METHOD AND SYSTEM FOR ANALYZING TRIAL AND REPEAT BUSINESS

Abstract: A method, system, and computer program product is disclosed for real-time monitoring of consumer behavioral data. The method, on which the system and computer program product are based, includes the steps of: accessing a database of consumer information in order to indentify based upon predetermined criteria a plurality of consumers to monitor, wherein each identified consumer is uniquely identified within the database; storing in a data structure behavioral measuring data corresponding to a frequency in which a product is purchased by each identified consumer; periodically receiving data extracts including data collected during a predefined monitoring period; updating the behavioral measuring data stored in the data structure based on the data extracts; and generating messages directed to selected consumers during the predefined monitoring period requesting attitudinal measuring data regarding the product.

Description: Method And System For Analyzing Trial and Repeat Business BACKGROUND OF THE INVENTION Field of the Invention The present invention relates generally to the use of a computer system, and more particularly to the use of a computer system in monitoring information regarding new product introductions to the market. Discussion of the Background Marketing research is used by advertisers, manufacturers, retailers, and consumer advocacy groups as well as other people, groups, and organizations to provide information on consumer psychology and trends. In particular, manufacturers are concerned with information regarding their own products as well as competitors' products. Information derived from marketing research is used to increase sales and to deliver to consumers products that are more likely to be well received by the public. One form of marketing research involves analyzing data regarding consumer products including the introduction of a new consumer products to the market. The pertinent data collected for this type of analysis includes behavioral data reflecting the sales performance of the product and attitudinal data reflecting the consumer's awareness, acceptance, and satisfaction regarding the new product. Presently, detailed behavioral data regarding the introduction of a new consumer product to the market takes nearly a year to filter back to marketers. The filtering delay is primarily a function of the time it takes for manufacturers to receive sales data from third party researchers. This delay is primariK the result of small household panels and the time it takes a new product to penetrate the panel to have adequate sample sizes for analysis. Sample sizes for certain consumer segments will never be adequate. Furthermore, in order to obtain attitudinal data, mass mailings, e-mails, and telephone calls to random consumers have been required. These consumers are invited to participate in surveys, answer questionnaires, and to participate in live interviews with market surveyors. Thus, such marketing research is conducted in a random or quasi-random manner. As a result, many consumers invited to participate in marketing research may have little or no knowledge of the subject matter of the marketing research. As a result, many of the consumers who participate in marketing research are not helpful because they do not use or purchase products that are the subject of the research. Additionally, many consumers are annoyed by invitations, often in the form of "junk mail," because the subject matter of the marketing research is unrelated to the consumers' purchasing behavior and habits. SUMMARY OF TFIE INVENTION Accordingly, an object of the present invention is to provide a novel method and system for obtaining immediate behavioral data on new product performance in the market. Another object of the present invention is to provide a novel method and system for obtaining immediate attitudinal data on consumer awareness, acceptance, and satisfaction of new products. Yet another object of the present invention is to provide a novel method and system for identifying potential future sales volume regarding new products for various segments of the consumer community. Still yet another object of the present invention is to provide a novel method and system for targeting consumers identified as failing to try a newly introduced product. These and other objects are achieved by providing a novel method, system, and computer program product for obtaining real-time behavioral and attitudinal data on product performance in the market. The method, on which the system and computer program product are based, includes the steps of: accessing a database of consumer information in order to identify based upon predetermined criteria a plurality of consumers to monitor, wherein each identified consumer is uniquely identified within the database: storing in a data structure behavioral measuring data corresponding to a frequency in which a product is purchased by each identified consumer; periodically receiving data extracts including data of the identified consumers collected during a predefined monitoring period; updating the behavioral measuring data stored in the data structure based on the data extracts; and generating messages directed to selected consumers during the predefined monitoring period requesting attitudinal measuring data regarding the product. According to another aspect of the invention, the method, on which the sy tem and computer program product are based, includes the steps of: storing in a data structure behavioral measuring data corresponding to a frequency in which a product is purchased by consumers; periodically receiving data extracts including data collected during a predefined monitoring period; updating the behavioral measuring data stored in the data structure based on the data extracts; identifying triers and non-triers of the product based on the behavioral measuring data stored in the data structure upon determining that a purchase rate of the product is below a predetermined level; and automatically generating a promotion to be delivered to each non-trier incenting the non-trier to purchase the product. As used herein, the word "promotion" means any offer, incentive, advertisement, commercial, coupon, and/or communication for promoting one or more goods and/or services. In the manner described above, the present invention overcomes problems associated with monitoring new product introductions utilizing conventional marketing techniques. The present invention enables manufacturers to analyze their products as well as their competitors. Thus, the present invention will enable manufacturers to modify marketing plans and product characteristics while a product is being introduced to consumers in order to increase new product success rates. BRIEF DESCRIPTION OF THE DRAWINGS A more complete appreciation of the invention and many of the attendant advantages thereof will be readily obtained as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings, wherein: Figure 1 is a computerized system for storing purchase histories of consumers and monitoring new product introductions in accordance with an embodiment of the present invention; Figure 2 is a purchase history table for associating customer identifiers (CTDs) with purchase histories of consumers; Figure 3 is a target segment table for storing behavioral measuring