JP2018163605A - Program, information processor, and information processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a program or the like capable of achieving efficient marketing.SOLUTION: A computer is caused to execute processing of: acquiring attributes of a customer related to purchase, referring to a storage part storing attributes and behavior information related to the attributes for each customer, extracting the behavior information of the customer not having the acquired attributes, counting a first customer count based on a combination of the pieces of extracted behavior information, and calculating a second customer count for each of the pieces of extracted behavior information based on the counted first customer count in each combination.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a program, an information processing apparatus, and an information processing method.

  2. Description of the Related Art Conventionally, an information processing system or the like that can provide information useful for consumer targeting in advertising distribution or the like is known (see, for example, Patent Documents 1 to 4).

International Publication No. 2016/136148 Japanese Patent No. 5913722 Japanese Patent No. 599644 Japanese Patent No. 4728229

  However, the conventional technology has a problem that it cannot perform marketing to customers efficiently.

  In one aspect, an object is to provide a program or the like that enables efficient marketing.

  In one proposal, attributes related to customer purchases are acquired, and for each customer, reference is made to the storage unit storing the attributes and the behavior information related to the attributes, and the behavior information of the customers not having the acquired attributes is extracted and extracted. The computer counts the number of first customers based on the combination of behavior information, and causes the computer to execute processing for calculating the number of second customers for each extracted behavior information based on the number of first customers of each counted combination.

  In one aspect, marketing can be performed efficiently.

It is explanatory drawing which shows the outline | summary of an information processing system. It is a block diagram which shows the hardware group of a computer. It is explanatory drawing which shows the record layout of DNADB. It is explanatory drawing which shows the record layout of DNA action tag DB. It is explanatory drawing which shows the record layout of action tag DB. It is explanatory drawing which shows the record layout of customer DB. It is explanatory drawing which shows the relationship between DNA and an action tag. It is explanatory drawing which shows a combination. It is explanatory drawing which shows the record layout of result DB. It is a flowchart which shows the calculation procedure of the number of customers. It is explanatory drawing which shows the display image of the 2nd customer number. It is a flowchart which shows a change procedure. It is a flowchart which shows the output process procedure of a candidate action. It is explanatory drawing which shows the image of recommendation information. It is a flowchart which shows a reference value calculation procedure. It is a functional block diagram which shows operation | movement of the computer of the form mentioned above. FIG. 16 is a block diagram illustrating a hardware group of a computer according to a fifth embodiment. FIG. 10 is an explanatory diagram showing an overview of an information processing system according to a sixth embodiment. FIG. 20 is a block diagram illustrating a hardware group of a computer according to a sixth embodiment. It is explanatory drawing which shows the record layout of contact DB. It is explanatory drawing which shows a 1st customer group. It is explanatory drawing which shows the record layout of result DB. It is explanatory drawing which shows the display image of a 2nd customer number and a 2nd customer group. It is a flowchart which shows the specific process sequence of customer ID. It is a functional block diagram which shows operation | movement of the computer of the form mentioned above.

Embodiment 1
Hereinafter, embodiments will be described with reference to the drawings. FIG. 1 is an explanatory diagram showing an outline of an information processing system. The information processing system includes an information processing apparatus 1 connected to a communication network N such as the Internet, a LAN (Local Area Network), and a public line network. The information processing apparatus 1 is a server computer, a personal computer, a tablet, a smartphone, or the like. Hereinafter, the information processing apparatus 1 will be described as being replaced with the computer 1.

  Based on the result of the purchase information analysis, the customer is given an attribute (category) such as “I am interested in fashion and limited items” or “Respond to a good deal”. Further, when an action related to the attribute satisfies a predetermined condition, the attribute is given to the customer. In the example in FIG. 1, “rise purchase”, “buy lucky bag”, and “shopping in a trendy store” are listed as actions (purchase characteristics). For example, when the target customer performs all three of these actions, the attribute “interested in fashion and limited items” is given. A customer who performs all three actions has an attribute of “interested in fashion and limited items” as shown by the solid line in FIG.

  On the other hand, as indicated by the dotted line in FIG. 1, there is a customer segment who does not perform all of these actions. For example, an attribute of “I'm interested in fashion and limited items” is given to customers who do the actions “rise purchase” and “shopping in trendy stores” but do not do the action “buy lucky bags” Not. In this case, in order to increase the number of customers who have the attribute of “interested in fashion and limited items”, measures related to the actions “buy up”, “buy lucky bags”, or “shopping in trendy stores” Need to do. In the embodiment, an appropriate measure is determined by the method described below. In the example of FIG. 1, a measure corresponding to the action “recommend measures for purchasing lucky bags” is displayed on the computer 1. Details will be described below.

