JP6533416B2 - Sales support server, sales support terminal and sales support system - Google Patents

Description

  The present invention relates to a sales support server, a sales support terminal, and a sales support system, and is particularly suitable for application to a sales support server, a sales support terminal, and a sales support system for optimizing explanatory materials used for sales.

  Generally, when selling a product to a customer, a salesperson describes the product using a prepared explanatory material. At this time, there is a technique for teaching a sales representative the appropriate explanatory material as a technique for performing the explanation smoothly (Patent Document 1). Specifically, the camera records the operation of the salesperson at the time of explanation, extracts the order relation between the explanation materials and the relation other than the explanation materials (supplemental relation, comparison relation, etc.), and other salespersons explain In some cases, a technique is disclosed for extracting an appropriate explanatory material using the extracted relationship.

Unexamined-Japanese-Patent No. 2015-11508

  However, the technology described in Patent Document 1 focuses on only the salesperson who explains the product, and does not focus on the customer who receives the description of the product. Depending on the customer, the order of the content and the description desired to be heard (collectively referred to as "content preference") differs, and the volume of the description that can be easily accepted, the direction of the material, and the screen size (collectively referred to as "business negotiation preference") May differ.

  Salespeople with insufficient experience or skills do not know what content to use during restricted business hours and in what order they should be explained. Also, at the time of explanation, it is not known at what volume to explain, whether the orientation of the material should be vertical or horizontal, and the size of the screen should be good. Therefore, there is a problem that it is not possible to give an optimal explanation in accordance with the preferences of each customer.

  The present invention has been made in consideration of the above points, and proposes a sales support server, a sales support terminal, and a sales support system capable of proposing an optimal sales method in accordance with the preference of a customer.

In order to solve the problem, in the present invention, the sales support server refers to a sales history table indicating sales history among the information on sales stored in the storage unit, and relates to a case where sales is successful. to extract the products, we extracted the explanatory materials are classified into each product, constitute extracted and product was, and classified instructional materials, and customers who received the products described below with reference to classified explanatory material, the classification was explanatory material Create an explanatory material tagging table that associates multiple content with a content that has a good reputation for the customer's response entered by the sales representative, and the score calculated higher as the content usage frequency increases, and the sales target When a customer preference table in which a customer is associated with the preference of the customer is created, and search information including customer information from a sales support terminal as an external terminal is input, Based on the information of the customer included in the search information, the referring customer preferences table, the retrieve preferences of the customer, the description with reference to the article tagging table number from the highest score given The content of the customer is acquired, and the acquired customer preference and content are proposed as a sales method adapted to the customer preference.

  According to the present invention, it is possible to propose an optimal sales method in accordance with the taste of the customer.

It is a whole block diagram of a sales support system. It is a conceptual diagram of pre-processing by a sales support server. It is an internal block diagram of a customer information table. It is an internal block diagram of a goods information table. It is an internal block diagram of a sales history table. It is an internal block diagram of a description data table. It is an internal block diagram of content operation history. It is an internal block diagram of feedback. It is a flowchart of explanatory material tagging table preparation processing. It is a flowchart of customer preference table creation processing. It is an internal block diagram of a description data tagging table. It is an internal block diagram of a customer preference table. It is a conceptual diagram of the content recommendation process by a sales support server and a sales support terminal. It is an internal block diagram of search information. It is an internal block diagram of recommendation information. It is a flowchart of a customer preference acquisition process. It is a flowchart of a content recommendation process. It is an example of screen composition.

  Hereinafter, the present embodiment will be described in detail with reference to the drawings. In the present embodiment, the configuration of the sales support terminal and the sales support server in the case where the salesperson performs sales activities using a tablet terminal (hereinafter referred to as "sales support terminal") will be described. More specifically, the sales support terminal called MR (medical information person in charge) meets a doctor at a medical institution and explains the configuration of the sales support terminal and the sales support server in the case of explaining the medicine to the doctor using the sales support terminal. Do.

  The sales support terminal can always communicate with the sales support server. In addition, it is assumed that the salesperson knows information about the doctor and the time limit of the business talk (the business talk time, the explanation time, etc.) in advance before the business talk with the doctor. Since the salesperson can explain the product using the explanatory material matched to the preference of the doctor while showing the sales support terminal to the doctor, the business negotiation can be advanced efficiently.

