Abstract:
Embodiments of the present invention disclose a method, computer program product, and system for performing a total unduplicated reach and frequency analysis that analyzes a large set of variables practically. A plurality of variables are received. A subset of the plurality of variables is identified as variables of interest. Variables are partitioned randomly into groups. Each of the groups of variables contains fewer than a predetermined maximum number of variables for each group. The best combination of variables within each group is found through a total unduplicated reach and frequency analysis of each group. The winning variable or variables within each group are identified based on the highest reach score. The winners from each group analysis are then pooled into a group, and the process is repeated. The combination from the second stage with the best reach is then reported. Additional variable combinations ranked by reach may also be reported.

Description:
BACKGROUND 
       [0001]    The present invention relates generally to the field of product marketing research, and more particularly to total unduplicated reach and frequency analysis. 
         [0002]    Rating scales are common tools in marketing for measuring customer preferences for products, or features of products, that may influence a company or business owner&#39;s use or promotion of a product or feature. For example, a common analysis tool to determine the optimal product or feature for sale is total unduplicated reach and frequency analysis, or “TURF.” TURF is a type of analysis that determines possible combinations of products or records that are attractive to the largest number of potential customers. TURF is also commonly used in conjunction with rating scales for product line optimization. The most common metrics are the percentage of potential customers that desire at least one product in a bundle of a limited number of products, the “reach,” or the total number of occurrences that any product in the bundle is selected, or “frequency.” The TURF analysis also deals with combinations of reach and frequency in order to demonstrate and approximate a customer desire for a certain product or feature of a product. A user of TURF analysis may also alter the definitions of reach or frequency by specifying the desirability threshold. As the number of products or features, collectively “variables,” increases, the computational resources required to perform the TURF analysis increase as each new variable requires an analysis of all combinations with previous variables and the new variable. While TURF analysis of 10-20 variables may be reasonable, analysis of 50 or more variables becomes resource and cost prohibitive. 
       SUMMARY 
       [0003]    Embodiments of the present invention disclose a method, computer program product, and system for performing a total unduplicated reach and frequency analysis that makes analysis of a large set of variables computationally practical. A plurality of variables are received. A subset of the plurality of variables is identified as variables of interest. Variables are partitioned randomly into groups. Each of the groups of variables contains fewer than a predetermined maximum number of variables for each group. The best combination of variables within each group is found through a total unduplicated reach and frequency analysis of each group. The winning variable or variables within each group are identified based on the highest reach score. The winners from each group analysis are then pooled into a group, and the process is repeated. The combination from the second stage with the best reach is then reported. Additional variable combinations ranked by reach may also be reported. 
     
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         [0004]      FIG. 1  is a functional block diagram illustrating a distributed data processing environment, in accordance with an embodiment of the present invention. 
           [0005]      FIG. 2  is a functional block diagram illustrating the components of an application within the distributed data processing environment, in accordance with an embodiment of the present invention. 
           [0006]      FIG. 3  is a flowchart depicting operational steps of an application on a computing device, within the data processing environment of  FIG. 1 , for performing a total unduplicated reach and frequency analysis, in accordance with an embodiment of the present invention. 
           [0007]      FIG. 4  depicts a block diagram of components of the computing device, in accordance with an embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0008]    The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
         [0009]    The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
         [0010]    Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
         [0011]    Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
         [0012]    Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
         [0013]    These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
         [0014]    The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
         [0015]    The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
         [0016]    A business owner engaged in the ecommerce space may find it important to be able to provide relevant products that a customer desires, as well as, attracting that customer to a particular store front or site. Various embodiments of the invention may aid in determining what variables resonate with customers, as that may be difficult to determine without direct feedback. Store owners may utilize surveys to determine what variables, or factors, determine whether a customer will shop at a particular store and what products they may purchase. 