  FIG. 2 is a block diagram illustrating a hardware group of the computer 1. The computer 1 includes a central processing unit (CPU) 11, a random access memory (RAM) 12 as a control unit, an input unit 13, a display unit 14, a storage unit 15, a clock unit 18, and a communication unit 16. The CPU 11 is connected to each part of the hardware via the bus 17. The CPU 11 controls each part of the hardware according to the control program 15P stored in the storage unit 15. The CPU 11 may be a multi-core processor equipped with a plurality of processor cores. The RAM 12 is, for example, SRAM (Static RAM), DRAM (Dynamic RAM), flash memory, or the like. The RAM 12 also functions as a storage unit, and temporarily stores various data generated when the CPU 11 executes various programs.

  The input unit 13 is an input device such as a mouse, a keyboard, a touch panel, or a button, and outputs received operation information to the CPU 11. The display unit 14 is a liquid crystal display, an organic EL (electroluminescence) display, or the like, and displays various information according to instructions from the CPU 11. The communication unit 16 is a communication module and transmits / receives information to / from another computer (not shown). The clock unit 18 outputs date information to the CPU 11. The storage unit 15 is a large-capacity memory, and stores a control program 15P, an attribute (hereinafter referred to as DNA) DB 151, and the like. In addition, the storage unit 15 stores a DNA behavior tag DB 152, a behavior tag DB 153, a customer DB 154, a result DB 155, and the like. In the embodiment, the example in which the DNADB 151 and the like are stored in the storage unit 15 is shown, but the present invention is not limited to this. For example, in addition to storing in the RAM 12, it may be stored in a DB server computer (not shown).

  FIG. 3 is an explanatory diagram showing a record layout of the DNADB 151. The DNADB 151 includes a DNA ID field, a DNA name field, a summary field, a minimum action tag number field, and the like. The DNAID field stores unique identification information (hereinafter referred to as DNAID) for specifying DNA. In the DNA name field, the name of the DNA is stored in association with the DNA ID. For example, there are names such as “weak to fashion and limited products”, “react to a good deal (point)”, and “react to a good deal (friends' group)” (not shown).

  In the summary field, a summary of DNA is stored in association with the DNA ID. For example, for the name “weak to fashion and limited items”, “extract people who tend to shop at the beginning of the season or buy limited items such as lucky bags” is stored. In the minimum action tag number field, the number of action tags (reference value) necessary to determine that there is DNA is stored in association with the DNA ID. For example, it is assumed that there are four action tags, action tag 1, action tag 2, action tag 3, and action tag 4, associated with DNA “weak to fashion and limited products”. Here, since the minimum number of action tags is stored as 3, the CPU 11 determines that if the customer has at least three of the action tag 1, action tag 2, action tag 3, and action tag 4, the customer. Judged to have DNA “weak to fashion and limited products”. In the embodiment, the minimum number of action tags is stored. However, when all (four in the above example) are included, it may be determined that DNA is included.

  Similarly, the minimum action tag number is stored as “2” for the DNA “Respond to a good deal (point)”. Assume that there are four action tags, action tag 1, action tag 3, action tag 4, and action tag 5, associated with the DNA “Respond to a good deal (point)”. Here, since the minimum number of action tags is stored as 2, the CPU 11 determines that if the customer has at least any one of the action tag 1, the action tag 3, the action tag 4 or the action tag 5, the customer. Is determined to have DNA “reacts to a good deal (point)”. In addition, DNA, action tag, etc. which are described in the embodiment are examples, and are not limited thereto. Moreover, the storage form of each DB is an example, and as long as the data relationship between each field is maintained, it may be stored in a different form.

  FIG. 4 is an explanatory diagram showing a record layout of the DNA action tag DB 152. The DNA action tag DB 152 includes a DNA ID field, an action tag ID field, and the like. In the action tag ID field, unique identification information (hereinafter referred to as action tag ID) for specifying action tags correlated with each other is stored in association with the DNA ID. For example, the behavior tag IDs “1”, “2”, etc. related to the DNA are stored in the DNA ID “1”.

  FIG. 5 is an explanatory diagram showing a record layout of the action tag DB 153. The behavior tag DB 153 includes a behavior tag ID field, a behavior tag name field, a summary field, and the like. In the action tag name field, the name of the action tag is described in association with the action tag ID. The summary field stores a summary of the behavior tag in association with the behavior tag ID. For example, the action tag name “rising purchase” stores, as a summary, “the purchase price at a fixed price of clothes in February, August and September exceeds a certain percentage”. In addition, the action tag name “has bought a lucky bag” stores, as an overview, “there is a certain number of purchases of a lucky bag within a certain period”. In this way, the behavior tag DB 153 stores behavior information including a behavior ID, a behavior tag name, or a behavior summary.

