JP7000691B2 - Programs, information processing equipment and information processing methods - Google Patents

Description

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

Conventionally, information processing systems and the like capable of providing information useful for consumer targeting when delivering advertisements and the like are known (see, for example, Patent Documents 1 to 4).

International Publication No. 2016/136148 Japanese Patent No. 5913722 Japanese Patent No. 5995644 Japanese Patent No. 4728229

However, there is a problem that the conventional technology cannot efficiently market to customers.

In one aspect, the purpose is to provide programs that can be marketed efficiently.

In one plan, the attributes and standard values related to the customer's purchase are acquired, the attributes and the storage unit that stores the behavior information related to the attributes are referred to for each customer, and the behavior information of the customers who do not have the acquired attributes is extracted. Then, the number of first customers is counted based on the combination of behavior information obtained by subtracting 1 from the acquired reference value, and the number of second customers is calculated for each extracted behavior information based on the number of first customers of each combination counted. Let the computer perform the processing to be done.

On one side, it is possible to carry out marketing efficiently.

It is explanatory drawing which shows the outline 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 the DNA action tag DB. It is explanatory drawing which shows the record layout of the action tag DB. It is explanatory drawing which shows the record layout of a customer DB. It is explanatory drawing which shows the relationship between DNA and a behavior tag. It is explanatory drawing which shows the combination. It is explanatory drawing which shows the record layout of the 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 the change procedure. It is a flowchart which shows the output processing procedure of a candidate action. It is explanatory drawing which shows the image of the recommended information. It is a flowchart which shows the reference value calculation procedure. It is a functional block diagram which shows the operation of the computer of the above-mentioned form. It is a block diagram which shows the hardware group of the computer which concerns on Embodiment 5. It is explanatory drawing which shows the outline of the information processing system which concerns on Embodiment 6. It is a block diagram which shows the hardware group of the computer which concerns on Embodiment 6. It is explanatory drawing which shows the record layout of the contact DB. It is explanatory drawing which shows the 1st customer group. It is explanatory drawing which shows the record layout of the result DB. It is explanatory drawing which shows the display image of the 2nd customer number and the 2nd customer group. It is a flowchart which shows the specific processing procedure of a customer ID. It is a functional block diagram which shows the operation of the computer of the above-mentioned form.

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 device 1 connected to a communication network N such as the Internet, a LAN (Local Area Network), and a public line network. The information processing device 1 is a server computer, a personal computer, a tablet, a smartphone, or the like. Hereinafter, the information processing apparatus 1 will be referred to as a computer 1 and will be described.

Based on the result of analysis of purchasing information, the customer is given an attribute (category) such as "interested in fashion or limited items" or "reacts to a good deal". Further, when the action related to the attribute satisfies a predetermined condition, the attribute is given to the customer. In the example in FIG. 1, "start-up purchase", "lucky bag purchase", and "shopping at a trendy store" are listed as behaviors (purchasing characteristics). When the target customer performs all three of these actions, for example, the attribute "interested in fashion or limited items" is given. Customers who perform all three actions will have the attribute of "interested in fashion and limited items" as surrounded by the solid line in FIG.

On the other hand, as shown by the dotted line in FIG. 1, there is also a customer group who does not perform all of these actions. For example, a customer who performs the actions "start-up purchase" and "shopping at a trendy store" but does not perform the action "purchase a lucky bag" is given the attribute "interested in fashion and limited items". Not done. In this case, in order to increase the number of customers who have the attribute of "interested in fashionable or limited items", measures related to actions "start-up purchase", "lucky bag purchase", or "shopping at fashionable stores" Need to be done. In the embodiment, appropriate measures are determined by the method described below. In the example of FIG. 1, the measure corresponding to the action is displayed on the computer 1 as "a measure for purchasing a lucky bag is recommended". The details will be described below.