  In the following, one embodiment of the present invention is described assuming MR sales, but the present invention is not necessarily limited thereto. For example, a scene where an insurance sales employee explains an insurance product to a customer, a branch of a financial institution. It is also possible to apply the present invention to a scene where a financial salesperson explains a financial product to a customer, a scene of real estate business, a scene of school education, and the like.

  Although the sales support terminal is a tablet terminal, the present invention is not limited to this. For example, the sales support terminal may be a node personal computer or a smartphone capable of displaying and operating an explanatory material.

  FIG. 1 shows the overall configuration of a sales support system 1 in the present embodiment. The sales support system 1 includes a sales support server 10, a sales information management server 20, and a sales support terminal 30. The sales support server 10, the sales information management server 20, and the sales support terminal 30 are communicably connected to each other via the network N1.

  The sales support server 10 includes a storage unit 101, an input unit 102, a central processing unit (CPU) 103, a display unit 104, and a communication unit 105. The storage unit 101 stores sales data and processing programs.

  Specifically, the sales data includes a content operation history 1011, a feedback 1012, a customer information table 1013, a product information table 1014, a sales history table 1015 and an explanatory material table 1016. The processing programs include an explanatory material tagging program 1101, a customer preference extraction program 1102, a content recommendation program 1103 and a customer preference acquisition program 1104.

  Further, when the sales support server 10 executes the pre-processing (FIG. 2), the storage unit 101 also stores data obtained as an execution result. Specifically, an explanatory material tagging table 1201 and a customer preference table 1202 are stored.

  The input unit 102 is an interface for receiving an instruction from the administrator, and is, for example, a keyboard or a mouse. The CPU 103 is a processor that generally controls the operation of the sales support server 10 in cooperation with a processing program that refers to sales data.

  The display unit 104 is a display device that displays sales data and the like stored in the storage unit 101 on a display screen, and is, for example, an LCD (Liquid Crystal Display). The communication unit 105 is an interface that transmits and receives data to and from an external device via the network N1, and is, for example, a NIC (Network Interface Card).

  The sales information management server 20 is a general server device, and includes, for example, a storage medium such as a CPU or a hard disk drive (HDD). The sales information management server 20 stores sales data necessary for the sales support server 10 and the sales support terminal 30 to execute the processing program, and transmits the sales data to the sales support server 10 as appropriate.

  The transmission timing of the sales data can be set arbitrarily by the manager. For example, when the sales data update operation is performed, the sales information management server 20 transmits the updated sales data to the sales support server 10 periodically (at the end of the business day). The sales support terminal 30 acquires the updated sales data from the sales support server 10 at the timing when the sales support server 10 updates the sales data, and updates its own sales data.

  The sales support terminal 30 is a portable tablet terminal, and includes a storage unit 301, a communication unit 302, an input unit 303, a CPU 304, and a display unit 305. The storage unit 301 stores terminal operation data indicating a sales representative's operation record and a processing program.

  Specifically, the terminal operation data is the content operation history 3011 and the feedback 3012. These are data acquired by the sales support terminal 30 itself when explaining to the customer. The processing programs are a negotiation status search program 3101, a recommended content adjustment program 3102, a screen display program 3103, a reputation collection program 3104, and a content operation history generation program 3105.

  There are two types of processing executed by the sales support server 10 in the present embodiment. One is pre-processing, which includes explanatory material tagging table creation processing and customer preference table creation processing. The other is processing executed based on the search condition from the sales support terminal 30 when the salesperson inputs the search information to the sales support terminal 30, and is configured from customer preference acquisition processing and content recommendation processing. Ru.

  In addition, the explanatory material which a salesperson uses at the time of description of goods is electronic data in which the whole description content of goods is included. For example, it is stored in the sales support terminal 30 in the form of a PowerPoint file, a PDF (Portable Document Format) file, or the like. The content is a component of the explanatory material. For example, the explanation content of the chapter of PowerPoint corresponds to the content.

