Analysis device, analysis method, and program

Abstract

This analysis device comprises: a detection means that detects a customer reaction to content that is output to an output device in a store; an acquisition means that acquires information pertaining to the purchasing behavior of the customer after detection has been performed; an analysis means that performs analysis on the basis of the information pertaining to the purchasing behavior; and an output means that outputs the results of the analysis.

Description

Analysis device, analysis method, and recording medium

[0001] The present disclosure relates to an analysis device, an analysis method, and a recording medium.

[0002] In order to confirm the effectiveness of targeted advertising, it is necessary to investigate the purchasing behavior of customers who have viewed the advertisement. For example, Patent Literature 1 discloses a method for detecting viewers of content based on captured images acquired by an imaging device installed in a store, detecting the direction of movement of the viewers after viewing the content, and analyzing the inducing effect of the content based on the detected direction of movement of the viewers.

[0003] Japanese Patent Application Laid-Open No. 2017-162221

[0004] The technology described in Patent Document 1 detects the direction of movement of viewers after viewing content and analyzes the effect of the content on audience induction based on the detected direction of movement of the viewers. However, there are cases where analysis based solely on the direction of movement of customers is insufficient.

[0005] An example of an objective of the present disclosure is to provide an analysis device that can analyze products in content from multiple perspectives.

[0006] An analysis device in one aspect of the present disclosure includes a detection means for detecting customer responses to content output to one of the output devices in the store, an acquisition means for acquiring information related to the customer's purchasing behavior after the detection, an analysis means for performing analysis based on the information related to the purchasing behavior, and an output means for outputting the results of the analysis.

[0007] In one aspect of the present disclosure, an analysis method involves a computer detecting customer reactions to content output to one of the output devices in a store, obtaining information regarding the customer's purchasing behavior after the detection, analyzing the information based on the purchasing behavior, and outputting the results of the analysis.

[0008] In one aspect of the present disclosure, a recording medium stores a program that causes a computer to execute a process of detecting customer reactions to content output to one of the output devices in a store, obtaining information regarding the customer's purchasing behavior after the detection, analyzing the information regarding the purchasing behavior, and outputting the results of the analysis.

[0009] According to the present disclosure, it is possible to provide an analysis device that can analyze products in content from multiple perspectives.

[0010] FIG. 1 is an explanatory diagram showing an example of a usage scene of an analysis device in a first embodiment. FIG. 2 is a block diagram showing an example of the configuration of an analysis system including the analysis device in the first embodiment. FIG. 3 is a diagram showing a hardware configuration in which the analysis device in the first embodiment is realized by a computer device and its peripheral devices. FIG. 4 is an example of an output of an analysis result in the first embodiment. FIG. 5 is a flowchart showing the operation of the analysis device in the first embodiment. FIG. 6 is a block diagram showing an example of the configuration of an analysis system including the analysis device in a second embodiment. FIG. 7 is an example of an output of a result of analyzing the appeal of content by the output unit in the second embodiment. FIG. 8 is an example of an output of a result of analyzing the appeal of a product by the output unit in the second embodiment. FIG. 9 is a flowchart showing the operation of the analysis device in the second embodiment.

[0011] Hereinafter, embodiments of an analysis device, an analysis method, and a non-transitory recording medium for recording a program according to the present disclosure will be described in detail with reference to the drawings. The disclosed technology is not limited to these embodiments.

[0012] In this embodiment, an output device in a store outputs content related to products sold in the store to customers who are shopping. The store is, for example, a retail store such as a supermarket or a home improvement store. The content in this embodiment is sales information for a specific product to be recommended to customers, and includes product features, price, display location, inventory information, etc. In this embodiment, the product included in the content is referred to as the target product.