FIG. 2 is a block diagram showing a hardware group of the computer 1. The computer 1 includes a CPU (Central Processing Unit) 11 as a control unit, a RAM (Random Access Memory) 12, an input unit 13, a display unit 14, a storage unit 15, a clock unit 18, a communication unit 16, and the like. The CPU 11 is connected to each part of the hardware via the bus 17. The CPU 11 controls each hardware unit 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, an SRAM (Static RAM), a DRAM (Dynamic RAM), a flash memory, or the like. The RAM 12 also functions as a storage unit, and temporarily stores various data generated when various programs are executed by the CPU 11.

The input unit 13 is an input device such as a mouse, a keyboard, a touch panel, and a button, and outputs the 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 the instructions of the CPU 11. The communication unit 16 is a communication module, and transmits / receives information to / from another computer (not shown) or the like. The clock unit 18 outputs date and time information to the CPU 11. The storage unit 15 is a large-capacity memory, and stores the control program 15P, the attribute (hereinafter referred to as DNA) DB 151, and the like. The storage unit 15 also stores the DNA action tag DB 152, the action tag DB 153, the customer DB 154, the result DB 155, and the like. In the embodiment, an example of storing the DNADB 151 or the like 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 DNADB 151. The DNADB 151 includes a DNAID field, a DNA name field, a summary field, a minimum number of action tags field, and the like. The DNAID field stores unique identification information (hereinafter referred to as DNAID) for identifying 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 items", "reacts to a good deal (point)", and "reacts to a good deal (friends' association)" (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 have a strong tendency to shop at the beginning of the season or buy limited items such as lucky bags." In the minimum number of action tags field, the number of action tags (reference value) required for determining that there is DNA in association with the DNA ID is stored. For example, it is assumed that there are four action tags associated with the DNA "vulnerable to fashion and limited items": action tag 1, action tag 2, action tag 3, and action tag 4. Here, since the minimum number of action tags is stored as 3, the CPU 11 determines that the customer has at least three of the action tag 1, the action tag 2, the action tag 3, and the action tag 4. Judges to have DNA "vulnerable to fashion and limited editions". In the embodiment, the minimum number of action tags is stored, but if all (4 in the above example) are possessed, it may be determined that the DNA is possessed.

Similarly, for the DNA "reacting to a good deal (point)", the minimum number of action tags is stored as "2". It is assumed that there are four action tags associated with the DNA "reacting to a good deal (point)": action tag 1, action tag 3, action tag 4, and action tag 5. Here, since the minimum number of action tags is stored as 2, the CPU 11 determines that the customer has at least any two of the action tag 1, the action tag 3, the action tag 4, and the action tag 5. Is determined to have DNA "reacts to a good deal (point)". The DNA, action tag, and the like described in the embodiments are examples and are not limited thereto. Further, 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 the 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 identifying a mutually correlated action tag is stored in association with the DNA ID. For example, in the DNA ID "1", a behavior tag having a correlation such as a behavior tag ID "1", "2", ..., Etc. related to the DNA is stored.

FIG. 5 is an explanatory diagram showing the record layout of the action tag DB 153. The action tag DB 153 includes an action tag ID field, an action 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. Further, in the summary field, the summary of the action tag is stored in association with the action tag ID. For example, in the action tag name "start-up buying", it is stored as an outline that "the purchase price of clothes in February, August, and September exceeds a certain percentage". In addition, the action tag name "I have bought a lucky bag" is remembered as an outline "I have bought a lucky bag more than a certain number of times within a certain period of time". In this way, the action tag DB 153 stores the action information including the action ID, the action tag name, or the summary of the action.

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 the purchase histories of a plurality of customers. The customer DB 154 is downloaded in advance via the communication unit 16 or the like and stored in the storage unit 15. In the customer ID field, unique identification information (hereinafter referred to as a customer ID) for identifying 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 / absence of a flag is stored for each DNA ID. For the customer ID "200102", the flag is set in DNA1 and the flag is not set in DNA2.

