Abstract:
A method for customizing direct marketing materials is provided. This method comprises developing models to predict customer purchases and then scoring potential customers for each predictive model. Next, specific layout areas are determined as well as where particular products may be placed in the layout. In one embodiment, preference multipliers are used to determine the increased likelihood of a product being purchased depending on its location in the layout (i.e. front cover). An optimization model is then used to customize the layout for potential customers, whether it be for a niche market or individual customers. The customized layout is only printed and sent if the expected profits exceed the production costs of the materials.

Description:
BACKGROUND OF THE INVENTION  
         [0001]    1. Technical Field  
           [0002]    The present invention relates to network computing. More specifically, the present invention relates to customizing direct marketing materials for customers.  
           [0003]    2. Description of Related Art  
           [0004]    Direct marketers are constantly seeking improved methods for effectively targeting their advertisements to potential customers. Efforts are increasingly focused on moving closer to “one-to-one” marketing, or mass customized marketing materials, in which marketing materials are tailored to the tastes and buying habits of particular individuals and niche markets.  
           [0005]    Unfortunately, it is very labor intensive to define direct mailers or web presentations for an individual customer. A key ingredient to customized marketing is data mining, which involves exploring detailed business transactions to uncover patterns and relationships within business activity and history. The process usually demands filtering through massive amounts of data and can be done manually or with programs that analyze the data automatically.  
           [0006]    When marketers adopt the combination of data mining and one-to-one customized printing, they are faced with two new business processes: identifying what products to offer to each of the niche markets (or individuals), and designing graphic layouts for each niche (or individual).  
           [0007]    Marketing and graphics labor availability will limit the number of niche markets (or individuals) that can be addressed, and will present a serious problem as marketers attempt to approach one-to-one marketing. Therefore, it would be desirable to have a method for automating these two business processes in order to increase the number of customized mailings without increasing the required staff support.  
         SUMMARY OF THE INVENTION  
         [0008]    The present invention provides a method for customizing direct marketing materials. This method comprises developing models to predict customer purchases and then scoring potential customers for each predictive model. Next, specific layout areas are determined as well as where particular products may be placed in the layout. In one embodiment, preference multipliers are used to determine the increased likelihood of a product being purchased depending on its location in the layout (i.e. front cover).  
           [0009]    An optimization model is then used to customize the layout for potential customers, whether it be for a niche market or individual customers. The customized layout is only printed and sent if the expected profits exceed the production costs of the materials.  
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0010]    The novel features believed characteristic of the invention are set forth in the appended claims. The invention itself, however, as well as a preferred mode of use, further objectives and advantages thereof, will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings, wherein:  
         [0011]    [0011]FIG. 1 depicts a pictorial representation of a network of data processing systems in which the present invention may be implemented;  
         [0012]    [0012]FIG. 2 depicts a block diagram of a data processing system that may be implemented as a server, such as server in accordance with a preferred embodiment of the present invention;  
         [0013]    [0013]FIG. 3 depicts a block diagram illustrating a data processing system in which the present invention may be implemented;  
         [0014]    [0014]FIG. 4 depicts a flowchart illustrating a method for automated and mass customization of marketing materials in accordance with the present invention;  
         [0015]    [0015]FIG. 5 depicts a diagram illustrating a grid layout in accordance with the present invention;  
         [0016]    [0016]FIG. 6 depicts a flow diagram illustrating the transportation optimization model in accordance with the present invention;  
         [0017]    [0017]FIG. 7 depicts a flow diagram illustrating the network optimization model in accordance with the present invention; and  
         [0018]    [0018]FIG. 8 depicts a flow diagram illustrating the generalized network optimization model in accordance with the present invention.  
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT  
       [0019]    With reference now to the figures, FIG. 1 depicts a pictorial representation of a network of data processing systems in which the present invention may be implemented. Network data processing system  100  is a network of computers in which the present invention may be implemented. Network data processing system  100  contains a network  102 , which is the medium used to provide communications links between various devices and computers connected together within network data processing system  100 . Network  102  may include connections, such as wire, wireless communication links, or fiber optic cables.  
