ITMI20010165A1 - PROCEDURE AND SYSTEM FOR ANALYZING ATTEMPTIVE AND REPEAT COMMERCIAL ACTIVITIES - Google Patents

Description

DESCRIPTION of the industrial invention entitled:

"PROCEDURE AND SYSTEM FOR ANALYZING TRIAL AND REPEAT COMMERCIAL ACTIVITIES"

DESCRIPTION

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention generally relates to the use of a computer system and, more particularly, to the use of a computer system in monitoring information relating to new product introductions to the market.

Background Discussion

Market research is used by advertisers, manufacturers, retailers and consumer association groups, as well as other individuals, groups and other organizations to provide information on consumer psychology and trends. In particular, manufacturers are interested in information relating to their own products as well as products from competitors. Market research information is used to increase sales and to deliver products to consumers that are more likely to be well received by the public.

One form of market research involves analyzing consumer product data including the introduction of a new consumer product to the market. Relevant data collected for this type of analysis includes behavioral data reflecting product sales performance and attitudinal data reflecting consumer awareness, acceptance and satisfaction with the new product. Currently, detailed behavioral data relating to the introduction of a new consumer product into the market takes nearly a year to be filtered and sent back to market participants. The filtration delay is primarily a function of the time it takes for manufacturers to receive sales data from third-party researchers. This delay is mainly the result of small family groups and the time it takes for a new product to enter the group to have adequate sample sizes for analysis. Sample sizes for certain consumer segments will never be adequate.

In addition, bulk mailing, e-mail, and telephone calls to casual consumers have proved necessary to obtain aptitude data. This consumers are encouraged to participate in prospecting, to answer questionnaires, and to participate in live interviews with market prospecting practitioners. Thus, such market research is conducted in a haphazard or quasi-random manner. Consequently, many consumers invited to participate in market research may have little or no knowledge of the subject of the market research itself. Thus, many of the consumers who participate in market research are not useful because they do not employ or purchase products that are the subject of the research. Additionally, many consumers are bothered by invitations, often in the form of "junk maìl" or pounding mail, as the subject matter of market research is unrelated to consumer behavior and buying habits.

SUMMARY OF THE INVENTION

Therefore, it is an object of the present invention to provide a novel method and system for obtaining immediate behavior data on the behavior of a new product in the market.

Another object of the present invention is to provide a new process and system for obtaining immediate aptitude data on consumer awareness or satisfaction, acceptance, and satisfaction with respect to new products.

Still another object of the present invention is to provide a novel method and system for identifying a potential future sales volume of new products for various segments of the consumer community. Still a further object of the present invention is to provide a new method and system for targeted addressing to consumers identified as lacking to try a recently introduced product.

These and other purposes are achieved by providing a new process, system and computer program product to obtain behavioral and aptitude data in real time on the behavior of products in the market. The process on which the system and the computer program product are based includes the steps of:

access a consumer information database in order to identify, on the basis of predetermined criteria, a plurality of consumers to be monitored, in which each identified consumer is uniquely identified within the database; store, in a data structure, behavior measurement data corresponding to a frequency with which a product is purchased by each identified consumer; periodically receive data extracts including identified consumer data collected during a predefined monitoring period; update the behavior measurement data stored in the data structure based on the data extracts; and generate direct messages to selected consumers during the predefined monitoring period requesting aptitude measurement data related to the product.

According to another aspect of the invention, the method, on which the system and the computer program product are based, includes the steps of: storing, in a data structure, behavior measurement data corresponding to a frequency with which a product is purchased by consumers; periodically receive data extracts including data collected during a predefined monitoring period; update the behavior measurement data stored in the data structure based on the data extracts;

identifying assayers and non-assayers of the product based on behavior measurement data stored in the data structure upon determining that a percentage of purchases of the product is below a predetermined level; and automatically generate a promotion to be given to each non-assayer, incentivizing the non-assayer to purchase the product. As used herein, the word "promotion" means any offer, incentive, advertisement, commercial message, coupon or discount coupon, and / or communication to promote one or more goods and / or one or more services.

In the manner described above, the present invention solves problems associated with monitoring new product introductions using conventional market techniques. The present invention allows manufacturers to analyze their products as well as those of their competitors. Thus, the present invention will allow manufacturers to change market plans and product characteristics while a product is being introduced to consumers in order to increase the success rates of new products.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete evaluation of the invention and many of the consequent advantages thereof will be readily obtained as it is better understood with reference to the following detailed description when considered in conjunction with the accompanying drawings, in which:

Figure 1 is a computerized system for storing consumer shopping histories and monitoring new product introductions according to an embodiment of the present invention;

Figure 2 is a purchase history table for associating customer identifiers (CIDs) with consumer shopping histories;

Figure 3 is a table of desired segments or targets for storing behavior measurement data reflecting a sequence in which a product is purchased;

Figure 4 is a table of sales chains to identify chains selling a product;

Figure 5 is a flowchart describing how the behavior data for each desired segment or target is processed and reported;

Figures 6A and 6B are flowcharts describing the process for identifying which consumers to monitor in order to obtain aptitude or aptitude measurement data;

Figure 7 is a flowchart for explaining how behavior measurement data and aptitude measurement data are collected during a launch or "re-launch" of new products;

Figure 8 is a flowchart to explain how to perform targeted marketing on non-assayers based on behavior measurement data collected during the launch of a new product; And

Figure 9 is 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 Reference is now made to the drawings, in which like reference numerals indicate identical or corresponding parts through the various views and, more particularly, to Fig. 1, which illustrates a computerized system for distributing advertising messages aimed at customers. The system of Figure 1 includes a central or host computer 101, a global purchasing database 103, one or more retail stores 105, a purchasing data computer 107, a local purchasing database 109, a The shop control unit 111, a shop base 113, and one or more points of sale 115, each including a printer 117, a terminal 119 and a scanning device 121.