  The explanatory material tagging table creation processing and the customer preference table creation processing are executed after the sales support server 10 has the sales data. The customer preference acquisition process and the content recommendation process are performed after the salesperson inputs the search information at the sales support terminal 30. As a result, the recommended content and the negotiation preference are displayed on the display screen of the sales support terminal 30.

  FIG. 2 shows the flow of pre-processing by the sales support server 10. The sales support server 10 acquires the content operation history 3011 and the feedback 3012 stored in the storage unit 301 of each sales support terminal 30, and stores the content operation history 1011 and the feedback 1012 in the storage unit 101.

  Further, the sales support server 10 acquires the customer information table 1013, the product information table 1014, the sales history table 1015, and the explanation material table 1016 stored in the storage unit of the sales information management server 20 and stores them in the storage unit 101. .

  The sales support server 10 executes the explanatory material tagging program 1101 and the customer preference extraction program 1102 with reference to the information 1011 to 1016. Then, an explanatory material tagging table 1201 and a customer preference table 1202 are generated and stored in the storage unit 101.

  FIG. 3 shows an internal configuration of the customer information table 1013. The customer information table 1013 stores information on customers with whom a sales representative has made a business negotiation. The customer information table 1013 includes a customer field 10131, a sex field 10132, an age field 10133, a specialty field 10134, a company field 10135, a scale field 10136, an address field 10137 and the like.

  The information stored in each field is acquired by the public information and the salesperson in contact with the customer inputting it, and is stored in the sales information management server 20. Then, it is transmitted from the sales information management server 20 to the sales support server 10. A field for storing information such as the customer contact address and the customer category may be further added.

  FIG. 4 shows an internal configuration of the product information table 1014. The product information table 1014 stores information on products. The product information table 1014 includes a product column 10141, a product classification column 10142, an application column 10143, an amount column 10144 and the like.

  In particular, the product classification column 10142 stores various classifications depending on the purpose. Here, classifications defined in the Pharmaceutical Affairs Act are stored. The purpose of use of the product is stored in the application field 10143, and the sales price of the product is stored in the amount field 10144. These pieces of information are accumulated by the sales information management server 20 and transmitted to the sales support server 10. A field for storing information such as the product form and the expiration date may be further added.

  FIG. 5 shows an internal configuration of the sales history table 1015. The sales history table 1015 stores information related to sales history. The business history table 1015 includes a case ID field 10151, a customer field 10152, a product field 10153, an explanatory material ID field 10154, a result field 10155, and the like.

  The case ID column 10151 stores a case number for uniquely specifying a case for which a business negotiation has been made in the sales support system 1. The customer column 10152 and the product column 10153 store the sales history input by the sales representative after the business negotiation. The explanatory material ID column 10154 stores an explanatory material number for uniquely identifying the explanatory material in the sales support system 1.

  Further, the result column 10155 stores information indicating “success” or “failure” which is input after the salesperson has made a business negotiation. The negotiation result here is determined based on whether or not the order has been won. The result of the item for which the order has been received is "success", and the result for the item for which the order is not obtained is "failure". These pieces of information are accumulated by the sales information management server 20 and transmitted to the sales support server 10.

  FIG. 6 shows the internal configuration of the explanatory material table 1016. The explanatory material table 1016 stores information on the explanatory material. The explanation material table 1016 includes a product field 10161, an explanation material ID field 10162, a content classification field 10163, a content ID field 10164, and a storage location field 10165.

  In particular, the storage location column 10165 stores information (for example, a file path) indicating the storage location of the content in the sales information management server 20. Based on the storage location of the content, the sales support server 10 can acquire the content from the sales information management server 20. These pieces of information are accumulated by the sales information management server 20 and transmitted to the sales support server 10.

  FIG. 7 shows the internal configuration of the content operation history 1011. The content operation history 1011 stores information on the operation history of the content. The content operation history 1011 includes a case ID field 10111, a terminal ID field 10112, a logon user name field 10113, a product field 10114, and a customer field 10115.

  Furthermore, it includes an explanatory material ID column 10116, a usage date column 10117, a content classification column 10118, a content ID column 10119, a sound volume column 101191, and a direction column 101192. These pieces of information are acquired by the sales support terminal 30, and transmitted to the sales support server 10.