         [0017]    Conventional approaches to determine these variables may involve surveying customers on each variable and having the customer rate each variable on a scale of desirability. Analysis of survey results may be needed to determine which variables are most attractive which is commonly achieved using a Total Unduplicated Reach and Frequency analysis, or TURF analysis. A TURF analysis of preference survey results determine the combination of variables that maximizes the “reach,” or the probability that a random customer will find at least one variable in the chosen variable set to which they would give a high rating. For example, an ice cream shop owner may only have the capacity for 3 ice cream flavors in a store, therefore, the combination of three flavors offered must have the maximum reach to maximize the potential customer base for the limited flavors available. When analyzing survey results without TURF the solution for maximum reach may seem to be the 3 most popular flavors amongst the survey results, for example, vanilla, chocolate, and strawberry. However this may not be the optimal solution as if all customers who like chocolate also like vanilla, having both of those overlapping flavors does not increase customer reach, where a slightly less rated flavor, for example, mint, may be determined to have a better reach via a TURF analysis. TURF is applicable to many industries, for example, products being advertised or the most popular location for a conference. 
         [0018]    Embodiments of the present invention will be described with reference to the Figures.  FIG. 1  is a functional block diagram illustrating a distributed data processing environment, generally designated  100 , in accordance with one embodiment of the present invention. 
         [0019]    Distributed data processing environment  100  includes computing device  110 , and data store  140 , interconnected over network  130 . 
         [0020]    Network  130  may include permanent connections, such as wire or fiber optic cables, or temporary connections made through telephone or wireless communications. Network  130  may represent a worldwide collection of networks and gateways, such as the Internet, that use various protocols to communicate with one another, such as Lightweight Directory Access Protocol (LDAP), Transport Control Protocol/Internet Protocol (TCP/IP), Hypertext Transport Protocol (HTTP), Wireless Application Protocol (WAP), etc. Network  130  may also include a number of different types of networks, such as, for example, an intranet, a local area network (LAN), or a wide area network (WAN). 
         [0021]    Each of computing device  110  and data store  140  may be a laptop computer, tablet computer, netbook computer, personal computer (PC), desktop computer, smart phone, or any programmable electronic device capable of an exchange of data packets with other electronic devices, for example, through a network adapter, in accordance with an embodiment of the invention, and which may be described generally with respect to  FIG. 4  below. Although data store  140  is described herein as programmable electronic device, this is merely one embodiment. Data store  140  may be a separate server or series of servers, a database, or other data storage, internal or external to computing device  110 . 
         [0022]    Computing device  110  includes turf application  120 , as described in greater detail below, with reference to  FIG. 2 . In various embodiments of the invention, computing device  110  operates generally to receive data from a data store, for example, data store  140 , and to host applications, for example, turf application  120 , which may process and store data. 
         [0023]    Turf application  120  may be for example, database oriented, computation oriented, or a combination of these. Turf application  120  may operate generally to receive and process one or more different survey results from a client or data store, for example, data store  140 , via computing device  110 . Survey results may contain numerical or textual data. Turf application  120  may process data by performing a TURF analysis on received data in a tournament style manner. 
         [0024]    In various embodiments of the invention, data store  140  may operate generally to receive and store customer survey results and to communicate customer survey results to another computing device, for example, computing device  110  via network  130 . 
         [0025]    In various embodiments of the invention, TURF application  120  may receive, from data store  140 , textual or numerical data based on customer survey results, or “variables,” that may represent the results of a customer survey. Turf application  120  may identify variables that should be analyzed. Turf application  120  may identify at least one variable that meets a desirability criteria, for example, identifying variables that have a rating score on a survey above a threshold value, or “rating threshold.” Identified variables may be dichotomized into desirable or undesirable, for example, a variable above a rating threshold may be stored in a data store in variable as a value of “1,” indicating desirability, or a variable below a rating threshold may be stored as a value of “0,” indicating undesirability. In various embodiments, a rating threshold may be predetermined by a user. In various embodiments, identification of desired variables for analysis may be based on client input, analysis history, or a saved profile saved on a data store on computing device  110 . Turf application  120  may partition identified variables into groups at random. Turf application  120  may perform a TURF analysis on each group, generating a corresponding reach scores for each group. 