The central or host computer 101 is any suitable workstation, server or other device such as a computer system 901 of Figure 9 for communicating with the purchase data computer 107 and for storing information in and retrieving information from the database. global purchasing data 103. The central computer 101 communicates with the purchasing data computer 107 and the global purchasing database 104 using any suitable protocol which may be implemented using the computer system 901 of Figure 9, for example.

The global purchasing database 103 is an archive that includes records or records containing information for monitoring new product introductions, according to the present invention. This information includes information about each purchase made by a customer in the retail store 105. This information may include, but is not limited to, the stock keeping unit (SKU), brand, size, price weight, date and time of purchase, and a customer identifier (CID). ) of the customer making the purchase, for example. In one embodiment, portions of this information are obtained from barcodes on purchased items, which are scanned by the scanning device 121 during a transaction. Barcodes can contain UPC, JAN and EAN information. Global purchase basic 103 records contain fields along with a set of operations for search, selection, recombination and other database functions. The global purchasing database 103 can be implemented as two or more databases, if desired. One or more of U.S. Patent Nos. 5 832 457; 5 649 114; 5 430 644; and 5 592 560 describe techniques for collecting consumer shopping history information and for storing that information in databases such as global purchases database 103 and store database 113, for example. US Patents No. 5 832 457; 5649 114; 5 430 644; and 5592 560 are incorporated herein by reference. Additionally, techniques for gathering consumer shopping information and for storing this information in databases, such as global shopping database 103, and store database 113, 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.

Retail store 105 is generally called a retail location and is a place where goods are held for retail to customers. A retail store is typically associated with a chain, (such as Ralph's). As noted above, many retail stores 105 can be connected to the central 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, workstation, server, or device for communicating with the central computer 101 for storing and retrieving information in the local purchase data base 109, for monitoring data transmitted between terminal 119 and store controller 111 (i.e. transaction data), and for controlling printer 117.

The records or records in the local purchase data base 109 contain fields to associate barcodes with products in the retail store 105 (e.g. using UPC, JAN, and / or EAN codes) and to associate consumer identifiers with information the history of customer purchases. The local purchasing database 109 also includes operations for search functions, selection or classification, recombination and other functions of the database. The local purchasing database 109 can 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 base 109 is retrieved from the purchase data computer 107 and transmitted to the central computer 101 which uses this information to update the information of the purchase history stored in database 103 of global purchasing data.

The store controller 111 is any computer or device for communicating with the terminal 119 to use information stored in the store database 113 to perform transactions at the point of sale (POS) 115. A description of a shop controller 111 can be found, for example, in U.S. Patent No.

5 173 851.

The store database 113 is an archive that includes records or records containing information for carrying out transactions at the store 115 by scanning barcodes printed on purchased items. The registrations or records in the store database 113 contain fields for associating barcodes with products and their corresponding prices. The store database 113 also includes operations for search, selection, recombination and other database functions, and can be implemented if desired as two or more databases.

The retail store 105 includes one or more outlets 115. Each store 115 preferably includes a corresponding printer 117, a terminal 119, and a scanning device 121. The printer 117 receives printing instructions from the computer 107 of the data of the shopping. Terminal 119 may be implemented as a standard cash register and may include a screen, credit card reader, and numeric keypad, for example. The terminal 119 communicates with the store controller 112 and the scanning device 121. The scanning device 121 can be implemented as any conventional scanning device for reading product information such as an item code (e.g. UPC, EAN, or JAN ) by barcodes or other markings on the product. According to the present invention, new UPC codes are to be entered into a UPC dictionary associated with the store database 113. Information read by the scanning device 121 is transmitted to the store controller 111 through the terminal 119. The store controller 111 uses the scanned information and the information stored in the store database 113 to determine transaction information including SKU, product price, product quantity and product description, for example.

If there are a plurality of outlets 115 within the retail store 105, then each terminal 119 is preferably disposed on a loop or twisted pair such as the store controller 111. The purchase data computer 107 is positioned in front of the store controller 111 on the loop or twisted pair 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 of Figure 1 is for exemplary purposes only, since many variants of the specific hardware and software employed to implement the present invention will be readily apparent to one of ordinary skill in the art. For example, the functionality of the shopping data computer 107 and the store controller 111 can be combined in a single device. This implementation and other implementations of retail computer systems are described in more detail in one or more of U.S. Patent Nos.

4,723 212; 4 910 672; 5173 851; 5612 868; and 6026 370, each of which is incorporated herein by reference. To implement these variants as well as other variants, a single computer (e.g., the 901 computer system of Figure 9) may be programmed to perform the special purpose functions of two or more of any of the devices depicted in Figure 1. D ' on the other hand, two or more programmed computers can be replaced in place of any of the devices depicted in Figure 1. Principles and advantages of distributed processing, such as redundancy and replication, can also be implemented as desired to increase system robustness and performance , for example.