         [0020]    In the depicted example, a server  104  is connected to network  102  along with storage unit  106 . In addition, clients  108 ,  110 , and  112  also are connected to network  102 . These clients  108 ,  110 , and  112  may be, for example, personal computers or network computers. In the depicted example, server  104  provides data, such as boot files, operating system images, and applications to clients  108 - 112 . Clients  108 ,  110 , and  112  are clients to server  104 . Network data processing system  100  may include additional servers, clients, and other devices not shown. In the depicted example, network data processing system  100  is the Internet with network  102  representing a worldwide collection of networks and gateways that use the TCP/IP suite of protocols to communicate with one another. At the heart of the Internet is a backbone of high-speed data communication lines between major nodes or host computers, consisting of thousands of commercial, government, educational and other computer systems that route data and messages. Of course, network data processing system  100  also may be implemented as a number of different types of networks, such as for example, an intranet, a local area network (LAN), or a wide area network (WAN). FIG. 1 is intended as an example, and not as an architectural limitation for the present invention.  
         [0021]    Referring to FIG. 2, a block diagram of a data processing system that may be implemented as a server, such as server  104  in FIG. 1, is depicted in accordance with a preferred embodiment of the present invention. Data processing system  200  may be a symmetric multiprocessor (SMP) system including a plurality of processors  202  and  204  connected to system bus  206 . Alternatively, a single processor system may be employed. Also connected to system bus  206  is memory controller/cache  208 , which provides an interface to local memory  209 . I/O bus bridge  210  is connected to system bus  206  and provides an interface to I/O bus  212 . Memory controller/cache  208  and I/O bus bridge  210  may be integrated as depicted.  
         [0022]    Peripheral component interconnect (PCI) bus bridge  214  connected to I/O bus  212  provides an interface to PCI local bus  216 . A number of modems may be connected to PCI bus  216 . Typical PCI bus implementations will support four PCI expansion slots or add-in connectors. Communications links to network computers  108 - 112  in FIG. 1 may be provided through modem  218  and network adapter  220  connected to PCI local bus  216  through add-in boards.  
         [0023]    Additional PCI bus bridges  222  and  224  provide interfaces for additional PCI buses  226  and  228 , from which additional modems or network adapters may be supported. In this manner, data processing system  200  allows connections to multiple network computers. A memory-mapped graphics adapter  230  and hard disk  232  may also be connected to I/O bus  212  as depicted, either directly or indirectly.  
         [0024]    Those of ordinary skill in the art will appreciate that the hardware depicted in FIG. 2 may vary. For example, other peripheral devices, such as optical disk drives and the like, also may be used in addition to or in place of the hardware depicted. The depicted example is not meant to imply architectural limitations with respect to the present invention.  
         [0025]    The data processing system depicted in FIG. 2 may be, for example, an IBM RISC/System 6000 system, a product of International Business Machines Corporation in Armonk, N.Y., running the Advanced Interactive Executive (AIX) operating system.  
         [0026]    With reference now to FIG. 3, a block diagram illustrating a data processing system is depicted in which the present invention may be implemented. Data processing system  300  is an example of a client computer. Data processing system  300  employs a peripheral component interconnect (PCI) local bus architecture. Although the depicted example employs a PCI bus, other bus architectures such as Accelerated Graphics Port (AGP) and Industry Standard Architecture (ISA) may be used. Processor  302  and main memory  304  are connected to PCI local bus  306  through PCI bridge  308 . PCI bridge  308  also may include an integrated memory controller and cache memory for processor  302 . Additional connections to PCI local bus  306  may be made through direct component interconnection or through add-in boards. In the depicted example, local area network (LAN) adapter  310 , SCSI host bus adapter  312 , and expansion bus interface  314  are connected to PCI local bus  306  by direct component connection. In contrast, audio adapter  316 , graphics adapter  318 , and audio/video adapter  319  are connected to PCI local bus  306  by add-in boards inserted into expansion slots. Expansion bus interface  314  provides a connection for a keyboard and mouse adapter  320 , modem  322 , and additional memory  324 . Small computer system interface (SCSI) host bus adapter  312  provides a connection for hard disk drive  326 , tape drive  328 , and CD-ROM drive  330 . Typical PCI local bus implementations will support three or four PCI expansion slots or add-in connectors.  
         [0027]    An operating system runs on processor  302  and is used to coordinate and provide control of various components within data processing system  300  in FIG. 3. The operating system may be a commercially available operating system, such as Windows  2000 , which is available from Microsoft Corporation. An object oriented programming system such as Java may run in conjunction with the operating system and provide calls to the operating system from Java programs or applications executing on data processing system  300 . “Java” is a trademark of Sun Microsystems, Inc. Instructions for the operating system, the object-oriented operating system, and applications or programs are located on storage devices, such as hard disk drive  326 , and may be loaded into main memory  304  for execution by processor  302 .  
         [0028]    Those of ordinary skill in the art will appreciate that the hardware in FIG. 3 may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash ROM (or equivalent nonvolatile memory) or optical disk drives and the like, may be used in addition to or in place of the hardware depicted in FIG. 3. Also, the processes of the present invention may be applied to a multiprocessor data processing system.  