[0013] Examples of output devices include customer terminals such as a tablet terminal provided in a shopping cart or the customer's own terminal, signage, or speakers. The output device outputs information about recommended products in the form of audio, moving images, or still images. The installation location of the output device is not particularly limited. For example, the output device may be installed near the entrance or exit of the store, on a display shelf, a display shelf door, a store window, a ceiling, or the like, or on the floor of the store. The output device may also be configured as a projector and project content onto the wall or floor of the store.

[0014] 1 is an explanatory diagram showing an example of a usage scenario of an analysis device according to this embodiment. In an analysis system 10 including an analysis device 100, an image capture device 1 captures images of the behavior of a customer 3 in front of a display shelf 4 in response to content output to an output device 2. The image capture device 1 then transmits a video signal representing the captured image to the analysis device 100.

[0015] The imaging device 1 is, for example, a surveillance camera installed in a store or a camera attached to a signage. The imaging device 1 is installed in a predetermined position where it can capture, for example, the reactions of customers 3 to content and the display shelves 4. The camera may be installed in a position where it can capture images of the ceiling or aisles of the sales floor, a position where it can capture images in front of the display shelves such as a shelf-front camera, or in front of the store cash register, but the number of cameras and their installation locations are not particularly limited.

[0016] Furthermore, the imaging device 1 is assigned an ID (Identifier) ​​or the like in advance to identify the imaging device 1. In a store, the reactions of customers 3 to content and the display shelves 4 may be captured by different imaging devices. For example, an imaging device provided in a signage may capture images of customers 3's reactions to content, and a surveillance camera may capture images of the display shelves 4. The imaging device 1 acquires captured images. At this time, the imaging device 1 associates the captured image with the capture time, which is the time the captured image was acquired, by referring to, for example, its own clock. In this way, the imaging device 1 acquires captured images showing the reactions of customers 3 and the state of the display shelves 4, etc.

[0017] The video captured by the imaging device 1 may be a moving image or a series of still images. In this embodiment, the captured image acquired by the imaging device 1 may be a color image (hereinafter referred to as an RGB (Red Green Blue) image) or an image in a color space other than an RGB image.

[0018] As described above, the imaging device 1 transmits image data representing the captured image to the analysis device 100. The analysis device 100 stores the received image data in the storage device 505. The imaging device 1 may also store the image data in a storage device inside the imaging device 1 or in a storage device different from the analysis device 100.

[0019] 2 is a block diagram showing an example of the configuration of an analysis system 10 according to the first embodiment. In the analysis system 10, an analysis device 100 and an imaging device 1 are connected via a network. Referring to FIG. 2, the analysis device 100 includes a detection unit 101, an acquisition unit 102, an analysis unit 103, and an output unit 104.

[0020] FIG. 3 is a diagram illustrating an example of a hardware configuration of the analysis apparatus 100 according to the first embodiment of the present disclosure, implemented by a computer device 500 including a processor. The analysis apparatus 100 is implemented by an information processing device such as a computer device, and includes a CPU (Central Processing Unit) and memory. The analysis method according to the present disclosure may also be implemented as an information processing method performed by the information processing device. As shown in FIG. 2, the analysis apparatus 100 includes a CPU 501, memory such as a ROM (Read Only Memory) 502 and a RAM (Random Access Memory) 503, a storage device 505 such as a hard disk for storing a program 504, a communication interface 508 for network connection, and an input / output interface 509 for inputting and outputting data. In the first embodiment, the analysis apparatus 100 is connected to each component via a bus 510. The analysis apparatus 100 according to the first embodiment shown in FIG. 1 can also be configured using cloud computing or the like.

[0021] The CPU 501 runs an operating system to control the entire analysis device 100 according to the first embodiment of the present invention. The CPU 501 also reads programs and data into memory from a recording medium 506 mounted in, for example, a drive device 507. The CPU 501 also functions as the detection unit 101, acquisition unit 102, analysis unit 103, and output unit 104 according to the first embodiment, or as part of these, and executes processing or commands in the flowchart shown in FIG. 5, which will be described later, based on the program.