The present invention stores information relating to various customers who shop at retail stores 105 including the purchase histories of these customers. This information is stored in one or more memories, such as a hard disk, an optical disk, a magneto-optical disk and / or a RAM, for example. One or more databases, such as the global purchases database 103 and the store database 113, may store the information used to implement the present invention. Databases are organized using data structures (e.g. records, tables, matrices, fields, graphs, trees and / or lists) contained in one or more memories, such as the memories listed above or in any of the storage devices listed later in the discussion of Figure 9, for example.

Figures 2, 3 and 4 illustrate data structures used to implement a system for monitoring new product introductions according to an embodiment of the present invention. Data structures are illustrated in a relational format, using tables, whereby information stored in one column (i.e. field) of a table is mapped or concatenated to information stored in the same row (i.e. record or record) via the other column or the other columns of the table. These data structures are also employed by the central or 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 purchases database 103, the local purchases database 109 and / or any other suitable storage device or storage medium.

Figure 2 is a purchasing history table 201 which includes a 203 field for storing consumer identifiers (CID) and a 205 field for storing consumer shopping histories in field 203. A CID is any identifier that is scanned , read or otherwise introduced into a computer system at a checkout or checkout point to identify a customer. Each customer can have a multiplicity of CIDs. Preferably, the CID is represented as a barcode whereby it can be quickly scanned at checkout by the scanner 117, although any other type of machine-readable or non-machine-readable implementations for storing or displaying identifications can be employed. , including magnetic strips, platelets or memory chips, and so-called smart cards or "smart cards". Examples of possible consumer IDs are credit card numbers, debit card numbers, social insurance card numbers, driver's license numbers, bank account numbers, street addresses, names, email or email addresses. , telephone numbers, frequent customer card numbers, shopper card identifications (SCID) or shopper card identifications (SCID) or shopper card numbers issued by retail store 105, although almost any other form may be employed suitable identification. Preferably, field 205 is divided into various subfields to store purchase data separately, such as SKU, location of purchase, a description of the items purchased, the price of each item purchased, the date and time of the transaction, and any other desired information of consumer transactions.

Figure 3 is a table 301 of desired segments or targets, which includes a field 303 for storing CID and a field 305 for storing codes of target segments. According to an embodiment of the present invention, a static group of coherent or consistent buyer families are identified for monitoring. For example, (i) families who have made two visits in eight weeks for each of the last six (6) eight-week periods (i.e., for such a period of 48 weeks), would be identified as a family that would be monitored regardless of the dollars spent. during such visits or (ii) families who have visited in four weeks for the last twelve four-week periods (i.e. for an overall period of 52 weeks) would be identified as a family that would be monitored in dependence on the dollars spent during those visits . The identified families are segmented and are assigned target segment codes. According to an embodiment of the present invention, the segments would include category brands competing with loyalty / consumption breaks for basic brands, related category brands and buyers of new categories. The greater the number of defined segments, the greater the level of granularity (i.e. the level of definition) of the families that are monitored.

Segments can consist of any combination of buyers including but not limited to the following types of buyers or buyers. "Light" shoppers, generally defined as the lowest volume category user, typically, but not limited to, down to the bottom 50% of consumers. Average shoppers, generally defined as intermediate volume category users, typically the intermediate 25% of consumers. Strong shoppers, generally defined as the maximum volume category user, typically the top or top 25% of consumers. Uncategorized shoppers, generally defined as shoppers who haven't made a purchase within the category in the past 52 weeks. Loyal shoppers, generally defined as consumers who make the majority of their category purchases for a single brand, typically in an amount of 71% or more. Occasional shoppers, generally defined as consumers who switch between brands within a category (i.e. consumers who are not loyal to any brand). Competitor shoppers, generally defined as consumers who make the majority of their category purchases for a competing or competing brand, typically 71% or more. Buyers who never buy a brand, generally defined as consumers who have not bought a particular brand within a predetermined period (e.g. a 52 week period).

In addition, the following segment definitions apply. Category segment, generally defined as a group of UPCs for products that are generally interchangeable, which defines a particular purchasing behavior of consumers. Crossed or related category segment, generally defined as a group of UPCs for products that may be the same or used in association with another category that defines a particular consumer purchasing behavior. A lifestyle or "lifestyle" segment, generally defined as a group of UPCs that defines the shopping behavior of a demographic population (eg seniors, children). A segment of customized or customized definitions, generally defined as any group of UPCs that defines a particular consumer shopping behavior.

In addition to the previously identified segments, according to another embodiment of the present invention, segments can also be defined by causal information, such as price. Therefore, consumers can be "tailed" based on the price they paid for the monitored product. For example, one shopper segment may receive a temporary price reduction for a product, while other segments may not receive it. Thus, by monitoring retry and retry rates based on the price paid for retry and retry occasions, the causal effect of the price among other things can be determined.

All previous segments are stored in a target segment correlation table containing the descriptions of the target segment and the corresponding target segment code. Buyers will generally qualify for more than one segment. Thus, a segment hierarchy is defined such that a family is associated with only one target segment in target segment table 301.