In the action tag field, one or a plurality of flags indicating whether or not the customer has each action tag are 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", the action tag 1 is not set with the flag, and the action tag 2 is set with the flag. As for the presence / absence of DNA and the presence / absence of behavior tags of each customer shown in FIG. 6, various information is stored based on the result of the purchase information analysis performed in advance.

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

The CPU 11 refers to the DNA action tag DB 152 and reads out the action tag ID corresponding to the DNA ID. The CPU 11 obtains a combination of a number of action tags corresponding to the subtraction value for the plurality of action tag IDs read out.

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

Subsequently, the CPU 11 calculates the number of second customers for each action tag based on the number of first customers of each combination counted. Specifically, the CPU 11 calculates the number of second customers for each action tag based on the total value of the number of first customers corresponding to combinations other than the action tag. In the example of FIG. 8, if the behavior tag 1 is further present, the number of customers who will have the DNA is totaled. In this case, the CPU 11 totals 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 are set as the second customer number. calculate.

FIG. 9 is an explanatory diagram showing the 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 number of second customers calculated by the above 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 the behavior tag 1 is 28,900, and the number of customers who will have DNA1 if they have the behavior tag 4 is 50,000. can.

FIG. 10 is a flowchart showing a procedure for calculating the number of customers. The CPU 11 receives the target DNA via the input unit 13 or the communication unit 16 (step S101). The CPU 11 extracts the customer ID that does not have the received DNA with reference to the customer DB 154 (step S102). The CPU 11 reads the action tag corresponding to the DNA from the DNA action tag DB 152 (step S103). The CPU 11 refers to the DNA DB 151 and reads out the minimum number of action tags corresponding to the target DNA (step S104). The CPU 11 subtracts 1 from the minimum number of action tags to obtain a subtraction value (step S105).

The CPU 11 obtains all combinations of the number of action tags corresponding to the number of subtraction values from the action tags read in step S103 (step S106). The CPU 11 refers to the customer DB 154 and counts the number of first customers having a combination of action tags for each combination (step S107). The CPU 11 calculates the number of second customers for each action tag based on the total value of the number of first customers having a combination other than the target action tag (step S108).

The CPU 11 stores the number of second customers calculated in association with the action tag in the result DB 155 (step S109). The CPU 11 outputs the number of second customers corresponding to the action tag for each action tag to the display unit 14 (step S1010). 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 diagram showing a display image of the number of second customers. The CPU 11 outputs the calculated number of second customers to the display unit 14 together with the action tag. Further, the CPU 11 may also output a message indicating the relationship between the action tag and the number of second customers. For example, a message such as "If you take measures related to the displayed behavior tag, the number of customers who may have DNA1 (the number of second customers) is displayed." This makes it possible to understand which action tag should be taken. In addition, it is possible to compare and verify the effect on each action tag.

Embodiment 2
The second embodiment relates to a mode in which the minimum number of action tags is changed. FIG. 12 is a flowchart showing the change procedure. The CPU 11 receives the DNA ID from the input unit 13 (step S121). The CPU 11 receives the minimum number of action tags via the input unit 13 (step S122). The DNA ID and the minimum number of action tags may be received from another computer via the communication unit 16. The CPU 11 stores the minimum number of action tags 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 (matches) is used is shown, but the present invention is not limited to this. It may be changed to a number and used as a ratio (match ratio). For example, when the number of action tags related to DNA is 4, a numerical 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 base.

Since the second embodiment is as described above and the other parts are the same as those of the first embodiment, the corresponding reference numbers are assigned to the corresponding parts and detailed description thereof will be omitted.

Embodiment 3
The third embodiment relates to a form of selecting a candidate action. FIG. 13 is a flowchart showing an output processing procedure for a candidate action. The CPU 11 extracts the behavior tag and the number of second customers, which are the largest number among the number of second customers stored in step S109 (step S131). In the example of FIG. 9, action tags 4 and 50,000 people 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 DNA DB 151. The CPU 11 generates an explanatory text including the DNA name of the received DNA (step S132). Specifically, the CPU 11 combines the DNA name with the text sentence of the template stored in the storage unit 15. For example, as shown in Fig. 14, the following measures are effective in increasing the customer base that is "vulnerable to trends and limited items." ”, Etc. are generated.