         [0026]    In various embodiments of the invention, turf application  120  may group identified variables into random subsets and determine for each subset the variables with the highest reach. The best combinations from each group in the first stage are then combined into a new group, and the process is repeated. If the total number of variables is sufficiently small, all variables may form a single group, and only one stage is required. The total number of variables may be determined to be sufficiently small if the number of variables in a single group is below a second threshold, or “analysis threshold.” If the number of variables in a single group is below an analysis threshold, turf application  120  may perform a TURF analysis on the single group of variables. If the number of variables within the single group is above an analysis threshold, turf application  120  may repartition the variables of the single group into multiple groups and analyze as described above. 
         [0027]    For example, an ice cream shop owner may provide customers with a survey where customers rate ice cream flavors on a 10 point scale, for example, from 1 to 10 and are instructed to rate various flavors of ice cream. Data store  140  may receive flavors of ice cream with the corresponding rating, based on the provided survey results, and may store the results in memory. Turf application  120  may receive customer survey results from data store  140  via computing device  110 . Turf application  120  may receive 50 flavors of ice cream and turf application  120  may assign a 1 or 0 to each of the flavors if the flavor received a rating above a rating threshold, for example, 5 on the 10 point scale. Turf application  120  may randomly partitioned the flavors into groups of 10 and perform a TURF analysis on each group. Turf application  120  may regroup 25 flavors that have the greatest reach, as determined by the TURF analysis. The number of flavors that have been regrouped may be above an analysis threshold and turf application  120  may repartition the flavors into a “second heat” of groups of 5 at random. Turf application  120  may perform a TURF analysis on each of the second heat groups and regroup 10 ice cream flavors that have the greatest reach score in the second heat. Turf application  120  may determine the group of 10 flavors is below the analysis threshold and may perform a final TURF analysis on the regroup of variables from the second heat. 
         [0028]      FIG. 2  is a functional block diagram illustrating the components of computing device  110 , including the components of turf application  120 , functioning within the distributed data processing environment, in accordance with an embodiment of the present invention. Turf application  120  may receive and perform TURF analysis on data sets as described above. Turf application  120  includes receiving module  210 , variable module  220 , partition module  230 , and turf module  240 . 
         [0029]    In reference to  FIGS. 1 and 2 , in various embodiments of the invention, receiving module  210  may receive data based on one or more customer survey results, from one or more clients, for example data store  140 , as described above. The received data may consist of textual data based on “variable labels” and/or numerical data based on ratings given to each variable. The variable labels and corresponding rating data may be stored, by receiving module  210 , in a list in a data store as “variables.” Receiving module  210  may communicate the list of variables to variable module  220 . 
         [0030]    In various embodiments of the invention, variable module  220  may receive a list of variables from receiving module  210  and store that list in memory. Variable module  220  may identify variables from the list of variables as “variables of interest.” Variables of interest may be determined by a product reach criteria. A product reach criteria may be predetermined by a user and may be based on the availability or willingness of a client to produce a certain variable. For example, if receiving module  210  may receive a list of fragrances from data store  140  via computing device  110 . Certain fragrances may not be desirable due to manufacture limitations, the time it would take to receive materials relative to a peak sales period, or an unwillingness the fragrance manufacture may attach to a certain fragrance(s). Variable module  220  may receive criteria eliminating certain fragrances from the list of fragrances received from receiving module  210 . Variable module  220  may communicate only variables of interest to partition module  230 . 