Target segment table 301 includes a 307 field of chain identifier elements. Initially, the 307 chain identifier field will not contain any values. This field will be populated when the chain associated with each CID appears in weekly excerpts downloaded from retail stores 105 (i.e. the actual week). Target segment table 301 also includes an assayer field 309, a first repeater field 311, a second repeater field 313, a third repeater field 315 and a fourth repeater field 317, etc. Initially, these fields will not contain values. These fields will be populated with the number of weeks of the calendar identifying the week in which a CID appeared in a weekly archive transmitted by the computer 107 of the purchase data (ie the weeks will be aligned). For example, if CID # 123 appears for the first time in a number of calendar weeks, then "1" will appear in that CID's assayers field. The other fields at that time will remain "blank" or "white". If that CID were to appear again in an excerpt of a subsequent week, then the number of calendar weeks for that week of that excerpt will be used to populate the first 311 repeater field. If the same CID were to appear for a third time, then the second repeater field 313 would be populated for that CID.

Target segment table 301 also includes a 319 week field and an aligned week field 321. Initially, the aligned week 321 field does not contain any values. This field will be populated with a number reflecting the number of weeks a consumer has waited to experience or try a product after the product manufacturer's initial launch of the product, taking into account the period of time during which the chain associated with the consumer took to offer the product after the initial launch. For example, if a consumer tries or experiences a product during the first week that the chain associated with the consumer offers that product, then a "1" will appear in the aligned weeks field associated with that consumer's CID. A "1" will appear in the aligned weeks field even if the product has been offered in other chains for a longer period than it has been offered in that chain associated with that consumer. Therefore, behavior measurement data collected for a plurality of chains can be aligned. Thus, the confusing effect of distribution earnings in the first part of the product launch can be eliminated.

Figure 4 is a table 401 to identify all the chains that sell the new product. Table 401 includes a 403 field to identify each chain and a 404 chain-week field to indicate in which week each chain began selling the new product. Field 405 is based on the current week of tracking the new product.

Figure 5 is a flowchart illustrating how the behavior data for each target segment is processed and reported. In step 501, central computer 101 joins target segment table 301 and chain sales table 401 in order to set field 405 for each CID in target segment table 301.

In step 503, the central computer analyzes duplicate CIDs from the weekly extracts downloaded from the retail stores 105.

In step 505, the central computer 101 populates the assayers field 309, the first repeater field 311, the second repeater field 313, the third repeater field 315, and the fourth repeater field 317 for each CID based on that 'weekly extract or that week. In step 507, the central computer 101 counts the number of assayers, first repeaters, second repeaters, third repeaters and fourth repeaters. In step 509, the percentage of assayers, first repeaters, second repeaters, third repeaters and fourth repeaters is determined by the central computer as a function of the total number of new product assayers in the analyzed segment. In step 511, the central computer 101 generates reports showing (1) the total number of CIDs in each universe of the target segments, (2) the total number of CIDs for each target segment that purchased the product (i.e. performed a test of brand), for the week the report is covering, and (3) the percentages of CID who performed a brand test that week (i.e., (2) / (1)). 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 week markers comparing the universe of the target segments and the universe of category buyers. In step 513, a purchase cycle is determined. The purchase cycle can either be determined manually by an operator or automatically by the central computer 101 based on the average purchase cycles for each CID in the competitive buyer segment.

The weekly extracts can also be used to forecast volumetric sales for each of the target segments. Future sales volume can be predicted based on the number of buyers reflected in the target segments table 301 taking into account past new product sales trends and collected aptitude measurement data.

Figures 6A and 6B are flowcharts describing the process for identifying which consumers to prospect or monitor in order to obtain aptitude measurement data for newly introduced products. According to an embodiment of the present invention, there are four types of purchasers to be monitored in a targeted manner for prospecting. The four types are non-assayers, assayers, assayers-discards, and repeaters.In step 601, central 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 for each category can be the desired mass). In step 603, the central computer 101 places the CIDs that have been counted in a weekly archive. In step 605, the central computer 101 filters the weekly archive in order to ensure that no consumer has to be subjected to surveillance twice.

In step 607, the central computer 101 creates a file identifying non-assayers or non-experimenters for each segment. In order to populate this archive, access is made to table 301 of the target segments, and any CID in which the associated assayers field 309 is null and the associated chain-in-week field 405 is not null is added to the database of non-assayers. In step 609, central computer 101 randomly selects for CID surveillance from the non-assayer file. According to one embodiment of the invention, to be a non-assayer (1.5 x purchase cycle or some other designated number of weeks), weeks must have passed before a consumer is considered a non-assayer. Additionally, for the competitive purchaser segment, CIDs may be requested as acquiring a competitive brand in order to be considered a non-assayer. For all other target segments, this is not a requirement.

In step 611, central computer 601 creates a file identifying assayers for each segment. In order to populate this archive, access is made to table 301 of the target segments, and any CID in which the associated assayer field 309 is non-null, and the first associated repeater field 311 is null, is added to the assayer archive. . In step 613, central computer 101 randomly selects to monitor CID from the assayer archive.

Referring to Figure 6B, in step 605 the central computer 101 creates a file identifying assayers-rejects for each segment. In order to populate this archive, access is made to table 301 of the target segments, and any CID in which field 309 of the assayers is populated, the first field 311 of the repeaters is null and a predetermined time related to the purchase cycle has passed is added to the field of assayers-discards. Additionally, for the competitive segment, it has to be determined that CID are still purchasing a competitive brand in order to be considered an assayer-discarder. From step 617, central computer 101 randomly selects to monitor CID from the assayer-discarder archive.