data reflecting a frequency in which a product is purchased; Figure 4 is a chain selling table for identifying chains selling a product; Figure 5 is a flow chart describing how the behavioral data for each target segment is processed and reported; Figures 6A and 6B are flow charts describing the process for identifying which consumers to survey in order to obtain attitudinal measuring data; Figure 7 is a flow chart for explaining how behavioral measuring data and attitudinal measuring data are collected during a new product launch or restage; Figure 8 is a flow chart for explaining how to perform targeted marketing on non- triers based on behavioral measuring data collected during a new product launch; and Figure 9 a schematic illustration of a computer system programmed to perform one or more of the special purpose functions of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Referring now to the drawings, wherein like reference numerals designate identical or corresponding parts throughout the several views, and more particularly to Fig. 1 which shows a computerized system for delivering targeted advertisements to customers. The system of Figure 1 includes a host computer 101, a global purchase database 103, one or more retail stores 105, a purchase data computer 107, a local purchase database 109, a store controller 1 1 1 , a store database 1 13, and one or more points of sale 1 15, each including a printer 1 17, a terminal 1 19, and a scanner 121. The host computer 101 is any suitable workstation, server, or other device, such as the computer system 901 of Figure 9, for communicating with the purchase data computer 107 and for storing information in and retrieving information from the global purchase database 103. The host computer 101 communicates with the purchase data computer 107 and the global purchase database 103 using any suitable protocol and may be implemented using the computer system 901 of Figure 9, for example. The global purchase database 103 is a file that includes records containing information for monitoring new product introductions, in accordance with the present invention. This information includes information of each purchase made by a customer in the retail store 105. Such information may include, but is not limited to the stock keeping unit (SK-U), brand, size, weight, price, date and time of purchase, and customer identifier (CID) of the customer making the purchase, for example. In one embodiment, portions of this information are obtained from bar codes on purchase items, which are scanned by the scanner 121 during a transaction. These bar codes may contain UPC, JAN, and EAN information. Records in the global purchase database 103 contain fields together with a set of operations for searching, sorting, recombining, and other database functions. " 1 he global purchase database 103 may be implemented as two or more databases, if desired. One or more of U.S. Pat. Nos. 5,832,457; 5,649, 1 14; 5,430,644; and 5,592,560 describe techniques for collecting consumer purchase history information and for storing such information in databases such as the global purchase database 103 and the store database 1 13, for example. U.S. Pat. Nos. 5,832,457: 5,649, 144; 5,430,644; and 5,592,560 are incorporated herein by reference. Additionally, techniques for collecting consumer purchase information and for storing such information in databases, such as the global purchase database 103 and the store database 1 13, are described in other patents owned by Catalina Marketing and/or Catalina Marketing International. Each patent owned by Catalina Marketing and/or Catalina Marketing International is incorporated herein by reference. The retail store 105 is generically referred to as a retail location and is a place where goods are kept for retail sale to customers. A retail store is typically associated with a chain (e.g. Ralph's). As noted above, many retail stores 105 may be connected to the host computer 101. The purchase data computer 107 may be implemented using the computer system 901 of Figure 9, for example, or any other suitable PC, work station, server, or device for communicating with the host computer 101 , for storing and retrieving information in the local purchase database 109, for monitoring data transmitted between the terminal 1 19 and the store controller 1 1 1 (i.e., transaction data), and for controlling the printer 1 17. The records in the local purchase database 109 contain fields for associating bar codes w ith products in the retail store 105 (e.g., by using UPC, JAN, and/or EAN codes), and associating consumer identifiers with purchase history information of customers. The local purchase database 109 also includes operations for searching, sorting, recombining, and other database functions. The local purchase database 109 may be implemented as two or more databases, if desired. Periodically (e.g., daily), sales transaction information (i.e., data extracts) stored in the local purchase database 109 is retrieved by the purchase data computer 1 7 and sent to the host computer 101 , which uses the information to update the purchase history information stored in the global purchase database 103. The store controller 1 1 1 is any computer or device for communicating with the terminal 1 19 and for using information stored in the store database 1 13 to carry out transactions at the point of sale (POS) 1 15. A description of a store controller 1 1 1 is found in U.S. Patent No. 5,173,851 , for example. The store database 1 13 is a file that includes records containing information for carrying out transactions at the point of sale 1 15 by scanning bar codes printed on purchased items. The records in the store database 1 13 contain fields for associating bar codes with products and their corresponding prices. The store database 1 13 also includes operations for searching, sorting, recombining, and other database functions, and may be implemented as two or more databases, if desired. The retail store 105 includes one or more points of sale 1 15. Each point of sale 1 15 preferably includes a corresponding printer 1 17, a terminal 1 19, and a scanner 121. The printer 1 17 receives printing instructions from the purchase data computer 107. The terminal 1 19 may be implemented as a standard cash register and may include a screen, credit card reader, and numeric key pad, for example. The terminal 1 19 communicates with the store controller 1 1 1 and the scanner 121. The scanner 121 may be implemented as any conventional scanning device for reading product information such as an item code (e.g., UPC, EAN, or JAN) from bar codes or other indicia on the product. In accordance with the present invention, new UPC codes must be entered into a UPC dictionary associated with the store database 1 13. Information read by the scanner 121 is transmitted to the store controller 1 1 1 via the terminal 1 19. The store controller 1 11 , uses the scanned information and the information stored in the store database 1 13 to determine information of the transaction including SKU, product price, quantity, and product description, for example. If there are multiple points of sale 1 15 within the retail store 105, then each terminal 1 19 is preferably arranged on a loop with the store controller 1 1 1. The purchase data computer 107 is located in front of the store controller 1 1 1 on the loop so that information