[0022] The recording medium 506 is, for example, an optical disk, a flexible disk, a magneto-optical disk, an external hard disk, or a semiconductor memory. The semiconductor memory or the like that is part of the recording medium is a non-volatile storage device that stores the program. The program may also be downloaded from an external computer (not shown) that is connected to a communication network.

[0023] As described above, the first embodiment shown in Fig. 2 is realized by the computer hardware shown in Fig. 3. However, the means for realizing each unit included in the analytical device 100 of Fig. 2 is not limited to the configuration described above. Furthermore, the analytical device 100 may be realized by a single physically coupled device, or may be realized by a system consisting of two or more physically separated devices connected by wire or wirelessly.

[0024] The detection unit 101 is a means for detecting a customer's reaction to content output on one of the output devices in the store. A reaction is a customer's behavior showing interest in the content, including viewing the content and actions taken after viewing the content. The detection unit 101 may detect that a customer has viewed the content when the customer has viewed the content for a predetermined period of time or longer. The detection unit 101 may also detect that content about products in the same category has been viewed multiple times on different output devices.

[0025] The detection unit 101 may detect a customer's behavior after viewing the content, such as approaching the output device. The behavior may also include the customer's gaze. The detection unit 101 may also detect a customer encouraging a companion to view the content or moving toward the display of products displayed in the content. The detection unit 101 may also detect a customer's positive attitude toward the content based on the customer's mouth movements or facial expressions. The detection unit 101 may change the criteria for the behavior to be detected depending on the customer's attributes, or may detect the customer's level of interest in the products included in the content based on the content of the detected behavior. However, the detection by the detection unit 101 is not limited to the above example, and any known method may be used. The detection unit 101 outputs appearance information, such as features and images, that identify the appearance of the customer who reacted to the content to the acquisition unit 102.

[0026] The acquisition unit 102 is a means for acquiring information about the purchasing behavior of a customer after detecting the customer's reaction. In this embodiment, the acquisition unit 102 acquires, as information about purchasing behavior, the time required for the customer to perform a specific purchasing behavior after detection. The customer is a customer whose appearance matches that of the customer who responded to the content. When the acquisition unit 102 receives customer appearance information from the detection unit 101, it acquires information about the purchasing behavior of the customer. Purchasing behavior is the behavior a customer performs in a store before purchasing a target product.

[0027] In this embodiment, the purchasing behavior from when a customer's reaction to content is detected until the customer moves to the display shelf of the target product is defined as the first purchasing behavior, and the purchasing behavior from when the customer moves to the display shelf of the target product until the customer places the target product in the product storage unit is defined as the second purchasing behavior. The product storage unit is a container for storing products purchased by the customer, such as a shopping basket or shopping cart. The shopping cart may be a smart shopping cart equipped with a product scanning function. If the customer uses a product storage unit equipped with a scanning function, the purchasing behavior from when the customer moves to the display shelf of the target product until the customer scans the target product is defined as the second purchasing behavior. The acquisition unit 102 may acquire the first purchasing behavior and the second purchasing behavior by analyzing image data captured by the same imaging device whose imaging range includes the display shelf on which the target product is displayed. In this case, both purchasing behaviors can be acquired from a single image data, thereby reducing the load on image processing.

[0028] The acquisition unit 102 acquires the fact that a specific purchasing behavior has been performed and the time when the purchasing behavior was performed. The acquisition unit 102 analyzes image data captured in front of the display shelf of the target product to acquire the customer's purchasing behavior. For example, by analyzing the image data, the acquisition unit 102 acquires the customer's purchasing behavior up to the time when the customer moves to the display shelf of the target product. Furthermore, by analyzing the image data, the acquisition unit 102 acquires the customer's purchasing behavior of removing the target product from the display shelf from movements of the customer's arms, hands, etc. Furthermore, the acquisition unit 102 may use image data captured by a camera in front of the display shelf on which the target product is displayed to detect changes such as a decrease in the number of products on the display shelf, and acquire the behavior of the customer picking up the target product.