  FIG. 6 is an explanatory diagram showing a record layout of the customer DB 154. The customer DB 154 includes a customer ID field, a DNA flag field, an action tag flag field, and the like. The customer DB 154 stores purchase histories of a plurality of customers. The customer DB 154 is downloaded in advance via the communication unit 16 and stored in the storage unit 15. In the customer ID field, unique identification information (hereinafter referred to as customer ID) for specifying a customer is stored. In the DNA flag field, a flag indicating whether or not the customer has each DNA is stored in association with the customer ID. Specifically, the presence or absence of a flag is stored for each DNA ID. For customer ID “200102”, a flag is set in DNA1 and no flag is set in DNA2.

  In the action tag field, one or more flags indicating whether or not the customer has each action tag is stored in association with the customer ID. Specifically, the presence / absence of a flag is stored for each action tag ID. For the customer ID “200102”, no flag is set in the action tag 1 and a flag is set in the action tag 2. As for the presence / absence of DNA of each customer and the presence / absence of an action tag shown in FIG.

  FIG. 7 is an explanatory diagram showing the relationship between DNA and action tags. The DNA ID and the action tag are associated as shown in the DNA action tag DB 152 in FIG. In the example of FIG. 7, DNA1 and action tags 1 to 4 are associated with each other. The CPU 11 refers to the DNADB 151 and reads the minimum action tag number corresponding to the DNAID. In this embodiment, 3 is stored. The CPU 11 subtracts 1 from the read minimum action tag number to obtain a subtraction value 2.

  The CPU 11 reads the action tag ID corresponding to the DNA ID with reference to the DNA action tag DB 152. CPU11 calculates | requires the combination of the number of action tags corresponding to a subtraction value with respect to several read action tag ID.

  FIG. 8 is an explanatory diagram showing combinations. The action tags associated with DNA1 are action tags 1 to 4. Since the subtraction value is 2, all combinations of two action tags are obtained. As shown in FIG. 8, action tag 1 and action tag 2, action tag 1 and action tag 3, action tag 1 and action tag 4, action tag 2 and action tag 3, action tag 2 and action tag 4, action tag 3 And 6 types of combinations of action tags 4 are obtained. The CPU 11 refers to the customer DB 154 and reads a customer ID that does not have a target DNA ID (DNA1 in this example). The CPU 11 refers to the customer DB 154 and counts the number of customers (first customer number) having the obtained action tag combination. For example, the number of customers having the action tag 1 and the action tag 2 is 16000.

  Subsequently, the CPU 11 calculates the second customer number for each action tag based on the counted first customer number of each combination. Specifically, CPU11 calculates the 2nd customer number for every action tag based on the total value of the 1st customer number corresponding to combinations other than the said action tag. In the example of FIG. 8, if the action tag 1 is further provided, the number of customers who will have DNA is totaled. In this case, the CPU 11 adds up 11,000 people of the action tag 2 and the action tag 3, 9000 people of the action tag 2 and the action tag 4, and 8900 people of the action tag 3 and the action tag 4, and 28900 people as the second customer number. calculate.

  FIG. 9 is an explanatory diagram showing a record layout of the result DB 155. The result DB 155 includes an action tag field, a second customer number field, and the like. The CPU 11 stores the second customer number calculated by the above-described method in association with the action tag. In the example of FIG. 9, it is understood that the number of customers who will have DNA1 if they have after-action tag 1 is 28,900 and the number of customers who will have DNA1 if they have after-action tag 4 is 50,000. it can.

  FIG. 10 is a flowchart showing the procedure for calculating the number of customers. The CPU 11 accepts the target DNA via the input unit 13 or the communication unit 16 (step S101). CPU11 extracts customer ID which does not have received DNA with reference to customer DB154 (step S102). CPU11 reads the action tag corresponding to DNA from DNA action tag DB152 (step S103). The CPU 11 refers to the DNADB 151 and reads the minimum action tag number corresponding to the target DNA (step S104). The CPU 11 obtains a subtraction value by subtracting 1 from the minimum number of action tags (step S105).

  CPU11 calculates | requires all the combinations of the number of action tags corresponding to the number of subtraction values from the action tag read by step S103 (step S106). CPU11 counts the 1st customer number which has the combination of an action tag for every combination with reference to customer DB154 (step S107). CPU11 calculates the 2nd customer number for every action tag based on the total value of the 1st customer number which has combinations other than the action tag used as object (step S108).

