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 Art

[0002] There is a service industry in which, for customers who visit a store, a person in charge who is in charge of the customer provides services. Examples of such business formats include beauty salons, massage parlors, etc. In such business formats, it is important in management to increase the ratio of so-called repeat customers who continuously repeat visits to the store. Therefore, management efforts are being made to increase the return visit rate, which is the ratio of customers who made a reservation for the next visit when they visited the store.

[0003] However, as the number of visitors and reservations increases, the labor involved in grasping the return visit rate and increasing the return visit rate increases. Therefore, means for supporting management efforts to increase the return visit rate are required.

[0004] Regarding means for supporting management efforts to increase the return visit rate, Patent Document 1 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 analysis data including the productivity to a terminal of the hair salon to display the analysis data on the terminal, wherein the database further stores at least one of the number of customers who reserved the next use and the number of customers who named an employee of the hair salon. The calculation unit further calculates at least one of the next reservation rate, which is the ratio of customers who reserved the next use, and the naming rate, which is the ratio of customers who named an employee of the hair salon, by referring to the database. The transmission unit discloses an information processing apparatus that transmits the analysis data further including at least one of the next reservation rate and the naming rate. The technology of Patent Document 1 can more accurately support the operation of the hair salon by including the next reservation rate, which can be said to be one of the important guidelines for the operation of the hair salon, in the analysis data and providing it to the hair salon.

Prior Art Documents

Patent Document

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] By the way, in order to increase the next reservation rate, it is important to improve the services of individual employees. The technology of Patent Document 1 can calculate the next reservation rate for each hair salon and provide the hair salon with analysis data including the calculated next reservation rate and the like. However, the technology of Patent Document 1 has room for further improvement in grasping the next reservation rate of individual employees and supporting them to increase the next reservation rate of individual employees.

[0007] The present invention has been made in view of such circumstances, and its purpose is to support improving the management of a store by increasing the next reservation rate of individual employees in the service industry such as beauty salons.

Means for Solving the Problems

[0008] As a result of intensive studies to solve the above problems, the present inventors have found that the above object can be achieved by extracting low-evaluation persons who are persons in charge with a low next reservation rate and requesting the extracted low-evaluation persons to submit reports. And the present inventors have completed the present invention. Specifically, the present invention provides the following.

[0009] The present invention provides an information management apparatus including: a visit database that stores visit information regarding visitors, the visit information including reservation information that can identify whether the visitor has reserved a return visit and staff information that can identify a staff member in charge of the visitor; an extraction unit configured to extract from the visit database a staff member (low - evaluation staff member) with a low next - reservation rate, which is the ratio of visitors who have reserved a return visit among the visitors in charge; and a report request unit configured to request the low - evaluation staff member to submit a report.

[0010] In the present invention, the extraction unit extracts a low - evaluation staff member and requests the extracted low - evaluation staff member to submit a report. As a result, in the process of the low - evaluation staff member creating a report, the present invention can assist the staff member in reviewing and improving their service - providing procedures and the like, thereby assisting the low - evaluation staff member in increasing their own next - reservation rate. Further, thereby, the present invention can assist the store manager or the like in grasping and reviewing the service - providing procedures of the low - evaluation staff member and the service - providing procedures of employees working in the store based on the report, and thus can assist the store manager or the like in increasing the next - reservation rate of the store and improving the management through the review.

Advantages of the Invention

[0011] The present invention can assist in increasing the next - reservation rate of individual employees and improving the management of a store in the service industry such as a beauty salon.

Brief Description of the Drawings

[0012]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Mode for Carrying Out the Invention

[0013] The following is a detailed description of an example of an embodiment of the present invention with reference to the drawings.

[0014] <System S> FIG. 1 is a block diagram showing an example of a preferable aspect of the hardware configuration and software configuration in the system S of the present embodiment. Hereinafter, an example of a preferable aspect of the hardware configuration and software configuration in the system S of the present embodiment will be described with reference to FIG. 1. The system S includes an information management apparatus 1 and a terminal T that can communicate with the information management apparatus 1 via a network N.

[0015] 〔Next Reservation Rate〕 The system S of the present embodiment supports improving the management of a store by increasing the next reservation rate of each employee in a service industry that provides services to customers, such as a beauty salon. The "next reservation rate" in the present embodiment refers to the ratio of customers who have reserved a return visit among the customers who have visited the store. That is, when the number of customers visited by a certain person in charge is C and the number of customers who have reserved a return visit among the customers visited by the person in charge is R, the next reservation rate r is obtained by r = R / C. As shown in the above formula, in calculating the next reservation rate r in the present embodiment, a customer who has made a next reservation but has canceled is generally treated in the same manner as a customer who has made a next reservation and has not canceled. Thereby, the information management apparatus 1 prevents a customer who has canceled due to the convenience of the customer from being treated as a customer who has not become a repeater due to insufficient sales efforts of the person in charge. Therefore, the information management apparatus 1 can appropriately evaluate the sales efforts of the person in charge.

[0016] The system S of this embodiment increases the proportion of customers who continuously visit the store, such as repeaters and regular customers, by increasing the next reservation rate, and stabilizes the sales amount. Also, the system S of this embodiment enables managers and the like to predict future sales by increasing the next reservation rate. Therefore, the system S of this embodiment improves the management of the store 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, a communication unit 13, and the like. The type of the information management device 1 is not particularly limited. Examples of the type include a server, a cloud server, and the like. The information management device 1 of this embodiment calculates the next reservation rate and supports the improvement of store management through 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. And the control unit 11 realizes a reception unit 111, an extraction unit 112, a report request unit 113, an information providing unit 114, an information updating unit 115, a generation 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 are shown in the description of the preferred flow of the information management process described later.

[0019] [Storage Unit 12] The storage unit 12 is a device in which data and / or files are stored, and has a data storage unit using a hard disk, a semiconductor memory, a recording medium, a memory card, and the like.

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

[0021] The memory unit 12 stores programs executed by a microcomputer, a visit database 121, a report database 122, a person-in-charge improvement neural network 123, a store improvement neural network 124, and the like.

[0022] (Visit Database 121) The visit database 121 stores visit information related to the visits of visitors. The visit information includes at least reservation information and person-in-charge information. The reservation information is information that can identify whether the visitor has reserved a return visit. The person-in-charge information is information that can identify the person-in-charge who was in charge of the visitor.