  FIG. 8 shows an internal configuration of the feedback 1012. The feedback 1012 stores feedback when the salesperson operates the sales support terminal 30. The feedback 1012 includes a case ID field 10121, a product field 10122, a customer field 10123, an explanatory material ID field 10124, a content classification field 10125, a content ID field 10126, and a reputation field 10127.

  When explaining the product using the content, the salesperson determines the reaction (good / bad) to the content of the customer, and inputs the determination result at the sales support terminal 30. The judgment result input here is held as a reputation in the sales support server 10. The method of recording the feedback is not limited to this, and for example, the doctor may input while explaining the salesperson, or the doctor may fill in the questionnaire after the business talk is over.

  FIG. 9 shows a flowchart of an explanatory material tagging table creation process. The explanatory material tagging table creation process is executed by the explanatory material tagging program 1101 of the sales support server 10. First, the explanatory material tagging program 1101 refers to the sales history table 1015, selects the case for which the result is “successful”, and extracts customer information, product information, content operation history, and feedback regarding the successful case (SP1 ).

  For example, the explanatory material tagging program 1101 extracts customer information by searching the customer information table 1013 with the customer of the row in which “success” is stored in the result column 10154 of the sales history table 1015 as a key.

  The explanatory material tagging program 1101 extracts the product information by searching the product information table 1014 using the product in the row in which “success” is stored in the result column 10154 of the sales history table 1015 as a key.

  In addition, the explanation material tagging program 1101 extracts the content operation history by searching the content operation history 1011 using the item ID of the row in which “success” is stored in the result column 10154 of the sales history table 1015 as a key. Do.

  The explanatory material tagging program 1101 extracts feedback by searching the feedback 1012 using the case ID whose “success” is stored in the result column 10154 of the sales history table 1015 as a key.

  Next, the explanatory material tagging program 1101 classifies the explanatory material for each product and stores the explanatory material in the explanatory material tagging table 1201 (SP2). Generally, a plurality of sales representatives create different explanatory materials for one product.

  Next, the explanation material tagging program 1101 extracts the customer who received the item description using the explanation material, and stores the customer in the description material tagging table 1201 (SP3). Generally, several different customers receive explanations for one explanatory material.

  Next, the instruction material tagging program 1101 refers to the feedback 1012 to calculate the score of the content based on the reputation and the number of times of use of the content. Then, the calculated score is stored in the explanatory material tagging table 1201 (SP4). The number of times of use of content is counted by the number of times the content ID appears in the explanatory material tagging table 1201.

  As a score calculation method, the score is added based on the reputation (good / bad) of the content. For example, if the reputation when the content ID "A0010" appears once is "good", 0.1 is added to the score of this content. If the reputation when it appears twice is "good", the score of this content will be 0.2. If the third reputation is "bad", add -0.1 to get a score of 0.1.

  Next, the explanatory material tagging program 1101 refers to the content operation history 1011 to calculate the average usage time of the content. Then, the calculated average usage time is stored in the explanation material tagging table 1201 (SP5), and this processing is ended.

  As a method of calculating the average usage time, the usage date and time and content ID of the content are extracted from the content operation history 1011, and the average value of usage time of each content is calculated based on the difference in usage date and time of each content and the number of usage.

  FIG. 10 shows a flowchart of customer preference table creation processing. This process is executed by the customer preference extraction program 1102 of the sales support server 10. First, the customer preference extraction program 1102 refers to the feedback 1012 to sort out content ID with high reputation, and extracts customers, content classification, content description order, volume, direction, and terminal ID for content with high reputation (SP11) ).

  For example, the customer preference extraction program 1102 extracts the customer and content classification of the row in which “good” is stored in the reputation column 10127 of the feedback 1012. In addition, the customer preference extraction program 1102 searches the content operation history 1011 using the case ID and content ID of the line for which “good” is stored in the reputation column 10127 of the feedback 1012 as a key.

  Then, the customer preference extraction program 1102 extracts the description order of content having good reputation based on the order before and after the use date and time of the row obtained as the search result, and extracts the volume and direction when using this content. Furthermore, the terminal ID using this content is extracted.