  The CPU 11 stores the second customer number calculated in association with the action tag in the result DB 155 (step S109). CPU11 outputs the 2nd customer number corresponding to an action tag for every action tag to the display part 14 (step S1010). Note that the CPU 11 may output the number of second customers corresponding to the action tag to another computer (not shown) via the communication unit 16.

  FIG. 11 is an explanatory view showing a display image of the second number of customers. CPU11 outputs the calculated 2nd customer number to the display part 14 with an action tag. The CPU 11 may also output a message indicating the relationship between the action tag and the second number of customers. For example, a message such as “If a measure related to the displayed action tag is taken, the number of customers who may have DNA 1 (the number of second customers) is displayed” may be displayed. Thereby, it becomes possible to grasp which action tag should be taken. In addition, it is possible to compare and verify the effect on each action tag.

Embodiment 2
Embodiment 2 relates to a mode in which the minimum number of action tags is changed. FIG. 12 is a flowchart showing the change procedure. CPU11 receives DNAID from the input part 13 (step S121). CPU11 receives the minimum number of action tags via the input part 13 (step S122). The DNAID and the minimum number of action tags may be received from another computer via the communication unit 16. The CPU 11 stores the minimum action tag number in the DNA DB 151 in association with the received DNA ID (step S123). In the embodiment, an example in which the minimum number of action tags (number of matches) is used has been described, but the present invention is not limited to this. Instead of the number, the ratio (matching ratio) may be used. For example, when the number of action tags related to DNA is 4, a value of 100% may be accepted instead of the minimum action tag number 4, and 75% may be accepted instead of the minimum action tag number 3. This makes it possible to expand or contract the target customer segment.

  The second embodiment is as described above, and the other parts are the same as those of the first embodiment. Therefore, the corresponding parts are denoted by the same reference numerals, and detailed description thereof is omitted.

Embodiment 3
The third embodiment relates to a mode for selecting a candidate action. FIG. 13 is a flowchart showing an output process procedure of candidate actions. CPU11 extracts the action tag and the 2nd number of customers with the largest number among the 2nd number of customers memorized by Step S109 (Step S131). In the example of FIG. 9, action tags 4 and 50,000 are extracted as candidate actions. FIG. 14 is an explanatory diagram showing an image of recommended information. The CPU 11 reads the DNA name corresponding to the received DNA from the DNADB 151. CPU11 produces | generates the explanatory note containing the DNA name of received DNA (step S132). Specifically, the CPU 11 combines the template text text stored in the storage unit 15 with the DNA name. For example, as shown in Figure 14, the following measures are effective to increase the number of customers who are “weak to fashion and limited products”. Is generated.

  CPU11 reads the action tag name corresponding to the extracted action tag. CPU11 produces | generates the explanatory note containing the action tag name corresponding to an action tag (step S133). For example, as illustrated in FIG. 14, an explanatory note “a measure relating to“ shopping in a trendy store ”” is generated. CPU11 produces | generates the explanatory note containing the 2nd customer number (step S134). For example, as illustrated in FIG. 14, an explanatory note “50,000 customers expected to increase due to the measure” is generated. The CPU 11 outputs the explanatory text generated in steps S132 to S134 to the display unit 14 (step S135). This makes it possible to adopt measures that can be expected to be more effective.

  The third embodiment is as described above, and the others are the same as in the first and second embodiments. Therefore, the corresponding parts are denoted by the same reference numerals, and detailed description thereof is omitted.

Embodiment 4
The fourth embodiment relates to a mode for outputting a reference value. FIG. 15 is a flowchart showing a reference value calculation procedure. The action tag and the second customer number stored in the result DB 155 in step S109 are read (step S151). The CPU 11 refers to the customer DB 154 and calculates the percentage of customers having action tags with respect to the total number of customers (step S152). The CPU 11 calculates a reference value by multiplying the number of second customers corresponding to the calculated ratio (step S153). The CPU 11 stores the reference value in the storage unit 15 in association with the action tag (step S154). CPU11 judges whether the process mentioned above was complete | finished about the action tag relevant to all DNA (step S155).

  If the CPU 11 determines that the process has not been completed for all the action tags (NO in step S155), the process proceeds to step S156. CPU11 reads an unprocessed action tag and the 2nd customer number (step S156). Thereafter, the CPU 11 shifts the process to step S152. By repeating the above processing, reference values for all target action tags are derived.