[0023] In addition to the reservation information and the person-in-charge information, the visit information may further include various types of information related to the visit, such as store information that can identify the store visited, information related to the background of the visit (such as the number of times the visitor has visited, whether there is a reservation, the number of days elapsed since the previous visit, etc.), information related to the timing of the visit (such as the visit date, visit time, etc.), information related to the services provided to the visitor (such as service name, waiting time, time when the service was received, amount charged, etc.), and information related to the products provided to the visitor (such as product name, sales amount, number of sales, etc.).

[0024] By including information related to the background of the visit in the visit information, the information management device 1 can analyze the next reservation rate based on the background of the visit and perform processing based on the analysis. Thereby, the information management device 1 can extract, for example, low-evaluation person-in-charges, etc., based on various next reservation rates based on the background of the visit, such as the next reservation rate for visitors who visited for the first time, the next reservation rate for visitors who have visited a specific number of times such as the second time or the fifth time or more, the next reservation rate for visitors who visited with / without a reservation, or the next reservation rate for visitors whose number of days elapsed since the previous visit is within a predetermined range.

[0025] By including information related to the timing of the visit in the visit information, the information management device 1 can analyze the next reservation rate based on the timing and perform processing based on the analysis. Thereby, the information management device 1 can extract low-evaluation person-in-charges, etc., based on various criteria according to seasons, time zones, etc.

[0026] By including information related to the services provided to the visitors in the store visit information, the information management device 1 can analyze the next reservation rate based on this information and perform processing based on the analysis. As a result, the information management device 1 can, for example, extract low-evaluation responsible persons, etc., based on various next reservation rates based on information related to the provided services, such as the next reservation rate of visitors who received a specific service (such as a head spa, hair dyeing, etc.), the next reservation rate of visitors with a waiting time within a certain range, the next reservation rate of visitors whose service receiving time is within a certain range, and the next reservation rate of visitors with a billing amount within a certain range.

[0027] By including information related to the products provided to the visitors in the store visit information, the information management device 1 can analyze the next reservation rate based on this information and perform processing based on the analysis. As a result, the information management device 1 can, for example, extract low-evaluation responsible persons, etc., based on various next reservation rates based on information related to the provided services, such as the next reservation rate of visitors who purchased a specific product (such as shampoo and conditioner for customers with specific attributes), and the next reservation rate of visitors with a sales amount / sales volume within a certain range.

[0028] The reservation information may include information related to the reservations made after the store visit in addition to the information related to the reservations made when visiting the store. As a result, the information management device 1 can, for example, calculate the next reservation rate by regarding the visitors who made a reservation before a given period (for example, 3 days, 1 week, 1 month, etc.) has passed after the store visit as those who made a reservation when visiting the store. To enable reservation management using the information management device 1 of the present embodiment, it is preferable that the reservation information includes various information related to the reservation, exemplified by the reserved date and time, the reserved service, etc.

[0029] The person-in-charge information may further include information that can identify the person-in-charge who was in charge of the customer who visited the store, information that can identify whether the person-in-charge was named by the customer who visited the store, and various information related to the person-in-charge exemplified by the position and years of continuous service of the person-in-charge. Thereby, the information management device 1 can extract low-evaluated person-in-charges, etc., based on various next reservation rates based on information related to the person-in-charge, such as the next reservation rate for a named person-in-charge / unnamed person-in-charge, the next reservation rate for a person-in-charge in a predetermined position, and the next reservation rate for a person-in-charge with years of continuous service within a predetermined range.

[0030] Figure 2 is an example of the store visit information stored in the store visit database 121. Among the store visit information shown in Figure 2, the store visit information related to the customer "Kozue Yamada" who visited the store "△△" at the time "10:00 on April 1, 2023" includes reservation information indicating that the next store visit is not reserved, person-in-charge information indicating that the person-in-charge "Shoji Miyamoto" with the position name "Senior Stylist" was in charge "without naming", and that the customer is a first-time customer with a total of "1" store visit, "no reservation", the service name "Standard Course" was provided, the service was provided for "1 hour" after a waiting time of "0.5 hour", and the billing amount was "8,000 yen", and various other information.

[0031] In addition, among the store visit information shown in Figure 2, the store visit information related to the customer "Kojiro Sasaki" who visited the store "△△" at the time "12:00 on April 1, 2023" includes reservation information indicating that the next store visit was reserved at the time "10:00 on July 1, 2023", three days after the store visit, person-in-charge information indicating that the person-in-charge "Shoji Miyamoto" with the position name "Senior Stylist" was in charge "with naming", and that the customer is a regular customer with a total of "5 or more" store visits, "with reservation", the service name "Full Course" was provided, the service was provided for "1.5 hours" after no waiting time, the billing amount was "10,000 yen", and at the time of the store visit, "1" item of the product name "Damage Care Shampoo" was sold, and its sales amount was "5,000 yen", and various other information.

[0032] When the store visit information shown in FIG. 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, "Masashi Miyamoto", and execute various processes based on the calculated next reservation rate. In addition, the information management device 1 can calculate the next reservation rate for the store visit information narrowed down by various information such as the date and time related to the store visit, the total number of store visits, the presence or absence of a name request, the name of the service provided, the waiting time, the service providing time, the amount billed, the name of the product provided and the sales amount, etc., and execute various processes based on the calculated next reservation rate. Thereby, the information management device 1 can assist in improving the management of the store.

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

[0034] FIG. 3 is an example of a report stored in the report database 122. In the example shown in FIG. 3, there are reports related to the low evaluation in-charge person "Masashi Miyamoto": "I would like to report the shortage of the next reservation. There were sometimes oversights in communicating with customers at the end of the treatment, and I was unable to secure a sufficient number of next reservations. I am very sorry.", reports related to the high evaluation in-charge person "Ichiro Suzuki": "We emphasized communication with customers, carefully listened to the customers' requests, and made efforts to encourage the next reservation. Also, we created a relaxed atmosphere through small talk with customers and repeatedly made efforts to build a trust relationship. These efforts increased the customers' satisfaction and led to securing the next reservation.", and reports related to the high evaluation store "△△ Store": "We thoroughly checked the procedures at the regular meeting and further posted the procedures at the cash register to create an environment where all staff can respond consistently. As a result, it became possible to provide customers with information about the next reservation without omission. Also, we put effort into raising the awareness and training of the staff and improved the communication ability with customers." etc. are stored.