transmitted from the terminals to the store controller is monitored by the purchase data computer 107. It is to be understood that the system in Figure 1 is for exemplary purposes only, as many variations of the specific hardware and software used to implement the present invention will be readily apparent to one having ordinary skill in the art. For example, the functionality of the purchase data computer 107 and the store controller 1 1 1 may be combined in a single device. These implementations and other implementations of retail computer systems are described in greater detail in one or more of U.S. Pat. Nos. 4,723,212; 4,910,672: 5, 173,851 ; 5,612,868; and 6,026,370, each of which is incorporated herein by reference. To implement these variations as well as other variations, a single computer (e.g., the computer system 901 of Figure 9) may be programmed to perform the special purpose functions of two or more of any of the devices shown in Figure 1. On the other hand, two or more programmed computers may be substituted for any one of the devices shown in Figure 1. Principles and advantages of distributed processing, such as redundancy and replication, may also be implemented as desired to increase the robustness and performance of the system, for example. The present invention stores information relating to various customers who shop at the retail stores 105 including the purchase histories of those customers. This information is stored in one or more memories such as a hard disk, optical disk, magneto-optical disk, and/or RAM, for example. One or more databases, such as the global purchase database 103 and the store database 1 13, may store the information used to implement the present invention. The databases are organized using data structures (e.g., records, tables, arrays, fields, graphs, trees, and/or lists) contained in one or more memories, such as the memories listed above or any of the storage devices listed below in the discussion of Figure 9, for example. Figures 2, 3, and 4 depict data structures used for implementing a system for monitoring new product introductions in accordance with an embodiment of the present invention. The data structures are depicted in a relational format, using tables, whereby information stored in one column (i.e., field) of a table is mapped or linked to information stored in the same row (i.e., record) across the other column(s) of the table. These data structures are also used by the host computer 101 and/or the purchase data computer 107 to provide targeted promotions to consumers in accordance with the present invention. The data structures shown in Figures 2, 3, and 4 are stored in the global purchase database 103, the local purchase database 109, and/or any other suitable storage device(s) or medium(s). Figure 2 is a purchase history table 201 that includes a field 203 for storing consumer identifiers (CIDs) and a field 205 for storing purchase histories of the consumers in the field 203. A CID is any identifier that is scanned, read, or otherwise entered into a computer system at checkout to identify a customer. Each customer may have multiple CIDs. Preferably, the CID is represented as a bar code so that it can be quickly scanned at checkout by the scanner 1 17, although any other type of machine readable or non-machine readable implementations for storing or displaying identifications may be used, including magnetic strips, memory chips, and smart cards. Examples of possible consumer IDs are credit card numbers, debit card numbers, social security card numbers, driver's license numbers, checking account numbers, street addresses, names, e-mail addresses, telephone numbers, frequent customer card numbers, shopper card identifications (SCIDs), or shopper loyalty card numbers issued by the retail store 105, although any other suitable form of identification may be used. Preferably, the field 205 is divided into several subfields for separately storing purchase data such as the SKU, location of the purchase, a description of the items purchased, the price of each item purchased, date and time of the transaction, and any other desired information of consumers' transactions. Figure 3 is a target segment table 301 that includes a field 303 for storing CIDs and a field 305 for storing target segment codes. According to an embodiment of the present invention, a static group of consistent shopping households are identified for monitoring. For example, (i) households that have made two trips in eight weeks for each of the last six (6) eight week periods (i.e., over a 48 week period) would be identified as a household that would be monitored regardless of the dollars spent during those visits or (ii) households that have made one trip in four weeks for the last 12 four week periods (i.e., over a 52 week period) would be identified as a household that would be monitored dependent on the dollars spent during those visits. The identified households are segmented and assigned target segment codes. According to an embodiment of the present invention, the segments would include competing category brands with loyalty/consumption breaks for key brands, cross- category brands, and new category buyers. The greater the number of defined segments, the greater the level of granularity (i.e., the level of definition) of the households being monitored. The segments can be made of any combination of buyers including but not limited to the following types of buyers. Light buyers, defined generally as the lowest volume category user, typically but not limited to the bottom 50° o of consumers. Medium buyers, defined generally as middle volume category users, typically the middle 25% of consumers. Heavy buyers, defined generally as the highest volume category user, typically the top 25% of consumers. Non-category buyers, generally defined as buyers that have not made a purchase within the category in the last 52 weeks. Loyal buyers, generally defined as consumers who give the majority of their category purchases to a single brand, typically 71 % or more. Occasional buyers, generally defined as consumers who switch between brands within a category (i.e., consumers not loyal to any one brand). Competitive buyers, generally defined as consumers who give the majority of their category purchases to a competing brand, typically 71 % or more. Never-buy-brand buyers, generally defined as consumers who have not bought a particular brand over a predetermined period (e.g., a 52 week period). Moreover, the following segment definitions are applicable. Category segment, generally defined as a group of UPCs for products that are generally substitutable for one another which defines a particular consumer purchase behavior. Cross Category segment, generally defined as a group of UPCs for products that may be alike or used in conjunction with another category which defines a particular consumer purchase behavior. Lifestyle segment, generally defined as a group of UPCs that defines the purchase behavior of a demographic population (e.g., seniors, babies). Custom definitions segment, generally defined as any group of UPCs which defines a particular consumer purchase behavior. In addition to the above identified segments, according to another embodiment of the present invention, segments can also be defined by causal information such as price. Accordingly, consumers can be tracked based on the price they