  If the CPU 11 determines that the process for all the action tags has been completed (YES in step S155), the process proceeds to step S157. CPU11 reads the reference value corresponding to each action tag from the memory | storage part 15, sorts in order with a large reference value, and outputs a reference value and an action tag to the display part 14 (step S157). In addition, you may output to the display part 14 in order of an action tag, without sorting a reference value. Further, the reference value and the action tag may be output to another computer (not shown) via the communication unit 16. This makes it possible to set a measure related to the action tag with higher accuracy.

  The fourth embodiment is as described above, and the others are the same as those of the first to third embodiments. Therefore, the corresponding parts are denoted by the same reference numerals and detailed description thereof is omitted.

Embodiment 5
FIG. 16 is a functional block diagram showing the operation of the computer 1 of the above-described form. When the CPU 11 executes the control program 15P, the computer 1 operates as follows. The acquisition part 161 acquires the attribute regarding a customer's purchase. The extraction unit 162 refers to the storage unit 15 that stores the attribute and the behavior information related to the attribute for each customer, and extracts the behavior information of the customer that does not have the acquired attribute. The counting unit 163 counts the number of first customers based on the extracted combination of behavior information. The calculating unit 164 calculates the second customer number for each extracted behavior information based on the counted first customer number of each combination.

  FIG. 17 is a block diagram illustrating a hardware group of the computer 1 according to the fifth embodiment. A program for operating the computer 1 causes the storage unit 15 to read a portable recording medium 1A such as a CD-ROM, a DVD disk, a memory card, or a USB memory by a reading unit 10A such as a disk drive or a memory card slot. You may remember. Further, a semiconductor memory 1B such as a flash memory storing the program may be mounted in the computer 1. Further, the program can be downloaded from another server computer (not shown) connected via a communication network N such as the Internet. The contents will be described below.

  The computer 1 shown in FIG. 17 reads a program for executing the above-described various software processes from the portable recording medium 1A or the semiconductor memory 1B or downloads it from another server computer (not shown) via the communication network N. To do. The program is installed as the control program 15P, loaded into the RAM 12, and executed. Thereby, it functions as the computer 1 described above.

  The fifth embodiment is as described above, and the other parts are the same as those of the first to fourth embodiments. Therefore, the corresponding parts are denoted by the same reference numerals and detailed description thereof is omitted.

Embodiment 6
Embodiment 6 relates to a form for specifying a customer group. In the first embodiment, the number of second customers corresponding to each behavior tag is calculated, but the sixth embodiment relates to a mode for specifying the second customer. FIG. 18 is an explanatory diagram showing an overview of an information processing system according to the sixth embodiment. The computer 1 is shopping at a trendy store for the number of second customers who may be given the attribute “interested in trendy and limited items” with the action tags “rise purchase”, “buy lucky bag”, and "Every time. The computer 1 further outputs a second customer group corresponding to each action tag in order to support the approach to the customer. In the example of FIG. 18, customer IDs “200105, 200106...” Are displayed as the second customer group that should take the action tag “buy lucky bag”. Details will be described below.

  FIG. 19 is a block diagram illustrating a hardware group of the computer 1 according to the sixth embodiment. In addition to the first embodiment, a contact DB 156 is further provided. FIG. 20 is an explanatory diagram showing a record layout of the contact DB 156. As shown in FIG. The contact DB 156 includes a customer ID field, a name field, a mail address field, an address field, and the like. In the name field, the name of the customer is stored in association with the customer ID. In the mail address field, the mail address of the customer is stored in association with the customer ID. In the address field, an address is stored in association with the customer ID. The contact DB 156 is referred to when approaching the identified customer, and may store a telephone number, a store member number, etc. in addition to the data described above.

  FIG. 21 is an explanatory diagram showing a first customer group. The CPU 11 refers to the customer DB 154 and extracts a customer ID that does not have the designated DNA. The CPU 11 refers to the customer DB 154 from the extracted customer IDs and extracts, for each combination of action tags, a customer ID having all the action tags related to the combination as a first customer group. In the example of FIG. 21, for the combination of the action tag 1 and the action tag 2, the first customer group “200105, 200107...” In which the flag is set in both the action tag 1 and the action tag 2 is extracted. . Similarly, for the combination of the action tag 2 and the action tag 3, the first customer group “200108,...” In which flags are set in both the action tag 2 and the action tag 3 is extracted.