The CPU 11 reads out the action tag name corresponding to the extracted action tag. The CPU 11 generates an explanatory text including an action tag name corresponding to the action tag (step S133). For example, as shown in FIG. 14, an explanatory note is generated that says "measures related to" shopping at a trendy store "". The CPU 11 generates an explanatory text including the number of second customers (step S134). For example, as shown in FIG. 14, an explanatory statement stating that "the number of customers whose increase is expected due to the measures is 50,000" 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.

Since the third embodiment is as described above and the other parts are the same as those of the first and second embodiments, the same reference numbers are assigned to the corresponding parts and detailed description thereof will be omitted.

Embodiment 4
The fourth embodiment relates to a mode in which a reference value is output. FIG. 15 is a flowchart showing a reference value calculation procedure. The action tag and the number of second customers stored in the result DB 155 in step S109 are read out (step S151). The CPU 11 refers to the customer DB 154 and calculates the ratio of customers having an action tag to the total number of customers (step S152). The CPU 11 calculates the 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). The CPU 11 determines whether or not the above-mentioned processing has been completed for the behavior tags related to all the DNAs (step S155).

When the CPU 11 determines that the processing has not been completed for all the action tags (NO in step S155), the CPU 11 shifts the processing to step S156. The CPU 11 reads out the unprocessed action tag and the number of second customers (step S156). After that, the CPU 11 shifts the process to step S152. By repeating the above processing, reference values for all the target action tags are derived.

When the CPU 11 determines that the processing for all the action tags has been completed (YES in step S155), the CPU 11 shifts the processing to step S157. The CPU 11 reads the reference values corresponding to each action tag from the storage unit 15, sorts them in descending order of the reference values, and outputs the reference values and the action tags to the display unit 14 (step S157). It should be noted that the reference values may be output to the display unit 14 in the order of the action tags without sorting. 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 measures related to action tags more accurately.

Since the fourth embodiment is as described above and the other parts are the same as those of the first to third embodiments, the same reference numbers are assigned to the corresponding parts and detailed description thereof will be omitted.

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

FIG. 17 is a block diagram showing a hardware group of the computer 1 according to the fifth embodiment. The program for operating the computer 1 is such that the reading unit 10A such as a disk drive or a memory card slot reads a portable recording medium 1A such as a CD-ROM, a DVD disk, a memory card, or a USB memory, and the storage unit 15 is used. You may remember it. Further, a semiconductor memory 1B such as a flash memory storing the program may be mounted in the computer 1. Further, the program can also 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 various software processes described above from the portable recording medium 1A or the semiconductor memory 1B, or downloads the program from another server computer (not shown) via the communication network N. do. The program is installed as a control program 15P, loaded into the RAM 12, and executed. As a result, it functions as the computer 1 described above.

Since the fifth embodiment is as described above and the other parts are the same as those of the first to fourth embodiments, the same reference numbers are assigned to the corresponding parts and detailed description thereof will be omitted.

Embodiment 6
The sixth embodiment relates to a form for specifying a customer group. In the first embodiment, the number of second customers corresponding to each action tag is calculated, but the sixth embodiment relates to a form for specifying the second customer. FIG. 18 is an explanatory diagram showing an outline of the information processing system according to the sixth embodiment. The computer 1 sets the number of second customers who may be given the attribute "interested in fashionable or limited items" by the action tags "start-up purchase", "lucky bag purchase", and "shopping at a trendy store". It was output 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, the customer ID “200105, 20016 ...” is displayed as the second customer group for which the action tag “purchase of lucky bag” should be taken. The details will be described below.

