in which the segment identifier  199  corresponds to the segment identifier  404  for the entry  400 A (“None” in the case of “All Products Not in Promotion”). As will be understood, in the case of the “All Products in Promotion” entry  400 A′, the sales data  406  is calculated by summing the sales data  194  for the campaign  190 ,  401 A from each segmented measurables data record  39  in which the segment identifier  199  is not “None”.  
         [0082]     Each entry  400 A will preferably also include number of saleables data  408  corresponding to the segment  404  for the particular location  402 . The number of saleables data  408  is calculated by totaling the number of different saleables  192  corresponding to the location  402 ,  193  for the campaign  190 ,  401 A from each segmented measurables data record  39  in which the segment identifier  199  corresponds to the segment identifier  404  for the entry  400 A (“None” in the case of “All Products Not in Promotion”). As will be understood, in the case of the “All Products in Promotion” entry  400 A′, the number of saleables data  408  is calculated by totaling the number of different saleables  192  corresponding to the location  402 ,  193  for the campaign  190 ,  401 A from each segmented measurables data record  39  in which the segment identifier  199  is not “None”.  
         [0083]     Each entry  400 A will preferably also be provided with sales % data  410  for the location  402  corresponding to the ratio of the sales value  406  of the segment  404  relative to the total of all sales  194  corresponding to the location  192 ,  402  in the segmented measurables data records  39  for the campaign  190 ,  401 A. Alternatively, the total of all sales  194  may be calculated by adding the sales  406  values in the “All Products Not in Promotion”  400 A″ and “All Products in Promotion”  400 A′ entries.  
         [0084]     Each entry  400 A will preferably also be provided with number of saleables % data  412  for the location  402  corresponding to the ratio of the number of saleables  408  of the segment  404  relative to the total number of all saleables  27 . In the example entry  400 A″, it can be seen that since there was a total of “5” saleables  408  in the “All Products Not in Promotion” segment  404 , and there were “14” saleables  27  in total, the number of saleables % data  412  is illustrated as “36%” (5/14=36%).  
         [0085]     An efficiency value  414  is also provided. The efficiency value  414  for each entry  400 A is calculated as the ratio of the sales % value  410  relative to the number of saleables % value  412  for that entry  400 A. For example, referring to entry  400 A′, the efficiency value  414  is listed as 121% (78%/64%=121%)  
         [0086]     While the ROMI efficiency report  300  and alternate efficiency report  400  illustrate two different types of efficiency analyses which can be conducted on raw data  13  once the marketing programs have been segmented as described herein, it should be understood that other types of efficiency analyses can be performed. Furthermore, while various data entities and data have been illustrated and described herein, it should be understood that other structures for the entities and data may be created and used in accordance with the present invention.  
         [0087]     Thus, while what is shown and described herein constitute preferred embodiments of the subject invention, it should be understood that various changes can be made without departing from the subject invention, the scope of which is defined in the appended claims.