paid for the monitored product. For example, one segment of buyers may receive a temporary price reduction for a product, while other segments do not. Thus, by monitoring trial and repeat rates based upon the price paid for the trial and repeat occasions, the causal effect of price among other things can be determined. All of the above segments are stored in a target segment lookup table containing the descriptions of the target segment and the corresponding target segment code. Buyers generally will qualify for more than one segment. Thus, a segment hierarchy is defined such that a household is associated with only one target segment in the target segment table 301. The target segment table 301 includes a chain identifier field 307. Initially, the chain identifier field 307 will contain no values. This field will be populated as the chain associated with each CID appears in weekly extracts downloaded from retail stores 105 (i.e., the actual week). The target segment table 301 also includes a trier field 309, a first repeater field 31 1 , a second repeater field 313, a third repeater field 315, and a fourth repeater field 17, etc. Initially, these fields will contain no values. These fields will be populated with the calender week number identifying the week that a CID appeared in a weekly file transmitted from the purchase data computer 107 (i.e, the weeks will be aligned). For example, if CID #123 appears for the first time in calender week number 1 , then "1" will appear in that CID's trier field. The other fields at that time would remain blank. If that CID appeared again in a subsequent week's extract, the calender week number for that extract week would be used to populate the first repeater field 31 1. If the same CID appeared for a third time, the second repeater field 313 would be populated for that CID. The target segment table 301 also includes a week field 319 and an aligned week field 321. Initially, the aligned week field 321 contains no value. This field will be populated with a number reflecting the number of weeks that a consumer waited to try a product after an initial launch of the product by the product's manufacturer, taking into consideration the length of time that that consumer's associated chain has taken to offer the product after the initial launch. For example, if a consumer tries a product the first week that the consumer's associated chain offers that product, then a "1 " will appear in the aligned week field associated with that consumer's CID. A "1 " will appear in the aligned week field even if the product has been offered in other chains for a longer period than it has been offered at that consumer's associated chain. Accordingly, behavioral measuring data collected for a plurality of chains can be aligned. Thus, the confounding effect of distribution gains in the early part of a product launch can be eliminated. Figure 4 is a table 401 for identifying all of the chains selling the new product. Table 401 includes a field 403 for identifying each chain and chain-in-week field 405 for indicating which week each chain began selling the new product. The field 405 is based on the current week of tracking the new product. Figure 5 is a flow chart describing how the behavioral data for each target segment is processed and reported. In step 501 , the host computer 101 joins the target segment table 301 and the chain selling table 401 in order to set the field 405 for each CID in the target segment table 301. In step 503, the host computer parses duplicate CIDs from the weekly extracts downloaded from retail stores 105. In step 505, the host computer 101 populates the trier field 309, the first repeater field 31 1 , the second repeater field 313, the third repeater field 315, and the fourth repeater field 317 for each CID based on that week's extract. In step 507, the host computer 101 counts the number of triers, first repeaters, second repeaters, third repeaters, and fourth repeaters. In step 509, the percentage of triers, first repeaters, second repeaters, third repeaters, and fourth repeaters is determined by the host computer as a function of the total number of triers of the new product in the analyzed segment. In step 51 1, the host computer 101 generates reports showing (1) the total number of CIDs in each target segments universe, (2) the total number of CIDs for each target segment that purchased the product (i.e., made a brand trip) for the week the report is covering, and (3) the percentages of CIDs that made a brand trip that week (i.e., (2)/(D). The reports are typically generated either daily or weekly. According to an embodiment of the present invention, two separate reports are generated at the 13 week and 26 w r eek markers comparing the target segment universe and the category buyer universe. In step 513, a purchase cycle is determined. The purchase cycle can either be determined manually by an operator or automatically by the host computer 101 based on the average purchase cycle for each CID in the competitive buyers segment. The weekly extracts can also be used for volumetric sales forecasting for each of the targeted segments. Future sales volume can be forecasted based on the number of buyers reflected in the target segment table 301 taking into consideration past sales trends of new products and collected attitudinal measuring data. Figures 6A and 6B are flow charts describing the process for identifying which consumers to survey in order to obtain attitudinal measuring data regarding newly introduced products. According to an embodiment of the present invention, there are four types of buyers to target for surveying. The four types are non-triers, triers, trier-rejecters, and repeaters. In step 601 , the host computer 101 creates a table in order to count the number of consumers (CIDs) that fit into each of these four categories (e.g., 1 ,500 consumers from each category may be the desired pool). In step 603, the host computer 101 places the CIDs which have been counted into a weekly file. In step 605, the host computer 101 filters the weekly file in order to ensure that no consumer gets surveyed twice. In step 607, the host computer 101 creates a file identifying non-triers for each segment. In order to populate that file, the target segment table 301 is accessed and any CID wherein the associated trier field 309 is null and the associated chain-in-week field 405 is not null is added to the file of non-triers. In step 609, the host computer 101 randomly selects for surveying CIDs from the file of non-triers. According to an embodiment of the invention, in order to be a non-trier, (1.5 x purchase cycle or some other designated number of weeks) weeks must have passed before a consumer is deemed to be a non-trier. Additionally, for the competitive buyers segment, the CIDs may be required to be buying a competing brand in order to be considered a non-trier. For all other target segments this is not a requirement. In step 61 1 , the host computer 101 creates a file identifying triers for each segment. In order to populate that file, the target segment table 301 is accessed and any CID wherein the associated trier field 309 is not null, and the associated first repeater field 31 1 is null is added to the file of triers. In step 613, the host computer 101 randomly selects for surveying CIDs from the file of triers. With reference to Figure 6B, in step 615, the