         [0029]    As another example, data processing system  300  may be a stand-alone system configured to be bootable without relying on some type of network communication interface, whether or not data processing system  300  comprises some type of network communication interface. As a further example, data processing system  300  may be a Personal Digital Assistant (PDA) device, which is configured with ROM and/or flash ROM in order to provide non-volatile memory for storing operating system files and/or user-generated data.  
         [0030]    The depicted example in FIG. 3 and above-described examples are not meant to imply architectural limitations. For example, data processing system  300  also may be a notebook computer or hand held computer in addition to taking the form of a PDA. Data processing system  300  also may be a kiosk or a Web appliance.  
         [0031]    Referring now to FIG. 4, a flowchart illustrating a method for automated and mass customization of marketing materials is depicted in accordance with the present invention. The process begins with the development of a model to predict whether or not a consumer will purchase a particular product (step  401 ). This step would be performed by a marketing and data mining group. The data mining could be performed using a program such as Intelligent Miner and possibly a custom data mining program to run Intelligent Miner in batch to generate response models for each product. The model might provide the probability that a customer would buy a particular product or would respond to a particular marketing approach. The next step would be to score all customers for each predictive model (step  402 ). This step could be performed by an Information Technology (IT) or data mining group.  
         [0032]    The next step requires the graphics design team to determine the minimum and maximum sizes for each product layout (step  403 ). From there, the team must then determine actual layout areas and the size of each area (step  404 ).  
         [0033]    The graphics design team and data mining group must then develop a preference multiplier for each layout area (step  405 ). This might be done using a test mailing, although initial mailings might have preferences chosen by expert designers. An example of a preference multiplier would be putting a product on the cover and getting 25% increase in the likelihood that the product will be purchased. The preference multiplier would then be 1.25.  
         [0034]    The graphics design team can then determine the layout areas where a particular product can be placed (step  406 ). This determination could be made using the sizes of product layouts, the sizes of layout areas and the suitability of a product for a layout area (i.e. cover).  
         [0035]    A custom application would need to be provided to perform steps  403 ,  404  and  406  and to enter this information into a database. The custom user interface would update a Product Layout table with products numbers, the graphic/text file name to be used by InfoPrint Manager, and maximum and minimum size. A Layout Exclusion table would be updated with the product number and the layout area where the product cannot be printed. The application would also update a Layout Area table with layout area numbers, preference multipliers and layout area sizes.  
         [0036]    After making the determinations above, the IT group can then use one of the optimization models described below to automate the creation of a layout that will maximize the expected profit for each customer (step  407 ). A custom application would generate a model and then run it for each customer. The model would be based on Product Layout, Layout exclusion, Layout Area and Customer Score. A program such as, for example, IBM Optimization Subroutine Library could solve the model and generate an output file that can be read by InfoPrint Manager.  
         [0037]    It must then be determined if the expected profit from a particular customer is higher than the production cost of the catalog (step  408 ). If the expected profits do not exceed the production cost, the catalog is not printed and sent to the customer (step  409 ). If the expected profits do exceed the production costs, then the output of the optimization model is passed on to a print manager for printing (step  410 ). An application such as, for example, InfoPrint Manager could be used for this function.  
         [0038]    The following sections describe three optimization models that could be used to automate and optimize mass customization. The first model is the simplest, and is the recommended model. The other two models are presented as reference material. They remove restrictions used in the simple mode, but require greater computation and may be less reliable. The models use graphic descriptions of networks (rather than equations) in order to make them easier to read.  
         [0039]    A “grid” layout system is frequently used in graphic design. A page is divided into grids, with each design element occupying one or more grids. In the optimization model, a particular product might use a single grid location, or might use a collection of contiguous grid locations. The different models presented offer different levels of flexibility for layouts, with increasing processing time for more complex layouts.  
         [0040]    Referring to FIG. 5, a diagram illustrating a grid layout is depicted in accordance with the present invention. The grid layout has three grids horizontally and four grids vertically, resulting in  12  grid locations. In this example, the  12  grid locations have been assigned to six layout areas A-F, as illustrated. FIG. 5 shows how the layout areas are mapped to the grid locations, and gives example preference factors for each location.  
         [0041]    The optimization models are of the form:  
         Max  cx    
         Subject to  
         
