Information management device

Abstract

To provide an information management device for improving management of stores by increasing a next reservation rate of each employee in a service business such as beauty parlors.SOLUTION: An information management device 1 includes a visit database 121 for storing visit information, the information relating to visits of visitors, the visit information including reservation information for identifying whether or not the visitor has reserved a revisit and person-in-charge information for identifying a person taking charge of the visitor; an extraction part 112 configured to extract persons in charge (low-rated persons in charge) with low next reservation rates, the ratio of visitors reserving revisits among visitors whom the low-rated persons take charge of, from a visit database 121, based on reservation information and person-in-charge information; and a report request part 113 configured to request the low-rated persons in charge to submit reports.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information management device. [Background technology]

[0002] There are service industries in which a person in charge of a customer provides services to a customer who visits the store. Examples of such businesses include beauty salons and osteopathic clinics. In these types of businesses, it is important for management to increase the ratio of repeat customers who visit the store on an ongoing basis. Therefore, management efforts are being made to increase the next visit rate, which is the ratio of customers who make a reservation for their next visit when they visit the store.

[0003] However, as the number of customers and reservations increases, the effort required to grasp and increase the next visit rate increases. Therefore, there is a need for a means to support management efforts to increase the next visit rate.

[0004] Regarding a means for supporting management efforts to increase the rate of next visits, Patent Document 1 discloses an information processing device that includes a calculation unit that refers to a database that stores the number of employees and sales of a hair salon and calculates the productivity per employee of the hair salon based on the number of employees and sales, and a transmission unit that transmits analytical data including the productivity to a terminal of the hair salon to display the analytical data on the terminal, the database further stores at least one of the number of customers who have made a reservation for their next visit and the number of customers who have designated an employee of the hair salon, the calculation unit refers to the database and further calculates at least one of a next reservation rate, which is the rate at which customers have made a reservation for their next visit, and a nomination rate, which is the rate at which customers have designated an employee of the hair salon, and the transmission unit transmits the analytical data that further includes at least one of the next reservation rate and the nomination rate. The technology of Patent Document 1 can more accurately support the operation of a hair salon by providing the analysis data to the hair salon including the next reservation rate, which can be said to be one of the important guidelines for the operation of a hair salon. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2018-060407 A Summary of the Invention [Problem to be solved by the invention]

[0006] In order to increase the next appointment rate, it is important to improve the service of each employee. The technology of Patent Document 1 can calculate the next appointment rate for each hair salon and provide the hair salon with analysis data including the calculated next appointment rate. However, the technology of Patent Document 1 has room for further improvement in terms of grasping the next appointment rate for each employee and supporting each employee to increase the next appointment rate.

[0007] The present invention has been made in consideration of the above circumstances, and its purpose is to support an increase in the next reservation rate of individual employees in the service industry, such as beauty salons, and to improve store management. [Means for solving the problem]

[0008] As a result of intensive research into solving the above problem, the inventors discovered that the above object can be achieved by extracting low-rated staff members who have a low next reservation rate and requesting the extracted low-rated staff members to submit a report. The inventors then completed the present invention. Specifically, the present invention provides the following:

[0009] The present invention provides an information management device comprising: a store visit database in which store visit information is stored, the store visit information being information related to a visitor's visit, including reservation information capable of identifying whether the visitor has made a reservation for a return visit and staff member information capable of identifying the staff member who served the visitor; an extraction unit configured to extract staff members with a low next reservation rate (low-rated staff members) which is the ratio of visitors who have made a reservation for a return visit among the visitors they served, from the store visit database based on the reservation information and the staff member information; and a report request unit configured to request the low-rated staff members to submit a report.

[0010] In the present invention, the extraction unit extracts low-rated staff members and requests the extracted low-rated staff members to submit a report. As a result, the present invention can support low-rated staff members in reviewing and improving their own service provision procedures, etc. in the process of creating a report. Thus, the present invention can support low-rated staff members to increase their own next reservation rate. Furthermore, as a result, the present invention can support store managers, etc. to understand and review the service provision procedures, etc. of the low-rated staff members and the service provision procedures, etc. of employees working at the store based on the report. Thus, the present invention can support store managers, etc. to increase the store's next reservation rate and improve management through such review. Effect of the Invention

[0011] The present invention can support businesses in the service industry, such as beauty salons, in increasing the next reservation rate for individual employees and improving store management. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a block diagram showing an example of a preferred embodiment of the hardware configuration and software configuration of a system S of this embodiment. [Diagram 2] FIG. 2 is an example of the store visit information stored in the store visit database 121. As shown in FIG. [Diagram 3] FIG. 3 is an example of a report stored in the report database 122. [Figure 4]FIG. 4 is a main flowchart showing an example of a preferable flow of the information management process executed by the information management device 1 of this embodiment. [Diagram 5] FIG. 5 is a continuation of FIG. [Figure 6] FIG. 6 is a continuation of FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, an example of an embodiment of the present invention will be described in detail with reference to the drawings.

[0014] <System S> Fig. 1 is a block diagram showing an example of a preferred embodiment of the hardware configuration and software configuration of the system S of this embodiment. Hereinafter, an example of a preferred embodiment of the hardware configuration and software configuration of the system S of this embodiment will be described with reference to Fig. 1. The system S includes an information management device 1 and a terminal T capable of communicating with the information management device 1 via a network N.

[0015] [Next booking rate] The system S of this embodiment supports improving the next reservation rate of each employee and improving the management of a store in a service industry that provides services to customers, such as a beauty salon. In this embodiment, the "next reservation rate" refers to the ratio of customers who made a reservation to visit again. In other words, if the number of customers that a certain staff member served is C and the number of customers who made a reservation to visit again is R, the next reservation rate r is calculated as r = R / C. As shown in the above formula, in the calculation of the next reservation rate r in this embodiment, a customer who made a next reservation but canceled it is treated in principle the same as a customer who made a next reservation but did not cancel it. In this way, the information management device 1 prevents a customer who canceled due to the customer's convenience from being treated as a customer who did not become a repeat customer due to the lack of sales effort of the staff member. Therefore, the information management device 1 can appropriately evaluate the sales effort of the staff member.

[0016] By increasing the next reservation rate, the system S of this embodiment increases the proportion of customers who visit the store on an ongoing basis, known as repeat customers or regulars, and stabilizes sales. Furthermore, by increasing the next reservation rate, the system S of this embodiment enables managers to predict future sales. Thus, the system S of this embodiment improves store management by increasing the next reservation rate.

[0017] [Information management device 1] The information management device 1 includes a control unit 11, a storage unit 12, and a communication unit 13. The type of the information management device 1 is not particularly limited. Examples of the type include a server and a cloud server. The information management device 1 of this embodiment calculates the next reservation rate and supports store management improvement by various processes based on the calculation result.

[0018] [Control unit 11] The control unit 11 cooperates with the storage unit 12 and / or the communication unit 13 as necessary. The control unit 11 then realizes a receiving unit 111, an extracting unit 112, a report requesting unit 113, an information providing unit 114, an information updating unit 115, a generating unit 116, and the like, which are software components of the program of this embodiment executed by the information management device 1. The functions provided by each of the software components of the program of this embodiment will be shown in the description of a preferred flow of the information management process described later.