FIG. 19 is a block diagram showing a hardware group of the computer 1 according to the sixth embodiment. In addition to the first embodiment, the contact DB 156 is further provided. FIG. 20 is an explanatory diagram showing a record layout of the contact DB 156. The contact DB 156 includes a customer ID field, a name field, an email address field, an address field, and the like. In the name field, the customer's name is stored in association with the customer ID. In the e-mail address field, the customer's e-mail address is stored in association with the customer ID. In the address field, the address is stored in association with the customer ID. The contact DB 156 is referred to when approaching the specified customer, and may store a telephone number, a store membership number, and the like in addition to the above-mentioned data.

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. From the extracted customer IDs, the CPU 11 refers to the customer DB 154, and for each combination of action tags, extracts a customer ID having all the action tags related to the combination as the 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 flags are set for 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 the flags are set for both the action tag 2 and the action tag 3 is extracted.

FIG. 22 is an explanatory diagram showing the record layout of the result DB. The result DB 155 is further provided with a second customer group field. In the second customer group field, the 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 a second customer group in the result DB 155 in association with the action tag. 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 of 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 number of second customers and the second customer group. In addition to the contents shown in FIG. 11, the CPU 11 displays the 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 e-mail address or the like in place of the customer ID or in addition to the customer ID.

FIG. 24 is a flowchart showing a procedure for specifying a customer ID. The CPU 11 receives the designation of the target DNA via the input unit 13 or the communication unit 16 (step S241). The CPU 11 extracts the customer ID that does not have the received DNA with reference to the customer DB 154 (step S242). The CPU 11 reads the action tag corresponding to the DNA from the DNA action tag DB 152 (step S243). That is, the CPU 11 acquires an action tag corresponding to DNA, which is information on the purchase history, from the customer DB 154 that stores the purchase history. The CPU 11 refers to the DNA DB 151 and reads out the minimum number of action tags corresponding to the target DNA (step S244). The CPU 11 subtracts 1 from the minimum number of action tags to obtain a subtraction value (step S245).

The CPU 11 obtains all combinations of the number of action tags corresponding to the number of subtraction values from the action tags read in step S243 (step S246). The CPU 11 refers to the customer DB 154 and extracts the customer ID having the combination of action tags for each combination as the first customer group (step S247). The CPU 11 collects customer IDs of combinations other than the target action tag for each action tag, and stores the collected customer IDs as a second customer group in the result DB 155 (step S248).

The CPU 11 outputs the number of second customers corresponding to the action tag and the customer ID related to the second customer group for each action tag to the display unit 14 (step S249). The CPU 11 may output the number of second customers and the 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 out the e-mail address corresponding to the customer ID related to the second customer group for each action tag (step S2410). This makes it possible to identify customers who should take measures corresponding to the behavior tags. In addition, measures can be sent to the customer's address, and it becomes possible to promote the customer's DNA addition.

Since the sixth embodiment is as described above and the other parts are the same as those of the first to fifth embodiments, the same reference numbers are assigned to the corresponding parts and detailed description thereof will be omitted.

Embodiment 7
FIG. 25 is a functional block diagram showing the operation of the computer 1 in the above-described form. When the CPU 11 executes the control program 15P, the computer 1 operates as follows. The acquisition unit 251 acquires information on the purchase history of a plurality of customers. The specifying unit 252 analyzes the acquired purchase histories of the plurality of customers, and identifies one or a plurality of correlated purchasing characteristics among the plurality of purchasing characteristics for each of the plurality of customers. The customer identification unit 253 correlates with the purchase characteristics that satisfy the criteria selected as candidates belonging to the specific category among the plurality of purchase characteristics associated with the specific category according to the designation of the specific category. Identify the customers who are determined to be. The output unit 254 outputs the specified customer as a candidate belonging to the specific category.

Since the seventh embodiment is as described above and the other parts are the same as those of the first to sixth embodiments, the same reference numbers are assigned to the corresponding parts and detailed description thereof will be omitted. It should be noted that the above-described embodiments can be combined with each other.