  There are two types of customer preferences in the present embodiment: customer content preferences and negotiation preferences. The content preference is a preference for the customer's explanatory material, and is composed of the content to be heard and the order of the explanation. The negotiation preference is the preference for the negotiation style of the customer, and is constituted by the volume of the description, the orientation of the terminal (the orientation of the explanatory material), and the size of the screen.

  Steps SP12 and SP13 are extraction processing of the content preference of the customer, and steps SP14 to SP16 are extraction processing of the negotiation preference of the customer. First, in step SP12, the customer preference extraction program 1102 classifies preference factors for each customer (SP12).

  Next, the customer preference extraction program 1102 extracts the content preference of the customer and stores it in the customer preference table 1202 (SP13). Specifically, the number of times of use of each content classification is totaled for each customer, the usage frequency of the content classification is calculated, and the content classification with high frequency is extracted as the content preference of the customer. For example, the content classification within the top three most frequently used is extracted as the content preference of the customer.

  Next, the customer preference extraction program 1102 extracts the customer's explanation order preference and stores it in the customer preference table 1202 (SP14). Specifically, the pattern of the order of the description of the content is collected, and the pattern having the high appearance rate is stored in the customer preference table 1202 as the description order preference of the customer.

  Next, the customer preference extraction program 1102 extracts the volume preference of the customer and stores it in the customer preference table 1202 (SP15). Specifically, the average value of the volume at the time of description of content having good reputation is calculated for each customer, and this is extracted as volume preference and stored in the customer preference table 1202.

  Next, the customer preference extraction program 1102 extracts the orientation preference of the customer and stores it in the customer preference table 1202 (SP16). Specifically, the frequency of the direction at the time of description of the content having good reputation is calculated for each customer, the direction with high frequency is extracted as the direction preference of the customer, and stored in the customer preference table 1202. Here, the direction of the sales support terminal 30 is only two in the horizontal direction and the vertical direction, and the determination criterion that the ratio is high is determined based on whether or not the direction ratio exceeds 51%.

  Next, the customer preference extraction program 1102 extracts the screen size preference of the customer, stores it in the customer preference table 1202 (SP17), and ends this processing. Specifically, the screen size of the terminal used at the time of description of content with good reputation for each customer is acquired. Then, the use frequency of the screen size is calculated, the screen size with high frequency is extracted as the screen size preference, and stored in the customer preference table 1202.

  FIG. 11 shows an internal configuration of the explanatory material tagging table 1201. The explanation material tagging table 1201 stores the processing result of the explanation material tagging table creation process (FIG. 9). The explanatory material tagging table 1201 comprises a product column 12011, an explanatory material ID column 12012, a customer column 12013, a total required time column 12014, a content classification column 12015, a content ID column 12016, a score column 12017, and a content time column 12018. .

  In particular, in the total required time column 12014, the total required time of the explanatory material obtained by adding the average time of each content of the explanatory material is stored. In the score column 12017, the score of each content calculated in step SP4 of the explanation material tagging table creation process (FIG. 9) is stored. The content time column 12018 stores the average usage time of each content calculated in step SP5 of the explanatory material tagging table creation processing.

  FIG. 12 shows an internal configuration of the customer preference table 1202. The customer preference table 1202 stores the processing result of the customer preference table creation process (FIG. 10). The customer preference table 1202 includes a customer field 12021, a content preference field 12022, and a negotiation preference field 12023.

  There are two types of customer preferences, as shown in the customer preference table 1202, content preference and negotiation preference. The content preference of the customer is stored in the content column in the content preference column 12022, and the description order preference is stored in the description order column in the content preference column 12022. The customer's volume preference, orientation preference and screen size preference are stored in the volume, orientation and screen size fields in the negotiation preference column 12023, respectively.

  For example, among the content preferences of “A teacher”, the content preferences are the “feature”, “effect” and “price” of the medicine, and the description preference is “feature → effect → price”. Further, of the negotiation preferences of “A teacher”, the volume preference is “50 dB”, the orientation preference is “sideways”, and the screen size preference is “10 inches”. The customer preference is not limited to these contents, and may include preferences such as the color of the explanatory material and the style of the content (video and audio).