[0035] As a result, the information management device 1 can provide reports from low-evaluated personnel to store managers and the like. In addition, the information management device 1 can update improvement information through machine learning based on reports from high-evaluated personnel. Furthermore, the information management device 1 can generate store improvement information indicating guidelines for improving the store's next reservation rate by using machine learning based on reports submitted from high-evaluated stores.

[0036] (Responsible Person Improvement Neural Network 123) The Responsible Person Improvement Neural Network 123 is a neural network that generates improvement information indicating guidelines for improving the next reservation rate for a responsible person. The Responsible Person Improvement Neural Network 123 is not particularly limited as long as it generates the above-described improvement information. The Responsible Person Improvement Neural Network 123 is, for example, a neural language model related to a large-scale language model that has been learned and adjusted to specialize in natural language processing for generating text related to the above-described improvement information.

[0037] "Natural language processing" enables a computer to understand text and voice data written in natural language and execute processing according to the purpose. Specifically, morphological analysis that assigns information such as part of speech by decomposing into "morphemes", which are the smallest units that make up natural language, syntactic analysis that clarifies the structure and meaning of a sentence by analyzing the grammatical structure of natural language, semantic analysis that understands the meaning of words and sentences by analyzing the meaning of natural language and performs logical judgment and inference, context analysis that understands natural language while considering the context before and after a sentence, intention analysis that extracts the intention of the speaker or writer from conversations and texts using natural language, etc. are exemplified. As a result, "natural language processing" processes natural language by combining processes such as morphological analysis, syntactic analysis, semantic analysis, context analysis, and intention analysis, enabling the generation of advice related to the repair of the electronic computer in this embodiment.

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

[0039] It is preferable that the operator improvement neural network 123 has been previously subjected to machine learning using, as learning data, materials related to the work procedures of the operator exemplified by work manuals and the like. Thereby, the information management device 1 can generate guidelines for the operator to improve the next reservation rate based on words, texts, etc. shown in the materials.

[0040] It is preferable that the operator improvement neural network 123 has been previously subjected to machine learning using, as learning data, materials related to the communication of the operator exemplified by the content of text communication performed via the terminal T. The text communication includes, for example, short messages transmitted and received via a mobile phone network, text chats transmitted and received via a chat tool such as LINE (registered trademark), articles posted on a social networking service (SNS) such as Facebook (registered trademark), and comments on the articles. Thereby, the information management device 1 can machine-learn the actions of the operator related to the improvement of the next reservation rate from the materials related to communication, and generate guidelines for the operator to improve the next reservation rate based on words, texts, etc. shown in the materials.

[0041] In addition, it is preferable that the person-in-charge improvement neural network 123 has previously performed machine learning related to generating the contribution of the person-in-charge to management based on the next reservation rate, using learning data that associates the next reservation rate of the person-in-charge with the contribution of the person-in-charge to management corresponding to the next reservation rate. Thereby, the information management device 1 can generate the contribution of the person-in-charge to management (for example, contribution to sales, contribution to customer lifetime value (LTV), etc.) based on the next reservation rate. In order to perform machine learning related to the next reservation rate, which is an indicator closely related to other indicators related to management 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 indicating guidelines for improving the next reservation rate in the store. The store improvement neural network 124 is not particularly limited as long as it generates the above-described improvement information. The store improvement neural network 124 is, for example, a neural language model related to a large language model that has been learned and adjusted to specialize in natural language processing for generating the text related to the above-described improvement information.

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

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

[0045] In addition, it is preferable that the store improvement neural network 124 has previously performed machine learning related to the generation of an analysis result of the business situation based on the next reservation rate, using learning data in which the next reservation rate across the store is associated with the analysis result of the business situation corresponding to the next reservation rate. Thereby, the information management device 1 can generate an analysis result of the business situation (for example, sales prediction, LTV, etc.) based on the next reservation rate.

[0046] In addition, it is preferable that the store improvement neural network 124 has previously performed machine learning related to the generation of advice for management based on the next reservation rate, using learning data in which the next reservation rate across the store is associated with the advice for management corresponding to the next reservation rate. Thereby, the information management device 1 can generate advice for management (for example, the timing of opening a new store, etc.) based on the next reservation rate. Since machine learning related to the next reservation rate, which is an indicator closely related to other indicators related to management such as future sales and LTV, is performed, the information management device 1 can generate improvement information that contributes to management.

[0047] [Communication unit 13] The communication unit 13 is not particularly limited as long as it can connect the information management device 1 to the network N and enable communication with the terminal T and the like. 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 and the like. Examples of the type of the network N include 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, or the like. The terminal T can execute processes such as providing information related to store visits, reservation information, reports, and information related to communication with visitors to the information management device 1, and displaying information provided from the information management device 1.

[0050] 〔Main flowchart of information management process〕 FIG. 4 is a main flowchart showing an example of a preferred flow of the information management process executed by the information management device 1 of the present embodiment. FIG. 5 is a figure following FIG. 4. FIG. 6 is a figure following FIG. 5. The following is an example of a preferred flow of the information management process executed by the information management device 1 of the present embodiment using FIGS. 4 to 6.

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

[0052] The means for receiving store visit information is not particularly limited. The means may be, for example, means for receiving a message from a messenger application exemplified by LINE (registered trademark), means for receiving a message from an SNS exemplified by Instagram (registered trademark), means for receiving an input via a form provided on a web page, means for receiving an email, or the like. The store visit information to be determined in step S1 is not particularly limited as long as it can identify whether the visitor has reserved a return visit and can identify the person in charge who was in charge of the visitor, and is the same as the store visit information described above in the item of the store visit database 121.

[0053] [Step S2: Store in the store visit database] The control unit 11, in cooperation with the storage unit 12 and the communication unit 13, executes the reception unit 111. Then, the control unit 11 executes a process of storing the store visit information determined to be received in step S1 in the store visit database 121 by the reception unit 111 (step S2, store visit information storage step). The control unit 11 moves 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 the low-evaluation personnel, high-evaluation personnel, high-evaluation stores, and other processing targets from the store visit database 121 (step S3, extraction necessity determination step). If it is determined to extract, the control unit 11 moves the process to step S4. If it is not determined to extract, the control unit 11 moves the process to step S14.