host computer 101 creates a file identifying trier-rejecters for each segment. In order to populate that file, the target segment table 301 is accessed and any CID wherein the trier field 309 is populated, the first repeater field 31 1 is null and a predetermined time related to the purchase cycle has past is added to the file of trier-rejecters. Additionally, for the competitive segment, it must be determined that CIDs are still buying a competitive brand in order to be deemed a trier-rejecter. In step 617, the host computer 101 randomly selects for surveying CIDs from the file of trier- rejecters. In step 619, the host computer 101 creates a file for each type of identified repeater ( first, second, third, fourth, etc.) for each segment. In order to populate the first repeater file, the target segment table 301 is accessed and any CID wherein a value in the associated first repeater field 31 1 is populated is added to the first repeater file. The same process is repeated for the second, third, and fourth repeater files. In step 621 , the host computer 101 randomly selects for surveying CIDs from the file of each repeater file. The processing described to implement the steps described in Figures 5 and 6 are performed by the host computer 101 or alternatively by one or more different computers with access to the data. The surveys provide a mechanism to obtain immediate attitudinal feedback on consumer awareness, acceptance, and satisfaction regarding the newly introduced product. Marketers will quickly obtain valuable information regarding the different surveyed categories. Marketers will be able to obtain (re early-triers) attitudinal measuring data through this type of targeted surveying reflecting (1) the source of early triers awareness of the product, (2) the degree of ad awareness of early triers, (3) the ease in which early triers found the store, (4) the reasons why the product was purchased, (5) how the product performed, and (6) the future shopping intent of early triers. With regard to early non-triers, marketers will able to obtain attitudinal measuring data reflecting (1) whether the early non- triers ever considered the new product, (2) reasons for the non-triers current loyalty to a competing product, (3) why the product was not purchased, and (4) whether or not the early non-trier has any interest in future purchases of the new product. With regard to trier- rejecters, marketers will able to obtain attitudinal measuring data reflecting (1) why the trier- rejecter initially purchased the product, (2) unmet expectations of the trier- rejecter, and (3) reasons why the trier- rejecter did not purchase the product again. With regard to repeaters, marketers will able to obtain attitudinal measuring data reflecting (1) why the repeater initially purchased the product and (2) why the repeater purchased the product repeatedly. Profiles of all households surveyed can be obtained through surveying. Figure 7 is a flowchart for explaining how behavioral measuring data and attitudinal measuring data are collected by host computer 101 or other computers during a new product launch. In step 701, a host computer accesses a database of consumer information in order to identify based upon predetermined criteria a plurality of consumers to monitor, wherein each identified consumer is uniquely identified within the database. In step 703, the host computer stores in a data structure behavioral measuring data corresponding to a frequency in which a product is purchased by each identified consumer. In step 705, the host computer periodically receives data extracts including data collected from the identified consumers during a predefined monitoring period. The predefined monitoring period is generally determined by the manufacturer of the product and typically lasts long enough to obtain information regarding repeat buyers. In step 707, the host computer updates the behavioral measuring data stored in the data structure based on the data extracts. Lastly, in step 709, the host computer generates messages directed to selected consumers during the predefined monitoring period requesting attitudinal measuring data regarding the product. The messages can be in the form of direct mail addressed to the consumer, an electronic message transmitted via the Internet, or any other suitable form for communicating the message to the consumer. Upon receiving attitudinal measuring data from the surveyed consumers, the received attitudinal measuring data is processed by associating the data with each consumer and subsequently compiling the data for each category of consumers (i.e., triers, non-triers, trier-rejecters, and repeat triers). Figure 8 is a flowchart for explaining how to perform targeted marketing on non- triers, triers, or trier-rejectors based on behavioral measuring data collected during a new product launch. In step 801 , a host computer stores in a data structure behavioral measuring data corresponding to a frequency in which a product is purchased. In step 803, the host computer periodically receives data extracts including data collected during a predefined monitoring period. In step 805, the host computer updates the behavioral measuring data stored in the data structure based on the data extracts. Lastly, in step 807, targeted marketing is performed based on the updated behavioral measuring data. According to an embodiment of the invention, consumers identified as non-triers, triers, or trier-rejectors of the product could be targeted for promotion. For example, during the next visit to their associated store and at the point of sale, a promotion would be generated for each identified (i) non-trier (ii) trier having a predetermined trial rate level, or (iii) repeater having a predetermined repeat rate level. Alternatively, a promotion could be mailed to each identified non-trier. The present invention is not limited to these two types of promotions. Other types of promotions can be instituted which incent the consumer to purchase the subject product. All or a portion of the invention may be conveniently implemented using conventional general purpose computers or microprocessors programmed according to the teachings of the present invention, as will be apparent to those skilled in the computer art. Appropriate software can be readily prepared by programmers of ordinary skill based on the teachings of the present disclosure, as will be apparent to those skilled in the software art. Figure 9 illustrates a computer system 901 upon which an embodiment according to the present invention may be implemented. Computer system 901 includes a bus 903 or other communication mechanism for communicating information, and a processor 905 coupled with bus 903 for processing the information. Computer system 901 also includes a main memory 907. such as a random access memory (RAM) or other dynamic storage device (e.g., dynamic RAM (DRAM), static RAM (SRAM), synchronous DRAM (SDRAM), flash RAM), coupled to bus 903 for storing information and instructions to be executed by processor 905. In addition, main memory 907 may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 905. Computer system 901 further includes a read only memory (ROM) 909 or other static storage device (e.g., programmable ROM (PROM), erasable PROM (EPROM), and electrically erasable PROM (EEPROM)) coupled to bus 903 for storing static information and instructions