[0019] [Storage section 12] The storage unit 12 is a device in which data and / or files are stored, and includes a data storage unit such as a hard disk, a semiconductor memory, a recording medium, and a memory card.

[0020] The storage unit 12 may have a mechanism that enables connection via the network N to a storage device or storage system such as a NAS, a SAN, cloud storage, a file server, and / or a distributed file system.

[0021] The memory unit 12 stores a program executed by the microcomputer, a store visit database 121, a report database 122, a staff member improvement neural network 123, a store improvement neural network 124, and the like.

[0022] (Visit database 121) Store visit database 121 stores store visit information related to a visitor's visit. Store visit information includes at least reservation information and staff information. Reservation information is information that makes it possible to identify whether a store visitor has made a reservation to visit again. Staff information is information that makes it possible to identify the staff who served the store visitor.

[0023] In addition to reservation information and staff information, the store visit information may further include various information related to the visit, such as store information capable of identifying the store visited, information regarding the circumstances of the visit (the visitor's total number of visits, whether or not a reservation was made, the number of days since the last visit, etc.), information regarding the timing of the visit (date of visit, date and time of visit, etc.), information regarding the services provided to the visitor (name of service, waiting time, time the service was provided, amount charged, etc.), and information regarding the products provided to the visitor (product name, sales amount, sales quantity, etc.).

[0024] Since the store visit information includes information on the history of the store visit, the information management device 1 can analyze the next reservation rate in consideration of the history of the store visit and perform processing based on the analysis. As a result, the information management device 1 can extract low-rated agents with low next reservation rates based on various next reservation rates in consideration of the history of the store visit, such as the next reservation rate for customers who visit for the first time, the next reservation rate for customers who have visited a specific number of times such as the second time, the fifth time or more, the next reservation rate for customers who visit with / without a reservation, or the next reservation rate for customers whose number of days since their last visit is within a specified range.

[0025] Since the store visit information includes information on the timing of the visit, the information management device 1 can analyze the next reservation rate based on the timing and perform processing based on the analysis. This allows the information management device 1 to extract low-rated staff members, etc., based on various criteria such as seasons and time periods.

[0026] Since the store visit information includes information on the service provided to the customer, the information management device 1 can analyze the next reservation rate based on the information and perform processing based on the analysis. As a result, the information management device 1 can extract low-rated staff members, etc., based on various next reservation rates based on information on the service provided, such as the next reservation rate for customers who received a specific service (head spa, gray hair dye, etc.), the next reservation rate for customers whose waiting time is within a certain range, the next reservation rate for customers whose time of receiving the service is within a certain range, and the next reservation rate for customers whose billing amount is within a certain range.

[0027] Since the store visit information includes information on the product provided to the customer, the information management device 1 can analyze the next reservation rate based on the information and perform processing based on the analysis. As a result, the information management device 1 can extract low-rated staff members, etc., based on various next reservation rates based on information on the provided services, such as the next reservation rate for customers who purchased a specific product (such as shampoo and conditioner for customers with specific attributes) and the next reservation rate for customers whose sales amount / sales volume is within a certain range.

[0028] The reservation information may include information about reservations made at the time of visiting the store, as well as information about reservations made after visiting the store. This allows the information management device 1 to calculate the next reservation rate by regarding a customer who made a reservation before a given period (e.g., three days, one week, one month, etc.) after visiting the store as having made a reservation at the time of visiting the store. In order to make it possible to realize reservation management using the information management device 1 of this embodiment, it is preferable that the reservation information includes various information about reservations, exemplified by the reserved date and time, the reserved service, etc.

[0029] The staff member information may further include information that identifies the staff member who served the customer, as well as information that identifies whether the staff member was designated by the customer, and various information related to the staff member, exemplified by the staff member's job title, years of service, etc. This allows the information management device 1 to extract low-rated staff members, etc., based on various next reservation rates that take into account information about the staff member, such as the next reservation rate for designated / non-designated staff members, the next reservation rate for staff members with a specified job title, and the next reservation rate for staff members with years of service within a specified range.

[0030] FIG. 2 is an example of store visit information stored in the store visit database 121. Among the store visit information shown in FIG. 2, the store visit information related to the customer "Yamada Kozue" who visited the store "△△" store at the date and time "April 1, 2023 10:00" includes reservation information that the next visit is not reserved, and in charge information that the in charge "Miyamoto Masashi" with the job title "Senior Stylist" was in charge "without nomination", as well as various information such as the total number of visits was "1", the customer was a first-time customer, "no reservation", the service name "standard course" was provided, the service was provided for "1 hour" after the waiting time of "0.5 hour", and the billed amount was "8,000 yen".

[0031] In addition, among the store visit information shown in Figure 2, the store visit information related to customer "Torajiro Sasagi" who visited store "△△" at the date and time "April 1, 2023, 12:00" includes reservation information that the next visit was booked "three days after the visit" at the date and time "July 1, 2023, 10:00", and information on the person in charge that the person in charge "Masashi Miyamoto" with the job title "Senior Stylist" was in charge of the visit "with a request". In addition, it includes various information such as the customer is a regular customer with a total of "5 or more" visits, has a "reservation", the service name "full course" was provided, the service was provided for "1.5 hours" after "no" waiting time, the billed amount was "10,000 yen", "1" item of the product name "Damage Care Shampoo" was sold at the time of the visit, and the sales amount was "5,000 yen".

[0032] 2 is stored in the store visit database 121, the information management device 1 can calculate the next reservation rate for the person in charge "Miyamoto Masashi" and execute various processes based on the calculated next reservation rate. The information management device 1 can also calculate the next reservation rate for store visit information narrowed down by various information such as the date and time of the visit, the total number of visits, whether or not there was a request, the name of the service provided, the waiting time, the time of service provision, the billing amount, the product name and sales amount provided, and execute various processes based on the calculated next reservation rate. In this way, the information management device 1 can support the improvement of store management.

[0033] (Report Database 122) The report database 122 stores reports submitted by users of the terminal T. The users of the terminal T are, for example, low evaluation personnel, high evaluation personnel, managers of low evaluation stores, managers of high evaluation stores, other personnel and managers, etc.

[0034] FIG. 3 is an example of a report stored in the report database 122. The example shown in FIG. 3 includes a report related to a low-rated agent "Masashi Miyamoto" saying "We would like to inform you about the lack of next reservations. We sometimes forgot to talk to customers after the treatment, and were unable to secure a sufficient number of next reservations. We are very sorry.", a report related to a high-rated agent "Ichiro Suzuki" saying "We placed importance on communication with customers, and made efforts to encourage them to make next reservations while carefully listening to their requests. We also made efforts to create a relaxed atmosphere through small talk with customers and build trusting relationships. These efforts increased customer satisfaction and led to the securing of next reservations.", and a report related to a high-rated store "△△ store" saying "We thoroughly confirmed procedures in regular meetings and posted the procedures at the cash register, creating an environment in which all staff members can respond consistently. This made it possible to guide customers to next reservations without any omissions. We also focused on raising awareness and training for staff members, and improved their communication skills with customers.", etc.

[0035] This allows the information management device 1 to provide reports from low-rated staff to store managers, etc. Also, the information management device 1 can update improvement information by machine learning based on reports from high-rated staff. In addition, the information management device 1 can generate store improvement information that shows guidelines for improving the store's next reservation rate by using machine learning based on reports submitted by high-rated stores.