  FIG. 22 is an explanatory diagram showing a record layout of the result DB. The result DB 155 further includes a second customer group field. In the second customer group field, a corresponding customer ID is stored in association with the action tag. The CPU 11 selects an action tag and extracts all customer IDs (first customer group) related to combinations other than the selected action tag. The CPU 11 stores all the extracted customer IDs as second customer groups in association with the action tags in the result DB 155. Regarding the action tag 1, in the example of FIG. 21, the customer ID of “behavior tag 2, action tag 3”, the customer ID of “behavior tag 2, action tag 4”, and “behavior tag 3, action tag 4” All the customer IDs are extracted and stored in the result DB 155 as the second customer group.

  FIG. 23 is an explanatory diagram showing a display image of the second number of customers and the second customer group. In addition to the contents shown in FIG. 11, the CPU 11 displays a customer ID (second customer group) corresponding to the action tag on the display unit 14. The display example is an example and is not limited to this. The CPU 11 may refer to the contact DB 156 and display a corresponding mail address or the like instead of the customer ID or in addition to the customer ID.

  FIG. 24 is a flowchart showing a customer ID identification processing procedure. The CPU 11 accepts designation of the target DNA via the input unit 13 or the communication unit 16 (step S241). CPU11 extracts customer ID which does not have received DNA with reference to customer DB154 (step S242). CPU11 reads the action tag corresponding to DNA from DNA action tag DB152 (step S243). That is, CPU11 acquires the action tag corresponding to DNA which is the information of purchase history from customer DB154 which stored purchase history. The CPU 11 refers to the DNADB 151 and reads the minimum number of action tags corresponding to the target DNA (step S244). CPU11 calculates | requires a subtraction value by subtracting 1 from the minimum action tag number (step S245).

  CPU11 calculates | requires all the combinations of the action tag number corresponding to the number of subtraction values from the action tag read by step S243 (step S246). CPU11 extracts customer ID which has a combination of an action tag for every combination as a 1st customer group with reference to customer DB154 (step S247). For each action tag, the CPU 11 collects a combination of customer IDs other than the target action tag, and stores the collected customer ID as a second customer group in the result DB 155 (step S248).

  CPU11 outputs the customer ID which concerns on the 2nd customer number and 2nd customer group corresponding to an action tag for every action tag to the display part 14 (step S249). Note that the CPU 11 may output the second customer number and customer ID corresponding to the action tag to another computer (not shown) via the communication unit 16. The CPU 11 refers to the contact DB 156 and reads an email address corresponding to the customer ID related to the second customer group for each action tag (step S2410). Thereby, it becomes possible to specify the customer who should take the measure corresponding to the action tag. In addition, the measure can be transmitted to the customer's address, and the customer's DNA can be promoted.

  The sixth embodiment is as described above, and the other parts are the same as those of the first to fifth embodiments. Accordingly, the corresponding parts are denoted by the same reference numerals, and detailed description thereof is omitted.

Embodiment 7
FIG. 25 is a functional block diagram showing the operation of the computer 1 of the above-described form. When the CPU 11 executes the control program 15P, the computer 1 operates as follows. The acquisition unit 251 acquires purchase history information of a plurality of customers. The specifying unit 252 analyzes the acquired purchase histories of the plurality of customers, and specifies one or more purchase characteristics having a correlation among the plurality of purchase characteristics for each of the plurality of customers. In response to designation of a specific category, the customer specifying unit 253 correlates purchase characteristics satisfying a criterion selected as a candidate belonging to the specific category among a plurality of purchase characteristics associated with the specific category. Identify customers that are determined to be. The output unit 254 outputs the specified customer as a candidate belonging to the specific category.

  The seventh embodiment is as described above, and the others are the same as those of the first to sixth embodiments. Therefore, the corresponding parts are denoted by the same reference numerals, and detailed description thereof is omitted. The embodiments described above can be combined with each other.

  With respect to the embodiments including the above first to seventh embodiments, the following additional notes are further disclosed.