for processor 905. A storage device 911 , such as a magnetic disk or optical disc, is provided and coupled to bus 903 for storing information and instructions. The computer system 901 may also include special purpose logic devices (e.g., application specific integrated circuits (ASICs)) or configurable logic devices (e.g., generic array of logic (GAL) or reprogrammable field programmable gate arrays (FPGAs)). Other removable media devices (e.g., a compact disc, a tape, and a removable magneto-optical media) or fixed, high density media drives, may be added to the computer system 901 using an appropriate device bus (e.g., a small computer system interface (SCSI) bus, an enhanced integrated device electronics (IDE) bus, or an ultra-direct memory access (DMA) bus). The computer system 901 may additionally include a compact disc reader, a compact disc reader- writer unit, or a compact disc juke box, each of which may be connected to the same device bus or another device bus. Computer system 901 may be coupled via bus 903 to a display 913, such as a cathode ray tube (CRT), for displaying information to a computer user. The display 913 may be controlled by a display or graphics card. The computer system includes input devices, such as a keyboard 915 and a cursor control 917, for communicating information and command selections to processor 905. The cursor control 917, for example, is a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 905 and for controlling cursor movement on the display 913. In addition, a printer may provide printed listings of the data structures shown in Figures 2. 3 and 4 or any other data stored and/or generated by the computer system 901. The computer system 901 performs a portion or all of the processing steps of the invention in response to processor 905 executing one or more sequences of one or more instructions contained in a memory, such as the main memory 907. Such instructions mav be read into the main memory 907 from another computer-readable medium, such as storage device 91 1. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in main memory 907. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions. Thus, embodiments are not limited to any specific combination of hardware circuitry and software. As stated above, the system 901 includes at least one computer readable medium or memory programmed according to the teachings of the invention and for containing data structures, tables, records, or other data described herein. Stored on any one or on a combination of computer readable media, the present invention includes software for controlling the computer system 901 , for driving a device or devices for implementing the invention, and for enabling the computer system 901 to interact with a human user, e.g., a consumer. Such software may include, but is not limited to, device drivers, operating systems, development tools, and applications software. Such computer readable media further includes the computer program product of the present invention for performing all or a portion (if processing is distributed) of the processing performed in implementing the invention. The computer code devices of the present invention may be any interpreted or executable code mechanism, including but not limited to scripts, interpreters, dynamic link libraries, Java classes, and complete executable programs. Moreover, parts of the processing of the present invention may be distributed for better performance, reliability, and/or cost. The term "computer readable medium" as used herein refers to any medium that participates in providing instructions to processor 905 for execution. A computer readable medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, optical, magnetic disks, and magneto-optical disks, such as storage device 91 1. Volatile media includes dynamic memory, such as main memory 907. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 903. Transmission media also may also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications. Common forms of computer readable media include, for example, hard disks, floppy disks, tape, magneto-optical disks, PROMs (EPROM, EEPROM, Flash EPROM), DRAM, SRAM, SDRAM, or any other magnetic medium, compact disks (e.g., CD-ROM), or any other optical medium, punch cards, paper tape, or other physical medium with patterns of holes, a carrier wave (described below), or any other medium from which a computer can read. Various forms of computer readable media may be involved in carrying out one or more sequences of one or more instructions to processor 905 for execution. For example, the instructions may initially be carried on a magnetic disk of a remote computer. The remote computer can load the instructions for implementing all or a portion of the present invention remotely into a dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 901 may receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector coupled to bus 903 can receive the data carried in the infrared signal and place the data on bus 903. Bus 903 carries the data to main memory 907, from which processor 905 retrieves and executes the instructions. The instructions received by main memory 907 may optionally be stored on storage device 91 1 either before or after execution by processor 905. Computer system 901 also includes a communication interface 919 coupled to bus 903. Communication interface 919 provides a two-way data communication coupling to a network link 921 that is connected to a local network (e.g., LAN 823). For example, communication interface 919 may be a network interface card to attach to any packet switched local area network (LAN). As another example, communication interface 919 may be an asymmetrical digital subscriber line (ADSL) card, an integrated services digital network (ISDN) card, or a modem to provide a data communication connection to a corresponding type of telephone line. Wireless links may also be implemented. In any such implementation, communication interface 919 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information. Network link 921 typically provides data communication through one or more networks to other data devices. For example, netwwk link 921 may provide a connection through LAN 923 to a host computer 925 or to data equipment operated by a service provider, which provides data communication services through an IP (Internet Protocol) network 927 (e.g., the Internet 61 ) or any other suitable network using any known protocol (e.g., IPX). LAN 923 and IP network 927 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 921 and through communication interface 919, which carry the digital data to and from computer system 901 , are exemplary forms of carrier waves transporting the information. Computer system 901 can transmit notifications and receive data, including program code, through the network(s), network link 921 and communication interface 919. Obviously, numerous modifications and variations of the present invention are possible in light of the above teachings. For example, the present invention is not limited to analyzing new product launches, but is equally applicable to analyzing behavioral and attitudinal data of consumers regarding products which have sales histories. It is therefore to be understood that within the scope of the appended claims, the invention may be practiced otherwise than as specifically described herein.