[0036] (Personnel Improvement Neural Network 123) The staff improvement neural network 123 is a neural network that generates improvement information that indicates a guideline for improving the next reservation rate for the staff. The staff improvement neural network 123 is not particularly limited as long as it generates the above-mentioned improvement information. The staff improvement neural network 123 is, for example, a neural language model related to a large-scale language model that has been trained and adjusted to specialize in natural language processing that generates text related to the above-mentioned improvement information.

[0037] "Natural language processing" allows a computer to understand text and voice data written in a natural language and execute processing according to the purpose. Specifically, examples include morphological analysis, which breaks down natural language into "morphemes," the smallest units that make up the language, and assigns information such as parts of speech; syntactic analysis, which clarifies the structure and meaning of a sentence by analyzing the grammatical structure of the natural language; semantic analysis, which understands the meaning of words and sentences by analyzing the meaning of natural language, and performs logical judgment and inference; contextual analysis, which understands natural language while considering the context before and after a sentence; and intent analysis, which extracts the intent of a speaker or writer from a conversation or sentence using a natural language. Thus, "natural language processing" processes natural language by combining processes such as morphological analysis, syntactic analysis, semantic analysis, contextual analysis, and intent analysis, and enables the generation of advice related to repairing a computer in this embodiment.

[0038] A "language model" is a type of probabilistic model used in natural language processing, and is a model for probabilistically predicting how likely a given word or sentence is to occur as natural language. Specifically, the language model predicts the next sentence by calculating the occurrence probability of a given sentence, etc., comparing the occurrence probability of multiple sentences, etc. In this way, the language model can automatically generate the most likely sentence based on the context related to the given sentence, etc. In particular, the language model of this embodiment is a large-scale language model that is trained on a large amount of text of at least 500 GB or more, exemplified by ChatGPT-3.5 and ChatGPT-4 by OpenAI, LLaMa by Meta AI, etc., and has a large number of parameters of at least 10 billion or more, so that it can generate natural response sentences.

[0039] It is preferable that the staff member improvement neural network 123 has previously undergone machine learning using materials related to staff member work procedures exemplified in a work manual, etc., as learning data. This allows the information management device 1 to generate guidelines for staff members to improve the next reservation rate based on words, sentences, etc., shown in the materials.

[0040] It is preferable that the staff member improvement neural network 123 has previously undergone machine learning using materials relating to staff member communication, exemplified by the contents of text communication made via the terminal T, as learning data. The text communication includes, for example, short messages transmitted and received via a mobile phone network, text chats transmitted and received via chat tools such as LINE (registered trademark), articles posted on social networking services (SNS) such as Facebook (registered trademark), and comments on the articles. This allows the information management device 1 to perform machine learning of staff member behavior relating to improving the next reservation rate from the materials relating to communication, and generate guidelines for the staff member to improve the next reservation rate based on words, sentences, etc. shown in the materials.

[0041] In addition, the staff improvement neural network 123 preferably performs machine learning in advance to generate a staff member's contribution to management based on the next reservation rate, using learning data that associates the staff member's next reservation rate with the staff member's contribution to management corresponding to the next reservation rate. This allows the information management device 1 to generate a staff member's contribution to management based on the next reservation rate (for example, contribution to sales, contribution to customer lifetime value (LTV), etc.). Since machine learning is performed on the next reservation rate, which is an index closely related to other management-related indexes such as future sales and LTV, the information management device 1 can generate improvement information that contributes to improving the management situation.

[0042] (Store Improvement Neural Network 124) The store improvement neural network 124 is a neural network that generates improvement information that indicates guidelines for improving the next reservation rate at the store. The store improvement neural network 124 is not particularly limited as long as it generates the above-mentioned improvement information. The store improvement neural network 124 is, for example, a neural language model related to a large-scale language model that has been trained and adjusted to specialize in natural language processing that generates text related to the above-mentioned improvement information.

[0043] The natural language processing and language model related to the store improvement neural network 124 may be similar to those related to the person in charge improvement neural network 123.

[0044] It is preferable that the store improvement neural network 124 has previously undergone machine learning using materials relating to store management improvement procedures exemplified by a manager's manual, etc., as learning data. This allows the information management device 1 to generate guidelines for improving the next reservation rate at the store based on words, sentences, etc., shown in the materials.

[0045] In addition, the store improvement neural network 124 preferably performs machine learning in advance for generating analysis results of the business situation based on the next reservation rate, using learning data that associates the next reservation rate for the entire store with the analysis results of the business situation corresponding to the next reservation rate. This allows the information management device 1 to generate analysis results of the business situation based on the next reservation rate (e.g., sales forecast, LTV, etc.).

[0046] In addition, the store improvement neural network 124 preferably performs machine learning in advance for generating advice for management based on the next reservation rate, using learning data that associates the next reservation rate for the entire store with advice for management corresponding to the next reservation rate. This allows the information management device 1 to generate advice for management based on the next reservation rate (for example, the timing of opening a new store). Since machine learning is performed for the next reservation rate, which is an index closely related to other indexes related to management such as future sales and LTV, the information management device 1 can generate improvement information that contributes to management.

[0047] [Communications Section 13] The communication unit 13 is not particularly limited as long as it connects the information management device 1 to the network N and enables communication with the terminal T, etc. Examples of the communication unit 13 include a wireless device compatible with a mobile phone network, a device connectable to a wireless LAN, and a network card compatible with the Ethernet standard.

[0048] [Network N] The type of the network N is not particularly limited as long as it enables communication between the information management device 1 and the terminal T, etc. The type of the network N is, for example, the Internet, a mobile phone network, a wireless LAN, etc.

[0049] [Terminal T] The terminal T is, for example, a personal computer, a laptop computer, a smartphone, a tablet terminal, etc. The terminal T can execute a process of providing information related to visits, information related to reservations, reports, information related to communication with customers, etc. to the information management device 1, a process of displaying the information provided by the information management device 1, etc.

[0050] [Main Flowchart of Information Management Processing] Fig. 4 is a main flow chart showing an example of a preferred flow of information management processing executed by the information management device 1 of this embodiment. Fig. 5 is a diagram continuing from Fig. 4. Fig. 6 is a diagram continuing from Fig. 5. The following is an example of a preferred flow of information management processing executed by the information management device 1 of this embodiment, using Figs. 4 to 6.

[0051] [Step S1: Determine whether store visit information has been received] The control unit 11 executes the receiving unit 111 in cooperation with the storage unit 12 and the communication unit 13. Then, the control unit 11 executes a process of determining whether the receiving unit 111 has received store visit information related to a customer's visit from the terminal T (step S1, store visit information receiving step). If it is determined that the information has been received, the control unit 11 shifts the process to step S2. If it is determined that the information has not been received, the control unit 11 shifts the process to step S3.

[0052] The means for receiving the store visit information is not particularly limited. For example, the means may be a means for receiving a message from a messenger application such as LINE (registered trademark), a means for receiving a message from an SNS such as Instagram (registered trademark), a means for receiving an input via a form on a web page, a means for receiving an email, etc. The store visit information to be determined in step S1 is not particularly limited as long as it includes reservation information that can identify whether the store visitor has made a reservation for a revisit or not and staff information that can identify the staff member who served the store visitor, and is the same as the store visit information described above in the store visit database 121 section.