[0029] In this embodiment, the acquisition unit 102 can acquire customer purchasing behavior based on information from various sensors installed on the display shelves, in addition to image data captured by the imaging device. For example, an infrared sensor, a weight sensor, or the like may be installed on the display shelves to acquire purchasing behavior of removing a target product from the display shelves. For example, a weight sensor can detect a change in the weight of a target product placed on the display shelves. In other words, the acquisition unit 102 acquires purchasing behavior of a customer removing a target product from the display shelves when it is detected that the weight of the display shelves on which the target product is placed has decreased.

[0030] The acquisition unit 102 acquires the image capture time associated with the captured image as the time of purchase behavior. When the acquisition unit 102 recognizes the behavior of the customer picking up the target product, it acquires the behavior of picking up the target product and the time. When the acquisition unit 102 recognizes that the customer picked up the target product and then looked at the product package, it acquires the behavior of looking at the product package and the time. Furthermore, when the acquisition unit 102 recognizes that the customer has placed the target product in a product storage unit, it acquires the behavior of placing the target product in the product storage unit and the time. When the acquisition unit 102 recognizes that the customer has returned the target product to the shelf, it acquires the behavior of returning the target product to the shelf and the time. For example, by acquiring the behavior of the customer putting the target product in the product storage unit, it can be determined that the customer is likely to purchase the target product. Furthermore, by detecting that the customer has returned the product to the shelf, it can be determined that the customer has discontinued purchasing the product. The acquisition unit 102 outputs the content of the purchase behavior and the time at which the purchase behavior occurred to the analysis unit 103.

[0031] The analysis unit 103 is a means for performing analysis based on information related to purchasing behavior. In this embodiment, the analysis unit 103 analyzes the appeal of content based on the time required for the first purchasing behavior. For example, the analysis unit 103 calculates the average time required for the first purchasing behavior of multiple customers and compares the average time required with a predetermined standard time. The standard time is not particularly limited as long as it serves as a reference time for analyzing appeal, but it may be, for example, the time required for an adult to walk to a display shelf at an average walking speed. The analysis unit 103 then analyzes that the shorter the average time required for the first purchasing behavior is compared to the standard time, the higher the appeal of the content. On the other hand, the analysis unit 103 analyzes that the longer the average time required for the first purchasing behavior is compared to the standard time, the lower the appeal of the content. The analysis unit 103 may also infer attributes such as gender and age from the customer's appearance and analyze the appeal for each attribute. However, the method of analyzing the appeal of content by the analysis unit 103 is not limited thereto.

[0032] The analysis unit 103 may analyze the appeal of the target product based on the time required for the second purchasing behavior. The analysis unit 103, for example, calculates the average time required for the second purchasing behavior of multiple customers and compares the average time required with a predetermined standard time. For example, the analysis unit 103 then analyzes that the shorter the average time required for the second purchasing behavior is compared to the standard time, the higher the appeal of the target product. On the other hand, the analysis unit 103 analyzes that the longer the average time required for the second purchasing behavior is compared to the standard time, the lower the appeal of the target product. However, the method of analyzing the appeal of the target product by the analysis unit 103 is not limited to this.

[0033] The output unit 104 is a means for outputting the analysis results. The output unit 104 outputs the analysis results to an output device on the store side. The output device on the store side is, for example, a display device in the back room of the store or a display on a terminal carried by an employee. The output unit 104 may also output the analysis results to a terminal of a salesperson in charge of the sales floor where the target product is displayed.

[0034] FIG. 4 is an example of an output of the analysis results. As shown in FIG. 4, the difference between each purchasing behavior and the standard time required for that behavior is shown. In the example of FIG. 4, the time required for the first purchasing behavior is 10% longer than the standard time, so the analysis unit 103 analyzes that the appeal of the content is low. On the other hand, in the example of FIG. 4, the time required for the second purchasing behavior is 20% shorter than the standard time, so the analysis unit 103 analyzes that the appeal of the product is high. However, the method of outputting the analysis results is not limited to this.