In step 619, the central computer 101 creates an archive for each type of identified repeater (first, second, third, fourth, etc.) for each segment. In order to populate the first repeater archive, access is made to the target segment table 301, and any CID in which a value in the associated first repeater field 311 is populated is added to the first repeater archive. The same process is repeated for the second, third and fourth repeater archives. In step 621, the central computer 101 randomly selects to monitor CID from the archive of each repeater archive. The processing described for implementing the steps illustrated in Figures 5 and 6 is carried out by the central computer 101 or alternatively by one or more different computers that access the data.

Prospecting or surveillance provides a mechanism for obtaining immediate attitudinal feedback on consumers' awareness, acceptance and satisfaction with the newly introduced product. Market participants will quickly obtain valid information relating to the different categories monitored. Market participants will be able to obtain (from the first assayers) aptitude measurement data through this type of targeted surveillance reflecting (1) the source of awareness or liking of the first assayers of the product, (2) the degree of and awareness- first assayers' liking, (3) the ease with which early assayers found the shop, (4) the reasons why the product was purchased, (5) how the product performed, and (6) future intentions of purchases of the first assayers. With respect to first non-assayers, market participants will be able to obtain reflective aptitude measurement data (1) whether the first non-assayers have ever considered the new product, (2) reasons for the current loyalty of non-assayers to a competitive or competing product, (3) because the product was not purchased, and (4) whether or not the first non-assayer has any interest in future purchases of the new product. With respect to the assayers-rejects, market participants will be able to obtain aptitude measurement data reflecting (1) why the assayer-discarder initially purchased the product, (2) the unmet expectations of the assayer-discarder, and (3) reasons as to why the assayer-discarder did not purchase the product again. With respect to repeaters, market participants will be able to obtain reflective aptitude measurement data (1) why the repeater initially purchased the product and (2) why the repeater purchased the product repeatedly. Profiles of all supervised households can be obtained through surveillance or prospecting.

Figure 7 is a flowchart to explain how behavior measurement data and aptitude measurement data are collected from the central computer 101 or other computers during the launch of a new product. In step 701, a central computer accesses a consumer information database in order to identify, based on predetermined criteria, a plurality of consumers to be monitored, in which each identified consumer is uniquely identified within the database. In step 703, the central computer stores, in a data structure, behavior measurement data corresponding to a frequency with which a product is purchased by each identified consumer. In step 705, the central computer periodically receives data extracts including collected data. by consumers identified during a predefined monitoring period. The default monitoring period is usually determined by the product manufacturer and typically lasts long enough to obtain information from repetitive buyers. In step 707, the central computer updates the behavior measurement data stored in the data structure based on the data extracts. Finally, in step 709, the central computer generates direct messages to selected consumers during the predefined monitoring period requesting aptitude measurement data relating to the product. The messages can be in the form of direct mail addressed to the consumer, an electronic message transmitted over the Internet, or any other form suitable for communicating the message to the consumer. Upon receipt of aptitude measurement data from supervised consumers, the received aptitude measurement data is processed by associating the data with each consumer and subsequently compiling the data for each category of consumers (i.e. assayers, non-assayers, assayers-discards, and repetitive assayers).

Figure 8 is a flowchart to explain how to perform targeted marketing on non-assayers, assayers, or assayers-discards based on behavior measurement data collected during the launch of a new product. In step 801, a central computer stores, in a data structure, behavior measurement data corresponding to a frequency with which a product is purchased. In step 803, the central computer periodically receives data extracts including data collected during a predefined monitoring period. In step 805, the central computer updates the behavior measurement data stored in the data structure based on the data extracts. Finally, in step 807, targeted marketing is performed based on the updated behavior measurement data. According to an embodiment of the invention, consumers identified as non-experimenters, experimenters, or experimenter-discarders of the product could be selected in a targeted manner for promotion. For example, during the next visit to their partner store and point of sale, a promotion would be generated for each identified (i) non-assayer (ii) assayer having a predetermined assay percentage level, or (iii) a repeater having a predetermined levels of repetition rates. Alternatively, a promotion could be mailed to each identified non-assayer. The present invention is not limited to these two types of promotions. Other types of promotions can be set up with incentives for the consumer to purchase the product in question.

The whole invention or a portion of the invention may be conveniently implemented using conventional general purpose or universal computers or microprocessors programmed in accordance with 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 experience based on the teachings of this disclosure, as will be apparent to those skilled in the field of software.

Figure 9 illustrates a computer system 901 on which an embodiment according to the present invention can 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 main memory 907, such as random access memory (RAM) or other dynamic storage device (e.g. dynamic RAM (DRAM), static RAM (SRAM), synchronous DRAM ( SDRAM), a flash RAM) coupled to bus 903 to store information and instructions to be executed by the processor 905. In addition, main memory 907 can be used to store temporary variables or other intermediate information during instruction execution which must be performed by the 905 processor. The 901 computer system also includes a 909 read-only memory (ROM) or other static storage device (e.g. a programmable ROM (PROM), an erasable PROM (EPROM)), and an electrically erasable PROM (EEPROM)) coupled to bus 903 to store static information and instructions for the 905 processor. A 911 storage device, such as a magnetic disk or an optical disk is provided and coupled to bus 903 to store information and instructions.