  FIG. 13 shows an outline of content recommendation processing by the sales support server 10 and the sales support terminal 30. This process is executed when the salesperson uses the sales support system 1 after the negotiation schedule is clarified. First, when the sales support server 10 receives the search query of the sales employee, the sales support server 10 executes the customer preference acquisition program 1104 to extract the content preference and negotiation preference 13011 of the customer.

  Next, the sales support server 10 receives the extracted content preference of the customer, and executes the content recommendation program 1103. The content recommendation program 1103 refers to the explanatory material tagging table 1201 to extract content having a high score that matches the content preference, determines a content order that matches the description preference of the customer, and generates the recommended content 13012.

  After generating the negotiation preference 13011 and the recommendation content 13012, the sales support server 10 transmits them as the recommendation information 1301 to the sales support terminal 30. Upon acquiring the recommendation information 1301 from the sales support server 10, the sales support terminal 30 displays the recommendation information 1301 on the display screen of the display unit 305 by the screen display program 3103 and receives an adjustment to the recommended content 13012 from the sales representative. .

  For example, the salesperson checks whether the date of the explanatory material is correct, whether the content is missing, or whether the explanation time is sufficient, and adjusts the date of the explanatory material to the check result, adjustment such as addition or deletion of content. Do.

  When the sales support terminal 30 receives an adjustment from a sales representative, the sales support terminal 30 executes the recommended content adjustment program 3102 to adjust the recommended content 13012. Then, according to the screen display program 3103, the adjusted recommended content 13012 is displayed on the display screen.

  At this time, the salesperson can look at the negotiation preference 13011 and the recommended content 13012 adjusted and consider the response to the customer. For example, it is possible to prepare a tablet terminal of a screen size the customer likes, observe the restriction on the explanation time, and practice the explanation in consideration of the volume of the explanation at the time of the negotiation and the direction of the terminal.

  When the negotiation starts, the salesperson displays explanatory material including the adjusted recommended content on the sales support terminal 30, and explains it to the customer in accordance with the customer's negotiation preference. When the salesperson explains, the content operation history generation program 3105 generates an operation history (content used and date and time of use) to the sales support terminal 30, and a method of explanation (volume, direction, terminal ID).

  In addition, the salesperson presses the reputation button on the screen of the terminal while observing the reaction of the customer when explaining. Based on the pressing of the evaluation button, the reputation collection program 3104 collects feedback on the content. The sales support terminal 30 stores the collected content operation history 3011 and feedback 3012 in the storage unit 301, and periodically transmits it to the sales support server 10 via the network N1.

  The information that can be collected by the sales support terminal 30 is not limited to the content operation history 3011 and the feedback 3012, but, for example, information of a web page used by a sales representative to explain and information of a doctor's voice etc. It is also good.

  FIG. 14 shows the internal structure of the search information 3031. The search information 3031 stores information input via the input unit 303 of the sales support terminal 30 when the sales representative uses the sales support system 1. The search information is information that the sales representative has already grasped before the business negotiation. The search information 3031 includes a product field 30311, a customer field 30312, a specialty field 30313, a company field 30314, a scale field 30315, an address field 30316, and an explanation time field 30317.

  FIG. 15 shows an internal configuration of the recommendation information 1301. The recommendation information 1301 stores the processing result of the content recommendation processing (FIG. 13). The recommendation information 1301 includes a negotiation preference field 13011, a recommended content field 13012, a score field 13013, and a total required time field 13014.

  The volume, the orientation of the terminal, and the screen size stored in the negotiation preference column 13011 are displayed on the display screen of the sales support terminal 30. The display screen also displays the recommended content stored in the recommended content column 13012 and the required time for the description of each recommended content, the score stored in the score column 13013, and the time stored in the total required time column 13014 Ru.

  FIG. 16 shows a flowchart of customer preference acquisition processing. The customer preference acquisition process is executed by the customer preference acquisition program 1104 of the sales support server 10. As a premise, the salesperson inputs the search information 3031 through the input unit 303 of the sales support terminal 30 after the negotiation schedule is decided. Then, the sales support terminal 30 transmits the search information 3031 to the sales support server 10.