       Ax=b  
     
           x&gt;= 0  
         [0042]    Wherein c is the vector of costs for each arc, x is the column vector of arc flows (1 if the layout area is used, 0 if the layout area is not used), A is a node-arc incidence matrix, and b is the column vector of supply and demand for each node in the network.  
         [0043]    The c vector is composed of the costs (profits) associated with using an arc in the network.  
         [0044]    The recommended optimization model is the transportation model. This model is the simplest, and probably the fastest of the possible models, but is also the most restrictive in layout flexibility because there is no overlap of locations.  
         [0045]    The table below is an approximation of a grid layout system.  
                                   TABLE 1                                       1   2   3   4           5   6   7   8           9   10   11   12           13   14   15   16                      
 
         [0046]    The following table lists an example of layout locations that the transportation model would support. Note that none of the possible layout locations overlap. The catalog sent to each customer would have the same “look”, based upon the layout.  
                   TABLE 2                       Layout Location   Grids                   A   1, 2, 3, 5, 6, 7       B   4, 8       C   9, 13       D   10, 11, 12, 14, 15, 16                  
 
         [0047]    This model could be implemented using software such as, for example, IBM Optimization Subroutine Library using the network solver (“ekknslv”). The model could also be solved with a specialized solver for transportation models. The following table illustrates the relationship between model characteristics and layout design.  
                                             TABLE 3                               Upper Bound on   Upper Bound on               Size for 1000   Size for 100       Model       products and 25   products and 25       Characteristics   Formula   locations   locations                                Number of rows   I + J + 1   1,026   126       Upper bound on   I * (J + 1)   26,000   2,600       number of       columns       Upper bound on   I * J   25,000   2,500       nonzero       objective       function       entries                                                  
 
         [0048]    Referring to FIG. 6, a flow diagram illustrating the transportation model is depicted in accordance with the present invention. Each product node has a supply of 1, and each layout location has a demand of −1. The products flow to the location along the route that maximizes profit. No more than one product can flow to a particular layout location. Products that are not selected would flow to the unused location, as indicated. The cost for each product-to-layout location arc in the network is:  
         (probability of product purchase)×(profit per purchase)×(preference of location)  
         [0049]    Moving on to another optimization model, customers wishing to use more flexible layouts could use a more complex network model where layout locations overlap to a limited extent. This would result in some unused space, and might result in unbalanced layouts.  
         [0050]    The network model would use the same grid layout system as illustrated in Table 1. Assuming the grid layout in Table 1, the following table shows an example of layout locations that the network model could use.  
                           TABLE 4                                   Overlap Node       Layout       Overlap   Represents       Location   Grids   Location   Grid                   A   1, 2, 3, 5, 6, 7   B   3, 7       B   3, 7, 4, 8   A   3, 7       C   9, 10, 11, 13, 14, 15   D   11, 15       D   11, 12, 15, 16   C   11, 15                  
 
         [0051]    One problem with this model is that it will result in unused space:  
         [0052]    If location A is used, location B will not be used. Grids 4 and 8 would not be used.  
         [0053]    If location B is used, location A will not be used.  
         [0054]    Grids 1, 2, 5 and 6 would not be used.  
         [0055]    If location C is used, location D will not be used. Grids 12 and 16 would not be used.  
         [0056]    If location D us used, location C will not be used. Grids 9, 10, 13 and 14 would not be used.  
         [0057]    This model uses a pure network with a single +1 and a single −1 entry the column for each arc. Like the first model, the present one could be solved using the IBM Optimization Subroutine Library network solver (ekknslv). The following table illustrates the relationship between model characteristics and layout design.  
                                             TABLE 5                               Upper Bound on   Upper Bound on               Size for 1000   Size for 1000       Model       products and 25   products and 25       Charac-       locations with   locations with       teristics   Formula   5 overlaps   5 overlaps                                Number of   I + J + L + 1   1,031   131       rows       Upper bound   I * (J + 1) + J * L   26,125   2,725       on number of       columns       Upper bound   I * J   25,000   2,500       on nonzero       objective       function       entries                  
 