Further, the following appendices will be disclosed with respect to the embodiments including the above embodiments 1 to 7.

(Appendix 1)
Gets the customer's purchasing attributes and
Refer to the storage unit that stores the attributes and the behavior information related to the attributes for each customer, and extract the behavior information of the customers who do not have the acquired attributes.
Count the number of first customers based on the combination of extracted behavior information,
A program that causes a computer to execute a process of calculating the number of second customers for each extracted behavior information based on the number of first customers of each combination counted.
(Appendix 2)
Get the reference value,
The program according to Appendix 1 that counts the number of first customers corresponding to the combination of the number of behavioral information based on the acquired reference value.
(Appendix 3)
Accepting changes to the reference value,
The program according to Appendix 2, which stores the received reference value in the storage unit.
(Appendix 4)
The program according to Appendix 2 or 3, wherein the reference value is the number or ratio of behavioral information related to the attribute.
(Appendix 5)
The program according to any one of Supplementary note 1 to 4, which calculates the number of second customers based on the total value of the number of first customers corresponding to the combination of behavior information having a combination other than the behavior information for each behavior information.
(Appendix 6)
Select candidate behavior information based on the number of second customers,
The program described in any one of Appendix 1 to 5 that outputs the selected candidate action information.
(Appendix 7)
The program described in any one of Supplementary notes 1 to 6 that outputs the number of second customers for each action information.
(Appendix 8)
With reference to the storage unit, the ratio of the number of customers who have behavior information is calculated.
The reference value is calculated by multiplying the ratio by the number of the second customers in the behavior information.
The program described in any one of the appendices 1 to 7 that outputs the reference value calculated for each action information.
(Appendix 9)
Gets the customer's purchasing attributes and
Refer to the storage unit that stores the attributes and the behavior information related to the attributes for each customer, and extract the behavior information of the customers who do not have the acquired attributes.
Extract the first customer group based on the combination of the extracted behavior information,
A program that causes a computer to execute a process for specifying a second customer group based on a first customer group corresponding to a combination of behavior information having a combination other than the behavior information for each behavior information.
(Appendix 10)
The acquisition department that acquires the attributes related to the customer's purchase,
An extraction unit that refers to the storage unit that stores attributes and behavior information related to the attributes for each customer, and extracts the behavior information of customers that do not have the acquired attributes.
A counting unit that counts the number of first customers based on the combination of extracted behavior information,
An information processing device including a calculation unit that calculates the number of second customers for each extracted behavior information based on the number of first customers of each combination counted.
(Appendix 11)
Gets the customer's purchasing attributes and
Refer to the storage unit that stores the attributes and the behavior information related to the attributes for each customer, and extract the behavior information of the customers who do not have the acquired attributes.
Count the number of first customers based on the combination of extracted behavior information,
An information processing method that causes a computer to execute a process of calculating the number of second customers for each extracted behavior information based on the number of first customers of each combination counted.
(Appendix 12)
Count the number of first customers based on the combination of extracted behavior information,
The program according to Appendix 9 for causing a computer to execute a process of calculating the number of second customers based on the total value of the number of first customers corresponding to the 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 a second customer group specified for each behavior information and a calculated number of second customers.
(Appendix 14)
Get the purchase history information of multiple customers and
The acquired purchase histories of the plurality of customers are analyzed, and one or a plurality of correlated purchasing characteristics among the plurality of purchasing characteristics are identified for each of the plurality of customers.
A customer who is determined to have a correlation with a purchase characteristic that meets the criteria selected as a candidate belonging to the specific category among a plurality of purchase characteristics associated with the specific category according to the designation of the specific category. Identify and
Output the specified customer as a candidate belonging to the specific category.
A program characterized by having a computer perform processing.
(Appendix 15)
The criteria can be set externally.
The program according to Appendix 14, wherein the program is characterized by the above.
(Appendix 16)
The criterion is the matching ratio or the number of matching of purchasing characteristics.
The program according to Appendix 14 or 15, characterized in that.
(Appendix 18)
Refer to the storage unit that stores the categories and purchasing characteristics related to the categories for each customer, and extract the purchasing characteristics of customers that do not belong to the specified category.
Extract the first customer group based on the combination of extracted purchasing characteristics,
The program according to any one of Supplementary note 14 to 16, which identifies a second customer group based on a first advisory group corresponding to a combination of purchasing characteristics having a combination other than the purchasing characteristics for each purchasing characteristic.
(Appendix 19)
An acquisition unit that acquires purchase history information for multiple customers,
A specific unit that analyzes the acquired purchase histories of the plurality of customers and identifies one or a plurality of correlated purchasing characteristics among the plurality of purchasing characteristics for each of the plurality of customers.
A customer who is determined to have a correlation with a purchase characteristic that meets the criteria selected as a candidate belonging to the specific category among a plurality of purchase characteristics associated with the specific category according to the designation of the specific category. With the customer identification department to identify
An output unit that outputs the specified customer as a candidate belonging to the specific category, and
Information processing device equipped with.
(Appendix 20)
Get the purchase history information of multiple customers and
The acquired purchase histories of the plurality of customers are analyzed, and one or a plurality of correlated purchasing characteristics among the plurality of purchasing characteristics are identified for each of the plurality of customers.
A customer who is determined to have a correlation with a purchase characteristic that meets the criteria selected as a candidate belonging to the specific category among a plurality of purchase characteristics associated with the specific category according to the designation of the specific category. Identify and
Output the specified customer as a candidate belonging to the specific category.
An information processing method characterized by having a computer execute processing.