[0055] The procedure of the process of determining whether to extract in the extraction necessity determination step is not particularly limited. For example, the procedure may be a procedure of determining to extract if it is a date determined by the store manager, operator, etc. as the monthly report date, or for any person in charge, a procedure of determining to extract when the number of store visitors in charge of the person in charge satisfies a given condition (the cumulative number 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-evaluation personnel] When it is determined in step S3 that the low-evaluation personnel are the extraction targets, the control unit 11 executes the processes from step S4 to step S6 related to the low-evaluation personnel. The "low-evaluation personnel" in the present embodiment refers to the personnel with a low next reservation rate among a plurality of personnel in charge.

[0057] In this embodiment, "low next reservation rate" means, for example, that the next reservation rate is below a given threshold, or belongs to a group determined by a given ratio or number of people from the lower next reservation rate when the persons in charge are rearranged based on the next reservation rate. By setting those with a next reservation rate below the given threshold as the persons in charge with low evaluation, the information management device 1 can determine the persons in charge with low evaluation using an absolute criterion that the next reservation rate is below the given threshold. Thereby, the computational processing amount related to extraction is reduced. By setting those who belong to a group determined by a given ratio or number of people from the lower next reservation rate when the persons in charge are rearranged based on the next reservation rate as the persons in charge with low evaluation, the information management device 1 can determine the persons in charge with low evaluation using a relative criterion. The relative criterion mentioned here includes, for example, a criterion that the next reservation rate of the person in charge is lower than the average value of the next reservation rates in the store to which the person in charge belongs calculated in the information management device 1, a criterion that the next reservation rate of the person in charge is lower than the median value of the next reservation rates in the store to which the person in charge belongs calculated in the information management device 1, and the like. Thereby, the information management device 1 can take measures such as providing reports to a part of the plurality of persons in charge who have a higher need for improvement than other persons in charge.

[0058] [Step S4: Extract Persons in Charge with Low Evaluation] 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 persons in charge with low evaluation from the store visit database 121 by the extraction unit 112 (Step S4, Persons in Charge with Low Evaluation Extraction Step). The control unit 11 moves the process to Step S5.

[0059] The extraction in the extraction unit 112 may be extraction based on the next reservation rate calculated for the store visit information narrowed down by various conditions related to various information such as the date and time related to the store visit, the total number of store visits, the presence or absence of a named person, the name of the service provided, the waiting time, the service provision time, the amount charged, the name of the product provided, and the sales amount. Thereby, the information management device 1 can extract low-evaluated persons, etc. based on the next reservation rate under various conditions, and support the improvement of the work related to the extracted low-evaluated persons. In particular, by changing the criteria for low-evaluated persons, etc. between the next reservation rate for first-time visitors with a total of 1 store visit and the next reservation rate for repeat customers with a total of 2 or more store visits, the information management device 1 can extract low-evaluated persons, 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 store visitors who made a reservation for a return visit within a given period (e.g., 3 months, half a year, 1 year, etc.) from the store visit as reservation makers who made a reservation for a return visit. Also, the next reservation rate in the extraction unit 112 may be calculated by regarding store visitors who promised to make a reservation for a return visit as reservation makers who made a reservation for a return visit. Thereby, the information management device 1 can flexibly determine whether a customer is a repeat customer and extract low-evaluated persons, etc. that are in line with the actual business situation. By treating store visitors who made a reservation for a return visit at a timing different from the time of the store visit in the same way as store visitors who made a reservation for a return visit at the time of the store visit, the information management device 1 can flexibly determine whether a customer is a repeat customer and extract low-evaluated persons, etc. that are in line with the actual business situation. In addition, it is preferable that the next reservation rate in the extraction unit 112 treats store visitors who made a next reservation but canceled it in the same way as store visitors who made a next reservation and did not cancel it.

[0061] [Step S5: Request the submission of a report to the low-evaluated person] The control unit 11, in cooperation with the storage unit 12 and the communication unit 13, executes the report request unit 113. Then, the control unit 11 executes, by the report request unit 113, a process of requesting the submission of a report to the low-evaluated person extracted in step S4 (step S5, low-evaluated person 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 related to improvement of service provision procedures (improvement information) to the low - evaluation responsible person. Thereby, the information management apparatus 1 not only supports the low - evaluation responsible person in reviewing and improving the service provision procedures and the like of the low - evaluation responsible person during the process of creating a report, but also can support the improvement of the service provision procedures based on the information provided by the low - evaluation responsible person.

[0063] [Step S6: Provide information to the low - evaluation responsible person] The control unit 11, in cooperation with the storage unit 12 and the communication unit 13, executes the information providing unit 114. Then, the control unit 11 executes, through the information providing unit 114, a process of providing improvement information to the low - evaluation responsible person extracted in step S4 (step S6, low - evaluation responsible 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 responsible person information providing step is not particularly limited. The procedure may be, for example, a procedure of generating improvement information by a process using the responsible - person improvement neural network 123 with a given prompt as an input and providing the generated improvement information, a procedure of generating improvement information by a process using the responsible - person improvement neural network 123 with the report submitted based on the request in step S5 as an input and providing the generated improvement information, a procedure of generating improvement information by a process using the responsible - person improvement neural network 123 with the calculated next reservation rate as an input and providing the generated improvement information, and the like. By providing improvement information using the responsible - person improvement neural network 123, the information management apparatus 1 can provide improvement information with a natural language expression adapted to the low - evaluation responsible person.

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

[0066] The step of providing low-evaluation person-in-charge information preferably provides the result of salary assessment and / or the result of personnel matters according to the next reservation rate or the change in the next reservation rate. Thereby, the information management apparatus 1 can support salary reduction, demotion, etc. according to the next reservation rate.

[0067] [High-evaluation person-in-charge] When it is determined in step S3 that the high-evaluation person-in-charge is the extraction target, the control unit 11 executes the processing from step S7 to step S8 related to the high-evaluation person-in-charge. The "high-evaluation person-in-charge" in the present embodiment refers to a person-in-charge with a high next reservation rate among a plurality of persons-in-charge. By executing the processing, the information management apparatus 1 can update the improvement information to more excellent content based on the report from the high-evaluation person-in-charge.