         [0031]    Partition module  230  may act generally to receive variables and place received variables into groups. Partition module  230  may identify at least one received variable that meets rating threshold, as described above. Identified variables may be dichotomized into desirable or undesirable, for example, a variable above a rating threshold may be stored in a data store in variable as a value of “1,” indicating desirability, or a variable below a rating threshold may be stored as a value of “0,” indicating undesirability. Partition module  230  may group desirable variable, undesirable variables, or a combination and may store groups in a data store in memory as distinct lists with a group identifier, or group label, with the corresponding variables in that group. Partition module  230  may distribute variables into groups at random, in order they are received, or in various distributions. Partition module  230  may distribute variables into groups evenly, for example, partition module  230  receives 30 variables and distributed the variables randomly into three groups of ten. This is merely one example and does not represent all the ways partition module  230  may distribute variables into groups. Group distribution may be predetermined by a user. Partition module  230  may communicate groups of variables to turf module  240 . 
         [0032]    Turf module  240  may act generally to receive groups of variables and perform a TURF analysis, as described above. Turf module  240  may receive a group of variables from partition module  230 . Turf module  240  may perform a TURF analysis on the group of variables, calculate a reach score for each of the variables within the group, and store the group of variables with the corresponding reach scores in a data store in memory. Turf module  240  may communicate the stored group of variables and corresponding reach scores to partition module  230 . The above description may represent a “first heat” in the tournament style TURF analysis. 
         [0033]    In various embodiments of the invention, partition module  230  may receive an analyzed group of variables from turf module  240 . Partition module  230  may partition variables received from turf module  240  that have the highest reach, or “winning groups.” Partition module  230  may determine the number of variables in the winning groups of analyzed variables is above an analysis threshold value. An analysis threshold value may be a value based on the number of variables that a TURF analysis can be performed upon without the analysis being time or resource prohibitive. Partition module  230  may redistribute the winning groups of analyzed variables into random groups and communicate the new groups to turf module  240  for analysis as described above. This may represent a “second heat” in the tournament style TURF analysis. In various embodiments, “heats” may continue until partition module  230  groups received variables and the number of variables in that group are below the analysis threshold. Partition module  230  may communicate the single group to turf module  240  and turf module  240  may perform a TURF analysis on the single group and communicate the results or store the results in a data store in memory. This may represent the “final heat” in the tournament style TURF analysis. 
         [0034]    For example, 10 groups of 10 variables each are received by turf module  240 . Turf module  240  performs a TURF analysis on each group and communicates the winning groups of variables to partition module  230 . Partition module  230  partitions variables with highest reach score, for example, 5. Partition module  230  may check a predetermined analysis threshold, for example 20. The 5 variables from each of the 10 groups of variables are, in total, more than the analysis threshold, therefore, partition module  230  may distribute the 50 variables into 5 groups of 10 and communicate the groups to turf module  240  for analysis. Turf module  240  may perform a TURF analysis on the variables in each group and communicate the results to partition module  230 . Distribution into groups by partition module  230  and TURF analysis by turf module  240  may continue until a winning group of analyzed variables below the analysis threshold. This may represent the final heat where partition module  230  groups all variables into a single group, communicates that group to turf module  240 , turf module  240  performs a TURF analysis on the group of variables and may communicate the results to a user or store in a data store in memory. 
         [0035]    In various embodiments of the invention partition module  230  may base the analysis threshold value on periodically determining the available resources of computing device  110 , or computational resource value, a received maximum amount of time, or temporal limitation value, or on the predetermined value where the number of variables in the final heat group cannot exceed 125% times the number of variables in the initial heat groups. 
         [0036]      FIG. 3  is a flowchart depicting operational steps of an application on a computing device, within the data processing environment of  FIG. 1 , for performing a total unduplicated reach and frequency analysis, in accordance with an embodiment of the present invention. Referring now to  FIGS. 1, 2, and 3 , receiving module  210  receives variables from data store  140  (step  310 ). The variables may contain numerical or textual data as described above. 
         [0037]    Variable module  220  identifies variables for analysis (step  320 ) as described above and communicated identified variables to partition module  230 . Partition module  230  randomly puts variables in to groups of a predetermined size (step  330 ). Partition module  230  communicate the group(s) of variables to turf module  240 . 
         [0038]    Turf module  240  performs a TURF analysis on each received group of variables (step  340 ). The results may include a reach score that is associated with the corresponding analyzed variable. Turf module  240  communicates the TURF analysis results to partition module  230 . 