[0053] [Step S2: Store in store visit database] The control unit 11 executes the receiving unit 111 in cooperation with the storage unit 12 and the communication unit 13. Then, the control unit 11 executes a process of storing the store visit information determined to have been received in step S1 by the receiving unit 111 in the store visit database 121 (step S2, store visit information storage step). The control unit 11 shifts the process to step S3.

[0054] [Step S3: Determine whether to extract] The control unit 11, in cooperation with the storage unit 12 and the communication unit 13, executes a process of determining whether to extract at least one of low-rated staff, high-rated staff, high-rated stores, and other processing targets from the store visit database 121 (step S3, extraction necessity determination step). If it is determined that extraction is to be performed, the control unit 11 shifts the process to step S4. If it is not determined that extraction is to be performed, the control unit 11 shifts the process to step S14.

[0055] The procedure of the process for determining whether to extract in the extraction necessity determination step is not particularly limited. For example, the procedure may be a procedure for determining that the date is to be extracted if it is a date set by the store manager, owner, etc. as the date for monthly reporting, or a procedure for determining that the number of customers who have been handled by any of the staff members satisfies a given condition (a cumulative total since the previous report is a certain number or more, a certain number or more in a certain period, less than a certain number in a certain period, etc.).

[0056] [Low Rating Author] If it is determined in step S3 that the low-rated staff member is to be extracted, the control unit 11 executes the processes from step S4 to step S6 related to the low-rated staff member. In this embodiment, the "low-rated staff member" refers to a staff member with a low next reservation rate among multiple staff members.

[0057] In this embodiment, the term "low next reservation rate" refers to, for example, a next reservation rate below a given threshold, or belonging to a group determined by a given ratio or number of people in descending order of next reservation rate when sorting staff members based on the next reservation rate. By determining a person whose next reservation rate is below a given threshold as a low-rated staff member, the information management device 1 can determine a low-rated staff member using the absolute criterion that the next reservation rate is below a given threshold. This reduces the amount of calculation processing involved in the extraction. By determining a person whose next reservation rate is below a given ratio or number of people in descending order of next reservation rate when sorting staff members based on the next reservation rate as a low-rated staff member, the information management device 1 can determine a low-rated staff member using a relative criterion. The relative criterion here includes, for example, a criterion that the next reservation rate of the staff member is lower than the average value of the next reservation rate of the store to which the staff member belongs calculated by the information management device 1, a criterion that the next reservation rate of the staff member is lower than the median value of the next reservation rate of the store to which the staff member belongs calculated by the information management device 1, and the like. This allows the information management device 1 to take measures such as having some of the multiple persons in charge who are more in need of improvement than the other persons provide a report.

[0058] [Step S4: Extract low-evaluation staff] The control unit 11 executes the extraction unit 112 in cooperation with the storage unit 12 and the communication unit 13. Then, the control unit 11 executes a process of extracting low-rated staff members from the store visit database 121 by the extraction unit 112 (step S4, low-rated staff member extraction step). The control unit 11 shifts the process to step S5.

[0059] The extraction in the extraction unit 112 may be based on the next reservation rate calculated for the visit information narrowed down by various conditions related to various information such as the date and time of the visit, the total number of visits, whether or not there was a request, the name of the service provided, the waiting time, the service provision time, the billing amount, the product name and sales amount provided, etc. In this way, the information management device 1 can extract low-rated staff members, etc. based on the next reservation rate under various conditions, and support business improvement related to the extracted low-rated staff members. In particular, by changing the criteria related to low-rated staff members, etc. between the next reservation rate related to new customers who have visited the store once in total and the next reservation rate related to repeat customers who have visited the store twice or more in total, the information management device 1 can extract low-rated staff members, etc. that are more in line with the actual business situation.

[0060] The next reservation rate in the extraction unit 112 may be calculated by regarding a visitor who has made a reservation for a re-visit within a given period (e.g., three months, six months, one year, etc.) from the visit as a person who has made a reservation for a re-visit. The next reservation rate in the extraction unit 112 may also be calculated by regarding a visitor who has promised to make a reservation for a re-visit as a person who has made a reservation for a re-visit. This allows the information management device 1 to flexibly determine whether or not a visitor is a repeater and extract low-rated staff members, etc., in accordance with the actual business situation. By treating a visitor who has made a reservation for a re-visit at a different time from the time of the visit in the same way as a visitor who has made a reservation for a re-visit at the time of the visit, the information management device 1 can flexibly determine whether or not a visitor is a repeater and extract low-rated staff members, etc., in accordance with the actual business situation. In addition, it is preferable that the next reservation rate in the extraction unit 112 treats a visitor who has made a reservation for the next visit but canceled it in the same way as a visitor who made a reservation for the next visit but did not cancel it.

[0061] [Step S5: Request low-rated person to submit a report] The control unit 11 executes the report request unit 113 in cooperation with the storage unit 12 and the communication unit 13. Then, the control unit 11 executes a process of requesting the low evaluation staff extracted in step S4 to submit a report by the report request unit 113 (step S5, low evaluation staff report request step). The control unit 11 moves the process to step S6.

[0062] The information management process preferably includes a process (step S6) of providing information (improvement information) related to improvement of the service provision procedure to the low evaluation staff. In this way, the information management device 1 can not only support the low evaluation staff to review and improve the service provision procedure etc. of the low evaluation staff in the process of creating a report, but also support the low evaluation staff to improve the service provision procedure based on the provided information.

[0063] [Step S6: Provide information to low-rated users] The control unit 11 executes the information providing unit 114 in cooperation with the storage unit 12 and the communication unit 13. Then, the control unit 11 executes a process of providing improvement information to the low evaluation person extracted in step S4 by the information providing unit 114 (step S6, low evaluation person information providing step). The control unit 11 moves the process to step S7.

[0064] The procedure for providing improvement information in the low evaluation agent information providing step is not particularly limited. The procedure may be, for example, a procedure for generating improvement information by processing using the agent improvement neural network 123 with a given prompt as input and providing the generated improvement information, a procedure for generating improvement information by processing using the agent improvement neural network 123 with a report submitted based on the request of step S5 as input and providing the generated improvement information, a procedure for generating improvement information by processing using the agent improvement neural network 123 with a calculated next reservation rate as input and providing the generated improvement information, etc. By providing improvement information using the agent improvement neural network 123, the information management device 1 can provide improvement information in natural language expression tailored to the low evaluation agent.

[0065] In particular, by providing improvement information through processing using the staff improvement neural network 123 that receives a submitted report as input, the information management device 1 can grasp the essential issues reported regardless of the linguistic expression used in the report and provide improvement information corresponding to the issues. This is because a large-scale language model that has been trained and adjusted to specialize in natural language processing that generates text related to improvement information can generate appropriate improvement information according to the meaning indicated by the text given as input, regardless of the linguistic expression of the text.

[0066] The step of providing information on low-rated staff preferably provides the results of salary assessment and / or personnel changes according to the next reservation rate or the transition of the next reservation rate. This allows the information management device 1 to support demotion, demotion, etc. according to the next reservation rate.