[0068] In this embodiment, "having a high next reservation rate" means, for example, that the next reservation rate exceeds a given threshold, or belongs to a group determined by a given ratio or number of people from the highest next reservation rate when rearranging the persons in charge based on the next reservation rate, and so on. By designating those with a next reservation rate exceeding the given threshold as high-evaluation persons in charge, the information management apparatus 1 can determine high-evaluation persons in charge using an absolute criterion that the next reservation rate exceeds the given threshold. The relative criterion mentioned here includes, for example, a criterion that the next reservation rate of the person in charge is higher than the average value of the next reservation rates in the store to which the person in charge belongs, which is calculated in the information management apparatus 1, and a criterion that the next reservation rate of the person in charge is higher than the median value of the next reservation rates in the store to which the person in charge belongs, which is calculated in the information management apparatus 1, and so on. Thereby, the amount of calculation processing related to extraction is reduced. By designating as high-evaluation persons in charge those who belong to a group determined by a given ratio or number of people from the highest next reservation rate when rearranging the persons in charge based on the next reservation rate, the information management apparatus 1 can determine high-evaluation persons in charge using a relative criterion. Thereby, the information management apparatus 1 can take measures such as providing reports to a part of a plurality of persons in charge who are more likely to obtain useful reports than other persons in charge.

[0069] [Step S7: Extract high-evaluation persons in charge] 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, by the extraction unit 112, a process of extracting high-evaluation persons in charge from the store visit database 121 (step S7, high-evaluation person in charge extraction step). The control unit 11 moves the process to step S8.

[0070] [Step S8: Request submission of a report to high-evaluation persons in charge] The control unit 11 cooperates with the storage unit 12 and the communication unit 13 to execute the report request unit 113. Then, the control unit 11 executes, by the report request unit 113, a process of requesting submission of a report to the high-evaluation persons in charge extracted in step S7 (step S8, high-evaluation person in charge report request step). The control unit 11 moves the process to step S9.

[0071] The step of requiring a report from high - evaluation personnel preferably provides the results of salary reviews and / or personnel decisions according to the next reservation rate or the trend of the next reservation rate. Thereby, the information management device 1 can support salary increases, promotions, etc. according to the next reservation rate.

[0072] [Low - evaluation store] When it is determined in step S3 that the low - evaluation store is the extraction target, the control unit 11 executes the processes from step S9 to step S11 related to the low - evaluation store. The "low - evaluation store" in the present embodiment refers to a store with a low next reservation rate among a plurality of stores. "Having a low next reservation rate" is the same as that of the low - evaluation personnel, except that it is related to the store instead of the person in charge. Also, the relative criteria for the low - evaluation store include, for example, the criterion that the next reservation rate of the store is lower than the average value of the next reservation rates of all stores calculated in the information management device 1, the criterion that the next reservation rate of the store is lower than the median value of the next reservation rates of all stores calculated in the information management device 1, and the like.

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

[0074] [Step S10: Require submission of a report to the low - evaluation store] The control unit 11 collaborates with the storage unit 12 and the communication unit 13 to execute the report request unit 113. Then, the control unit 11 executes the process of requiring the submission of a report to the low - evaluation store extracted in step S9 by the report request unit 113 (step S10, low - evaluation store report request step). The control unit 11 moves the process to step S11.

[0075] The information management process preferably includes a process of providing improvement information related to the improvement of service provision procedures to low-evaluation stores (step S11). Thereby, the information management apparatus 1 not only supports the administrator of the low-evaluation store or the like to review and improve the service provision procedures in the low-evaluation store during the process of creating a report, but also can support the improvement of the service provision procedures based on the information provided by the administrator of the low-evaluation store or the like.

[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 the improvement information to be provided to the low-evaluation store extracted in step S9 by the generation unit 116 (step S11, low-evaluation store information provision step). The control unit 11 moves the process to step S12.

[0077] The procedure for providing improvement information in the low-evaluation store information provision step is not particularly limited. For example, the procedure may be to generate improvement information by a process using the store improvement neural network 124 that takes a given prompt as input and provide the generated improvement information, or to generate improvement information by a process using the store improvement neural network 124 that takes the report submitted based on the request in step S10 as input and provide the generated improvement information. By providing improvement information using the store improvement neural network 124, the information management apparatus 1 can provide improvement information with a natural language expression adapted to the low-evaluation store.

[0078] In particular, by providing improvement information by a process using the store improvement neural network 124 that takes the submitted report as input, the information management apparatus 1 can capture the essential issues reported regardless of the language expression used in the report and provide improvement information corresponding to the issues. This is because a large language model that has been trained and adjusted to specialize in natural language processing for generating text related to improvement information can generate appropriate improvement information according to the meaning indicated by the text regardless of the language expression of the text given as input.

[0079] [High - evaluation store] When it is determined in step S3 that the high - evaluation store is the extraction target, the control unit 11 executes the processes from step S12 to step S13 related to the high - evaluation store. The "high - evaluation store" in the present embodiment refers to a store with a high next - reservation rate among a plurality of stores. "Having a high next - reservation rate" is the same as that of the high - evaluation person in charge, except that it is related to the store instead of the person in charge. Also, the relative criteria for the high - evaluation store include, for example, the criterion that the next - reservation rate of the store is higher than the average value of the next - reservation rates of all stores calculated in the information management device 1, the criterion that the next - reservation rate of the store is higher than the median value of the next - reservation rates of all stores calculated in the information management device 1, and the like.

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

[0081] [Step S13: Request submission of a report to the high - evaluation store] The control unit 11 collaborates with the storage unit 12 and the communication unit 13 to execute the report - request unit 113. Then, the control unit 11 executes the process of requesting the submission of a report to the high - evaluation store extracted in step S12 by the report - request unit 113 (step S13, high - evaluation store report - request step). The control unit 11 moves 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 storage unit 12 and the communication unit 13. Then, the control unit 11 executes a process of determining whether the reports requested in step S5, step S8, step S10, step S13, etc. are received from the terminal T by the receiving unit 111 (step S14, report reception step). If it is determined that the reports are received, the control unit 11 moves the process to step S15. If it is determined that the reports are not received, the control unit 11 moves the process to step S16. The means for receiving the reports is not particularly limited and may be the same as the means for receiving the store visit information.