[0035] 5 is a flowchart showing an outline of the operation of the analysis device 100 according to the first embodiment. The processing according to this flowchart may be executed based on program control by the processor described above. The processing according to this flowchart is based on the premise that, after the detection unit 101 detects a customer's response to content, the acquisition unit 102 acquires the time required for at least one of the first purchasing behavior and the second purchasing behavior.

[0036] 5 is a flowchart illustrating the operation of the analysis device 100 according to the first embodiment. When the detection unit 101 detects a customer's reaction to content output to one of the output devices in the store (step S101; YES), the acquisition unit 102 acquires the time required for a specific purchasing behavior (step S102). When the analysis unit 103 acquires the time required from detecting the customer's reaction to the content to moving the content to the display shelf (step S103; YES), the analysis unit 103 analyzes the appeal of the content based on the acquired time required (step S104). When the analysis unit 103 does not acquire the time required from detecting the customer's reaction to the content to moving the content to the display shelf (step S103; NO), the flow proceeds to step S105.

[0037] Next, if the analysis unit 103 acquires the time required for the target product to be placed in the product storage means used by the customer after being moved to the display shelf (step S105; YES), it analyzes the appeal of the target product based on the acquired time required (step S106). If the analysis unit 103 does not acquire the time required for the target product to be placed in the product storage means used by the customer (step S105; NO), the flow proceeds to step S107. The output unit 104 outputs the analysis results to an output device on the store side (step S107). This completes the analysis process for the analysis device 100.

[0038] As described above, in the first embodiment, the analysis device 100 analyzes the appeal of content or products based on the time required for customers to take a specific purchasing action after detecting a customer's reaction to the content. The output unit 104 then outputs the analysis results to an output device on the store side. As a result, for example, if a high percentage of customers took less than a predetermined time from reacting to the content to moving the product to the display shelf, it can be analyzed that many customers were interested in the target product within the content and that the content itself is appealing. Also, if a high percentage of customers took less than a predetermined time from moving the product to the display shelf to placing the target product in the product storage unit, it can be analyzed that many customers immediately decided to purchase the target product after seeing it and that the product's packaging is appealing. Thus, the analysis device 100 can analyze products within content from multiple perspectives.

[0039] [Second Embodiment] Next, a second embodiment of the present disclosure will be described in detail with reference to the drawings. Below, the description of the second embodiment will be omitted to the extent that it does not make the description of the present embodiment unclear.

[0040] The analysis device 100 in the first embodiment performs analysis based on the time required for a specific purchase behavior as information about the purchase behavior, whereas the analysis device 110 in the second embodiment performs analysis based on the behavior of the customer at the time of the specific purchase behavior as information about the purchase behavior.

[0041] Fig. 6 is a block diagram showing an example of the configuration of an analysis system 11 including an analysis device 110 according to the second embodiment. Similar to the computer device shown in Fig. 3, the functions of the analysis device 110 can be realized not only by hardware but also by a computer device or software based on program control.

[0042] 6, the analysis device 110 includes a detection unit 111, an acquisition unit 112, an analysis unit 113, and an output unit 114. The detection unit 111 has the same configuration and function as the detection unit 101 of the first embodiment.

[0043] The acquisition unit 112 acquires behavior during each purchasing behavior as information about the customer's purchasing behavior. The acquisition unit 112 acquires behavior during a first purchasing behavior from when the customer reacts to content until when the customer moves to the display shelf of the target product, or behavior during a second purchasing behavior from when the customer moves to the display shelf until when the target product is placed in the product storage means.