[0067] [Highly rated] If it is determined in step S3 that a highly rated staff member is to be extracted, the control unit 11 executes the process of steps S7 to S8 related to the highly rated staff member. In this embodiment, the "highly rated staff member" refers to a staff member with a high next reservation rate among multiple staff members. By executing this process, the information management device 1 can update the improvement information to better content based on the report from the highly rated staff member.

[0068] In this embodiment, "high next reservation rate" refers to, for example, a next reservation rate exceeding a given threshold, belonging to a group determined by a given ratio or number of people in the order of high next reservation rate when sorting staff members based on the next reservation rate, etc. By determining a person whose next reservation rate exceeds a given threshold as a highly evaluated staff member, the information management device 1 can determine a highly evaluated staff member using an absolute criterion that the next reservation rate exceeds a given threshold. The relative criterion here includes, for example, a criterion that the next reservation rate of the staff member is higher than the average value of the next reservation rate of the store to which the staff member belongs calculated by the information management device 1, a criterion that the next reservation rate of the staff member is higher than the median value of the next reservation rate of the store to which the staff member belongs calculated by the information management device 1, etc. This reduces the amount of calculation processing involved in the extraction. By determining a person who belongs to a group determined by a given ratio or number of people in the order of high next reservation rate when sorting staff members based on the next reservation rate as a highly evaluated staff member, the information management device 1 can determine a highly evaluated staff member using a relative criterion. This allows the information management device 1 to take measures such as having some of the multiple personnel who are more likely to obtain useful reports provide the reports than the other personnel.

[0069] [Step S7: Extract high-evaluation staff] The control unit 11 executes the extraction unit 112 in cooperation with the storage unit 12 and the communication unit 13. Then, the control unit 11 executes a process of extracting highly rated staff members from the store visit database 121 by the extraction unit 112 (step S7, highly rated staff member extraction step). The control unit 11 shifts the process to step S8.

[0070] [Step S8: Request high-evaluation staff to submit a report] The control unit 11 executes the report request unit 113 in cooperation with the storage unit 12 and the communication unit 13. Then, the control unit 11 executes a process of requesting the highly rated staff extracted in step S7 to submit a report by the report request unit 113 (step S8, highly rated staff report request step). The control unit 11 moves the process to step S9.

[0071] The highly evaluated staff report request step preferably provides the results of salary assessment and / or personnel decisions according to the next reservation rate or the transition of the next reservation rate. This allows the information management device 1 to support salary increases, promotions, etc. according to the next reservation rate.

[0072] [Low rated stores] If it is determined in step S3 that a low-rated store is to be extracted, the control unit 11 executes the processes from step S9 to step S11 for the low-rated store. In this embodiment, a "low-rated store" refers to a store with a low next reservation rate among multiple stores. "Low next reservation rate" is the same as for a low-rated staff member, except that it is related to a store instead of a staff member. Furthermore, relative criteria related to a low-rated store include, for example, a criterion that the next reservation rate of the store is lower than the average value of the next reservation rates of all stores calculated by the information management device 1, a criterion that the next reservation rate of the store is lower than the median value of the next reservation rates of all stores calculated by the information management device 1, and the like.

[0073] [Step S9: Extract low-rated stores] The control unit 11 cooperates with the storage unit 12 and the communication unit 13 to execute the extraction unit 112. Then, the control unit 11 executes a process of extracting low-rated stores from the store visit database 121 by the extraction unit 112 (step S9, low-rated store extraction step). The control unit 11 shifts the process to step S10.

[0074] [Step S10: Request low-rated stores to submit reports] The control unit 11 executes the report request unit 113 in cooperation with the storage unit 12 and the communication unit 13. Then, the control unit 11 executes a process of requesting the low-rated stores extracted in step S9 to submit reports by the report request unit 113 (step S10, low-rated store report request step). The control unit 11 shifts the process to step S11.

[0075] The information management process preferably includes a process of providing the low-rated store with improvement information related to the improvement of the service provision procedure (step S11). In this way, the information management device 1 can not only support the manager of the low-rated store to review and improve the service provision procedure in the process of creating a report, but also support the manager of the low-rated store to improve the service provision procedure based on the provided information.

[0076] [Step S11: Provide information to the store] The control unit 11 executes the generation unit 116 in cooperation with the storage unit 12 and the communication unit 13. Then, the control unit 11 executes a process of generating and providing improvement information to be provided to the low-rated stores extracted in step S9 by the generation unit 116 (step S11, low-rated store information providing step). The control unit 11 shifts the process to step S12.

[0077] The procedure for providing improvement information in the low-rated store information providing step is not particularly limited. The procedure may be, for example, a procedure for generating improvement information by processing using the store improvement neural network 124 with a given prompt as input and providing the generated improvement information, or a procedure for generating improvement information by processing using the store improvement neural network 124 with a report submitted based on the request in step S10 as input and providing the generated improvement information. By providing improvement information using the store improvement neural network 124, the information management device 1 can provide improvement information in natural language expression tailored to low-rated stores.

[0078] In particular, by providing improvement information through processing using the store improvement neural network 124 that receives submitted reports as input, the information management device 1 can grasp the essential issues reported regardless of the linguistic expression used in the report and provide improvement information corresponding to the issues. This is because a large-scale language model that has been trained and adjusted to specialize in natural language processing that generates text related to improvement information can generate appropriate improvement information according to the meaning indicated by the text given as input, regardless of the linguistic expression of the text.

[0079] [Highly rated store] If it is determined in step S3 that a highly rated store is to be extracted, the control unit 11 executes the processes of steps S12 to S13 relating to the highly rated store. In this embodiment, a "highly rated store" refers to a store with a high next reservation rate among multiple stores. "High next reservation rate" is the same as for a highly rated staff member, except that it relates to a store instead of a staff member. Furthermore, relative standards relating to a highly rated store include, for example, a standard that the next reservation rate of the store is higher than the average value of the next reservation rates of all stores calculated by the information management device 1, a standard that the next reservation rate of the store is higher than the median value of the next reservation rates of all stores calculated by the information management device 1, and the like.

[0080] [Step S12: Extract highly rated stores] The control unit 11 cooperates with the storage unit 12 and the communication unit 13 to execute the extraction unit 112. Then, the control unit 11 executes a process of extracting highly rated stores from the store visit database 121 by the extraction unit 112 (step S12, highly rated store extraction step). The control unit 11 shifts the process to step S13.

[0081] [Step S13: Request highly rated stores to submit reports] The control unit 11 executes the report request unit 113 in cooperation with the storage unit 12 and the communication unit 13. Then, the control unit 11 executes a process of requesting the highly rated store extracted in step S12 to submit a report by the report request unit 113 (step S13, highly rated store report request step). The control unit 11 shifts the process to step S14.

[0082] [Step S14: Determine whether a report has been received] The control unit 11 executes the receiving unit 111 in cooperation with the memory unit 12 and the communication unit 13. Then, the control unit 11 executes a process of determining whether the report requested in step S5, step S8, step S10, step S13, etc. has been received from the terminal T by the receiving unit 111 (step S14, report receiving step). If it is determined that the report has been received, the control unit 11 transfers the process to step S15. If it is determined that the report has not been received, the control unit 11 transfers the process to step S16. The means for receiving the report is not particularly limited, and may be the same as the means for receiving the store visit information.