  First, the customer preference acquisition program 1104 of the sales support server 10 refers to the customer preference table 1202 and determines whether there is a customer to be searched (SP21). If a negative result is obtained in this determination (SP21: N), the customer preference acquisition program 1104 determines whether a customer similar to the customer to be searched exists in the customer preference table 1202 (SP22).

  If a negative result is obtained in this determination (SP22: N), the customer preference acquisition program 1104 ends this processing. On the other hand, when a positive result is obtained in steps SP21 and SP22 (SP21: Y and SP22: Y), the customer preference acquisition program 1104 acquires the customer preference (SP23), and the present process ends.

  Although the customer preference acquisition program 1104 searches for a customer similar to the search target customer in step SP22, the customer similar to the search target customer is extracted by the following calculation. For example, information such as the search target customer and other customers' specialties, business locations, and company sizes, addresses, etc. of the search target is acquired as customer attributes, and the similarity to the customer attributes of the search target customer is calculated. The degree of similarity between customers is an average value of the degrees of similarity of all customer attributes. If the customer attribute is a numerical value, calculate the cosine similarity of the attribute.

  If the customer attribute is non-numeric, the calculation method of attribute similarity is defined as follows. If the customer's attributes are the same, then set the attribute similarity to 1. If the customer's attribute is different, set the similarity of the attribute to 0. The customer whose similarity calculation result is closest to 1 is extracted, and the preference of the customer is acquired as the preference of the search target customer.

  FIG. 17 shows a flowchart of content recommendation processing. This content recommendation process is executed by the content recommendation program 1103 of the sales support server 10 after the customer preference acquisition process (FIG. 16) is completed. First, the content recommendation program 1103 determines whether or not the explanation supporting material for the product to be searched exists in the sales support server 10 (SP31).

  If a negative result is obtained in this determination (SP31: N), the content recommendation program 1103 transmits information indicating "no information on the corresponding product" to the sales support terminal 30, and the present process is ended. On the other hand, if a positive result is obtained (SP31: Y), the content recommendation program 1103 extracts a plurality of content candidates with high scores (SP32). For example, five pieces of content of each category are extracted.

  Next, the content recommendation program 1103 refers to the customer preference table 1202 and extracts, as the recommended content 13012, a content candidate having the highest score regarding the content according to the content the customer likes (SP33). Note that other content candidates having high scores may also be extracted and transmitted to the sales support terminal 30. In this case, although the sales support terminal 30 receives a plurality of content candidates, it allows the sales representative to select an appropriate explanatory material.

  Next, the content recommendation program 1103 arranges the order of the recommended content 13012 in accordance with the description order of the content preference of the customer (SP34), and ends this processing. The content recommendation program 1103 thereafter transmits the recommended content 13012 to the sales support terminal 30 according to the preference of the customer.

  FIG. 18 shows an exemplary screen configuration displayed on the display screen of the sales support terminal 30. As shown in FIG. The sales support terminal 30 is equipped with a tilt sensor and a volume sensor, and can record the volume of voice and the operation of the direction of the terminal when the salesperson explains.

  The processing result of the customer preference acquisition processing and the content recommendation processing executed by the sales support server 10 is displayed in the processing result display area 3051. Specifically, outlines of the negotiation preference and the recommendation content are displayed. As the negotiation preference, there are an optimum volume at the time of explaining by a salesperson, a direction of a terminal and a screen size of the terminal.

  Also, the outline of the recommended content includes the score of the recommended content, the total required time, and the outline of the recommended content. The preview area 3052 displays a preview of the recommended content. The salesperson can use the information displayed on the display screen to practice the content and the tablet operation in advance.

  In the reputation collection area 3053, a reputation button is displayed to be able to be pressed in order to gather a reputation for content. When a sales representative negotiates with a customer, it can see the customer's reaction while pushing the reputation button while explaining the recommended content. The reputation button may be displayed also on the explanation screen for the customer, or may be processed so as not to be noticed by the customer. The customer may also press the reputation button directly.