[0083] [Step S15: Store in the 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 be received in step S14 in the report database 122 by the receiving unit 111 (step S15, report storage step). The control unit 11 moves the process to step S16.

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

[0085] The procedure for determining whether to update in the person-in-charge improvement neural network update necessity determination step is not particularly limited. The procedure may be, for example, a procedure for determining to update if it is a date determined by the store manager, operator, etc. as the update date, a procedure for determining to update when the number of reports satisfies a given condition (the cumulative number of reports since the previous update is a certain number or more, the number of reports within a certain period is a certain number or more, etc.).

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

[0087] The "machine learning" in the information update step is, for example, a procedure for fine-tuning the person-in-charge improvement neural network 123 using, as learning data, the reports received from highly evaluated persons in charge in step S14 based on the request in step S8, a procedure for fine-tuning the person-in-charge improvement neural network 123 using, as learning data, the reports received from poorly evaluated persons in charge in step S14 based on the request in step S5, and the like.

[0088] Since the machine learning in the information update step includes a procedure for fine-tuning the person-in-charge improvement neural network 123 using the reports from highly evaluated persons in charge with actual results related to the next reservation rate as learning data, the information management device 1 can update the improvement information based on the useful reports from highly evaluated persons in charge with actual results related to the next reservation rate. Since the machine learning in the information update step includes a procedure for fine-tuning the person-in-charge improvement neural network 123 using the reports from poorly evaluated persons in charge with negative actual results related to the next reservation rate as learning data, the information management device 1 can update the improvement information using the reports from poorly evaluated persons in charge with negative actual results related to the next reservation rate as the teacher.

[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, store improvement neural network update necessity determination step). If it is determined to update, the control unit 11 moves the process to step S19. If it is not determined to update, the control unit 11 returns the process to step S1 and repeats the processes from step S1 to step S19.

[0090] In the step of determining whether to update in the store improvement neural network update necessity determination step, the procedure of the process for determining whether to update is not particularly limited. For example, the procedure may be a procedure for determining to update if it is a date determined by the store manager, operator, etc. as the update date, a procedure for determining to update when the number of reports satisfies 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, in cooperation with the storage unit 12 and the communication unit 13, executes the generation unit 116. Then, the control unit 11 executes the 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 for fine-tuning the store improvement neural network 124 using the reports from the highly evaluated stores received in step S14 based on the request in step S13 as learning data, a procedure for fine-tuning the store improvement neural network 124 using the reports from the lowly evaluated stores received in step S14 based on the request in step S10 as learning data, and the like.

[0093] Since the machine learning in the information update step includes a procedure for fine-tuning the store improvement neural network 124 using the reports from the highly evaluated stores as learning data, the information management device 1 can update the improvement information based on the useful reports from the highly evaluated stores with performance related to the next reservation rate. Since the machine learning in the information update step includes a procedure for fine-tuning the store improvement neural network 124 using the reports from the lowly evaluated stores as learning data, the information management device 1 can update the improvement information using the reports from the lowly evaluated stores with negative performance related to the next reservation rate as the teaching signal.

[0094] [Marketing step] 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 information public to various services. Thereby, the information management device 1 can 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 making public information such as information on highly evaluated personnel and highly evaluated stores that have been extracted.

[0095] [Advertising management step] The information management process preferably includes an advertising management step of managing advertisements distributed via the network N or the like. Thereby, the information management device 1 can perform advertising management based on the next reservation rate.

[0096] [Provision of the next reservation 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 salary assessment, the period for bonus assessment, the fiscal year, etc.). Thereby, the information management device 1 can convey the situation of the person in charge's own work to the person in charge. In order to convey the situation of the person in charge's own work in more detail, the provision of the next reservation rate preferably includes the average value of the next reservation rate in the store to which the person in charge belongs, the ranking of the next reservation rate of the person in charge in the store to which the person in charge belongs, and the like.

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

[0098] [Penalty Step] The information management process preferably includes a penalty step of proposing a penalty for employees with a low next reservation rate (poorly evaluated employees). The criteria for identifying poorly evaluated employees in the penalty step may be the same as those in the "poorly evaluated employees" section. For example, it may be employees with the lowest next reservation rate in each store, employees with the lowest next reservation rate across all stores, etc. The penalty in the penalty step is not particularly limited as long as it gives the motivation for improvement for the low next reservation rate. The penalty includes, for example, reflection in salary reviews in the direction of decreasing salary, salary cuts, demotions, training instructions away from work, etc. The penalty step includes a procedure for automatically proposing a penalty according to the next reservation rate, using machine learning with the next reservation rate as an explanatory variable and the penalty as a target variable, or based on a given criterion. By including the penalty step in the information management process, the information management device 1 gives employees the motivation to improve the next reservation rate. As a result, the information management device 1 can increase the next reservation rate of individual employees in the service industry such as beauty salons and improve the store management.

[0099] [Corresponding learning step] The information management process uses the next reservation rate of the person in charge as an explanatory variable, and uses machine learning with learning data that takes the response of the person in charge to the customers in the next visit, exemplified by the second visit, the third visit, the fourth visit, etc., as the objective variable, to cause the person-in-charge improvement neural network 123 to perform machine learning for generating improvement information indicating a response to increase the next reservation rate. The person-in-charge improvement neural network 123 is realized by, for example, a large language model such as ChatGPT described above. The "response" in the learning data of the corresponding learning step includes the response (follow-up) of the person in charge to the specific action at the timing when the customer takes the specific action (also referred to as when the flag is set). As a result, the information management device 1 can provide the employees with improvement information (manual) indicating a response to increase the next reservation rate for a specific action of the customer. Thereby, the information management device 1 can increase the next reservation rate of individual employees in the service industry such as beauty salons and improve the management of the store.