[0083] [Step S15: Store in report database] The control unit 11 executes the receiving unit 111 in cooperation with the storage unit 12 and the communication unit 13. Then, the control unit 11 executes a process of storing the store visit information determined to have been received in step S14 by the receiving unit 111 in the report database 122 (step S15, report storage step). The control unit 11 shifts the process to step S16.

[0084] [Step S16: Determine whether to update the staff improvement neural network] The control unit 11, in cooperation with the storage unit 12 and the communication unit 13, executes a process of determining whether to update the staff member-improved neural network 123 (step S16, staff member-improved neural network update necessity determination step). If it is determined that the staff member-improved neural network should be updated, the control unit 11 shifts the process to step S17. If it is determined that the staff member-improved neural network should not be updated, the control unit 11 shifts the process to step S18.

[0085] The procedure of the process of determining whether to update in the step of determining whether to update the staff-improved neural network is not particularly limited. For example, the procedure may be a procedure of determining that the update should be performed if it is a day set as the update date by the store manager, owner, etc., or a procedure of determining that the update should be performed if the number of reports meets a given condition (the total number of reports since the previous update is a certain number or more, the number of reports in a certain period is a certain number or more, etc.).

[0086] [Step S17: Execute machine learning to improve personnel] The control unit 11 executes the information update unit 115 in cooperation with the storage unit 12 and the communication unit 13. Then, the control unit 11 executes a process of executing machine learning related to improving the person in charge by the information update unit 115 (step S17, information update step). The control unit 11 shifts the process to step S18.

[0087] The "machine learning" in the information update step includes, for example, a procedure for fine-tuning the agent improvement neural network 123 using reports from highly rated agents received in step S14 based on a request in step S8 as learning data, and a procedure for fine-tuning the agent improvement neural network 123 using reports from low rated agents received in step S14 based on a request in step S5 as learning data.

[0088] Since the machine learning in the information updating step includes a procedure for fine-tuning the agent improvement neural network 123 using reports from highly rated agents as learning data, the information management device 1 can update the improvement information based on useful reports from highly rated agents who have a track record related to the next reservation rate. Since the machine learning in the information updating step includes a procedure for fine-tuning the agent improvement neural network 123 using reports from low rated agents who have a track record related to the next reservation rate as learning data, the information management device 1 can update the improvement information using reports from low rated agents who have a negative track record related to the next reservation rate as a negative example.

[0089] [Step S18: Determine whether to update the store improvement neural network] The control unit 11, in cooperation with the storage unit 12 and the communication unit 13, executes a process of determining whether to update the store improvement neural network 124 (step S18, a step of determining whether to update the store improvement neural network). If it is determined that the store improvement neural network should be updated, the control unit 11 moves the process to step S19. If it is not determined that the store improvement neural network should be updated, the control unit 11 returns the process to step S1 and repeats the processes from step S1 to step S19.

[0090] The procedure of the process for determining whether to update in the step of determining whether to update the store improvement neural network is not particularly limited. For example, the procedure may be a procedure of determining that the update should be performed if it is a day set as the update date by the store manager, owner, etc., or a procedure of determining that the update should be performed if the number of reports meets a given condition (the total number of reports since the previous update is a certain number or more, the number of reports in a certain period is a certain number or more, etc.).

[0091] [Step S19: Execute machine learning related to store improvement] The control unit 11 executes the generation unit 116 in cooperation with the storage unit 12 and the communication unit 13. Then, the control unit 11 executes a process of executing machine learning related to store improvement by the generation unit 116 (step S19, generation unit machine learning step). The control unit 11 returns the process to step S1 and repeats the processes from step S1 to step S19.

[0092] The "machine learning" in the generation unit machine learning step includes, for example, a procedure of fine-tuning the store improvement neural network 124 using reports from highly rated stores received in step S14 based on a request in step S13 as learning data, and a procedure of fine-tuning the store improvement neural network 124 using reports from low rated stores received in step S14 based on a request in step S10 as learning data.

[0093] Since the machine learning in the information updating step includes a procedure for fine-tuning the store improvement neural network 124 using reports from highly rated stores as learning data, the information management device 1 can update the improvement information based on useful reports from highly rated stores that have a track record related to next reservation rates.Since the machine learning in the information updating step includes a procedure for fine-tuning the store improvement neural network 124 using reports from low rated stores that have a track record related to next reservation rates as learning data, the information management device 1 can update the improvement information using reports from low rated stores that have a negative track record related to next reservation rates as negative examples.

[0094] [Marketing Steps] The information management process preferably includes a marketing step of collecting information from various services such as messenger applications exemplified by LINE (registered trademark) and SNS exemplified by Instagram (registered trademark), or making various services publish information. This allows the information management device 1 to execute various procedures related to marketing, such as a procedure of requesting the submission of a report based on the collected information, a procedure of updating improvement information based on the collected information, and a procedure of publishing information on extracted highly rated managers, highly rated stores, etc.

[0095] [Advertisement Management Steps] The information management process preferably includes an advertisement management step of managing advertisements distributed, etc., via the network N. This enables the information management device 1 to perform advertisement management based on the next reservation rate.

[0096] [Next booking rate] The information management process preferably provides the terminal T with the next reservation rate calculated for a given period (for example, the month for which a salary assessment is conducted, the period for which a bonus assessment is conducted, or a fiscal year). This allows the information management device 1 to inform the person in charge of the status of their own work. In order to inform the person in charge of the status of their own work in more detail, it is preferable that the next reservation rate provided includes the average next reservation rate in the store to which the person in charge belongs, the ranking of the next reservation rate of the person in the store to which the person in charge belongs, etc.

[0097] [Reward Step] The information management process preferably includes a reward step of proposing a reward to be given to an employee with a high next reservation rate (highly evaluated employee). The criteria for determining a highly evaluated employee in the reward step may be the same as those in the "highly evaluated employee" section, and may be, for example, an employee with a high next reservation rate in each store, an employee with a high next reservation rate in all stores, etc. The reward in the reward step is not particularly limited as long as it is a reward (incentive) given for a high next reservation rate. The reward includes, for example, reflection in a salary assessment in the direction of increasing the salary, a salary increase, a promotion, etc. The reward step includes a procedure of automatically proposing a reward according to the next reservation rate based on machine learning using learning data with the next reservation rate as an explanatory variable and the reward as an objective variable or a given criterion. By including the reward step in the information management process, the information management device 1 provides employees with a motivation to further increase the next reservation rate. As a result, the information management device 1 can increase the next reservation rate of each employee in a service industry such as a beauty salon, and improve the management of the store.

[0098] [Penalty Step] The information management process preferably includes a penalty step that proposes a penalty to be given to an employee with a low next reservation rate (low-evaluation staff). The criteria for determining low-evaluation staff in the penalty step may be the same as those in the "low-evaluation staff" section, and may be, for example, staff with a low next reservation rate in each store, staff with a low next reservation rate in all stores, etc. The penalty in the penalty step is not particularly limited as long as it motivates the employee to improve the low next reservation rate. The penalty includes, for example, a reflection in the salary evaluation in the direction of reducing the salary, a salary cut, demotion, instructions for training away from work, etc. The penalty step includes a procedure for automatically proposing a penalty according to the next reservation rate based on machine learning using learning data with the next reservation rate as an explanatory variable and the penalty as an objective variable or a given criterion. By including the penalty step in the information management process, the information management device 1 motivates the staff to improve the next reservation rate. As a result, the information management device 1 can increase the next reservation rate of each employee in the service industry such as a beauty salon, and improve the management of the store.