(Appendix 1)
Get customer purchase attributes,
For each customer, refer to the storage unit storing the attribute and the behavior information related to the attribute, and extract the behavior information of the customer who does not have the acquired attribute,
Count the number of first customers based on the combination of extracted behavior information,
A program for causing a computer to execute a process of calculating a second customer number for each extracted behavior information based on the counted first customer number of each combination.
(Appendix 2)
Get the reference value
The program according to attachment 1, wherein the number of first customers corresponding to a combination of the number of behavior information based on the acquired reference value is counted.
(Appendix 3)
Accepting a change in the reference value;
The program according to appendix 2, wherein the received reference value is stored in the storage unit.
(Appendix 4)
The program according to appendix 2 or 3, wherein the reference value is the number or ratio of behavior information related to an attribute.
(Appendix 5)
The program according to any one of appendices 1 to 4, wherein, for each piece of behavior information, the second number of customers is calculated based on a total value of the number of first customers corresponding to a combination of behavior information having a combination other than the behavior information.
(Appendix 6)
Select candidate action information based on the number of second customers,
The program according to any one of appendices 1 to 5, which outputs the selected candidate action information.
(Appendix 7)
The program according to any one of appendices 1 to 6, wherein the number of second customers is output for each action information.
(Appendix 8)
Refer to the storage unit, calculate the percentage of customers who have behavior information,
A reference value is calculated by multiplying the ratio by the number of second customers of the behavior information,
The program according to any one of supplementary notes 1 to 7, wherein a reference value calculated for each action information is output.
(Appendix 9)
Get customer purchase attributes,
For each customer, refer to the storage unit storing the attribute and the behavior information related to the attribute, and extract the behavior information of the customer who does not have the acquired attribute,
The first customer group is extracted based on the extracted action information combination,
The program which makes a computer perform the process which specifies a 2nd customer group based on the 1st customer group corresponding to the combination of action information which has combinations other than this action information for every action information.
(Appendix 10)
An acquisition unit for acquiring attributes related to customer purchases;
An extraction unit that refers to the storage unit storing the attribute and the behavior information related to the attribute for each customer, and extracts the behavior information of the customer who does not have the acquired attribute;
A counting unit for counting the number of first customers based on the combination of the extracted behavior information;
An information processing apparatus comprising: a calculation unit that calculates a second customer number for each extracted behavior information based on the counted first customer number of each combination.
(Appendix 11)
Get customer purchase attributes,
For each customer, refer to the storage unit storing the attribute and the behavior information related to the attribute, and extract the behavior information of the customer who does not have the acquired attribute,
Count the number of first customers based on the combination of extracted behavior information,
An information processing method for causing a computer to execute a process of calculating a second number of customers for each extracted behavior information based on a counted number of first customers of each combination.
(Appendix 12)
Count the number of first customers based on the combination of extracted behavior information,
The program according to appendix 9, which causes a computer to execute a process of calculating a second customer number based on a total value of the first customer number corresponding to a combination of behavior information having a combination other than the behavior information for each behavior information.
(Appendix 13)
The program according to appendix 9, which causes a computer to execute a process of outputting the second customer group specified for each action information and the calculated second customer number.
(Appendix 14)
Get purchase history information for multiple customers,
Analyzing the acquired purchase histories of the plurality of customers, respectively, and specifying one or more correlated purchase characteristics among the plurality of purchase characteristics for each of the plurality of customers,
A customer determined to have a correlation with a purchase characteristic that satisfies a criterion selected as a candidate belonging to the specific category among a plurality of purchase characteristics associated with the specific category in accordance with the designation of the specific category Identify
Outputting the identified customer as a candidate belonging to the specific category;
A program that causes a computer to execute processing.
(Appendix 15)
The reference can be set from outside.
The program according to appendix 14, characterized by:
(Appendix 16)
The criterion is a matching ratio or number of purchase characteristics,
The program according to appendix 14 or 15, characterized in that:
(Appendix 18)
For each customer, refer to the storage unit storing the category and the purchase characteristics related to the category, and extract the purchase characteristics of the customers who do not belong to the specified category,
The first customer group is extracted based on the combination of the extracted purchase characteristics,
The program according to any one of appendices 14 to 16, wherein, for each purchase characteristic, a second customer group is specified based on a first customer group corresponding to a combination of purchase characteristics having a combination other than the purchase characteristic.
(Appendix 19)
An acquisition unit for acquiring purchase history information of a plurality of customers;
Analyzing the acquired purchase histories of the plurality of customers, and specifying one or more correlated purchase characteristics among the plurality of purchase characteristics for each of the plurality of customers;
A customer determined to have a correlation with a purchase characteristic that satisfies a criterion selected as a candidate belonging to the specific category among a plurality of purchase characteristics associated with the specific category in accordance with the designation of the specific category A customer identification department that identifies
An output unit for outputting the identified customer as a candidate belonging to the specific category;
An information processing apparatus comprising:
(Appendix 20)
Get purchase history information for multiple customers,
Analyzing the acquired purchase histories of the plurality of customers, respectively, and specifying one or more correlated purchase characteristics among the plurality of purchase characteristics for each of the plurality of customers,
A customer determined to have a correlation with a purchase characteristic that satisfies a criterion selected as a candidate belonging to the specific category among a plurality of purchase characteristics associated with the specific category in accordance with the designation of the specific category Identify
Outputting the identified customer as a candidate belonging to the specific category;
An information processing method characterized by causing a computer to execute processing.