1 sales support system 10 sales support server 20 sales information management server 30 sales support terminal

Claims (6)

A sales support server that proposes a sales method tailored to the tastes of customers,
The sales support server is
A storage unit that stores information on sales and a program that operates while referring to the information;
A processor that controls the operation of the sales support server in cooperation with the program;
The processor is
Among the information on sales stored in the storage unit, the sales history table indicating sales history is referenced to extract the products related to the successful sales, and the explanatory material is classified and extracted for each extracted product. products and the classified instructional materials, and the customer who received item described with reference to classified instructional material, a plurality of contents constituting the classification was described article of content indicating the customer response entered by sales employees Create an explanatory material tagging table that is associated with a score that has a good reputation and the higher the content usage frequency is,
Create a customer preference table that associates customers for sales with the preferences of the customers,
When search information including customer information from a sales support terminal, which is an external terminal, is input, the preference of the customer is obtained with reference to the customer preference table based on the customer information included in the search information. While acquiring and referring to the explanatory material tagging table, acquiring a predetermined number of contents from high scores, and proposing the acquired customer preference and content as a sales method adapted to the customer preference. Sales support server to be. The processor is
Among the information on sales stored in the storage unit, referring to the feedback indicating the reputation of the business case, the customer on the business case having a good reputation is extracted, and the content of the content and the content for each extracted customer The customer preference table is created by correlating the order of explanation calculated based on the use date and time, the volume at the time of explanation when using the content, the direction of the content, and the size of the content. The sales support server described in 1. The processor is
If you enter the search information,
With reference to the customer preference table, when there is a customer included in the search information, the preference of the customer is acquired, and when there is no customer included in the search information, based on the attribute of the customer The sales support server according to claim 1, wherein a customer preference similar to the extracted customer is acquired. The processor is
If you enter the search information,
The sales support server according to claim 1, wherein the content with the highest score is acquired with reference to the explanatory material tagging table. A sales support terminal that displays sales methods tailored to customer preferences on a display screen,
The sales support terminal is
A processor for generating search information including product and customer information;
The processor transmits the search information to a sales support server which is an external terminal;
The sales support server is
Refer to the sales history table that shows the sales history, extract the products related to the items for which sales were successful, classify the explanatory materials according to the extracted products, extract the classified products, the classified explanatory materials, and the classified explanatory materials and customers who received the products described using a plurality of content that make up the classification was explanatory material, good reputation of the content that shows the reaction of customers that have been input by the sales representative, is highly calculated the greater the number of uses of the content A descriptive material tagging table in which the customer's score is associated, a customer to be sold, and a customer preference table in which the customer's preferences are associated,
If you enter the search information, based on the information of the customer included in the search information, the referring customer preferences table, the retrieve preferences of the customer, with reference to the instructional material tagging table Acquire a predetermined number of contents from the one with the highest score, and propose the acquired customer preferences and contents as a sales method tailored to the customer preferences.
The processor displays on the display screen the preference and content of the customer included in the proposed sales method, and while the content is used by a sales representative, the operation history at the time of use and the reputation of the content A sales support terminal, which notifies the sales support server of the feedback shown. A sales support system that proposes sales methods tailored to customer preferences.
The sales support system includes a sales support server and a sales support terminal.
The sales support server is
Refer to the sales history table that shows the sales history, extract the products related to the items for which sales were successful, classify the explanatory materials according to the extracted products, extract the classified products, the classified explanatory materials, and the classified explanatory materials and customers who received the products described using a plurality of content that make up the classification was explanatory material, good reputation of the content that shows the reaction of customers that have been input by the sales representative, is highly calculated the greater the number of uses of the content Create an explanatory material tagging table that associates the score with the
Create a customer preference table that associates customers for sales with the preferences of the customers,
If you enter search information, including customer information from the sales support terminal, on the basis of the information of the customer that is included in the search information, by referring to the customer preference table, along with the get the taste of the customer, With reference to the explanatory material tagging table, acquire a predetermined number of contents from the highest score, and propose the acquired customer preference and content as a sales method adapted to the customer preference,
The sales support terminal displays the preference and content of the customer included in the proposed sales method on the display screen, and while the content is used by a sales representative, the operation history at the time of use, and the content A sales support system, which notifies the sales support server of feedback indicating a reputation.

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