[0044] The behavior in the first purchasing behavior is behavior that may indicate the ease of finding a display location for the target product, such as behavior of searching for a display location for the target product. The behavior in the second purchasing behavior is behavior that may indicate the degree of interest in the target product, such as where on the product package the customer looks when in front of the display shelf, whether the customer put the product in the product storage means, how long the customer looked at the product before putting it in the product storage means, or whether the customer was undecided between other products. The above-mentioned behaviors are examples of behaviors acquired by the acquisition unit 112, and are not limited to these.

[0045] The acquisition unit 112 acquires the above-described behavior of the customer based on the customer's hand movement, the positional relationship between the direction of the customer's face and the target product being held by the customer, the customer's line of sight, etc., which are obtained from image data captured by the imaging device. For example, the acquisition unit 112 may acquire which information on the package of the target product the customer is viewing based on the customer's line of sight.

[0046] The acquisition unit 121 may also acquire customer flow line information. The flow line information is generated by a flow line generation means (not shown) and stored in the storage device 505 or the like. The acquisition unit 112 acquires, from the storage device 505, flow line information of customers whose appearance matches that of the customers who responded to the content. The flow line generation means generates the flow line information by, for example, analyzing image data captured by an imaging device. The flow line information includes position information indicating the location of the customer. The position information is a set of points in a time series indicating the location. In addition to the position information, the flow line information may further include directional information indicating the direction of the customer's movement. The directional information can be obtained, for example, from points indicating the location of the customer in a time series. Furthermore, the flow line information may also include a dwell time indicating the time the customer spends at a specific location.

[0047] The method for generating a flow line is not limited to the above-described method. For example, the flow line generation means may track a customer's flow line by using a sensor to detect radio waves or infrared rays emitted from a beacon or the like installed in the product storage means used by the customer. The flow line generation means may generate a flow line by tracking location information acquired from a customer terminal, such as a tablet terminal installed in the shopping cart or the customer's own terminal. The flow line generation means may also acquire a position where the speed of the customer's flow line suddenly increases or decreases based on an acceleration sensor installed in the product storage means.

[0048] The analysis unit 123 analyzes the appropriateness of the display location of the target product or the appeal of the target product based on the customer's behavior up to the specific purchasing behavior. The analysis unit 123 analyzes the appropriateness of the display location of the target product based on the behavior in the first purchasing behavior. Specifically, the analysis unit 123 analyzes that the display location is inappropriate if a predetermined percentage or more of customers who moved to the display shelf of the target product exhibited behavior such as searching for a display location for the target product for a predetermined period of time or more. On the other hand, the analysis unit 123 analyzes that the display location is appropriate if the number of customers who exhibited behavior such as searching for a display location for the target product for a predetermined period of time or more is less than a predetermined percentage.

[0049] The analysis unit 123 may also analyze the appeal of the content based on the movement line information in the first purchasing behavior. For example, if a predetermined percentage or more of customers stayed around other products or sales areas for a certain period of time before coming to the display shelf of the target product, or if a predetermined percentage or more of customers slowed down their movement speed in front of other products or sales areas, the analysis unit 123 may infer that the customers were looking at other products that they were more interested in than the target product in the content, and may analyze that the appeal of the content or the target product is low. The analysis unit 123 may also identify other products that the customers are looking at by analyzing location information or image data.

[0050] The analysis unit 123 analyzes the appeal of the target product based on the behavior in the second purchasing behavior. Specifically, if the customer hesitates before placing the target product in the product storage means, hesitates between other products, or returns the target product to the display shelf, the analysis unit 123 infers that the customer was interested in the target product in the content but did not find it attractive enough to immediately decide to purchase it. Therefore, the analysis unit 123 analyzes that the target product has low appeal. The analysis unit 123 may also analyze which parts of the package the customer looked at to hesitate or not purchase it. For example, if the customer looks at the ingredient list of the target product and then returns it to the shelf, the analysis unit 123 infers that the ingredients contained an ingredient that the customer dislikes. On the other hand, if the customer takes the target product from the display shelf and places it in the product storage means within a predetermined time, the analysis unit 123 analyzes that the target product has appeal. In this case, the analysis unit 123 may identify which parts of the target product the customer looked at and analyze which parts were the deciding factors in the purchase.