[0099] [Correspondence learning step] The information management process preferably includes a response learning step in which the staff improvement neural network 123 is made to generate improvement information indicating a response to increase the next reservation rate by machine learning using learning data in which the next reservation rate of the staff is an explanatory variable and the response of the staff to the customer at the next visit, exemplified by the second visit, the third visit, the fourth visit, etc., is an objective variable. The staff improvement neural network 123 is realized, for example, by a large-scale language model such as ChatGPT described above. The "response" in the learning data in the response learning step includes a response (follow-up) taken by the staff against a specific behavior at the timing when the customer takes a specific behavior (also referred to as a flag being raised). This allows the information management device 1 to provide staff with improvement information (manuals) indicating a response to a specific behavior of the customer that will increase the next reservation rate. This allows the information management device 1 to increase the next reservation rate of each employee in the service industry, such as a beauty salon, and improve the management of the store.

[0100] [Communication learning steps] The information management process preferably includes a communication learning step in which the staff improvement neural network 123 is made to generate improvement information indicating communication that increases the next reservation rate by machine learning using learning data in which the staff's next reservation rate is used as an explanatory variable and the communication made by the staff via the terminal T (for example, a message sent to a customer using LINE (registered trademark)) is used as an objective variable. The staff improvement neural network 123 is realized, for example, by a large-scale language model such as ChatGPT described above. The "communication" in the learning data in the communication machine learning step includes message exchanges via chat apps such as LINE (registered trademark), information provision via SNS such as Instagram (registered trademark), and message exchanges via email. This allows the information management device 1 to provide staff with improvement information (manuals) indicating communication that further increases the next reservation rate. This allows the information management device 1 to increase the next reservation rate of each employee in the service industry such as a beauty salon, thereby improving the management of the store.

[0101] [Manual auto-generation step] The information management process preferably includes an automatic manual generation step in which the staff improvement neural network 123, which has undergone machine learning in the above-mentioned correspondence learning step and / or communication learning step, automatically generates a manual showing procedures for increasing the next reservation rate. This allows the information management device 1 to provide staff with a manual showing procedures for further increasing the next reservation rate. This allows the information management device 1 to increase the next reservation rate for each employee in the service industry, such as a beauty salon, and improve the management of the store.

[0102] [Average calculation step] The information management process includes an average calculation step of calculating the above-mentioned average of the next reservation rate. The average calculation step includes a procedure of calculating, for example, a store average, which is the average of the next reservation rates at each store, an all-store average, which is the average of the next reservation rates at all stores, etc. In this way, the information management device 1 can provide the calculated average when providing the next reservation rate to the person in charge. In this way, the information management device 1 can provide the person in charge with information to judge whether the person's next reservation rate is higher or lower than the average. In this way, the information management device 1 can increase the next reservation rate of each employee in the service industry, such as a beauty salon, and improve the management of the store.

[0103] [Contribution calculation step] The information management process includes a contribution calculation step of calculating the contribution of the person in charge to the management based on the next reservation rate of the person in charge. The contribution calculated in the contribution calculation step includes, for example, the contribution to sales and the contribution to LTV (customer lifetime value). The procedure for calculating the contribution in the contribution calculation step is not particularly limited. For example, the procedure includes a procedure for calculating the contribution using a neural network in which machine learning has been performed using learning data in which the next reservation rate is an explanatory variable and the contribution is an objective variable. In this way, the information management device 1 can provide the manager with materials for judging the degree to which the next reservation rate has contributed to the management. In addition, the information management device 1 can provide the person in charge with materials for judging the degree to which the next reservation rate has contributed to the management, and can motivate the person in charge to increase the next reservation rate by being aware of his or her own contribution. Therefore, the information management device 1 can increase the next reservation rate of each employee in the service industry such as a beauty salon, and improve the management of the store.

[0104] [Investment decision criteria generation step] The information management process includes a speculation judgment criterion generation step of generating an investment judgment criterion based on an index related to the next reservation rate. The index used as an input in the speculation judgment criterion generation step is not particularly limited as long as it is an index related to the next reservation rate, and includes, for example, various averages (for example, the average of the next reservation rate for a specific person in charge, the above-mentioned store average, the above-mentioned all-store average) collected over a given period such as one year. The investment judgment criterion generated in the speculation judgment criterion generation step includes, for example, a judgment criterion indicating whether or not to open a new store, a judgment criterion including a forecast of sales of a procedure, etc., a judgment criterion including a forecast of income and expenditure of a procedure, etc. This allows the information management device 1 to provide the manager with materials for judging whether or not to perform a speculative act in management. Therefore, the information management device 1 can increase the next reservation rate of each employee in the service industry such as a beauty salon, and improve the management of the store.

[0105] [Next booking rate notification step] The information management process preferably includes a next reservation rate notification step for automatically notifying the low-rated and / or high-rated staff of the next reservation rate. This allows the information management device 1 to provide the staff with the next reservation rate, which is a basis for judging the results of the staff's sales efforts. This allows the information management device 1 to increase the next reservation rate of each employee in the service industry, such as a beauty salon, and improve the management of the store.

[0106] [Calculation of next reservation rate] In this embodiment, the "next reservation rate" refers to the ratio of customers who have made a reservation to visit again. The "reserved customers" referred to here include customers who made a reservation at the time of visiting the store (those who made a reservation during the store visit) and customers who made a reservation by the date promised at the time of visiting the store (those who made a reservation within the advance notice period). In addition, even if a customer who made a reservation during the store visit and a customer who made a reservation within the advance notice period cancel the reservation after making it and did not actually visit the store, they will continue to be treated as customers who made a reservation during the store visit and customers who made a reservation within the advance notice period. In other words, the next reservation rate is the ratio of customers who visited the store next time among customers, and differs from the repeat rate which does not include customers who canceled due to the customer's convenience. In this way, the information management device 1 prevents customers who made a reservation but canceled due to the customer's convenience from being treated as customers who did not become repeat customers due to the lack of sales efforts of the person in charge. Therefore, the information management device 1 can appropriately evaluate the sales efforts of the person in charge. By increasing the next reservation rate, the information management device 1 can promote the conversion of customers into fans who become supporters of the person in charge and / or the store, increase the LTV of the store customers, and improve the management of the store.

[0107] [Effects of information management processing] The information management device 1 that executes the information management process of this embodiment can extract low-rated staff members using the extraction unit 112 and support the extraction of low-rated staff members by requesting the extracted low-rated staff members to submit a report. This allows the information management device 1 to support the low-rated staff members in reviewing and improving their own service provision procedures, etc. in the process of creating a report. Thus, the present invention can support the low-rated staff members to increase their own next reservation rate. This also allows the information management device 1 to support the store manager, etc. to understand and review the service provision procedures, etc. of the low-rated staff members and the service provision procedures, etc. of the employees working at the store based on the report. Thus, the present invention can support the store manager, etc. to increase the store's next reservation rate through the review.