[0100] [Communication learning step] The information management process preferably includes a communication learning step of causing the responsible person improvement neural network 123 to perform machine learning using learning data with the next reservation rate of the responsible person as an explanatory variable and the communication (for example, a message sent to a customer who visits the store using LINE (registered trademark), etc.) performed by the responsible person via the terminal T as an objective variable, to generate improvement information indicating communication that increases the next reservation rate. The responsible person improvement neural network 123 is realized by, for example, a large language model such as ChatGPT described above. "Communication" in the learning data of the communication machine learning step includes message exchanges via a chat app exemplified by LINE (registered trademark), provision of information via an SNS exemplified by Instagram (registered trademark), message exchanges via email, etc. As a result, the information management device 1 can provide the responsible persons with improvement information (manual) indicating communication that further increases the next reservation rate. Thereby, the information management device 1 can increase the next reservation rate of individual employees in the service industry such as a beauty salon and improve the management of the store.

[0101] [Manual Automatic Generation Step] The information management process preferably includes a manual automatic generation step of causing the responsible person improvement neural network 123 in which machine learning has been performed in the above-described correspondence learning step and / or communication learning step to automatically generate a manual indicating procedures for increasing the next reservation rate. As a result, the information management device 1 can provide the responsible persons with a manual indicating procedures for further increasing the next reservation rate. Thereby, the information management device 1 can increase the next reservation rate of individual employees 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 for the next reservation rate. The average calculation step includes procedures for calculating, for example, the store average which is the average of the next reservation rates at each store, the overall store average which is the average of the next reservation rates for all stores, and so on. Thereby, when the information management device 1 provides the next reservation rate to the person in charge, it can provide the calculated average. Thereby, the information management device 1 can provide the person in charge with materials for judging whether the person's next reservation rate is higher or lower than the average. Thereby, the information management device 1 can increase the next reservation rate for individual employees in the service industry such as beauty salons and improve the store management.

[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 contributions calculated in the contribution calculation step include, for example, the contribution to sales, the contribution to LTV (customer lifetime value), and so on. The procedure for calculating the contribution in the contribution calculation step is not particularly limited. The procedure includes, for example, a procedure for calculating the contribution using a neural network in which machine learning is performed using the next reservation rate as an explanatory variable and the contribution as a target variable. Thereby, 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. Also, thereby, 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 motivate the person in charge to increase the next reservation rate while being aware of their own contribution. Therefore, the information management device 1 can increase the next reservation rate for individual employees in the service industry such as beauty salons and improve the store management.

[0104] [Investment judgment criterion generation step] The information management process includes a speculative judgment criterion generation step of generating an investment judgment criterion based on an index related to the next reservation rate. The index used as input in the speculative judgment criterion generation step is not particularly limited as long as it is an index related to the next reservation rate. For example, it includes various averages aggregated over a given period such as one year (e.g., the average of the next reservation rates for specific staff, the above-mentioned store average, the above-mentioned average of all stores). The investment judgment criteria generated in the speculative judgment criterion generation step include, for example, judgment criteria indicating whether a new store should be opened, judgment criteria including a prediction of sales such as procedures, and judgment criteria including a prediction of revenue and expenditure such as procedures. Thereby, the information management device 1 can provide the manager with materials for judging whether to conduct speculative acts in management. Therefore, the information management device 1 can increase the next reservation rate for individual employees in the service industry such as beauty salons and improve the store management.

[0105] [Next reservation rate notification step] Preferably, the information management process includes a next reservation rate notification step of automatically notifying the above-mentioned low-evaluation staff and / or high-evaluation staff of the next reservation rate. Thereby, the information management device 1 can provide the staff with the next reservation rate, which is a material for judging the result of the person's sales efforts. Thereby, the information management device 1 can increase the next reservation rate for individual employees in the service industry such as beauty salons and improve the store management.

[0106] [Regarding the calculation of the next reservation rate] The "next reservation rate" in this embodiment refers to the ratio of those who have reserved a return visit among the customers who visited the store. The "reservers" mentioned here include customers who made a reservation when they visited the store (in-store reservers) and customers who made a reservation by the promised date after they visited the store (reservers within the notice period). Also, in-store reservers and reservers within the notice period are treated as in-store reservers and reservers within the notice period even if they canceled the reservation and did not actually visit the store. That is, the next reservation rate is the ratio of those who visited the store again among the customers who visited the store, which is different from the repeat rate that does not include those who canceled as customers who visited the store. Thereby, the information management device 1 prevents customers who made a reservation but canceled due to reasons of the customers from being treated as customers who did not become repeat customers due to insufficient sales efforts of the staff. Therefore, the information management device 1 can appropriately evaluate the sales efforts of the staff. By increasing the next reservation rate, the information management device 1 can promote the transformation of customers into fans who support the staff and / or the store, increase the LTV of the customers, and improve the management of the store.

[0107] [Effect of Information Management Processing] The information management device 1 that executes the information management processing of this embodiment can assist in extracting low-evaluation staff by the extraction unit 112 and requesting the extracted low-evaluation staff to submit a report. Thereby, the information management device 1 can assist the low-evaluation staff in reviewing their service procedures and the like during the process of creating the report and improving them. Therefore, the present invention can assist the low-evaluation staff in increasing their own next reservation rate. Also, thereby, the information management device 1 can assist the store manager and the like in grasping and reviewing the service procedures and the like of the low-evaluation staff and the service procedures and the like of the employees working in the store based on the report. Therefore, the present invention can assist the store manager and the like in increasing the next reservation rate of the store and improving the management through the review.

[0108] Therefore, the information management device 1 that executes the information management processing of this embodiment can assist in increasing the next reservation rate of individual employees and improving the management of the store in the service industry such as a beauty salon.

[0109] In addition, the information management device 1 that executes the information management process of the present embodiment can provide improvement information indicating guidelines for improving the next reservation rate to the person in charge of low evaluations by the information providing unit 114. Thereby, the information management device 1 can assist the administrator of the low-evaluation store or the like in improving the service providing procedure based on the improvement information. The information management device 1 that executes the information management process of the present embodiment can update the above-described improvement information based on reports from the person in charge of high evaluations by the information update unit 115. That is, the information management device 1 can update the improvement information by machine learning based on useful reports from the person in charge of high evaluations with results related to the next reservation rate, and can make the information more useful.

[0110] In addition, the information management device 1 that executes the information management process of the present embodiment can extract low-evaluation stores and high-evaluation stores not only for the person in charge but also for the stores. Then, the information management device 1 can request the submission of reports to the high-evaluation stores. Thereby, the information management device 1 can obtain and provide reports from high-evaluation stores that are useful for improving the management of other stores. In addition, the information management device 1 can perform machine learning based on reports from high-evaluation stores and the like, and can provide information that is even more useful for improving the management of other stores.