Computer system 901 may also include special purpose logic devices (e.g., application specific integrated circuits or "(ASIC))" or configurable logic devices (e.g., a generic logic matrix (GAL) or programmable gate arrays. Field Reprogrammable (FPGA)). Other removable media devices (such as a compact disc,

a tape, and removable magneto-optical media), or fixed media drives, of high density, can be added to the computer system 901 using an appropriate device bus (e.g., a small computer system interface bus (SCSI) , 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 player, a compact disc read-write unit, or a compact disc jukebox, each of which may be connected to the same device bus or to another device bus. .

Computer system 901 may be coupled via bus 903 to a display 913, such as a cathode ray tube (CRT), to display information for a computer user. The 913 display can be controlled by a display card 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 the processor 905. The cursor control 917, for example, is a mouse, trackball, or directional cursor keys for communicating direction information and command selections to the 905 processor and for controlling cursor movement on the 913 display or display. In addition, a printer can provide printed lists of the data structures represented in Figures 2, 3 and 4 or any other data stored by and / or generated by the 901 computer system.

Computer system 901 executes a portion of or all of the processing steps of the invention in response to the execution by processor 905 of one or more sequences of one or more instructions contained in a memory, such as main memory 907. These instructions can be read into main memory 907 from another computer readable medium, such as storage device 911. One or more processors in a multiprocessing arrangement may also be employed to execute the instruction sequences contained in main memory 907. In Alternate embodiments, hard-wired circuitry may be employed in place of or in combination with software instructions. Thus, the embodiments are not limited to any specific combination of hardware and software circuitry.

As indicated above, system 901 includes at least one computer readable medium or memory programmed in accordance with the teachings of the invention and for holding data structures, tables, records and other data described herein. Stored on any of or a combination of computer readable media, the present invention includes software for controlling the computer system 901, for operating one or more devices to implement the invention, and for allowing the computer system 901 to interact. with a human user (e.g. a consumer). This software may include, but is not limited to, piloting elements or device drivers, operating systems, development tools, and application software. This computer readable medium further includes the computer program product according to the present invention for performing all or a portion (if the 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, writings, interpreters, dynamic link libraries, Java classes, and complete executable programs.

1550 ti. Furthermore, parts of the processing of the present invention can be deployed for better performance, reliability and / or cost.

The term "computer readable medium" as used herein refers to any medium participating in providing instructions to the 905 processor for execution. Computer readable media can take many forms, including, but not limited to, non-volatile media, volatile media, and broadcast media. Non-volatile media include, for example, optical, magnetic, and magneto-optical disks, such as the 911 storage device. Volatile media include dynamic memory such as 907 main memory. Media or transmission means include coaxial cables, copper wire, and fibers optical, including the wires that make up the bus 903. The transmission media may also take the form of acoustic or light waves, such as those generated during radio wave communications and infrared data communications.

Common forms of computer readable media include, for example, hard disks, flexible disks, tape, magneto-optical disks, PROMs (EPROM, EEPROM, Flash EPROM), DRAM, SRAM, SDRAM, or any other magnetic media, compact-disc (e.g. CD-ROM) or any other optical media, punch cards, paper tape, or other physical media with hole patterns, a carrier wave (described below) or any other media that a computer can read from.

Various forms of computer readable media may be involved in executing one or more sequences of one or more instructions to the 905 processor for execution. For example, the instructions may initially be placed on a magnetic disk of a distant computer. The remote computer may load the instructions for implementing the whole or a portion of the present invention remotely into a dynamic memory and transmit the instructions over a telephone line using a modem. A local modem for the 901 computer system can receive data over the telephone line and use an infrared transmitter to convert the data into an infrared signal. An infrared detector coupled to bus 903 can receive data carried in the infrared signal and place the data on bus 903. Bus 903 carries data to main memory 907, from which processor 905 retrieves and executes instructions. The instructions received from main memory 907 may optionally be stored on the storage device 911 either before or after execution by the processor 905.

Computer system 901 also includes a communications interface 919 coupled to bus 903. Communications interface 919 provides a bi-directional data communications coupling to a network link 921 which is connected to a local area network (e.g. LAN 823) . For example, the communications interface 919 may be a network interface card for attachment to any packet-switched local area network (LAN). As another example, the communications interface 919 may be an asymmetric digital subscriber line card (ADSL), an integrated services digital network card (ISDN) 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, the communications interface 919 transmits and receives electrical, electromagnetic or optical signals carrying digital data streams representing various types of information.

Network link 921 typically provides data communication across one or more networks to other data devices. For example, the network link 921 may provide a connection through the LAN 923 to a central or guest computer 925 or to data equipment operated by a service provider or provider, which provides data communication services over an IP (Internet Protocol) 927 (e.g. Internet 615), or any other suitable network, using any known protocol (e.g. IPX). The LAN 923 and the IP network both employ electrical signals, electromagnetic or optical, which carry digital data streams. The signals through the various networks and the signals on the network link 921 and through the communications interface 919, which carry digital data to and from the computer system 901, are exemplary forms of carrier waves carrying the information. The computer system 901 may transmit notifications or signals and receive data, including program code, over the network or networks, the network link 921 and the communications interface 919.

Obviously, numerous modifications and variations of the present invention are possible in light of the previous teachings. For example, the present invention is not limited to the analysis of new product launches, but is equally applicable to the analysis of consumer behavior and attitudinal data relating to products that have sales histories. It should therefore be borne in mind that, within the scope of the appended claims, the invention can be practically implemented differently from what has been specifically described herein.