Claims: CLAIMS: 1. A computer implemented method, comprising the steps of: accessing a database of consumer information in order to identify based upon predetermined criteria a plurality of consumers to monitor, wherein each identified consumer is uniquely identified within the database; storing in a data structure behavioral measuring data corresponding to a frequency in which a product is purchased by each identified consumer; periodically receiving data extracts including data of the identified consumers collected during a predefined monitoring period; updating the behavioral measuring data stored in the data structure based on the data extracts; and generating messages directed to selected consumers during the predefined monitoring period requesting attitudinal measuring data regarding the product. 2. The computer implemented method according to claim 1, wherein said storing step further comprises the steps of: associating said each identified consumer with at least one of a plurality of consumer buying segments; and determining a hierarchy for the consumer buying segments such that said each identified consumer is associated in the data structure with only one of the consumer buying segments. 3. The computer implemented method according to claim 2, further comprising the step of: generating a report reflecting a number of identified consumers per consumer buying segment that purchased the product during a predefined period. 4. fhe computer implemented method according to claim 2, further comprising the step of: forecasting future sales volume of the product for each of the consumer buying segments based on the behavioral measuring data. 5. The computer implemented method according to claim 1 , wherein the message is a direct mailing addressed to said each selected consumer. 6. The computer implemented method according to claim 1, wherein the message is an electronic message addressed to said each selected consumer. 7. The computer implemented method according to claim 1, further comprising the step of: categorizing the identified consumers each as a trier, non-trier, trier rejecter, or repeat trier of the product based on behavioral measuring data stored in the data structure. 8. The computer implemented method according to claim 7, wherein said categorizing step further comprises the step of: categorizing the identified consumers each as at least one of heavy, medium, light, loyal, occasional, competitive, and never-buy based on behavioral measuring data stored in the data structure. 9. The computer implemented method according to claim 7, further comprising the steps of: receiving attitudinal measuring data from the selected consumers generated in response to said messages; associating the received attitudinal measuring data with said each identified consumer; and compiling aggregate attitudinal measuring data of at least one of trier, non-trier, trier rejecter, and repeat trier categories, respectively. 10. The computer implemented method according to claim 1 , wherein the attitudinal measuring data comprises data reflecting at least one of consumer awareness, acceptance, and satisfaction regarding the product. 1 1. The computer implemented method according to claim 1 , wherein the step of updating comprises the steps of: associating said each consumer with a chain, wherein a plurality of chains offer the product: modifying the data structure in order to reflect the association; and storing alignment data in the data structure reflecting how quickly said each consumer purchases the product after the product is initially offered, wherein said alignment data reflects how long said each consumer's associated chain takes to offer the product after the product is initially offered. 12. A computer implemented method, comprising the steps of: storing in a data structure behavioral measuring data corresponding to a frequency in which a product is purchased; periodically receiving data extracts including data collected during a predefined monitoring period; updating the behavioral measuring data stored in the data structure based on the data extracts; and performing targeted marketing based on the updated behavioral measuring data. 13. The computer implemented method according to claim 12, wherein the step of performing comprises the steps of: identifying non-triers of the product based on the behavioral measuring data stored in the data structure upon determining that a purchase rate of the product is below a predetermined level; and automatically generating a promotion to be delivered to each non-trier incenting the non-trier to purchase the product. 14. fhe computer implemented method according to claim 13. wherein the database of consumer information comprises data from a plurality of stores, each identified consumer is associated in the data structure with one store, and the generating step further comprises the step of generating the promotion at the point-of-sale at said each non-triers next visit to their associated store. 15. The computer implemented method according to claim 13, wherein the generating step further comprises the step of: generating a mailing including the promotion directed to said each non-trier. 16. A computer system, comprising: a memory device having embodied therein a database of consumer information; and a processor in communication with said memory device, said processor configured to: access the database of consumer information in order to identify based upon predetermined criteria a plurality of consumers to monitor, wherein each identified consumer is uniquely identified within the database; store in a data structure behavioral measuring data corresponding to a frequency in which a product is purchased by each identified consumer; periodically receive data extracts including data of the identified consumers collected during a predefined monitoring period; update the behavioral measuring data stored in the data structure based on the data extracts; and generate messages directed to selected consumers during the predefined monitoring period requesting attitudinal measuring data regarding the product. 17. The computer system according to claim 16, wherein said processor is further configured to associate said each identified consumer with at least one of a plurality of consumer buying segments, and to determine a hierarchy for the consumer buying segments such that said each identified consumer is associated in the data structure with only one of the consumer buying segments during the process of storing the data structure. 18. The computer system according to claim 17, wherein said processor is further configured to generate a report reflecting a number of identified consumers per consumer buying segment that purchased the product during a predefined period. 19. The computer system according to claim 17, wherein said processor is further configured to forecast future sales volume of the product for each of the consumer buying segments based on the behavioral measuring data. 20. The computer system according to claim 16, wherein the message is a direct mailing addressed to said each selected consumer. 21. The computer system according to claim 16, wherein the message is an electronic message addressed to said each selected consumer. (Include internet access) 22. The computer system according to claim 21 , wherein the electronic message is transmitted via the Internet. 23. The computer system according to claim 16, wherein said processor is further configured to categorize the identified consumers each as a trier, non-trier, trier rejecter, or repeat trier of the product based on behavioral measuring data stored in the data structure. 24. The computer system according to claim 23, wherein said processor is further configured to: receive attitudinal measuring data from the selected consumers generated in response to said messages; associate the received attitudinal measuring data with said each identified consumer; and compile aggregate attitudinal measuring data of at least one of trier, non-trier, trier rejecter, and repeat trier categories, respectively. 