[0111] <Usage Example> The following is a usage example of the information management device 1.

[0112] 〔Registration of Store Visit Information〕 "Masashi Miyamoto", an employee of a beauty salon, performed a "Standard Course" treatment on "Kozue Yamada", a customer who visited the store without a reservation, and obtained sales of "8,000 yen" but was unable to make a reservation for the next time. "Masashi Miyamoto" registered the store visit information related to the customer via the terminal T in the information management device 1. The information management device 1 received the store visit information transmitted from the terminal T and stored it in the store visit database 121.

[0113] "Masashi Miyamoto" performed a "full course" treatment on the customer "Kojiro Sasaki" who visited the store as scheduled, obtained sales of "10,000 yen", and received a commitment from "Kojiro Sasaki" to make a reservation later. "Masashi Miyamoto" registered the store visit information related to the customer through terminal T in information management device 1. Information management device 1 received the store visit information sent from terminal T and stored it in store visit database 121. "Kojiro Sasaki" made a reservation for the next visit three days later. "Masashi Miyamoto" registered the next reservation as the next reservation related to the previously registered store visit information through terminal T in information management device 1. Information management device 1 received the next reservation information sent from terminal T and stored it in store visit database 121 in association with the previously stored store visit information.

[0114] [Report from the high evaluation person in charge] Information management device 1 extracted the high evaluation person in charge "Ichiro Suzuki" from store visit database 121. Then, information management device 1 informed the store manager that the next reservation rate of "Ichiro Suzuki" was high, provided the next reservation rate to "Ichiro Suzuki", and requested the submission of a report. "Ichiro Suzuki" sent a report to information management device 1 through terminal T, stating that he had made efforts to promote the next reservation while emphasizing communication and build a trust relationship through small talk. Information management device 1 received the report sent from terminal T and stored it in report database 122. Then, information management device 1 executed machine learning based on the report and updated the improvement information.

[0115] [Report from the high evaluation store] Information management device 1 extracted the high evaluation store "△△ store" from store visit database 121. Then, information management device 1 requested the submission of a report from "△△ store". "△△ store" sent a report to information management device 1 through terminal T, stating that regular meetings, procedure posting, awareness raising, and training had been carried out. Information management device 1 received the report sent from terminal T and stored it in report database 122. Then, information management device 1 executed machine learning based on the report and updated the improvement information.

[0116] [Business improvement of the low evaluation person in charge] The information management device 1 extracted the low-evaluation person in charge, "Masashi Miyamoto", from the store visit database 121. Then, the information management device 1 provided the next reservation rate to "Masashi Miyamoto", informed him that the next reservation rate was low, and requested the submission of a report. "Masashi Miyamoto" reflected on his own work attitude and noticed that he had not thoroughly greeted customers at the end of the treatment. Then, "Masashi Miyamoto" sent a report to the information management device 1 via the terminal T stating that he had not thoroughly greeted customers at the end of the treatment. The information management device 1 received the report sent from the terminal T and stored it in the report database 122. In addition, the information management device 1 provided the improvement information updated based on the report from "Ichiro Suzuki" to the terminal T of "Masashi Miyamoto". "Masashi Miyamoto" carried out business improvements to promote the next reservation while emphasizing communication and build a trust relationship through small talk based on the improvement information.

[0117] 〔Business Improvement of Low-Evaluation Stores〕 The information management device 1 extracted low-evaluation stores from the store visit database 121. Then, the information management device 1 informed the low-evaluation stores that the next reservation rate was low and requested the submission of a report. The information management device 1 received the reports sent from the terminals T of the low-evaluation stores and stored them in the report database 122. In addition, the information management device 1 provided the improvement information updated based on the reports from "△△ Store" to the terminals T of the low-evaluation stores. The managers of the low-evaluation stores carried out business improvements such as holding regular meetings, posting procedures, raising awareness, and conducting training based on the improvement information.

[0118] In the scope of the idea of the present invention, those skilled in the art can conceive of various modification examples and correction examples. Therefore, it is understood that those modification examples and correction examples belong to the scope of the present invention. For example, for the above-described embodiments, those in which those skilled in the art appropriately add, delete, or change the design of components, or add, omit, or change the conditions of the steps, also fall within the scope of the present invention as long as they have the gist of the present invention.

Explanation of Reference Numerals

[0119] S System 1 Information Management Device 11 Control Unit 111 Receiver 112 Extractor 113 Report Request Unit 114 Information Provision Unit 115 Information Update Unit 116 Generator 12 Memory Unit 121 Store Visit Database 122 Report Database 123 Responsible Person Improvement Neural Network 124 Store Improvement Neural Network 13 Communication Unit N Network T Terminal

Claims

1. An information update unit that updates a staff improvement neural network for generating improvement information indicating guidelines for improving the staff's next reservation rate, which is the ratio of customers who made a reservation for a return visit among the customers served by the staff; An extraction unit that extracts highly evaluated staff members who have a high staff next reservation rate; A report request unit that requests the highly evaluated staff members to submit a report; An information providing unit that generates the improvement information by a process using the staff improvement neural network updated by the information update unit and provides the generated improvement information to low-evaluated staff members; Comprising; The information update unit is configured to update the staff improvement neural network by machine learning that fine-tunes the staff improvement neural network using the report submitted by the highly evaluated staff members as learning data; An information management device.

2. An information update unit that updates a store improvement neural network for generating improvement information indicating guidelines for improving the store's next reservation rate, which is the ratio of customers who made a reservation for a return visit among the customers who visited the store; An extraction unit that extracts highly evaluated stores that have a high store next reservation rate, which is the ratio of customers who made a reservation for a return visit among the customers who visited the store, based on reservation information that can identify whether a customer made a reservation for a return visit and store information that identifies the store where the customer visited; A report request unit that requests the highly evaluated stores to submit a report; An information providing unit that generates the improvement information by a process using the store improvement neural network updated by the information update unit and provides the generated improvement information to low-evaluated stores; Comprising; The information update unit is configured to update the store improvement neural network by machine learning that fine-tunes the store improvement neural network using the report submitted by the highly evaluated stores as learning data; An information management device.

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