Claims (45)

CLAIMS 1. A computer-implemented process, comprising the steps of: access a consumer information database in order to identify, on the basis of predetermined criteria, a plurality of consumers to be monitored, in which each identified consumer is uniquely identified within the database; storing, in a data structure, behavioral measurement data corresponding to a frequency with which a product is purchased by each identified consumer; periodically receive data extracts including identified consumer data collected during a predefined monitoring period; update the behavior measurement data stored in the data structure based on the data extracts; And generate direct messages to selected consumers during the predefined monitoring period requesting aptitude measurement data related to the product. A computer-implemented method according to claim 1, wherein said storing step further comprises the steps of: associating each said identified consumer with at least one of a plurality of consumer purchase segments; And determining a hierarchy for the consumer purchasing segments such that each said identified consumer is associated in the data structure with only one of the consumer purchasing segments. 3. A computer-implemented process according to claim 2, further comprising the step of: generate a report reflecting a number of consumers identified by purchase segment of consumers who purchased the product during a predefined period. 4. A computer-implemented process according to claim 2, further comprising the step of: predict future product sales volume for each of the consumer purchasing segments based on behavior measurement data. 5. A computer-implemented method according to claim 1, wherein the message is a direct mail message addressed to each said selected consumer. 6. A computer implemented method according to claim 1, wherein the message is an electronic message addressed to each said selected consumer. 7. A computer-implemented process according to claim 1, further comprising the step of: classify the consumers identified each as an assayer, non-assayer, assayer-discarder, or repetitive product assayer based on the behavior measurement data stored in the data structure. 8. A computer implemented process according to claim 7, wherein said classification step further comprises the step of: classify the identified consumers each as at least one strong, medium, light, loyal, occasional, competitive, and never buyer based on behavior measurement data stored in the data structure. 9. A computer-implemented process according to claim 7, further comprising the steps of: receive aptitude measurement data from selected consumers generated in response to said messages; associate the received aptitude measurement data with each said identified consumer; And compile aggregate aptitude measurement data from at least one of the categories of assayers, non-assayers, assayers-discards, and repetitive assayers, respectively. A computer-implemented method according to claim 1, wherein the aptitude measurement data comprises data reflecting at least one element of consumer awareness-liking, acceptance and satisfaction with respect to the product. 11. A computer-implemented process according to claim 1, wherein the updating step comprises the steps of: associating each said consumer with a chain, in which a plurality of chains offer the product; modify the data structure to reflect the association; And storing alignment data in the data structure reflecting how quickly each said consumer purchases the product after the product is initially offered, wherein said alignment data reflects how long each said chain associated with each said consumer takes to offer the product after the product was initially offered. 12. A computer-implemented process, comprising the steps of: storing, in a data structure, behavior measurement data corresponding to a frequency with which a product is purchased; periodically receive data extracts including data collected during a predefined monitoring period; update the behavior measurement data stored in the data structure based on the data extracts; And perform targeted marketing based on up-to-date behavioral measurement data. A computer-implemented process according to claim 12, wherein the execution step comprises the steps of: identifying non-assayers of the product based on behavior measurement data stored in the data structure upon determining that a percentage of purchases of the product is below a predetermined level; And automatically generate a promotion to be distributed to each non-assayer incentivizing the non-assayer to purchase the product. The computer-implemented method of claim 13, wherein the consumer information database comprises data from a plurality of stores, each identified consumer is associated in the data structure in a store, and the generation step further comprises the phase of generating the promotion at the sales point in a subsequent visit of each non-assayer to their associated shop. A computer-implemented process according to claim 13, wherein the generation step further comprises the step of: generating a mailing message including direct promotion to each said non-assayer. 16. Computer system comprising: a memory device having a consumer information database incorporated therein; And a processor in communication with said memory device, said processor being configured for: access the consumer information database in order to identify, on the basis of predetermined criteria, a plurality of consumers to be monitored, in which each identified consumer is uniquely identified within the database; storing, in a data structure, behavioral measurement data corresponding to a frequency with which a product is purchased by each identified consumer; periodically receive data extracts including identified consumer data collected during a predefined monitoring period; update the behavior measurement data stored in the data structure based on the data extracts; And generate direct messages to selected consumers during the predefined monitoring period requesting aptitude measurement data related to the product. 17. A computer system according to claim 16, wherein said processor is further configured to associate each said identified consumer with at least one of a plurality of consumer purchase segments, and to determine a hierarchy for the consumer purchase segments in such a way that each said identified consumer is associated in the data structure with only one of the consumer purchase segments during the process of storing the data structure. A computer system according to claim 17, wherein said processor is further configured to generate a report reflecting a number of identified consumers per purchase segment of the consumers who purchased the product during a predefined period. A computer system according to claim 17, wherein said processor is further configured to predict future sales volume of the product for each of the consumer purchase segments based on the behavior measurement data. 20. A computer system according to claim 16, wherein the message is a postal message addressed to each said selected consumer. A computer system according to claim 16, wherein the message is an electronic message addressed to each said selected consumer (including Internet access). A computer system according to claim 21, wherein the electronic message is transmitted over the Internet. 23. A computer system according to claim 16, wherein said processor is further configured to classify consumers identified each as an assayer, non-assayer, assayer-rejecter, or repetitive product assayer based on behavior measurement data stored in the data structure. 