[0051] The output unit 124 outputs the analysis results to an output device on the store side. FIG. 7 is an example of the output result of analyzing the appeal of content by the output unit 124. As shown in FIG. 7, the analyzed content name, the acquired customer behavior, and the analysis results are shown. In the example of FIG. 7, the acquisition unit 112 acquired the information that, regarding the behavior of customers who responded to content A, 50% of the customers searched for a display location for a predetermined period of time or more. In response to this, the analysis unit 113 analyzed that the display location was inappropriate. Furthermore, the acquisition unit 112 acquired the information that, regarding the behavior of customers who responded to content B, less than 10% of the customers searched for a display location for a predetermined period of time or more. In response to this, the analysis unit 113 analyzed that the display location was appropriate.

[0052] FIG. 8 is an example of an output result of analyzing the appeal of a product. As shown in FIG. 8, the analyzed product names, customer behavior, and analysis results are shown. In the example of FIG. 8, the acquisition unit 112 acquired information regarding customer behavior toward product A, indicating that 30% of customers placed the product in a product storage unit within a predetermined time. In response to this, the analysis unit 113 analyzed that the product has high appeal. Furthermore, the acquisition unit 112 acquired information regarding customer behavior toward product B, indicating that 40% of customers returned the product to the display shelf. In response to this, the analysis unit 113 analyzed that the product has low appeal. However, the method of outputting the analysis results is not limited to this.

[0053] 9 is a flowchart showing an outline of the operation of the analysis device 110 according to the second embodiment. The processing according to this flowchart may be executed based on program control by the processor described above. The processing according to this flowchart is based on the premise that, after the detection unit 111 detects a customer's reaction to content, the acquisition unit 112 acquires the customer's behavior in at least one of the first purchasing behavior and the second purchasing behavior.

[0054] 9 is a flowchart showing the operation of the analysis device 110 in the second embodiment. When the detection unit 111 detects a customer's reaction to content output to one of the output devices in the store (step S201; YES), the acquisition unit 112 acquires the customer's behavior up to a specific purchasing event as information about the customer's purchasing behavior after the detection (step S202). When the analysis unit 113 acquires the customer's behavior from the time the customer's reaction to the content is detected until the product is moved to the display shelf of the target product (step S203; YES), the analysis unit 113 analyzes the appropriateness of the display location based on the acquired behavior (step S204). When the analysis unit 113 does not acquire the customer's behavior from the time the customer's reaction to the content is detected until the product is moved to the display shelf of the target product (step S203; NO), the flow proceeds to step S205.

[0055] Next, if the analysis unit 113 acquires the behavior of the customer from the time the target product is moved to the display shelf until the time the target product is placed in the product storage means used by the customer (step S205; YES), it analyzes the product's appeal based on the acquired behavior (step S206). If the analysis unit 113 does not acquire the behavior of the customer from the time the target product is placed in the product storage means used by the customer (step S205; NO), the flow proceeds to step S207. The output unit 114 outputs the analysis results to an output device on the store side (step S207). This completes the analysis process for the analysis device 110.

[0056] As described above, in the first embodiment, the analysis device 110 analyzes the appropriateness of the display location or the appeal of the product based on the customer's behavior up to the specific purchasing action after detecting the customer's reaction to the content. The output unit 114 then outputs the analysis results to an output device on the store side. As a result, for example, if many customers were able to move without hesitation from the time they reacted to the content to the time they moved to the display shelf, it can be analyzed that the display location is appropriate for the content's output location. Also, if many customers moved to the display shelf and then placed the target product in the product storage means without hesitation, it can be analyzed that the target product has appeal. Thus, products within content can be analyzed from multiple perspectives.