25. The computer system according to claim 16, wherein the attitudinal measuring data comprises data reflecting at least one of consumer awareness, acceptance, and satisfaction regarding the product. 26. The computer system according to claim 16, wherein the processor is further configured to associate said each consumer with a chain wherein a plurality of chains offer the product, to modify the data structure in order to reflect the association, and to store alignment data in the data structure reflecting how quickly said each consumer purchases the product after the product is initially offered during the process of updating the behavioral measuring data, wherein said alignment data reflects how long said each consumer's associated chain takes to offer the product after the product is initially offered. 27. A computer system, comprising: a memory device having embodied therein a database of consumer information; and a processor in communication with said memory device, said processor configured to: store in a data structure behavioral measuring data corresponding to a frequency in which a product is purchased; periodically receive data extracts including data collected during a predefined monitoring period; update the behavioral measuring data stored in the data structure based on the data extracts; and perform targeted marketing based on the updated behavioral measuring data. 28. The computer system according to claim 27, wherein the processor is further configured to identify non-triers of the product based on the behavioral measuring data stored in the data structure upon determining that a purchase rate of the product is below r a predetermined level, and to automatically generate a promotion to be delivered to each non- trier incenting the non-trier to purchase the product during the process of performing targeted marketing. 29. The computer system according to claim 28, wherein the database of consumer information comprises data from a plurality of stores, each identified consumer is associated in the data structure with one store, and the processor is further configured to generate the promotion at the point-of-sale at said each non-triers next visit to their associated store during the process of automatically generating the promotion. 30. The computer system according to claim 27, wherein the processor is further configured to generate a mailing including the promotion directed to said each non-trier during the process of generating the promotion. 31. A system, comprising: means for accessing a database of consumer information in order to identify based upon predetermined criteria a plurality of consumers to monitor, wherein each identified consumer is uniquely identified within the database; means for storing in a data structure behavioral measuring data corresponding to a frequency in which a product is purchased by each identified consumer; means for periodically receiving data extracts including data of the identified consumers collected during a predefined monitoring period; means for updating the behavioral measuring data stored in the data structure based on the data extracts; and means for generating messages directed to selected consumers during the predefined monitoring period requesting attitudinal measuring data regarding the product. 32. The system according to claim 31 , wherein said means for storing further comprises: means for associating said each identified consumer with at least one of a plurality of consumer buying segments; and means for determining a hierarchy for the consumer buying segments such that said each identified consumer is associated in the data structure with only one of the consumer buying segments. 33. The system according to claim 32, further comprising: means for generating a report reflecting a number of identified consumers per consumer buying segment that purchased the product during a predefined period. 34. The system according to claim 32. further comprising: means for forecasting future sales volume of the product for each of the consumer buying segments based on the behavioral measuring data. 35. The system according to claim 31 , wherein the message is a direct mail addressed to said each selected consumer. 36. The system according to claim 31 , wherein the message is an electronic message addressed to said each selected consumer. 37. The system according to claim 31 , further comprising: means for categorizing the identified consumers each as a trier, non-trier, trier rejecter, or repeat trier of the product based on behavioral measuring data stored in the data structure. 38. The system according to claim 37, further comprising: means for receiving attitudinal measuring data from the selected consumers generated in response to said messages; means for associating the received attitudinal measuring data with said each identified consumer; and means for compiling aggregate attitudinal measuring data of at least one of trier, non- trier, trier rejecter, and repeat trier categories, respectively. 39. The system according to claim 31 , wherein the attitudinal measuring data comprises data reflecting at least one of consumer awareness, acceptance, and satisfaction regarding the product. 40. The system according to claim 31 , wherein the means for updating further comprises: means for associating said each consumer with a chain, wherein a plurality of chains offer the product; means for modifying the data structure in order to reflect the association: and means for storing alignment data in the data structure reflecting how quickly said each consumer purchases the product after the product is initially offered, wherein said alignment data reflects how long said each consumer's associated chain takes to offer the product after the product is initially offered. 41. A system, comprising: means for storing in a data structure behavioral measuring data corresponding to a frequency in which a product is purchased: means for periodically receiving data extracts including data collected during a predefined monitoring period; means for updating the behavioral measuring data stored in the data structure based on the data extracts; and means for performing targeted marketing based on the updated behavioral measuring data. 42. The system according to claim 41 , wherein the means for performing comprises: means for identifying non-triers of the product based on the behavioral measuring data stored in the data structure upon determining that a purchase rate of the product is below a predetermined level; and means for automatically generating a promotion to be delivered to each non-trier incenting the non-trier to purchase the product. 43. The system according to claim 42, wherein the database of consumer information comprises data from a plurality of stores, each identified consumer is associated in the data structure with one store, and the means for generating comprises point-of-sale generating means for generating the promotion at the point-of-sale at said each non-triers next visit to their associated store. 44. The system according to claim 42, wherein the generating means further comprises: mailing generating means for generating a mailing including the promotion directed lo said each non-trier. 45. A computer readable medium containing program instructions for execution on a computer system, which when executed by a computer, cause the computer to perform the method recited in any one of claims 1 to 15.

Inventors: AI-ATRAQCHI WALEED, VENKER PATRICK, AI-ATRAQCHI, WALEED, VENKER, PATRICK
Applicants: CATALINA MARKETING INT, CATALINA MARKETING INTERNATIONAL, INC.
Application year: 2001
Main classification: G06F17
IPC codes: G06Q10, G06Q30