[0108] Therefore, the information management device 1 that executes the information management process of this embodiment can support an individual employee in a service industry such as a beauty salon to increase the next reservation rate and improve the management of the store.

[0109] Furthermore, the information management device 1 that executes the information management process of this embodiment can provide the low-rated staff with improvement information indicating guidelines for improving the next reservation rate by the information providing unit 114. This allows the information management device 1 to support the manager of the low-rated store to improve the service provision procedure based on the improvement information. The information management device 1 that executes the information management process of this embodiment can update the above-mentioned improvement information based on reports from high-rated staff by the information updating unit 115. That is, the information management device 1 can update the improvement information by machine learning based on useful reports from high-rated staff who have a track record related to the next reservation rate, making it more useful information.

[0110] In addition, the information management device 1 that executes the information management process of this embodiment can extract low-rated and high-rated stores, not only for staff members but also for stores. The information management device 1 can then request the highly rated stores to submit reports. This allows the information management device 1 to acquire and provide reports from highly rated stores that are useful for improving the management of other stores. Furthermore, the information management device 1 can perform machine learning based on reports from highly rated stores, and provide information that is even more useful for improving the management of other stores.

[0111] <Usage example> The following is an example of how the information management device 1 is used.

[0112] [Registering store visit information] A beauty salon employee, "Miyamoto Masashi," performs a "standard course" treatment for a customer, "Yamada Kokozue," who comes to the salon without a reservation, and although he earns "8,000 yen," he is unable to make a next reservation. "Miyamoto Masashi" registers the visit information of the customer in the information management device 1 via terminal T. The information management device 1 receives the visit information transmitted from terminal T and stores it in the visit database 121.

[0113] "Miyamoto Masashi" provided a "full course" treatment to "Sasagi Torajiro" who came to the store as scheduled, and received a sales amount of "10,000 yen." He then obtained a promise from "Sasagi Torajiro" that he would make a reservation later. "Miyamoto Masashi" registered the visit information related to the visitor in the information management device 1 via terminal T. The information management device 1 received the visit information transmitted from terminal T and stored it in the visit database 121. "Sasagi Torajiro" made his next reservation three days later. "Miyamoto Masashi" registered the next reservation in the information management device 1 via terminal T as the next reservation related to the previously registered visit information. The information management device 1 received the next reservation information transmitted from terminal T and stored it in the visit database 121 in association with the previously stored visit information.

[0114] [Report from the highly rated person in charge] The information management device 1 extracted the highly rated staff member "Suzuki Ichiro" from the store visit database 121. The information management device 1 then notified the store manager that "Suzuki Ichiro" had a high next reservation rate, provided "Suzuki Ichiro" with the next reservation rate, and requested the submission of a report. "Suzuki Ichiro" sent a report to the information management device 1 via terminal T to the effect that he had made an effort to encourage the next reservation while placing emphasis on communication and to build a relationship of trust through small talk. The information management device 1 received the report sent from terminal T and stored it in the report database 122. The information management device 1 then performed machine learning based on the report, etc., and updated the improvement information.

[0115] [Reports from highly rated stores] The information management device 1 extracted the highly rated store "△△ store" from the store visit database 121. Then, the information management device 1 requested "△△ store" to submit a report. "△△ store" sent a report to the information management device 1 via terminal T to the effect that it had held regular meetings, posted procedures, raised awareness, and conducted training. The information management device 1 received the report sent from terminal T and stored it in the report database 122. Then, the information management device 1 performed machine learning based on the report, etc., and updated the improvement information.

[0116] [Improving the work of those with low ratings] The information management device 1 extracted the low-rated staff member "Miyamoto Masashi" from the store visit database 121. The information management device 1 then provided "Miyamoto Masashi" with the next reservation rate, notifying him that the next reservation rate was low, and requested him to submit a report. "Miyamoto Masashi" reflected on his own work attitude and realized that he had not been thorough in calling out to customers at the end of treatment. "Miyamoto Masashi" then transmitted a report to the information management device 1 via terminal T to the effect that he had not been thorough in calling out to customers at the end of treatment. The information management device 1 received the report transmitted from terminal T and stored it in the report database 122. The information management device 1 also provided "Miyamoto Masashi" with improvement information updated based on the report from "Suzuki Ichiro" and the like, to terminal T of "Miyamoto Masashi." "Miyamoto Masashi" made work improvements based on the improvement information, such as encouraging customers to make next reservations while placing emphasis on communication, and building trusting relationships through small talk.

[0117] [Improvement of operations at low-rated stores] The information management device 1 extracted low-rated stores from the store visit database 121. The information management device 1 then notified the low-rated store that the next reservation rate was low and requested the submission of a report. The information management device 1 received the report sent from the terminal T of the low-rated store and stored it in the report database 122. The information management device 1 also provided the terminal T of the low-rated store with improvement information updated based on the report from "Store △△". The manager of the low-rated store made operational improvements based on the improvement information by holding regular meetings, posting procedures, raising awareness, and providing training.

[0118] It should be noted that within the scope of the concept of the present invention, a person skilled in the art may come up with various modifications and alterations. It is understood that such modifications and alterations fall within the scope of the present invention. For example, even if a person skilled in the art appropriately adds, deletes, or modifies the design of the above-described embodiment, or adds, omits, or modifies the conditions of a process, these modifications and alterations are included in the scope of the present invention as long as they include the gist of the present invention. [Explanation of symbols]

[0119] S System 1 Information management device 11 Control section 111 Receiving unit 112 Extraction part 113 Report request section 114 Information Provision Department 115 Information Update Department 116 Generation part 12 Storage section 121 Visitor Database 122 Report Database 123 Personnel Improvement Neural Network 124 Store Improvement Neural Network 13. Communications Department N Network T-Terminal

Claims

1. a store visit database in which store visit information is stored, the store visit information including reservation information for identifying whether the store visitor has made a reservation for a return visit and staff member information for identifying the staff member who served the store visitor; An extraction unit configured to extract staff members with low next reservation rates (low-rated staff members) from the store visit database based on the reservation information and the staff member information, the staff member having a low next reservation rate, which is the ratio of customers who have made reservations to visit the store among the customers who the staff member has served, from the store visit database based on the reservation information and the staff member information; A report request unit configured to request the low evaluation person to submit a report; An information management device comprising:

2. The information management device according to claim 1 , further comprising an information providing unit configured to provide improvement information indicating a guideline for improving a next reservation rate to the person in charge of low ratings.

3. An information update unit that updates the improvement information, The extraction unit is configured to extract a highly rated staff member who has a high next reservation rate, which is a rate at which customers who have been in charge of the staff member make a reservation to visit the store again, The report request unit is configured to request the high evaluation person to submit a report; The information update unit is configured to update the improvement information by machine learning based on the report submitted by the high evaluator. The information management device according to claim 2 .

4. The store visit information further includes store information identifying the store where the customer visited, The extraction unit is configured to extract highly rated stores having a high store next reservation rate, which is a ratio of customers who make reservations to return to the store, based on the reservation information and the store information, The report request unit is configured to request the highly rated store to submit a report. The information management device according to claim 1 .

5. The information management device of claim 4, further comprising a generation unit configured to generate store improvement information that indicates guidelines for improving the store's next reservation rate using machine learning based on reports submitted by the highly rated stores.

Images