1 Computer 1A Portable recording medium 1B Semiconductor memory 10A Reader 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 Behavior 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 Specific unit 253 Customer identification unit 254 Output unit N Communication network

Claims (6)

Obtain the attributes and reference values related to the customer's purchase,
Refer to the storage unit that stores the attributes and the behavior information related to the attributes for each customer, and extract the behavior information of the customers who do not have the acquired attributes.
The number of first customers is counted based on the combination of behavior information obtained by subtracting 1 from the acquired reference value.
A program that causes a computer to execute a process of calculating the number of second customers for each extracted behavior information based on the number of first customers of each combination counted. Accepting changes to the reference value,
The program according to claim 1, wherein the received reference value is stored in the storage unit. The program according to claim 1 or 2, wherein the reference value is a number or a ratio of behavioral information related to an attribute. Obtain the attributes and reference values related to the customer's purchase,
Refer to the storage unit that stores the attributes and the behavior information related to the attributes for each customer, and extract the behavior information of the customers who do not have the acquired attributes.
The first customer group is extracted based on the combination of behavior information obtained by subtracting 1 from the acquired reference value.
A program that causes a computer to execute a process for specifying a second customer group based on a first customer group corresponding to a combination of behavior information having a combination other than the behavior information for each behavior information. The acquisition unit that acquires the attributes and reference values related to the customer's purchase,
An extraction unit that refers to the storage unit that stores attributes and behavior information related to the attributes for each customer, and extracts the behavior information of customers that do not have the acquired attributes.
A counting unit that counts the number of first customers based on a combination of behavior information that is subtracted by 1 from the acquired reference value.
An information processing device including a calculation unit that calculates the number of second customers for each extracted behavior information based on the number of first customers of each combination counted. Obtain the attributes and reference values related to the customer's purchase,
Refer to the storage unit that stores the attributes and the behavior information related to the attributes for each customer, and extract the behavior information of the customers who do not have the acquired attributes.
The number of first customers is counted based on the combination of behavior information obtained by subtracting 1 from the acquired reference value.
An information processing method that causes a computer to execute a process of calculating the number of second customers for each extracted behavior information based on the number of first customers of each combination counted.

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