         [0058]    The variables and constants in the above table are the same as in Table 3, with L representing the number of overlaps.  
         [0059]    Referring now to FIG. 7, a flow diagram illustrating the network model is depicted in accordance with the present invention. Each product node has a supply of 1, and each overlap location has a demand of −1. The products flow to the location along the route that maximizes profit. No more than one product can flow to a particular layout (and overlap) location. Products that are not selected would flow to the unused location. The cost for each product-to-layout location arc in the network is:  
         (probability of product purchase)×(profit per purchase)×(preference of location)  
         [0060]    The cost for each layout location-to-overlap is 0. The arc costs have been omitted from FIGS.  7  to improve the clarity of the figure.  
         [0061]    The third possible optimization model is a generalized network model. This model is the most flexible, but is likely to be the slowest, and may not solve properly in some or all cases. It would require using one of the previous models as a backup in the code. However, there are two benefits to this model. The first is more flexible layouts where layout locations overlap. This model would not result in any unused space. The second benefit is the use of conditional profits, such as, for example, the likelihood of purchasing an extended warranty with a product. This feature is not shown for sake of simplicity.  
         [0062]    Again, assuming the grid layout system from Table 1, the following table shows an example of layout locations that the generalized network model might use.  
                       TABLE 6                       Layout Location   Grids   Overlap Location                   A   1, 5, 2, 6, 3, 7   B, E, F       B   1, 5, 2, 6   B, C, F       C   1, 5   A       D   4, 8   D       E   3, 7, 4, 8   A, D, F       F   2, 6, 3, 7, 4, 8   A, B, E                  
 
         [0063]    This model uses a generalized network. This model could be solved with the IBM Optimization Subroutine Library&#39;s linear program solver (ekksslv), or a third party generalized network solver. In the event that this model does not produce an integer solution, the software would need to either revert to one of the previous models, use a heuristic to resolve the layout problems, or use the mixed integer program solver (ekkmslv). The following table illustrates the relationship between model characteristics and layout design.  
                                             TABLE 7                               Size for 1000   Size for 100       Model       products and   products and 25       Charac-       25 locations   locations and       teristics   Formula   and 200 grids   200 grids                                Number of   I + J + K + 1   1,226   326       rows       Upper bound   I * (J + 1) + J * K   31,000   7,600       on number of       columns       Upper bound   I * J   25,000   2,500       on nonzero       objective       function       entries                  
 
         [0064]    The variables and constants in the above table are the same as in Table 3, with K representing the number of grid locations.  
         [0065]    Referring to FIG. 8, a flow diagram illustrating the network model is depicted in accordance with the present invention. Each product node has a supply of +1, and each grid area has a demand of −1. The products flow to the location along the route that maximizes profit. Because each layout location uses several underlying grid locations, a product flowing to a layout location must “grab” each grid area used by the layout location to prevent layout areas from using the same grid (overlapping). To implement this,  
         [0066]    Each product to layout location arc is defined with a +1 and a −m in the arc column, where m is the number of grid areas used by the layout location  
         [0067]    Each layout location to grid area arc is defined with a +1 and −1 in the arc column  
         [0068]    No more than one product can flow to a particular layout location. Products that are not selected would flow to the unused location. The cost for each product-to-layout location arc in the network is:  
         (probability of product purchase)×(profit per purchase)×(preference of location)  
         [0069]    The cost for each layout location-to-grid area is 0. The arc costs and arc multipliers have been omitted from FIGS.  8  to improve the clarity of the figure.  
         [0070]    For example, a layout area that uses grids 1, 2, 5, and 6 above would have a +1 and −4 as column entries in the node-arc incidences matrix.  
         [0071]    The present invention allows marketing businesses to increase the number of customized mailings (whether paper or electronic) without an increase in design staff support. The invention also enables marketers to increase the number of niche markets (or individuals) that can be addressed and maximizes the profit per customer.  
         [0072]    It is important to note that while the present invention has been described in the context of a fully functioning data processing system, those of ordinary skill in the art will appreciate that the processes of the present invention are capable of being distributed in the form of a computer readable medium of instructions and a variety of forms and that the present invention applies equally regardless of the particular type of signal bearing media actually used to carry out the distribution. Examples of computer readable media include recordable-type media, such as a floppy disk, a hard disk drive, a RAM, CD-ROMs, DVD-ROMs, and transmission-type media, such as digital and analog communications links, wired or wireless communications links using transmission forms, such as, for example, radio frequency and light wave transmissions. The computer readable media may take the form of coded formats that are decoded for actual use in a particular data processing system.  
         [0073]    The description of the present invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiment was chosen and described in order to best explain the principles of the invention, the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.