DESCRIPTION OF SYMBOLS 1 Computer 1A Portable recording medium 1B Semiconductor memory 10A Reading part 11 CPU
12 RAM
13 Input unit 14 Display unit 15 Storage unit 15P Control program 16 Communication unit 18 Clock unit 151 DNADB
152 DNA Action Tag DB
153 Action tag DB
154 Customer DB
155 Result DB
161 Acquisition Unit 162 Extraction Unit 163 Counting Unit 164 Calculation Unit 251 Acquisition Unit 252 Identification Unit 253 Customer Identification Unit 254 Output Unit N Communication Network

Claims (13)

Get customer purchase attributes,
For each customer, refer to the storage unit storing the attribute and the behavior information related to the attribute, and extract the behavior information of the customer who does not have the acquired attribute,
Count the number of first customers based on the combination of extracted behavior information,
A program for causing a computer to execute a process of calculating a second customer number for each extracted behavior information based on the counted first customer number of each combination. Get the reference value
The program according to claim 1, wherein the number of first customers corresponding to the number of combinations of behavior information based on the acquired reference value is counted. Accepting a change in the reference value;
The program according to claim 2, wherein the received reference value is stored in the storage unit. The program according to claim 2 or 3, wherein the reference value is the number or percentage of behavior information related to an attribute. Get customer purchase attributes,
For each customer, refer to the storage unit storing the attribute and the behavior information related to the attribute, and extract the behavior information of the customer who does not have the acquired attribute,
The first customer group is extracted based on the extracted action information combination,
The program which makes a computer perform the process which specifies a 2nd customer group based on the 1st customer group corresponding to the combination of action information which has combinations other than this action information for every action information. An acquisition unit for acquiring attributes related to customer purchases;
An extraction unit that refers to the storage unit storing the attribute and the behavior information related to the attribute for each customer, and extracts the behavior information of the customer who does not have the acquired attribute;
A counting unit for counting the number of first customers based on the combination of the extracted behavior information;
An information processing apparatus comprising: a calculation unit that calculates a second customer number for each extracted behavior information based on the counted first customer number of each combination. Get customer purchase attributes,
For each customer, refer to the storage unit storing the attribute and the behavior information related to the attribute, and extract the behavior information of the customer who does not have the acquired attribute,
Count the number of first customers based on the combination of extracted behavior information,
An information processing method for causing a computer to execute a process of calculating a second number of customers for each extracted behavior information based on a counted number of first customers of each combination. Get purchase history information for multiple customers,
Analyzing the acquired purchase histories of the plurality of customers, respectively, and specifying one or more correlated purchase characteristics among the plurality of purchase characteristics for each of the plurality of customers,
A customer determined to have a correlation with a purchase characteristic that satisfies a criterion selected as a candidate belonging to the specific category among a plurality of purchase characteristics associated with the specific category in accordance with the designation of the specific category Identify
Outputting the identified customer as a candidate belonging to the specific category;
A program that causes a computer to execute processing. The reference can be set from outside.
The program according to claim 8. The criterion is a matching ratio or number of purchase characteristics,
The program according to claim 8 or 9, characterized by the above. For each customer, refer to the storage unit storing the category and the purchase characteristics related to the category, and extract the purchase characteristics of the customers who do not belong to the specified category,
The first customer group is extracted based on the combination of the extracted purchase characteristics,
The program according to any one of claims 8 to 10, wherein, for each purchase characteristic, a second customer group is specified based on a first customer group corresponding to a combination of purchase characteristics having a combination other than the purchase characteristic. An acquisition unit for acquiring purchase history information of a plurality of customers;
Analyzing the acquired purchase histories of the plurality of customers, and specifying one or more correlated purchase characteristics among the plurality of purchase characteristics for each of the plurality of customers;
A customer determined to have a correlation with a purchase characteristic that satisfies a criterion selected as a candidate belonging to the specific category among a plurality of purchase characteristics associated with the specific category in accordance with the designation of the specific category A customer identification department that identifies
An output unit for outputting the identified customer as a candidate belonging to the specific category;
An information processing apparatus comprising: Get purchase history information for multiple customers,
Analyzing the acquired purchase histories of the plurality of customers, respectively, and specifying one or more correlated purchase characteristics among the plurality of purchase characteristics for each of the plurality of customers,
A customer determined to have a correlation with a purchase characteristic that satisfies a criterion selected as a candidate belonging to the specific category among a plurality of purchase characteristics associated with the specific category in accordance with the designation of the specific category Identify
Outputting the identified customer as a candidate belonging to the specific category;
An information processing method characterized by causing a computer to execute processing.

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