         [0039]    Partition module  230  may receive a predetermined analysis threshold (not shown). Partition module  230  orders the analyzed variables of each group by the corresponding reach score of each group and partitions the variables from the group with the greatest reach score (step  350 ) into a new group. Partition module  230  determines if the number of variables, in the group of variables with the greatest reach score, exceeds an analysis threshold value (decision step  360 ). 
         [0040]    If the number of analyzed variables exceeds the analysis threshold value (decision step  360  “YES” branch) the analyzed variables are partitioned into random groups. If the number of analyzed variables does not exceed the analysis threshold (decision step  360  “NO” branch) the variables are partitioned into a single group and communicated to turf module  240 . Turf module  240  performs a TURF analysis on the single group of variables and stores or communicated the results (step  370 ). 
         [0041]      FIG. 4  depicts a block diagram of components of computing device  110  and data store  140  of distributed data processing environment  100 , of  FIG. 1 , in accordance with an embodiment of the present invention. It should be appreciated that  FIG. 4  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made. 
         [0042]    Computing device  110  and data store  140  may include one or more processors  402 , one or more computer-readable RAMs  404 , one or more computer-readable ROMs  406 , one or more computer readable storage media  408 , device drivers  412 , read/write drive or interface  414 , network adapter or interface  416 , all interconnected over a communications fabric  418 . Communications fabric  418  may be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. 
         [0043]    One or more operating systems  410 , and one or more application programs  411 , for example, turf application  120 , are stored on one or more of the computer readable storage media  408  for execution by one or more of the processors  402  via one or more of the respective RAMs  404  (which typically include cache memory). In the illustrated embodiment, each of the computer readable storage media  408  may be a magnetic disk storage device of an internal hard drive, CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk, a semiconductor storage device such as RAM, ROM, EPROM, flash memory or any other computer-readable tangible storage device that can store a computer program and digital information. 
         [0044]    Computing device  110  and data store  140  may also include a R/W drive or interface  414  to read from and write to one or more portable computer readable storage media  426 . Application programs  411  on computing device  110  and data store  140  may be stored on one or more of the portable computer readable storage media  426 , read via the respective R/W drive or interface  414  and loaded into the respective computer readable storage media  408 . 
         [0045]    Computing device  110  and data store  140  may also include a network adapter or interface  416 , such as a TCP/IP adapter card or wireless communication adapter (such as a 4G wireless communication adapter using OFDMA technology). Application programs  411  on computing device  110  and data store  140  may be downloaded to a computing device, for example, computing device  110 , from an external computer or external storage device via a network (for example, the Internet, a local area network or other wide area network or wireless network) and network adapter or interface  416 . From the network adapter or interface  416 , the programs may be loaded onto computer readable storage media  408 . The network may comprise copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. 
         [0046]    Computing device  110  and data store  140  may also include a display screen  420 , a keyboard or keypad  422 , and a computer mouse or touchpad  424 . Device drivers  412  interface to display screen  420  for imaging, to keyboard or keypad  422 , to computer mouse or touchpad  424 , and/or to display screen  420  for pressure sensing of alphanumeric character entry and user selections. The device drivers  412 , R/W drive or interface  414  and network adapter or interface  416  may comprise hardware and software (stored on computer readable storage media  408  and/or ROM  406 ). 
         [0047]    The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature. 
         [0048]    While the present invention is particularly shown and described with respect to preferred embodiments thereof, it will be understood by those skilled in the art that changes in forms and details may be made without departing from the spirit and scope of the present application. It is therefore intended that the present invention not be limited to the exact forms and details described and illustrated herein, but falls within the scope of the appended claims. 
         [0049]    Based on the foregoing, a computer system, method, and computer program product have been disclosed. However, numerous modifications and substitutions can be made without deviating from the scope of the present invention. Therefore, the present invention has been disclosed by way of example and not limitation.