24. A computer system according to claim 23, wherein said processor is further configured for: receive aptitude measurement data from selected consumers generated in response to said messages; associate the received aptitude measurement data with each said identified consumer; And compile aggregate aptitude measurement data from at least one of the categories of assayers, non-assayers, assayers-discards, and repetitive assayers, respectively. A computer system according to claim 16, wherein the aptitude measurement data comprises data reflecting at least one of consumer awareness-liking, acceptance and satisfaction with respect to the product. A computer system according to claim 16, wherein the processor is further configured to associate each said consumer with a chain in which a plurality of chains offer the product, to modify the data structure in order to reflect the association, and storing alignment data in the data structure reflecting how quickly each said consumer purchases the product after the product was initially offered during the behavior measurement data update process, wherein said alignment data reflects how long each said associated chain with each said consumer it takes to offer the product after the product was initially offered. 27. Computer system comprising: a memory device having a consumer information database incorporated therein; a processor in communication with said memory device, said processor being configured for: storing, in a data structure, behavior measurement data corresponding to a frequency with which a product is purchased; periodically receive data extracts including data collected during a predefined monitoring period; update the behavior measurement data stored in the data structure based on the data extracts; And perform targeted marketing based on up-to-date behavioral measurement data. A computer system according to claim 27, wherein the processor is further configured to identify product non-assayers based on the behavior measurement data stored in the data structure upon determining whether a percentage or rate of purchases of the product is below a predetermined level, and to automatically generate a promotion to be distributed to each non-assayer incentivizing the non-assayer to purchase the product during the targeted marketing execution process. A computer system according to claim 28, wherein the consumer information database comprises data from a plurality of stores, each identified consumer is associated in the data structure with a store, and the processor is further configured to generate the point of sale promotion during each said non-experimenter's next visit to their associated store during the process of automatically generating the promotion. A computer system according to claim 27, wherein the processor is further configured to generate a mail message including direct promotion to each said non-assayer during the process of generating the promotion. 31. System comprising: means for accessing a consumer information database in order to identify, on the basis of predetermined criteria, a plurality of consumers to be monitored, in which each identified consumer is uniquely identified within the database; means for storing, in a data structure, behavior measurement data corresponding to a frequency with which a product is purchased by each identified consumer; means for periodically receiving data extracts including identified consumers collected during a predefined monitoring period; means for updating the behavior measurement data stored in the data structure based on the data extracts; And means to generate direct messages to selected consumers during the predefined monitoring period requesting aptitude measurement data related to the product. 32. System according to claim 31, wherein said storage means further comprise: means for associating each said identified consumer with at least one of a plurality of consumer purchase segments; And means for determining a hierarchy for consumer purchasing segments such that each said identified consumer is associated in the data structure with only one of the consumer purchasing segments. A system according to claim 32, further comprising: means to generate a report reflecting a number of consumers identified by purchase segment of consumers who bought the product during a predefined period. A system according to claim 32, further comprising: means to predict future product sales volume for each of the consumer purchasing segments based on behavior measurement data. A system according to claim 31, wherein the message is a direct postal message addressed to each said selected consumer. A system according to claim 31, wherein the message is an electronic message addressed to each said selected consumer. A system according to claim 31, further comprising: means for classifying the consumers identified each as an assayer, non-assayer, assayer-discarder, or repetitive product assayer based on the behavior measurement data stored in the data structure. A system according to claim 37, further comprising: means for receiving aptitude measurement data from selected consumers generated in response to said messages; means for associating the received aptitude measurement data with each said identified consumer; And means for compiling aggregate aptitude measurement data of at least one of the categories of assayers, non-assayers, assayers-discards, and repetitive assayers, respectively. A system according to claim 31, wherein the aptitude measurement data includes data reflecting at least one of consumer awareness-liking, acceptance and satisfaction with respect to the product. A system according to claim 31, wherein the updating means further comprise: means for associating each said consumer with a chain, wherein a plurality of chains offer the product; means to modify the data structure to reflect the association; And means for storing alignment data in the data structure reflecting how quickly each said consumer purchases the product after the product has been initially offered, wherein said alignment data reflects how long each said chain associated with each said consumer takes to offer the product after the product was initially offered. 41. System comprising: means for storing, in a data structure, behavior measurement data corresponding to a frequency with which a product is purchased; means for periodically receiving data extracts including data collected during a predefined monitoring period; means for updating the behavior measurement data stored in the data structure based on the data extracts; And means to perform targeted marketing based on up-to-date behavioral measurement data. 42. System according to claim 41, wherein the means for performing comprises: means for identifying product non-assayers based on behavior measurement data stored in the data structure upon determining that a product purchase rate or rate is less than a predetermined level; and means for automatically generating a promotion to be distributed to each non-assayer incentivizing the non-assayer to purchase the product. A system according to claim 42, wherein the consumer information database comprises data from a plurality of stores, each identified consumer is associated in the data structure with a store, and the means for generating include point-of-sale generating means to generate the promotion at the sales point during the next visit of each non-assayer to their associated shop. System according to claim 42, wherein generating means further comprises: postal message generating means for generating a postal message including direct promotion to each said non-assayer. 45. Computer readable medium containing program instructions for execution on a computer system which, when executed from a computer, cause the computer to carry out the procedure according to any one of claims 1 to 15.