[0057] Although the present disclosure has been described above with reference to various embodiments, the present disclosure is not limited to the above embodiments. The configuration and details of each of the present disclosures may include embodiments to which various modifications that would be apparent to those skilled in the art are applied within the scope of the present disclosure. The present disclosure may also include embodiments in which the details described herein are appropriately combined or substituted as necessary. For example, details described using a particular embodiment may also be applied to other embodiments to the extent that no contradiction occurs. For example, although multiple operations are described in sequence in the form of a flowchart, the order of the descriptions does not limit the order in which the multiple operations are performed. Therefore, when implementing each embodiment, the order of the multiple operations may be changed as long as it does not interfere with the content.

[0058] Furthermore, the purchasing behavior analyzed by the analysis units 103 and 113 is not limited to information about the first purchasing behavior and the second purchasing behavior. For example, the analysis units 103 and 113 may analyze the appeal of a product based on information about the purchasing behavior from when a customer sees the product package until when the customer places the product in the product storage unit, or may analyze the appeal of a product based on information about the purchasing behavior from when a customer picks up the product until when the customer returns it to the display shelf.

[0059] 10, 11 Analysis system 100, 110 Analysis device 101, 111 Detection unit 102, 112 Acquisition unit 103, 113 Analysis unit 104, 114 Output unit 500 Computer device 501 CPU 502 ROM 503 RAM 504 Program 505 Storage device 506 Recording medium 507 Drive device 508 Communication interface 509 Input / output interface 510 Bus

Claims

1. a detection means for detecting a customer's reaction to the content outputted to one of the output devices in the store; an acquisition means for acquiring information regarding the purchasing behavior of the customer after the detection; an analysis means for performing an analysis based on the information about the purchasing behavior; An analysis device comprising: an output means for outputting the analysis results.

2. The analysis device according to claim 1 , wherein the acquisition means acquires, as the information about the purchasing behavior, a time required for the customer to perform a specific purchasing behavior after the detection.

3. the acquiring means acquires a time required for the first purchasing behavior from the detection until the customer moves to the display shelf of the target product; The analysis device according to claim 2 , wherein the analysis means analyzes the appeal of the content based on the time required for the first purchasing behavior.

4. the acquisition means acquires the time required for a second purchasing behavior from when the customer moves to the display shelf of the target product until when the target product is placed in the product storage means used by the customer; The analysis device according to claim 2 or 3, wherein the analysis means analyzes the appeal of the target product based on the time required for the second purchasing behavior.

5. The analysis device according to claim 1 , wherein the acquisition unit acquires, as the information on the purchasing behavior of the customer, behavior of the customer up to a specific purchasing behavior after the detection.

6. the acquiring means acquires behavior of the first purchasing behavior from the time of the detection until the time the customer moves to a display shelf of the target product; The analysis device according to claim 5 , wherein the analysis means analyzes the appropriateness of a display location of the target product based on the behavior of the first purchasing behavior.

7. the acquiring means acquires a second purchasing behavior of the customer up to the point where the customer places the target product in a product storage means; The analysis device according to claim 5 , wherein the analysis means analyzes the appeal of the target product based on the behavior in the second purchasing behavior.

8. The acquiring means further acquires, as information regarding the purchasing behavior of the customer, a path of movement of the customer leading to the specific purchasing behavior after the detection, The analysis device according to claim 5 , wherein the analysis means analyzes the appropriateness of a display location of the target product or the appeal of the target product based on the flow line.

9. The computer Detecting customer reactions to content output on one of the output devices in the store, obtaining information regarding the purchasing behavior of the customer after the detection; Analyzing based on the information on the purchasing behavior, An analytical method that outputs the results of the analysis.

10. Detecting customer reactions to content output on one of the output devices in the store, obtaining information regarding the purchasing behavior of the customer after the detection; Analyzing based on the information on the purchasing behavior, A program that causes a computer to execute a process that outputs the results of analysis.